JP4980410B2 - Air conditioner and air conditioning system - Google Patents

Description

  The present invention relates to an air conditioner and an air conditioning system that perform air conditioning control based on a human sleep state.

  Conventionally, as this type of air conditioner, for example, “the air conditioner 15 is installed in a room and the bed 10 used in the room is provided with a body movement detecting means 9 for detecting the body movement of a sleeping person. In addition, there are provided occupancy determination means 12 for detecting occupancy by signals from the body motion detection means, and sleep detection means 13 for detecting sleep, and signals from an outside air temperature sensor 14, an indoor temperature sensor 16, and a wake-up timer 17 are provided. The calculation means 18 for calculating the indoor set temperature is provided, and the air conditioner 15 is controlled via the control means 19 by a signal from the calculation means 18. Thereby, the indoor temperature during sleep is set when sleep onset is detected, Then, the operation of the air conditioner is stopped "(for example, see Patent Document 1).

JP-A-5-92040 (summary)

  In the prior art, after sleep onset is detected, the target temperature is controlled to be constant. It is known that body temperature changes according to sleep state during sleep, and if you keep the indoor thermal environment constant without considering this change in body temperature, you will wake up when the body temperature changes, It was not for energy saving.

  The present invention has been made in view of these points, and an object thereof is to provide an air conditioner and an air conditioning system capable of air conditioning control that is comfortable and energy-saving during sleep.

The air conditioner according to the present invention includes an air conditioner that air-conditions the indoor space, a sleep state acquisition system that determines the sleep state of the sleeper, and a sleep state and a driving mode that are determined by the sleep state acquisition system. Control means for determining the set temperature of the air-conditioning means and controlling the air-conditioning means so that the temperature of the indoor space becomes the set temperature. When the sleeper starts sleeping, the control means may be in the cooling operation mode. If, the set temperature, gradually lowered to a temperature below the initial set temperature, and then, if the sleeping person has entered a deep sleep, and set to a temperature higher than the initial setting temperature setting temperature, in the shallow sleep through the deep sleep When transitioning, the set temperature continues for a predetermined time while in deep sleep, and after the predetermined time has passed, the process of lowering the set temperature to the predetermined temperature is performed during shallow sleep, and when this sleep is passed through and deep sleep is entered ,Preset temperature Setting the current increase than during deep sleep and light sleep, but repeated.

  According to the present invention, the air conditioning control is performed according to the sleep state, and when the sleeper enters deep sleep, the temperature setting is set to suppress the air conditioning capability. An air conditioner capable of air conditioning control can be obtained.

It is a block diagram which shows the structure of the air conditioner which concerns on Embodiment 1 of this invention. It is a figure which shows the change of the sleep state from sleep start to awakening, and the change of the deep body temperature (temperature of the center part of the body (rectal temperature)) considered to be deeply related to sleep. It is a flowchart which shows the flow of the air_conditioning | cooling normal operation in the air conditioner which concerns on Embodiment 1 of this invention. It is a wave form diagram for demonstrating control of the normal cooling operation in the air conditioner which concerns on Embodiment 1 of this invention. It is a wave form diagram for demonstrating control of the air_conditioning | cooling energy saving driving | operation in the air conditioner which concerns on Embodiment 1 of this invention. It is a wave form diagram for demonstrating the time lag control of the cooling energy saving driving | operation in the air conditioner which concerns on Embodiment 1 of this invention. It is a wave form diagram for demonstrating control of the cooling energy saving operation with an error function in the air conditioner which concerns on Embodiment 1 of this invention. It is a wave form diagram for demonstrating control of the operation for hot weather in the air conditioner which concerns on Embodiment 1 of this invention. It is a flowchart which shows the flow of the heating normal operation in the air conditioner which concerns on Embodiment 1 of this invention. It is a wave form diagram for demonstrating control of the heating normal operation in the air conditioner which concerns on Embodiment 1 of this invention. It is a wave form diagram for demonstrating control of the linear mode driving | operation in the air conditioner which concerns on Embodiment 1 of this invention. It is a wave form diagram for demonstrating control of the heating operation with an error function in the air conditioner which concerns on Embodiment 1 of this invention. It is a figure which shows the structure of the air conditioning system which concerns on Embodiment 2 of this invention. It is a figure which shows the network structure of the air conditioning system which concerns on Embodiment 3 of this invention. It is a figure which shows an example of the ranking result screen published on the network in the system of FIG. It is a block diagram which shows the structure of the biological condition acquisition system provided with the biological condition acquisition apparatus which concerns on Embodiment 4 of this invention. It is a flowchart which shows the flow of the biological condition acquisition process in the arithmetic unit of FIG. It is a figure which shows IQ plane of the reflected wave according to the motion of the body surface at the time of human respiration. It is a figure which shows an example of the time series data of the norm of a velocity vector. It is explanatory drawing (the 1) of the arithmetic processing for detecting respiration from the time series data of FIG. It is explanatory drawing (the 2) of the arithmetic processing for detecting respiration from the time series data of FIG. It is explanatory drawing (the 3) of the arithmetic processing for detecting respiration from the time series data of FIG. It is a figure which shows an example of the time series data of the displacement amount (average value) of the body surface when the motion of the body surface is complicated. It is a figure which shows IQ plane at the time of measuring the motion of the body surface of FIG. It is a figure which shows the time series data of the norm of the velocity vector of IQ plane of FIG. It is a figure which shows the respiration cycle (breathing time) in case the respiration count is performed normally, and its distribution. It is a figure which shows the respiratory cycle (breathing time) in case the respiration count is not performed normally, and its distribution. It is a flowchart which shows the flow of the necessity determination process of correction | amendment of FIG. 17, and a correction process (when correction is required). It is explanatory drawing (the 1) of the arithmetic processing at the time of counting respiration from the time series data of the norm of the velocity vector of FIG. It is explanatory drawing (the 2) of the arithmetic processing at the time of counting respiration from the time series data of the norm of the velocity vector of FIG. It is explanatory drawing (the 3) of the arithmetic processing at the time of counting respiration from the time series data of the norm of the velocity vector of FIG. It is a block diagram which shows the structure of the biological condition acquisition system which concerns on Embodiment 5 of this invention. It is a flowchart which shows the flow of the biological condition acquisition process in the arithmetic unit which concerns on Embodiment 5 of this invention. It is a figure which shows IQ signal output of the state in which the heart rate signal and the body motion signal were mixed. It is a figure which shows the waveform of the vector norm after performing the low-pass filter process after the envelope process of the signal output of FIG. It is a figure which shows the time series data of the signal intensity | strength (Amplitude = The square root of each square sum of I and Q) of the I signal and Q signal after a low-pass filter process. It is explanatory drawing (the 1) of the arithmetic processing for detecting a heartbeat from the amplitude time series data of FIG. It is explanatory drawing (the 2) of the arithmetic processing for detecting a heartbeat from the amplitude time series data of FIG. It is explanatory drawing (the 3) of the arithmetic processing for detecting a heartbeat from the amplitude time series data of FIG. It is a figure which shows the time series data of the signal intensity | strength (amplitude = square root of the square sum of Q) of I signal and Q signal after a low-pass filter process in case a body surface pulsates in two steps in 1 beat. It is explanatory drawing (the 1) of the arithmetic processing at the time of counting a heart rate from the time series data of FIG. FIG. 41 is an explanatory diagram (part 2) of the arithmetic processing when counting heartbeats from the time series data of FIG. 40. FIG. 41 is an explanatory diagram (No. 3) of the arithmetic processing when counting heartbeats from the time series data of FIG. 40. It is a flowchart which shows the flow of the necessity determination process of correction | amendment of FIG. 33, and a correction process (when correction is required). It is a figure which shows the heart rate for every unit period when the heart rate count is performed normally, and its distribution. It is a figure which shows the heart rate for every unit period when the heart rate count is not performed normally, and its distribution. It is a block diagram which shows the structure of the biological condition acquisition system which concerns on Embodiment 6 of this invention. It is a flowchart which shows the flow of the biological condition acquisition process in the arithmetic unit which concerns on Embodiment 6 of this invention. It is a block diagram which shows the structure of the biological condition acquisition system which concerns on Embodiment 7 of this invention. It is the figure which showed the characteristic of the body movement, respiration, and heart rate in each case of shallow sleep (including awakening), deep sleep, and REM sleep. It is a flowchart which shows the flow of the biological condition acquisition process in the arithmetic unit which concerns on Embodiment 7 of this invention. It is a block diagram which shows the structure of the biological condition acquisition system which concerns on Embodiment 8 of this invention. It is a figure which shows the stable locus | trajectory (deep sleep) of IQ plane within a certain period. It is a figure which shows the unstable locus | trajectory (except deep sleep) of IQ plane within a certain period. It is a flowchart which shows the flow of the biological condition acquisition process in the arithmetic unit which concerns on Embodiment 8 of this invention.

Embodiment 1 FIG.
FIG. 1 is a block diagram showing a configuration of an air conditioner according to Embodiment 1 of the present invention. FIG. 1 shows only the configuration of the main part related to the present invention, and illustration of various components normally provided in the air conditioner is omitted.
The air conditioner 1 includes an air conditioner 2 that air-conditions the room, a temperature detector 3 that detects the temperature of the indoor space, a sleep state acquisition system 100 that determines the sleep state, and a calculation that controls the entire air conditioner. Device 4.

  The air conditioning means 2 is a means for performing an air conditioning operation according to an operation mode set by a button (not shown) or the like installed on the remote controller 5 or the air conditioner body. As the operation mode, in the cooling operation, there are a normal cooling operation, a cooling energy saving operation, a cooling energy saving operation (time lag control), a cooling energy saving operation with an error function, and a hot summer operation. In the heating operation, there are a heating normal operation, a heating energy saving operation, a linear mode operation, and a heating operation with an error function, which can be switched by a remote controller 5 or the like. Further, for example, an energy saving setting button may be provided so that the cooling normal operation or the heating normal operation can be switched to the energy saving operation. The air conditioning unit 2 performs a series of operations until the temperature of the indoor space detected by the temperature detection unit 3 is adjusted to the set temperature so as to be discharged into the indoor space. In addition, conventionally well-known structure can be employ | adopted about structures, such as a specific refrigeration cycle for an air conditioning, Since detailed relationship is abbreviate | omitted with the meaning of this invention about this component part. In addition, this example particularly explains cooling operation and heating operation during sleep, and conventionally known control can be adopted for general cooling operation and heating operation modes other than during sleep. Since it is not related to the gist of the invention, detailed description is omitted.

  The sleep state acquisition system 100 determines the sleep state of a human (sleeping person) and outputs a sleep state signal indicating the determination result to the computing device 4. Here, the sleep state will be described.

FIG. 2 is a diagram showing changes in sleep state from sleep start to wake-up and changes in body deep body temperature (temperature at the center of the body (rectal temperature)) that is deeply related to sleep. Moreover, the relationship between a sleep state and a body movement is also illustrated as a reference for an embodiment described later.
First, sleep is broadly classified into REM sleep, which is generally light sleep, and non-REM sleep, which is deep sleep. Sleep states are further defined in detail, and six states of awakening, REM sleep, and sleep depths 1, 2, 3, and 4 are defined. The sleep depths 1, 2, 3, and 4 are obtained by further dividing the non-REM sleep into four stages. In the non-REM sleep, the sleep depth 1 is the shallowest and the sleep depth 4 is the deepest.

  REM sleep, sleep depth 1, 2, 3, 4 and so on until the next awakening from the start of sleep onset, then sleep depth 3, 2, 1, REM The sleep cycle of shifting to sleep is usually repeated with a period of about 90 minutes.

  The sleep state acquisition system 100 determines the sleep state of such a sleeper, and enters a deep sleep with a shallow sleep signal indicating that it has entered a shallow sleep (REM sleep, sleep depth 1 and sleep depth 2). At least a deep sleep signal indicating this and a wake-up prediction signal are output. The sleep signal output from the sleep state acquisition system 100 outputs a sleep state signal indicating the sleep state to the arithmetic device every time the sleep state (REM sleep, sleep depth 1, 2, 3, 4) is transferred. Alternatively, a sleep state signal indicating the current sleep state may be periodically output to the arithmetic device. Although the configuration of the sleep state acquisition system 100 is not particularly limited, in this example, biological state information such as respiration, heartbeat, and body movement is acquired in a non-contact manner using a Doppler radar sensor, and the acquired biological state information is acquired. Based on the above, a system is adopted in which a sleep state is determined by threshold determination using a preset threshold. A specific configuration example of the sleep state acquisition system 100 will be described in an embodiment described later. The wake-up prediction signal is a signal indicating that it is a predetermined time (for example, 60 minutes) before wake-up. Such wake-up prediction may be performed based on a human biological signal (such as a respiratory signal or a heartbeat signal), or may be performed based on a wake-up time set in advance by a sleeping person.

  The temperature detection means 3 is composed of a thermistor or something similar. The temperature detection means 3 detects the room temperature and outputs a temperature detection signal indicating the temperature to the arithmetic device 4.

  The arithmetic device 4 is constituted by a microcomputer, and includes a CPU, a ROM, and a RAM inside, and operates according to various programs stored in the ROM. The CPU controls the entire air conditioner by executing a control program stored in the ROM.

  Moreover, the arithmetic unit 4 receives the sleep state signal from the sleep state acquisition system 100 and the temperature detection signal from the temperature detection means 3, and performs control according to the received signal. The arithmetic device 4 stores air conditioning control data for performing air conditioning control in accordance with each operation mode. The air-conditioning control data specifies the set temperature for each sleep state according to the operation mode, and the air-conditioning control means 4a sets the set temperature of the air-conditioning means 2 according to the sleep state of the sleeping person based on the air-conditioning control data. And the air conditioning means 2 is controlled so that the temperature of the indoor space becomes the set temperature.

Here, prior to describing the specific control of the air conditioner 1, the temperature change in the deep body according to the sleep state will be described with reference to FIG.
The deep body temperature rises once after the start of sleep, and then gradually decreases as the sleep state becomes deeper. Then, at the time when the sleep state becomes shallow (the transition time from sleep depth 4 to sleep depth 3), the deep body temperature starts to rise this time and gradually rises toward awakening.

  Thus, deep body temperature rises after sleep start. Therefore, it is possible to promote sleep onset by lowering the room temperature and promoting heat dissipation from the limbs at the start of sleep. In addition, during deep sleep, the sense of change in the thermal environment of the indoor space is dull, while the body temperature regulation function works. For this reason, even if the room temperature exceeds the set temperature set by the sleeping person before going to bed, there is a low possibility that halfway awakening will occur. Therefore, it is preferable to increase the room temperature during deep sleep in consideration of energy saving.

  The air conditioner 1 according to the first embodiment performs air-conditioning control that can provide comfortable sleep to the sleeper based on the characteristics such as the change in the deep body temperature during sleep described above.

  Hereinafter, operation | movement of the air conditioner 1 of Embodiment 1 of this invention is demonstrated. The air conditioner 1 according to the first embodiment includes a cooling operation (cooling normal operation, cooling energy saving operation, cooling energy saving operation (time lag control), cooling energy saving operation with an error function, operation for hot weather), and heating operation (normal heating operation). Heating energy-saving operation, linear mode operation, heating operation with error function), and will be described below in order. The air conditioner 1 is preliminarily set with a comfortable temperature setting for the sleeper by the sleeper. Hereinafter, this temperature is referred to as an initial set temperature T0. Furthermore, it is assumed that the wake-up time is set in advance by the sleeping person and is set in the memory in the arithmetic device 4. Moreover, when the start of each operation mode is instructed by a remote control operation or the like by the sleeper, the sleep state acquisition system starts determination of the sleep state of the sleeper and sends a sleep state signal indicating the sleep state determination result to the arithmetic device 4. Assume that it is outputting.

  First, the cooling operation in summer will be described sequentially.

(Normal cooling operation)
FIG. 3 is a flowchart showing a processing flow of the arithmetic device when a sleep state signal is received. FIG. 4 is a waveform diagram for explaining the control of the cooling normal operation. In FIG. 4, the alternate long and short dash line indicates the change in the sleep state. Here, the sleep state is REM, the shallow sleep of 1 and 2 and the deep sleep of the sleep state of 3 and 4 are shown. In FIG. 4, the solid line shows the control waveform of the set temperature. The way of reading the diagrams is the same in each waveform diagram described later.

  The air conditioning control means 4a of the computing device 4 receives the sleep state signal from the sleep state acquisition system 100 (S1), and the received sleep state signal is the first shallow sleep signal (first deep sleep signal after the start of sleep). The following control is performed. That is, the air conditioning control means 4a gradually lowers the set temperature to a temperature lower than the initial set temperature T0 (T0-1 ° C. in FIG. 4) (S2, S3). FIG. 4 shows an example of lowering in stages. And the air-conditioning control means 4a controls the air-conditioning means 2 so that the temperature of indoor space becomes preset temperature (here, T0-1 degreeC) (S4). And it returns to step S1 and waits for the next sleep state signal to come, and maintains preset temperature. Since the human body temperature rises after the start of sleep as described above, the heat release of body heat can be promoted by gradually lowering the set temperature after the start of sleep, and sleep can be promoted.

  When the sleeper enters deep sleep and the air-conditioning control means 4a receives a deep sleep signal from the sleep state acquisition system 100 (S2), this time, it is set to a temperature that exceeds the initial set temperature T0 (T0 + 2 ° C. in FIG. 4). The temperature is updated (S5). During deep sleep, the sensitivity to changes in the thermal environment of the indoor space is low, while the body temperature regulation function works. Therefore, even if the set temperature exceeds the comfortable initial set temperature of the sleeping person, there is a low possibility that mid-wake awakening will occur. Therefore, in consideration of energy saving, the set temperature is raised during deep sleep. The air conditioning control means 4a controls the air conditioning means 2 so that the temperature of the indoor space becomes the set temperature (here, T0 + 2 ° C.) (S4). In the summertime during the cooling operation, the outside air temperature is higher than that in the air-conditioned room. Therefore, the temperature of the indoor space naturally increases by turning off the air-conditioning means 2. Specific control of the air-conditioning means 2 for setting the temperature of the indoor space to the set temperature is arbitrary, but considering energy saving, the air-conditioning means 2 may be turned off to stop the inverter control of the compressor. .

  Then, when the sleeper enters light sleep and the air conditioning control means 4a receives a light sleep signal from the sleep state acquisition system 100 (S2), the set temperature is returned to the initial set temperature T0 (S6). Since it is sensitive to changes in the thermal environment during light sleep, mid-level awakening occurs in an uncomfortable temperature zone. For this reason, by returning to the initial set temperature T0 set by the sleeping person, it becomes possible to prevent awakening during the middle and provide a comfortable sleep. A good sleep can be obtained, and the health condition can be maintained well.

  Then, when deep sleep is entered next, steps S5 and S4 are performed as in the case of the first deep sleep. The air conditioning control means 4a repeats the above control during deep sleep and light sleep. And if the air-conditioning control means 4a receives the wake-up prediction signal (a signal indicating a predetermined time (for example, 60 minutes) before the wake-up time) from the sleep state acquisition system 100, the set temperature is a temperature equal to or higher than the set temperature during deep sleep ( FIG. 4 shows an example in which the temperature is set to the same temperature T0 + 2 ° C. as the set temperature during deep sleep. Since the deep body temperature is lowered at midnight, by setting the set temperature to a temperature higher than the initial set temperature T0 and increasing the room temperature, it is possible to provide a comfortable wake-up for the sleeping person with less awakening.

(Cooling energy saving operation)
FIG. 5 is a waveform diagram for explaining control of cooling energy saving operation. Hereinafter, the cooling energy-saving operation is basically the same as the above-described cooling normal operation, and here, a description will be given of portions where the cooling energy-saving operation is different from the cooling normal operation.

As is clear from comparison between FIG. 4 and FIG. 5, in the cooling and energy-saving operation, even if the user goes through deep sleep and goes into shallow sleep, the set temperature is not returned to the initial set temperature T0, and the set temperature during deep sleep ( Here, T0 + 2 ° C. is maintained, and this is continued until getting up.
Thereby, it is possible to further save energy compared to the normal cooling operation.

(Cooling energy-saving operation (time lag control))
FIG. 6 is a waveform diagram for explaining the time lag control of the cooling energy saving operation.
The above-mentioned sleep energy saving control has an energy saving effect, but after entering the first deep sleep, there is no change in the thermal environment until getting up, and a temperature (T0 + 2 ° C.) higher than the initial set temperature T0 is maintained. For this reason, if heat dissipation from the end of the body starts toward deep sleep after shallow sleep, the sleeper may be hot and awake. Therefore, in this time lag control, a control having both an energy saving effect and a comfort in sleep is realized.

  Hereinafter, the time lag control of the cooling energy saving operation will be described. Here, the time lag control shown in FIG. 6 will be described focusing on the difference from the cooling normal operation shown in FIG. Although the numerical values in FIG. 6 indicate time, this time is an example and is not limited to this time.

  In the time lag control, when entering the first deep sleep, the set temperature is raised and updated to the set temperature during deep sleep (here, T0 + 2 ° C.). And when it shifts to shallow sleep through this deep sleep, it does not immediately lower the set temperature as in the case of normal cooling operation, but after a predetermined time lag (for example, 45 minutes in FIG. 6) has elapsed, The temperature is lowered (in this case, set to T0 + 1 ° C.) to induce the next deep sleep. This temperature is set to T0 + 1 ° C. here, but is not limited to this, and is set to a temperature equal to or higher than the initial set temperature T0 in consideration of energy saving. When the next deep sleep is entered, the set temperature is similarly updated to T0 + 2 ° C. And when it shifts to shallow sleep through this deep sleep, after a predetermined time lag (for example, 45 minutes in FIG. 6) elapses, the set temperature is lowered step by step to induce the next deep sleep. Here, an example is shown in which the set temperature is lowered stepwise up to the initial set temperature T0. And after lowering in steps, the set temperature is raised to T0 + 1 ° C. so as not to get too cold.

  By performing such time lag control, it is possible to perform energy-saving control while maintaining comfort during sleep as compared to sleep cooling energy-saving control.

(Cooling energy saving operation with error function)
FIG. 7 is a waveform diagram for explaining control of cooling energy saving operation with an error function. Hereinafter, the cooling energy-saving operation with an error function is basically the same as the above-described cooling energy-saving operation, and here, the difference between the cooling energy-saving operation with an error function and the cooling energy-saving operation will be described.

  As is clear from comparison between FIG. 7 and FIG. 5, in the cooling energy-saving operation with an error function, the temperature lower than the preset temperature T0 of the air conditioning means 2 after the start of sleep (here, T0-1 ° C.). After the temperature is gradually lowered (stepwise), the set temperature is returned to the initial set temperature T0 after a certain period of time. This is different from the cooling energy-saving operation. When the sleep state of the sleeper is actually deep sleep but the deep sleep signal is not input from the sleep state acquisition system 100 to the arithmetic device 4 due to some error or the like, the set temperature is set to the initial set temperature T0. It will remain set at a lower temperature (here, T0-1 ° C.). In this case, not only there is a problem in energy saving, but also after a deep sleep, a thermal environment lower than the initial set temperature T0 is maintained during a shallow sleep that is sensitive to changes in the thermal environment. A comfortable sleep cannot be obtained. For this reason, in the cooling energy saving operation with an error function, the set temperature is forcibly returned to the initial set temperature T0 after a certain period of time has elapsed after the set temperature of the air conditioning means 2 is lowered to T0-1 ° C. . The subsequent operation is the same as in the cooling energy-saving operation, and the set temperature is updated to T0 + 2 ° C.

(Driving for hot weather)
FIG. 8 is a waveform diagram for explaining the control of the hot summer driving. The operation for hot weather has a waveform shape substantially similar to the time lag control shown in FIG. 6, and performs an operation based on a waveform shifted to a lower temperature while maintaining the waveform shape. Here, a description will be given of the difference between the hot summer operation and the time lag control of the normal cooling operation.

  In hot summer driving, the point of lowering the set temperature at the start of sleep is the same as in normal cooling operation, but the set temperature should be lowered to a lower temperature (here, T0-2 ° C.) than in normal cooling operation. I have to. When the first deep sleep is entered, the set temperature is set to T0 + 2 ° C. in the normal cooling operation, but is set to a lower temperature (here, T0 + 1 ° C.) in the hot operation.

  With this control, it is possible to perform comfortable air conditioning control for a hot user and to save energy.

Next, the heating operation in winter will be described.
(Heating normal operation)
FIG. 9 is a flowchart showing a processing flow of the arithmetic device 4 when a sleep state signal is received in the heating normal operation. FIG. 10 is a waveform diagram for explaining the control of the heating normal operation. In the heating operation, if the room temperature detected by the temperature detecting means 3 is more than a set temperature (for example, 1 ° C.) or more, the air conditioner 2 is automatically turned on or off to set the room temperature. It is assumed that a process for controlling the air conditioning unit 2 so as to reach the temperature is incorporated in the air conditioning control unit 4a.

  The air conditioning control means 4a of the computing device 4 receives the sleep state signal from the sleep state acquisition system 100 (S11), and the received sleep state signal is the first shallow sleep signal (first deep sleep signal after sleep starts). If it is, the same processing (S12, S13) as the normal cooling operation is performed. That is, the air conditioning control means 4a gradually (stepwise) lowers the set temperature of the air conditioning means 2 to a temperature lower than the initial set temperature T0 (here, T0-1 ° C.) (S13). FIG. 10 shows an example in which the level is lowered in two stages.

  And the air-conditioning control means 4a controls the air-conditioning means 2 so that the temperature of indoor space becomes preset temperature (here, T0-1 degreeC) (S14). Since the outside air temperature is low during the heating operation, the temperature of the indoor space naturally decreases by turning off the air conditioning means 2. Specific control of the air conditioning means 2 for setting the temperature of the indoor space to the set temperature is arbitrary, but considering energy saving, the air conditioning means 2 may be turned off to stop the inverter control.

  And the air-conditioning control means 4a returns to step S1, waits for the next sleep state signal to come, and maintains temperature T0-1 degreeC until deep sleep is detected. Since the human body temperature rises after the start of sleep as described above, by gradually lowering the set temperature after the start of sleep, heat dissipation can be promoted and sleep can be promoted.

  When the sleeper enters deep sleep and the air conditioning control means 4a receives a deep sleep signal from the sleep state acquisition system 100 (S11), the set temperature is set to a temperature T0-2 ° C that is lower than the current set temperature T0-1 ° C. Update (S15). That is, the setting is made to lower the heating temperature by lowering the set temperature. And the air-conditioning control means 4a controls the air-conditioning means 2 so that the temperature of indoor space may be T0-2 degreeC (S14). As a specific control here, the air conditioning control means 4a turns off the air conditioning means 2. As a result, the room temperature gradually decreases. In addition, since the temperature in a bed does not fall rapidly even if the air-conditioning means 2 is turned off, and it falls only gradually, the sleeper who sleeps will be in the state of head cold foot heat. In winter, it is preferable to have a fever in the cold, but if it is in a cold region above a certain level, the room temperature is too low and the head cools down rapidly. This stimulation can interfere with sleep, so it is important to prevent this. Therefore, the air-conditioning control means 4a periodically determines whether the room temperature measured by the temperature detection means 3 has fallen by a certain temperature range (for example, 1 ° C.) or more than the set temperature (here, T0-2 ° C.). If the room temperature falls below the set temperature by a temperature range (for example, 1 ° C.) or more, the air-conditioning means 2 is turned on so that the room temperature does not drop any further, and the heating operation is performed.

  Further, when the set temperature is changed, the time until the temperature in the bed reaches the set temperature is longer than the time until the room temperature reaches the set temperature. That is, the temperature in the bed that the sleeper feels as a real feeling changes more slowly than the room temperature, and even if the set temperature is slightly changed, it takes time for the set temperature to be transmitted to the sleeper. Therefore, as long as the room temperature does not fall below the set temperature by a predetermined temperature range (here, 1 ° C.), even if a shallow sleep signal or a deep sleep signal is received thereafter, sleep is performed as shown in FIG. The set temperature inside is not changed.

  And if the air-conditioning control means 4a receives the wake-up prediction signal (a signal indicating a predetermined time (for example, 60 minutes) before the wake-up time) from the sleep state acquisition system 100 (S11, S12), it has entered the wake-up stage. Judgment is made, and the set temperature is raised to a temperature (here, T0 + 1 ° C.) higher than the current set temperature (here, T0-2 ° C.) (S16), thereby creating an environment where it is easy to get up. In this way, the indoor space is gradually warmed to such an extent that the sleeper does not wake up due to a temperature change, and the cold at the time of wakeup is reduced.

(Heating energy saving operation)
In the heating energy-saving operation, at the predicted wake-up timing (wake-up stage) shown in FIG. 10, the temperature set in the normal heating operation exceeds the initial set temperature T0 (here, T0 + 1 ° C.) exceeds the initial set temperature T0. The temperature is higher than the current range (T0-1 ° C. (dotted line in FIG. 10)). Also in this case, since the set temperature is raised from the set temperature T0-2 ° C. during sleep, the cold at the time of waking up can be reduced. In this operation, since the heating capacity at the time of getting up is suppressed as compared with the normal heating operation described above, energy saving is possible.

(Linear mode operation)
The linear mode driving is a driving that can be applied to sleep with a thin futon, for example, and can provide a comfortable sleep.

FIG. 11 is a waveform diagram for explaining control of linear mode operation. Here, the linear mode operation will be described focusing on the difference from the normal heating operation shown in FIG.
As is obvious from comparison between FIG. 11 and FIG. 10, in the linear mode operation, when the user goes through deep sleep and enters into light sleep, the air conditioning control means 4a updates the set temperature to the initial set temperature T0, and When deep sleep is resumed, the set temperature is updated to T0-2 ° C. Then, when the user goes through this deep sleep and enters a light sleep, the set temperature is updated again to the initial set temperature T0. The air conditioning control means 4a repeats the above control during deep sleep and light sleep. Then, as in the normal heating operation, when the wake-up prediction timing is reached, the set temperature is updated to T0 + 1 ° C., and the cold at the time of wake-up is reduced. In the case of a thin futon, if sleep is entered with the set temperature at T0-2 ° C., it may be cold and awake. In the linear mode operation, by controlling as described above, it is possible to reduce the inconvenience of being awake during sleep.

(Heating operation with error function)
FIG. 12 is a waveform diagram for explaining the control of the heating operation with an error function. The heating operation with an error function is basically the same as the normal heating operation described above, and here, a description will be given of a portion where the heating operation with an error function is different from the normal heating operation.

  As apparent from comparison between FIG. 12 and FIG. 10, in the heating operation with an error function, after the sleep is started, the set temperature of the air-conditioning means 2 is reduced to a temperature lower than the initial set temperature T0 (here, T0-1 ° C.). After a gradual (stepwise) decrease, the temperature is forcibly lowered below the set temperature (here, T0-2 ° C.) after a certain period of time. This is different from normal heating operation. If the sleep state of the sleeper is actually deep sleep, but the deep sleep signal is not input from the sleep state acquisition system 100 to the computing device 4 due to some error or the like, the air conditioning control means 4a is the air conditioning means 2 Will remain off. In this case, the room temperature continues to decrease, the temperature in the bed continues to decrease gradually, and the user feels cold and awakes midway, making it impossible to obtain a comfortable sleep. For this reason, in the heating operation with an error function, after the set temperature of the air-conditioning means 2 is lowered to T0-1 ° C., the set temperature is forcibly lowered after a certain period of time (here, T0-2). C). In other words, the setting temperature is lowered to reduce the heating capacity. The subsequent operation is the same as that in the normal heating operation. By performing the above control, air conditioning control capable of realizing comfortable sleep and energy saving even when some error occurs can be realized.

  In addition, in the linear mode driving | operation of heating operation and the heating operation with an error function, when the energy saving setting was made, you may make it combine with heating energy saving operation. That is, in the wake-up stage, the temperature that has been set higher than the initial set temperature T0 (here, T0 + 1 ° C.) is changed to a temperature that exceeds the initial set temperature T0 (here, T0-1 ° C. A dotted line in FIG. 10)) may be used.

As described above, according to the first embodiment, it is possible to obtain an air conditioner 1 that can perform comfortable air-conditioning control during sleep and has energy saving performance.
In the above description, the temperature range when the temperature is higher or lower than the initial set temperature is an example, and is not limited to the above temperature.

  In the first embodiment, it is assumed that there is only one sleeping person. However, the number of sleeping persons is not limited to one, and a plurality of persons may be used. In this case, a plurality of sensor portions in the sleep state acquisition system 100 are arranged, the sleep states of a plurality of persons are determined, and the following air conditioning control is performed. If two people are explained as an example, an intermediate or higher one of both comfortable temperatures is set as the initial set temperature T0. In the example of cooling, here, at the start of sleep, the set temperature is gradually lowered to a temperature lower than the initial set temperature T0 (for example, T1 = T0-1 ° C.). And when both go into deep sleep, the set temperature is updated to a temperature T1 that exceeds the initial set temperature T0. And even if one sleeper goes to deep sleep through deep sleep, when the other sleeper is still in the deep sleep state, the set temperature is not returned to the initial set temperature T0 but remains at the temperature T1. That is, the set temperature is not returned to the initial set temperature T0 until both sleepers transition from deep sleep to shallow sleep. Thereby, the temperature fall of the indoor space during the deep sleep where body temperature adjustment does not work can be prevented, and the awakening in the middle can be prevented.

  In addition, by predicting the wake-up, the room can be warmed in the late-night power hours. For example, the temperature tracking performance of the room is learned, and the set temperature is warmed higher than usual in the late-night power period so that the room becomes a comfortable temperature during the predicted wake-up time. As a result, a comfortable temperature is ensured even when the air-conditioning means 2 is OFF when waking up. When the temperature is set higher than the set temperature, if the heat load is large, the operation is started during the deep sleep period so as not to wake up due to a change in room temperature.

  In addition, in this Embodiment 1, although the example which performs an air-conditioning control based on the sleep state determined by the sleep state acquisition system 100 was provided, another sensor was provided, and sleep combined with the detection result of the sensor, It is possible to perform effective air conditioning control comfortably. Specifically, for example, a thermal pixel image sensed by scanning an infrared sensor, for example, a matrix thermopile, a monocular or a compound eye thermopile, an image by a camera such as a CMOS, a CCD, or a night vision function is used. The amount of clothes and the amount of futon of a sleeping person is estimated by infrared technology and image processing technology using these sensors. For example, in the case of a thermal pixel image, it is possible to discriminate body parts such as the head, limbs, and the surface temperature. Therefore, when the body is out of the futon, it can be determined which part is out of the futon, and the amount of clothing is determined, and the air conditioning control data (each set temperature in the waveform diagram shown in FIG. 4 etc.) And a certain period of time) may be changed. For example, during the heating operation in the winter season, the state of head cold foot heat is ideal, so the following control is performed. That is, in the first embodiment, the temperature of the indoor space is lowered at the start of sleep, but when the limbs are out of the futon, the set temperature is set to the normal set temperature (T0-1 ° C. in this example). The flaps and vanes for controlling the wind direction are controlled so that the temperature is set higher than the initial temperature T0 and the warm air is delivered toward the limbs so as not to warm the head. The futon position can be estimated from the position estimation based on the weight and distance detected by the contact sensor or non-contact sensor, and the condition of the futon (such as the amount and thickness of the futon) is determined by the thermistor. It can be grasped according to how it is changing.

  In addition, as described above, the air conditioning control may be controlled according to the determination result while actually determining the sleep state of the sleeper, or applied to the basic sleep cycle pattern (sleep state transition pattern). Air conditioning control may be performed. When a basic sleep cycle pattern is used, predictive control is possible. That is, since the basic sleep cycle can be predicted to become deep sleep after several hours from the start of falling asleep, it is possible to control to lower the set temperature and induce deep sleep before entering deep sleep. Further, it is possible to perform control such that the timing of exiting REM sleep is predicted based on the basic sleep cycle pattern, and the set temperature is changed to the wake-up temperature early.

  However, even in the same individual, the sleep cycle pattern varies depending on the life cycle (for example, in the case of a night shift worker, wakes up at 19:00, works from 21:00 to 5:00, and goes to bed at 12:00) and health conditions. Due to the change, the sleep cycle of the sleeper may be a different sleep cycle pattern from the basic sleep cycle. For this reason, a large amount of sample data is actually measured, and the feature amount of the sleep state change is patterned into a database for each life cycle and health state. Then, during actual use, the database is searched based on the sleep cycle pattern obtained from the start of sleep to the initial stage of sleep of, for example, about 3 hours, the matching sleep cycle pattern is extracted, and the prediction is performed based on the extracted sleep cycle pattern Control may be performed. In patterning, for each combination of life cycle and sleep control, or for each combination of health condition and sleep control, points are given by the time until sleep onset, the deep sleep ratio, and the number of cycles.

  In the above description, it is assumed that the air conditioning control data is uniquely determined for each operation mode. However, the user can set the temperature setting of the air conditioning control data in a temperature range that does not deviate from the characteristics of each operation mode. The temperature may be changed by input means such as the remote controller 5. And it is set as the structure which can switch air-conditioning control data for every user individual. Moreover, when the system which determines a sleep state from a sleeper's biological state (respiration, heartbeat, etc.) is adopted as the sleep state acquisition system 100, an individual is specified from the biological state information, and air conditioning control is performed for each individual. Data may be switched automatically. By individually registering the air-conditioning control data, it is possible to make optimum air-conditioning settings suitable for the individual, and it is possible to expect more comfortable and energy-saving air-conditioning control.

  In the sleep state acquisition system 100, since the sleeper's biological state information is detected in order to determine the sleep state, a change in the sleeper's biological state may be added to the air conditioning control. For example, in the normal cooling operation in summer, when deep sleep is reached and light sleep is reached, the set temperature is lowered to the initial set temperature T0 as shown in FIG. 4, and the initial set temperature T0 is continued during the shallow sleep. Yes. During control based on such air conditioning control data, the arithmetic device 4 determines the comfort level of the sleeper from the biological state information detected by the sleep state acquisition system 100. If the comfort level is high, the air conditioning control is set to save energy. You may make it switch to. It is assumed that the conventional method can be adopted as a method for determining the comfort level.

  The energy-saving setting here means, for example, that the set temperature is gradually increased from the initial set temperature T0 (for example, the temperature is increased by 0.5 ° C. every 15 minutes and increased to the maximum set temperature during deep sleep), and the cooling capacity Reduce energy consumption and save energy. In addition, when the comfort level of the sleeper is lowered by raising the set temperature (IQ plane disturbance (a lot of body movement and breathing is disturbed) in the fourth embodiment described later), if the heart beat, the sympathetic nerve by the RR interval When the condition becomes shorter, cancel the energy saving setting and stop raising the set temperature any more. By such control, it is possible to automatically set the energy saving mode without losing comfort and health. The energy saving setting here is merely an example, and is not limited to this.

  In addition, the air conditioner 1 is provided with a display panel to display the difference in electricity costs due to the energy-saving operation and by gradually raising the set temperature without changing to the initial set temperature T0 as in the above example. You may make it do.

Embodiment 2. FIG.
The first embodiment is configured assuming an embodiment in which the air conditioner 1 is used indoors independently. In the second embodiment, the air conditioner 1 is connected to a network such as the Internet, and a server is provided on the network to constitute an air conditioning system.

FIG. 13 is a diagram showing a configuration of an air conditioning system according to Embodiment 2 of the present invention.
The air conditioner system includes an air conditioner 1 that further includes a communication function in the air conditioner 1 of the first embodiment, and a server 101 that can communicate with the air conditioner 1. Connected via a network such as the Internet. The network connection may be any method as long as it can connect to the Internet, such as Ethernet (registered trademark), wireless LAN, and telephone line, and any method may be used.

  Further, in the air conditioning system of FIG. 13, one or more devices 102 installed in the same user's home as the air conditioner 1, and a user terminal (for example, a mobile phone) 103 used by a sleeper (user) Shows an example of being connected to the network. The device 102 is a device that has a function of acquiring biological information such as body movement by a mattress or a pyroelectric sensor and performing sleep determination, and is controlled based on the sleep determination result. For example, a dehumidifier, a humidifier, or the like is used. Applicable. Although FIG. 1 shows an example in which only one device 102 is connected, of course, a plurality of devices 102 may be connected. Note that the network configuration diagram of FIG. 13 is merely an example, and is not limited to this.

Hereinafter, usage forms that can be realized by connecting the air conditioner 1 on a network will be described. In the following description, various usage modes will be described as appropriate in addition to the usage modes corresponding to the network configuration of FIG.
By configuring the air conditioner 1 to be connected to the network in this way, various data set as defaults in the air conditioner 1 (threshold values used for sleep state determination in the sleep state acquisition system 100 (in Embodiment 4) Various data that enable more detailed sleep state determination and comfortable air conditioning control according to the physical condition of the sleeper than the other). The server 101 can be downloaded from the server 101. Various software (for example, a defect correction program or a patch program for improving performance) can be downloaded from the server 101 by connecting the air conditioner 1 to a network. Thereby, the software of the air conditioner 1 can always be updated.

  In addition, the air conditioner 1 acquires data detected by a sensor (biological information acquisition unit) mounted on the device 102 from the device 102 via the network, and uses it when determining a threshold for sleep state determination. It can also be configured. The threshold for determining the sleep state will be described in the fourth embodiment to be described later, but the sleep state determination is performed by analyzing the sleep data acquired by providing the learning period by the sleep state acquisition system 100 based on a predetermined algorithm. When the threshold value for the automatic setting is automatically set, the data obtained from the sensor (biological information acquisition means) mounted on the device 102 can be used to determine the threshold value. If the air conditioner 1 is equipped with sensors (biometric authentication sensors including a Doppler radar sensor) necessary for acquiring biological state information in the sleep state acquisition system 100, the cost becomes high. If the same data is acquired from the mounted sensor, the cost of the air conditioner 1 can be reduced.

  Also, sleep state determination data (threshold value for sleep state determination) according to race, sex, age, etc. may be prepared in advance and registered in the server 101 on the network. Then, parameters such as race, sex, and age may be input by the user on the air conditioner 1 side, and a threshold value may be requested from the server 101 together with these parameters to obtain the corresponding threshold value. In this case, more accurate sleep state determination becomes possible. It should be noted that, instead of the server 101, it may be registered in the memory of the air conditioner 1 so as not to use a network.

  In addition to the number of steps per day and physical condition (such as company residence time, commuting congestion, etc.), environmental information such as the thermal environment, sound, and light conditions of each location is taken into account. The air conditioner 1 may determine the physical condition using an existing program. The environmental information may be acquired from a sensor provided in the user terminal (for example, a mobile phone) 103, or may be acquired using an API of a management server such as a train or a road. Such environmental information may be centrally managed by the server 101 through the network and transmitted from the server 101 to the air conditioner 1. Alternatively, environmental information may be registered in the user terminal 103, connected to a home network when returning home, and the environmental information may be transferred from the user terminal 103 to the server 101 or the air conditioner 1.

  By these methods, the degree of change in the sleep state can be learned or registered in the database as a pattern, and the sleep state determination data and the air conditioning control data can be set by referring to it. For example, for a user who does not have a good physical condition, in order to reduce the load on the body, the upper and lower set temperatures of the cooling are kept within a certain range, the temperature is changed slowly, and the control is set to further reduce the load on the body.

  In addition, various information used in the sleep state acquisition system 100 (Doppler radar sensor or the like (see Embodiment 4 described later)) or biological information, health information, personal information (race, It is possible to upload personal attribute information such as sex, age, etc.) and life information (life cycle, etc.) to the server 101 via the network and analyze / manage / accumulate it on the server 101 side. Then, personal attribute information is transmitted from the air conditioner 1 side to the server 101 via the network, and sleep determination data (such as a threshold necessary for sleep state determination) and air conditioning control data that match the characteristics of the sleeper are transmitted to the server 101 side. It is also possible to obtain upon request. In addition, the personal attribute information accumulated on the server 101 side is classified into the same trader, the same age, the same sex, the region, the person type, etc., and the sleep state is analyzed and used for determining a threshold for determining the sleep state. Is possible.

  If the database is provided not on the air conditioner 1 but on the server 101, a large-capacity database can be used, so that a large amount of patterns can be accumulated. Moreover, since the pattern matching can be processed at high speed on the server 101 side, the air conditioner 1 can be configured at low cost.

  In addition, personal attribute information that has been approved for registration by an individual may be immediately updated and stored in the server 101 via a network and used as reference data for determining sleep determination data. Thereby, it is possible to keep the sleep determination data on the server 101 side in the latest state, and when performing the sleep state determination on the air conditioner 1 side, the latest sleep determination data can be acquired or referred to from the server 101. It becomes. As a result, more accurate sleep state determination software can be created and updated.

  The personal attribute information can be used for medical and health support, such as a health check in a hospital or the like, and adjustment of drug administration by a body clock. The personal attribute information can also be used for power company peak cuts. For example, the peak cut is not performed or restricted for the device 102 whose biological condition deteriorates when the power is limited, or the peak temperature level is determined by determining the temperature state of sensation. As a result, peak cuts that affect health or impair comfort can be avoided as much as possible, and power can be limited from the device 102 that is less likely to cause dissatisfaction.

  In addition, as a method for specifying an individual, various devices capable of acquiring information that can specify an individual (a biometric authentication device different from the sleep state acquisition system 100 of the air conditioner 1, an ID of a portable device, or an RFID of clothes) Etc. may be installed at the user's home, and an individual may be identified based on the personal identification information acquired by the device. The personal identification information for collation for collating with the personal identification information acquired at the user's home may be registered in the air conditioner 1 in advance, or registered in the server 101 and acquired via the network. You may make it collate. When biological information such as a Doppler radar sensor can be acquired, the frequency, sympathetic nerve trends such as the RR interval, respiratory / heartbeat amplitude, etc. are acquired when breathing / heartbeat is stable, such as when relaxing or sleeping. From these pieces of information, an individual may be specified using a conventionally known method. Further, the locus of an IQ plane according to the fourth embodiment to be described later may be patterned and matched to specify an individual.

  By the way, in general, devices connected via a network may have a function to identify each other's location, such as GPS and RFID, but even if there is no such function, depending on the communication method such as wireless It is possible to specify the distance and location between devices. It is also possible to employ the position specifying function in the air conditioner 1 to realize the following configuration.

  That is, in the sleep state acquisition system 100, a detection unit that detects biological information of a sleeping person is physically separated from the air conditioner 1 to form a separation type device, and the air conditioner 1 and the separation type device are mutually connected. It is assumed that data communication can be performed by a communication method that can specify the position of. As a result, the air conditioner 1 can identify the position of the separation-type device. Therefore, if the separation-type device is placed near a person such as a bedside and the air flow is sent avoiding the placement position of the separation-type device. Therefore, it is possible to avoid direct air flow to the person and to air-condition the vicinity of the person with gentle convection. By performing such control, air conditioning that does not cause the human body to feel a strong airflow is possible.

  In addition, a separate device is placed near a child sleeping in a baby bed, and the child's abnormality is detected (for example, apnea syndrome, respiratory failure, etc.) by detecting the respiratory rate and amplitude of the child. It is also possible.

  Moreover, you may make it control an air conditioning as follows according to a child's sleep cycle. During REM sleep, the brain is actively active, and it is said to be the time to make the brain. Also, unlike adults, children go to REM sleep immediately after falling asleep. Since it is not sensitive to temperature change at the end of REM sleep and deep sleep, when temperature change by air conditioning control is performed, it is performed at this timing (end of REM sleep and deep sleep). On the other hand, when it is desired to wake up a child, it is effective to change the temperature immediately before entering the REM, which is sensitive to temperature changes. It is also possible to gradually create a sleep rhythm by controlling lighting and music equipment from the air conditioner 1.

  Here, as a wireless communication system between the air conditioner 1 and the separation type apparatus, there are various systems such as infrared, weak, specific low power, 2.4 GHz band ISM such as a wireless LAN. If the synchronization accuracy between the air conditioner body and the internal clock of the separation-type device is high, the distance between each other can be specified from the time difference between the signals returned by the other party after a certain time from the signal transmitted by either one. Further, the position can be specified by the triangle method using the remote controller 5. Even if the synchronization of the internal clock is not accurate, it is determined by the transmission / reception time difference, and if reception processing is performed and processing for returning signals is minimized, a time proportional to the communication speed flying in space can be calculated. In the case of full-duplex communication, the time from when the bit of the transmission side signal is inverted during communication until the bit of the reception side signal is inverted may be used. If there is noise in the air, it may be performed after a random time, or by changing the frequency channel and performing again, or may be determined by calculating the distance based on the average of values excluding the upper and lower times of a plurality of channels.

  In addition, home appliances (humidifiers, dehumidifiers, air cleaners, lighting, etc.) that do not have a function to make sleep determination, and can be operated by infrared communication from the attached remote controller, for example. When the device is in the user's home, the home appliance may be controlled from the air conditioner 1 according to the sleep state determination result by the air conditioner 1. That is, by controlling home appliances, it is possible to create a comfortable and comfortable sleep environment such as indoor humidity and brightness. Therefore, a control signal such as a humidity setting signal and an illumination intensity signal for creating a comfortable sleep environment according to the sleep state is transmitted from the air conditioner 1 to the home appliance so as to create a comfortable sleep environment according to the sleep state. It may be.

Embodiment 3 FIG.
The third embodiment relates to an air conditioning system that allows a user to freely change the set temperature of air conditioning control data and allows the changed air conditioning control data to be widely disclosed on a network and provided to a plurality of users. Is.

FIG. 14 is a diagram illustrating a network configuration of the air-conditioning system according to the third embodiment. Similarly to the second embodiment, the air conditioner 1 of FIG. 14 is also provided with a communication function in the air conditioner 1 of the first embodiment, and a plurality of (here, two are illustrated) on the network. .
The air conditioner 1 has a configuration in which the user can set the set temperature of the air-conditioning control data prepared for each operation mode to the user's favorite temperature within a temperature range that does not depart from the characteristics of the operation mode. The set temperature may be changed by input means such as the remote controller 5.

  Here, an example of setting change will be described. As described in the first embodiment, in the hot operation, the temperature is set to be shifted to a lower temperature while maintaining the waveform shape substantially the same as the time lag control shown in FIG. The shift temperature is set to 1 ° C. in the hot operation in FIG. 8, but for example, if the user is hotter, air conditioning control data set to 2 ° C. is created.

  In addition, the air conditioner 1 includes a transmission unit that transmits the air conditioning control data created by the user to the server 101 via the network. In addition, a plurality of air conditioners 1 are connected on the network, and a plurality of users can similarly disclose the air conditioning control data created by themselves on the network. The arithmetic device 4 of the air conditioner 1 downloads desired air conditioning control data from a plurality of air conditioning control data disclosed on the network in response to a request from the user, and based on the downloaded air conditioning control data. The air conditioning control is executed, and sleep information including a sleeping state change (sleep cycle) of the sleeper in the air conditioning control is acquired by the sleep state acquisition system 100 and transmitted to the server 101.

  In addition, the server 101 publishes a plurality of air conditioning control data transmitted from each air conditioner 1 on the network, and votes the evaluation results by a plurality of users for the air conditioning control based on the disclosed air conditioning control data. Control means 101a that receives and collects the voting results, ranks them, and discloses them. For each air conditioning control data, the control unit 101a also has a function of calculating an average sleep comfort level based on sleep information from a plurality of users who have downloaded the air conditioning control data and publishing it according to the ranking result. The sleep comfort level is a score obtained by scoring the sleep time, deep sleep ratio, cycle number, and sleep time for each parameter for each age and sex, and adding the scores (100 points maximum). Moreover, the evaluation result by the user is evaluated with a maximum score of 10 points, for example.

FIG. 15 is an example of a ranking result screen showing a ranking result of a plurality of air conditioning control data disclosed on the network.
FIG. 15 shows an example in which the ranking of the air conditioning control data created by the user is disclosed together with the average value of evaluation by the user who tried the air conditioning control data and the average sleep (average of sleep comfort level).

  As described above, in the third embodiment, the user can change the air conditioning control data, and the air conditioning control data created by the user is disclosed on the network and ranked. Thereby, it can be expected that the air conditioning control data can be improved efficiently in the community. In addition, by using the air conditioning system of the third embodiment, air conditioning that reflects the user's evaluation while improving the variety of air conditioning control data tests such as energy-saving operation, hot summer operation, etc. in the market It is also useful for creating control data in a short period of time.

Embodiment 4 FIG.
The fourth embodiment and the subsequent embodiments will explain details of the sleep state acquisition system 100 of the first embodiment. The sleep state acquisition system 100 includes a Doppler radar sensor, transmits an electromagnetic wave to a sleeping human, receives a reflected wave thereof, and acquires biological state information such as respiration without contact using the received wave. It has a biological state acquisition device as a state detection unit. The sleep state acquisition system 100 determines the sleep state based on the biological state information acquired by the biological state acquisition device. Sleep state acquisition system 100 is not limited to the configuration described in the fourth embodiment and subsequent embodiments, and may be a device that determines a sleep state from other biological signals such as brain waves. Below, the biological state acquisition system 100 as an example of the sleep state acquisition system 100 is demonstrated in detail.

  In the biological state acquisition device described below, the activity state of a living body such as breathing, heartbeat, and body movement is acquired as basic data, and further, based on this basic data, the state of the autonomic nerve (sympathetic dominant, parasympathetic nerve) Superiority) and the sleep state of the living body. In each of the following embodiments, a case where the living state of a living body (for example, a human) during sleep is acquired will be described as an example.

(Respiration detection)
FIG. 16: is a block diagram which shows the structure of the biological condition acquisition system provided with the biological condition acquisition apparatus which concerns on Embodiment 4 of this invention. In the fourth embodiment, respiration information related to respiration is acquired as basic data of the biological state. A case where the state of the autonomic nerve and the human sleep state are calculated (acquired) based on the acquired respiratory information will be described.
The biological state acquisition system 100 transmits an electromagnetic wave (microwave) to a sleeping human and receives a reflected wave from the human of the transmitted wave, an IQ detector 20, and a bandpass filter. 30 and an AD converter 40. The biological state acquisition system 100 further includes an arithmetic device 50 as a biological state acquisition device and a storage device 60 that stores various data (such as learning data described later).

  The Doppler radar sensor 10 radiates electromagnetic waves toward a sleeping human and is arranged so as to be able to capture reflected waves reflected from the human body surface. The Doppler radar sensor 10 is composed of a module containing an antenna that receives a reflected wave as a received wave, an input / output amplifier, an oscillator, an IQ mixer (detector), a power source, and peripheral components (both shown in FIG. Not shown).

  The IQ detector 20 decomposes the reflected wave received by the antenna of the Doppler radar sensor 10 into an in-phase component (I signal) and a quadrature component (Q signal) with respect to the incident wave, and outputs them to the bandpass filter 30. The band-pass filter 30 has a low-frequency band-pass filter 31 for respiration detection, extracts a target signal and outputs it to the AD converter 40. Since the output of the IQ detector 20 is a signal in which not only respiration but also heartbeat and body movement are all superimposed, a respiration signal is extracted by passing the signal through the band-pass filter 31. Then, the signal after passing through the band pass filter 31 is converted into a digital signal by the AD converter 40 and output to the arithmetic unit 50. Note that the pass frequency band of the bandpass filter 31 for respiration detection is set in advance.

  The arithmetic device 50 is constituted by a microcomputer and includes a CPU, a ROM and a RAM inside, and operates according to various programs stored in the ROM. When the CPU executes the biological state acquisition program stored in the ROM, the IQ signal acquisition unit 51 that acquires the IQ signal from the AD converter 40 and the biological state acquisition unit 52 are functionally configured. .

  The biological state acquisition unit 52 applies the Doppler radar sensor 10 to a sleeping human and uses the Doppler effect caused by the movement of the human body surface by body movement such as breathing, heartbeat, and turning over the biological state (breathing). , Autonomic nerve state, sleep state, etc.).

  The biological state acquisition means 52 detects the respiration of the living body and calculates respiration information such as the respiration rate, and the autonomy for determining the autonomic nervous state of the living body based on the respiration information calculated by the respiration detection means 53. And a nerve state determination means 54. The biological state acquisition unit 52 further includes a sleep state determination unit 55 that determines the sleep state of the biological body based on the respiration information calculated by the respiration detection unit 53.

  FIG. 16 shows an example in which the band pass filter 31 and the AD converter 40 are provided in the subsequent stage of the Doppler radar sensor 10. However, the band pass filter 31 and the AD converter 40 may be incorporated in the module of the Doppler radar sensor 10. good. Further, the band pass filter 31 may be configured by a digital filter and disposed at the subsequent stage of the AD converter 40. Further, when the output of the IQ detector 20 is insufficient, an amplifier may be further arranged in front of the AD converter 40, and the configuration may be any configuration as long as the IQ signal can be accurately filtered and input to the arithmetic unit 50. .

Hereinafter, the operation of the biological state acquisition system 100 will be described.
The Doppler radar sensor 10 emits electromagnetic waves toward a sleeping human and receives a reflected wave from the human with an antenna (not shown). Then, the Doppler radar sensor 10 amplifies the received reflected wave with an amplifier and outputs it to the IQ detector 20. The signal input to the IQ detector 20 is decomposed into an I signal and a Q signal, a respiratory signal is extracted by a band pass filter, converted into a digital signal by the AD converter 40, and then output to the arithmetic unit 50. Is done. Respiration signals (I signal and Q signal) from the AD converter 40 are sequentially input to the arithmetic device 50 in time series.

  The arithmetic device 50 acquires the I signal and the Q signal from the AD converter 40 by the IQ signal acquisition means 51, and performs respiration detection based on the trajectory of the acquired signal on the IQ plane. Hereinafter, the respiration detection method in the respiration detection means 53 of the arithmetic unit 50 will be described in detail.

  First, the measurement principle of the biological state will be briefly described. In humans, the body surface moves due to movements of respiratory muscles and diaphragm due to breathing, pulsation due to heartbeat, and body movement. In the case of breathing, the body surface of the chest is moved by breathing. For this reason, a Doppler shift corresponding to the speed of movement of the body surface due to respiration occurs in the reflected wave from the person received by the antenna of the Doppler radar sensor 10.

  When the behavior of the body surface during breathing is analyzed in detail, the speed of movement of the body surface is substantially zero before the start of breathing (start of inhalation), and after the inhalation starts, the speed gradually increases and reaches a peak. Then, the velocity decreases this time toward the end point of breathing (when switching to sucking and exhaling), and becomes substantially zero at the end point of breathing. After the start of exhalation, the speed of movement of the body surface gradually increases and reaches a peak, and then the speed decreases toward the exhalation end point (at the end of exhalation), and the speed is almost zero at the exhalation end point. It becomes. Respiration is detected by detecting such a change in the velocity of the body surface from the detection result of IQ detection.

FIG. 17 is a flowchart showing the flow of the biological state acquisition process in the arithmetic device of FIG. Hereinafter, the flow of the biological state acquisition process will be described with reference to FIG.
(S1: I signal and Q signal acquisition)
The IQ signal acquisition means 51 of the arithmetic device 50 acquires IQ signals sequentially output in time series from the AD converter 40 in accordance with the movement of the human body surface. The biological state acquisition unit 52 first calculates the norm of the velocity vector based on the acquired I signal and the locus of the Q signal on the IQ plane. Hereinafter, calculation of the norm of the velocity vector will be described.

(S2: Speed vector norm calculation, S2: Respiration detection)
When the acquired signal acquired by the IQ signal acquiring means 51 is plotted on the IQ plane, a locus as shown in FIG. 18 is drawn according to the movement of the human body surface.

FIG. 18 is a diagram illustrating an example of an IQ plane of a reflected wave according to the movement of the body surface during human breathing. 18A shows a case where the body surface approaches the Doppler radar sensor 10, and FIG. 18B shows a case where the body surface moves away from the Doppler radar sensor 10.
The arrow in the counterclockwise direction in FIG. 18A indicates the direction of the locus of coordinates on the IQ plane of the IQ signal when the body surface approaches the Doppler radar sensor 10. The arrow in the clockwise direction in FIG. 18B indicates the direction of the locus of the coordinates on the IQ plane of the IQ signal when the body surface moves away from the Doppler radar sensor 10. Further, points 1 to 9 in FIG. 18A and points 1 to 9 in FIG. 18B are plots of IQ signal coordinates for each sampling time.
As described above, the movement of the body surface during breathing reaches a peak when the inhalation is started and the speed gradually increases after the inhalation starts. Then, the velocity decreases this time toward the end point of breathing (when switching to sucking and exhaling), and becomes substantially zero at the end point of breathing. When the body surface moves quickly, the phase change of the reflected wave due to the Doppler effect increases. For this reason, the interval between the points in FIG. 18A on the IQ plane is narrow at the start of operation, gradually increases, and becomes the longest in the middle portion of the trajectory, and then decreases again. The state of going is shown. An IQ plane having similar characteristics is also obtained during discharge.

  The velocity vector corresponds to a vector difference between points (point 1 to point 9 in the figure) on the IQ plane obtained at each sampling interval. Arrow (a) indicates the velocity vector between points 5 and 6. The length of the vector difference between each point corresponds to the norm of the velocity vector at that timing. If the norm of the velocity vector is illustrated in time series, the following diagram is shown in FIG.

FIG. 19 is a diagram illustrating time-series data of norms of velocity vectors. In FIG. 19, the horizontal axis represents time, and the vertical axis represents the norm of the velocity vector.
When the body surface moves due to breathing, as described above, the body surface moves at the highest speed approximately halfway between the inhalation and exhalation operations, and at the end point of breathing (when switching between inhalation and exhalation), the body surface The speed of is almost zero. Therefore, the time-series data of the norm of the velocity vector draws a mountain-shaped curve in each of the suction operation and the discharge operation, as shown in FIG. Therefore, two peaks in the time series data of the norm of the velocity vector indicate one breath. Therefore, it is possible to sequentially calculate the norm of the velocity vector from the IQ signal and to detect respiration from the time series data of the norm.

  In this way, using the feature of the movement of the body surface due to respiration, one respiration is extracted from the respiration signal, with the trajectory speed on the IQ plane being substantially zero at the end point of respiration. That is, a respiratory signal corresponding to one breath is detected based on periodic fluctuations in the waveform of the time series data of the velocity vector norm due to the velocity vector norm being substantially zero when switching between breathing in and breathing out. . Since no frequency analysis is required when calculating the norm, it is possible to detect respiration with a simple and low load.

  The method shown in FIGS. 20 to 22 can be used as a specific calculation process for detecting two mountain curves as one breath from time-series data of the velocity vector norm. In the method of FIG. 20, a constant value is subtracted from the time series data of the velocity vector norm, and the zero cross point of the time series data after the subtraction is detected. One breath is counted for every four zero-cross points. As another method, as shown in FIG. 21, the peak of velocity norm vector data may be extracted, and the time from the appearance of one peak to the appearance of the second peak may be counted as one breath. As the reverse method of FIG. 21, the bottom of the valley of the velocity vector norm data is extracted as shown in FIG. 22, and the time from the appearance of one valley to the appearance of the second valley is counted as one breath. May be. In the method shown in FIG. 20, when the amplitude of the norm of the velocity vector varies, a portion that does not zero-cross occurs depending on the setting of a constant numerical value, and there is a possibility that respiration of that portion cannot be counted. On the other hand, in the case of the method of FIGS. 21 and 22, respiration can be counted even when the amplitude of the norm of the velocity vector is not constant.

(S3: Calculation of respiratory rate and fluctuation range (standard deviation) of respiratory cycle)
The respiration detection means 53 detects two mountain curves as one respiration from the time-series norm calculation result as described above, and calculates respiration information. As the respiration information, the respiration rate for a certain period (for example, the past 2 minutes) is calculated. In addition, the fluctuation width of the respiratory cycle for a certain period (the fluctuation width of the time required for one breath (standard deviation)) is calculated.

(S4: Respiratory rate correction necessity determination / correction (if correction is required))
During sleep, when the movement of the body surface due to respiration is stable, respiration can be detected by the above method, but when the movement of the body surface is complicated, the respiration may not be accurately counted. For example, a case where a plurality of muscles are active to cancel the Doppler shift and the movement of the body surface cannot be detected can be considered. In such a case, it is necessary to correct the respiratory rate. Therefore, in step S4, a respiration rate correction necessity determination process is performed to determine whether respiration can be accurately counted. The details of the correction necessity determination process will be described later. Here, the description of the biological state acquisition process when it is determined that the respiration rate can be accurately counted and correction is unnecessary is continued.

(S5: Autonomic nerve state determination)
When the state of the autonomic nerve is the sympathetic nerve dominant state, the respiratory rate is large and the fluctuation range of the respiratory cycle is large. On the other hand, when the parasympathetic nerve is dominant, the respiratory rate is small and the fluctuation range of the respiratory cycle is small. Therefore, the state of the autonomic nerve can be determined by obtaining the fluctuation range of the respiratory rate and the respiratory cycle.

  The autonomic nerve state determination unit 54 calculates an index for determining the state of the autonomic nerve based on the respiration information calculated by the respiration detection unit. This index may be the value of the fluctuation rate itself of the respiratory rate or the respiratory cycle, or may be a value obtained by substituting each into a certain function, and takes a larger value as the sympathetic nerve is dominant. Shall. The state of the autonomic nerve can be determined by this index. For example, the index is compared with a preset threshold. If the index is larger than the threshold, it is determined that the sympathetic nerve is dominant, and if the index is less than the threshold, it is determined that the parasympathetic nerve is dominant. In addition, for example, the degree of sympathetic nerve activity may be determined.

(S6-S10: Sleep state determination)
Moreover, the sleep state determination means 55 determines a sleep state based on respiratory information. Next, the operation of the sleep state determination unit 55 will be described.

  Here, prior to describing the operation of the sleep state determination means 55, the sleep state will be described first. Sleep is generally divided into REM sleep, which is light sleep, and non-REM sleep, which is deep sleep. Sleep states are further defined in detail, and six states of awakening, REM sleep, and sleep depths 1, 2, 3, and 4 are defined. The sleep depths 1, 2, 3, and 4 are obtained by further dividing the non-REM sleep into four stages. In the non-REM sleep, the sleep depth 1 is the shallowest and the sleep depth 4 is the deepest.

  REM sleep, sleep depth 1, 2, 3, 4 and so on until the next awakening from the start of sleep onset, then sleep depth 3, 2, 1, REM The sleep cycle of shifting to sleep is usually repeated with a period of about 90 minutes. In this example, one of REM sleep, shallow sleep (for example, sleep depths 1 and 2), and deep sleep (for example, sleep depths 3 and 4) is determined based on the respiratory rate and the fluctuation range of the respiratory cycle.

  It is known that the aspect of human respiratory rate during sleep changes depending on the sleep state. In general, the respiratory rate during deep sleep is low and stable (the fluctuation range of the respiratory cycle is small), and the respiratory rate during shallow sleep is high and unstable (the fluctuation range of the respiratory cycle is large). Moreover, it is the most unstable during REM sleep, and the fluctuation range of the respiratory cycle is even larger. Therefore, the first respiratory rate threshold value, the second respiratory rate threshold value (<first respiratory rate threshold value), the first variation width threshold value, and the second variation width threshold value (<first variation) for determining REM sleep, shallow sleep, and deep sleep. (Variation width threshold value) is obtained and set in advance by experiments or the like, and the sleep state is determined by comparison with each threshold value.

  That is, if the respiration rate is equal to or greater than the first respiration rate threshold and the variation width of the respiration cycle is equal to or greater than the first variation width threshold, it is determined that REM sleep (S6, S8). If the respiratory rate is less than the first respiratory rate threshold value and greater than or equal to the second respiratory rate threshold value, and the fluctuation range of the respiratory cycle is less than the first fluctuation range threshold value and greater than or equal to the second fluctuation range threshold value, it is determined to be shallow sleep (S6, S9). . If the respiration rate is less than the second respiration rate threshold value and the fluctuation range of the respiration cycle is less than the second fluctuation range threshold value, deep sleep is determined (S6, S10).

  These threshold values may be set for each individual based on the sleep data obtained by providing a learning period and acquiring sleep data of one sleep cycle or more. Further, sleep data during the learning period may be analyzed based on a predetermined algorithm, and a threshold value may be determined and automatically set.

(Detailed description of determination of necessity of correction of respiration rate in step S4 in FIG. 17)
Hereinafter, details of the determination of necessity / unnecessity of respiration rate will be described. The case where the correction of the respiration rate is necessary corresponds to the case where the movement of the body surface during respiration is complicated and the Doppler shift is canceled as described above. Next, FIG. 23 shows an example of time-series data of the displacement amount (average value) of the body surface when the movement of the body surface is complicated.

FIG. 23 is a diagram illustrating an example of time-series data of the displacement amount (average value) of the body surface when the movement of the body surface is complicated. The solid line shows the case where the movement of the body surface is complicated. Note that the dotted line extending in the vertical direction in FIG. 23 shows the case where the body surface moves normally for reference, and the amount of displacement of the body surface gradually increases over time, and then starts to decrease. It shows how the operation is repeated.
In the example of FIG. 23, as an example of a case where the movement of the body surface is complicated, an example in which there is a period in which the average value of the movement of the entire body surface does not fluctuate during each of the suction and discharge operations. A period surrounded by a horizontally long ellipse in FIG. 23 indicates the period. The IQ plane when the movement of the body surface indicated by the solid line in the figure is measured is as shown in FIG.

FIG. 24 is a diagram showing an IQ plane when the movement of the body surface as shown in FIG. 23 is measured. Note that FIG. 24 illustrates the suction operation and the discharge operation in one breath of FIG.
In the period in which the average value of the movement of the entire body surface does not change, the movement close to the Doppler radar sensor and the movement away from the Doppler radar sensor are mixed on the entire body surface. As a result, the Doppler shift is offset. Therefore, the velocity vector on the IQ plane of the IQ signal in that period becomes zero. In FIG. 24, a portion surrounded by a vertically long ellipse indicates a portion where the velocity vector becomes zero. Therefore, the time series data of the norm of the velocity vector as shown in FIG. 24 is as shown in FIG.

FIG. 25 is a diagram showing time-series data of norms of velocity vectors on the IQ plane of FIG.
In the case of the IQ plane of FIG. 24, in the time series data of the norm of the velocity vector, each of the suction operation and the discharge operation appears as two peaks as shown in FIG. That is, one breath appears as four mountains. As described above, the breath detection means 53 employs an algorithm that counts two mountains as one breath. Therefore, if one breath appears as four mountains, the portion that is one breath accurately counts as two breaths. It will be done.

26 and 27 are diagrams showing a respiratory cycle (breathing time) and its distribution. FIG. 26 shows a case where the breath count is performed normally, and FIG. 27 shows a case where the breath count is not performed normally. Yes. 26 and 27 are diagrams in which the respiratory cycle of each breath is plotted as the vertical axis in the order of each breath taken on the horizontal axis.
In the case of the norm of the velocity vector in FIG. 19, the respiratory cycle is a time indicated by times Ta, Tb, and Tc. If the frequency distribution of the respiratory cycle is taken, a substantially normal distribution shape is obtained as shown in FIG. On the other hand, in the case of the norm of the velocity vector in FIG. 25, exactly four peaks should be counted as one breath and the breathing cycle should be calculated as time TA + TB. Calculated as TA, TB... Therefore, as shown in FIG. 27, the respiratory cycle frequency distribution is clearly a bipolar distribution with two peaks. Therefore, the respiration detecting means 53 calculates the frequency distribution of the respiration cycle after counting the respiration rate, and can determine whether or not the respiration count is normally performed depending on whether or not the frequency distribution has a substantially normal distribution shape. .

  By the way, the case where the movement of the body surface due to breathing is complicated specifically corresponds to, for example, the case where the person is sleeping sideways, and the posture at the time of sleep influences. Therefore, it is unlikely that a normal respiratory count and an abnormal respiratory count are mixed in a short period of time. Even if they are mixed, the number is small. Based on this assumption, it is possible to determine whether respiration counting is normally performed or not from the frequency distribution of the respiration cycle.

FIG. 28 is a flowchart showing the flow of the correction necessity determination process and the correction process (when correction is necessary) in FIG. The flow of the necessity determination process and the correction process (when correction is necessary) will be described below with reference to FIG.
The respiration detection means 53 calculates the frequency distribution of the respiration cycle (S21), and checks whether the distribution has a substantially normal distribution shape (S22). If the distribution is a substantially normal distribution shape, it is determined that normal respiration rate counting is being performed, and it is determined that correction is unnecessary (S23). On the other hand, if the respiratory cycle distribution deviates from the substantially normal distribution shape, it is determined that normal respiration rate count is not performed, and it is determined that correction is necessary (S24). Whether or not the frequency distribution of the respiratory cycle has a substantially normal distribution shape is determined by calculating a moment of the frequency distribution of the respiratory cycle and determining the threshold value. Specifically, for example, the degree of kurtosis, which is a third moment, is calculated, and if the degree of kurtosis deviates from a preset threshold value 3 (normal distribution), it is determined that the degree of kurtosis deviates from a substantially normal distribution shape. .

  When it is determined that correction is required by the above algorithm, the respiration detecting means 53 estimates the number N of peaks in the frequency distribution of the respiration cycle (for example, by maximum likelihood estimation) (S25). 23 to 27 exemplify and explain the case of N = 2, but the number is not limited to two depending on the movement of the body surface, and there may be a plurality of cases. Based on the number N, the respiration rate and the respiration cycle in the past fixed period calculated in step S3 (see FIG. 17) are corrected (S26). Hereinafter, this correction will be described with a specific example.

FIGS. 29 to 31 are explanatory diagrams of calculation processing when counting respiration from the time series data of the norm of the velocity vector of FIG. 29 shows the count by the zero cross point, FIG. 30 shows the count by the peak extraction, and FIG. 31 shows the count by the valley extraction. Here, it is assumed that the number N of peaks in the distribution of the respiratory cycle is N = 2.
In step S3, since an algorithm that counts two mountains as one breath is adopted, in the case of the measurement data in FIG. 25, as shown in FIGS. Be counted. Therefore, correction is performed to recount 2 breaths as 1 breath and the respiratory cycle is corrected. Specifically, after calculating the respiration rate for a certain period, the respiration rate of the calculation result is divided by 2 to obtain a corrected respiration rate. It should be noted that, from the time-series data of the norm of the velocity vector for a certain period, for example, correction is performed by recounting one breath for 4 (number of zero crosses) × N (number of peaks) by a counting method using zero cross points, for example. It may be. In addition, for example, the correction of the respiratory cycle is performed by correcting the time TA + TB as the respiratory cycle, as described in the example of FIG.

  As described above, if it is determined in step S24 that correction is necessary, correction is performed. Then, based on the corrected respiratory rate and respiratory cycle, the determination of the state of the autonomic nerve in step S5 and the determination of the sleep state in steps S6 to 10 are performed.

  The arithmetic device 50 outputs the determination result of the state of the autonomic nerve and the sleep state determined as described above to an external device such as an air conditioner. On the device side that has received the determination result, device control according to the determination result is performed. A specific example of device control will be described in a later-described sixth embodiment.

  As described above, in the fourth embodiment, a difference vector (velocity vector) is calculated from the trajectory of the IQ plane, a norm of the difference vector is calculated, and respiratory information (respiration rate and respiration) is calculated from the time series data of the norm. Period). Therefore, processing with a high load such as frequency analysis as in the past is not necessary, and respiratory information can be obtained using an inexpensive arithmetic device 50. In addition, the state of the autonomic nerve and the sleep state of the living body that are closely correlated with the respiratory information can be determined at high speed. Since the state of the autonomic nerve and the sleep state are related to each other, the sleep state may be determined based on the index indicating the state of the autonomic nerve. By estimating the activity of the autonomic nerve accompanying breathing, it becomes possible to determine the sleep state according to the physiological model.

  Further, according to the fourth embodiment, it is possible to detect a counting error that counts one breath as a plurality of breaths. When a counting error is detected, the respiratory rate is corrected. Calculation accuracy can be improved. As a result, the determination accuracy of the state of the autonomic nerve and the sleep state can be improved.

  In addition, since the determination of the state of the autonomic nerve and the determination of the sleep state are performed in a multidimensional space using both the respiratory rate and the fluctuation range of the respiratory cycle, it is possible to determine with high accuracy. Although some accuracy degradation cannot be denied, the determination of the state of the autonomic nerve and the determination of the sleep state may be performed based on either the respiratory rate or the fluctuation range of the respiratory cycle. In the fourth embodiment, the respiratory information and the fluctuation range of the respiratory cycle are given as examples of the respiratory information. However, the present invention is not limited to this, and the respiratory information may further include the fluctuation range of the respiratory rate. Good.

  In addition, when the moving speed of the body surface is fast, the phase of the IQ signal may change by 360 degrees or more. In this case, since the coordinates on the IQ plane are the same, it is difficult to count one breath from simple IQ plane coordinates. However, in the fourth embodiment, since the norm of the velocity vector of the IQ signal between each sampling is used and the respiration is detected based on an event such as whether the body surface has moved or stopped during each sampling time, Respiration can be counted regardless of the moving speed of the body surface.

  In the fourth embodiment, the case of detecting respiration has been described. However, if the change can be acquired as information by IQ detection such as heartbeat and pulse wave, it can be detected by the same method as described above. In the case of respiration, since the body surface has a larger movement range than that of heartbeats and pulse waves and the movement is slow, it is easy to find that the speed becomes substantially zero at the turnaround point of respiration. For this reason, the method of Embodiment 4 is particularly preferable in the case of breathing. In the case of breathing, since the body surface has a large movement width, detection is possible even at a position away from the body surface.

  In the fourth embodiment, an example of detecting respiration mainly during sleep has been described. However, not only during sleep, but also in a relaxed state or during driving, respiration detection can be performed by changing the filter. Is possible. If it is a digital filter, it is possible to change the filter automatically, and it is also possible to automatically change the setting according to the scene from which the biological state is acquired.

  As a method of preventing multiple false counts of breathing, for example, the locus on the IQ plane itself is filtered with a moving average to absorb the portion where the velocity becomes zero in the waveform of FIG. Based on this, the respiratory rate may be counted. In this case, it is possible to prevent multiple breaths from being erroneously counted. The filter is a moving average, but any filter can be used as long as it can suppress minute changes and steep changes.

Embodiment 5 FIG. (Heart rate detection)
The fifth embodiment describes a method suitable for acquiring a biological state in which the movement of the body surface due to body movement is small, such as a heartbeat, and the movement is complicated.

FIG. 32 is a block diagram showing a configuration of the biological state acquisition system according to Embodiment 5 of the present invention. In FIG. 32, the same parts as those of the fourth embodiment shown in FIG.
In the biological state acquisition system 200 according to the fifth embodiment, the bandpass filter 30 includes a high-frequency bandpass filter 32 for heartbeat / body movement instead of the respiratory bandpass filter 31 according to the fourth embodiment. The biological state acquisition unit 52 includes a heart rate detection unit 56 that detects a heartbeat of the living body and calculates heart rate information such as a heart rate, an autonomic nerve state determination unit 54A, and a sleep state determination unit 55A. Other configurations are the same as those in the fourth embodiment.

  Since the output of the IQ detector 20 is a signal in which all of respiration, heartbeat, and body movement are superimposed, the heartbeat and body movement are obtained by passing this signal through a high-frequency bandpass filter 32 for detecting heartbeat and body movement. Signal is extracted. The extracted signal is converted into a digital signal by the AD converter 40 and input to the arithmetic unit 50. The pass frequency band of the band pass filter 32 is set in advance.

  The heartbeat detection unit 56 includes a low-pass filter (not shown) that removes high-frequency components from the acquisition signals (I signal and Q signal) acquired by the IQ signal acquisition unit 51. The heartbeat signal is extracted by passing the measurement data input to the arithmetic unit 50 through a low-pass filter. The heartbeat detecting means 56 detects a heartbeat signal corresponding to one heartbeat based on the periodic fluctuation of the waveform of the heartbeat signal, and calculates the heart rate in the unit period as heartbeat information.

  The autonomic nerve state determination unit 54 </ b> A calculates an index indicating the autonomic nerve state based on the heartbeat information calculated by the heartbeat detection unit 56.

  The sleep state determination unit 55A determines the sleep state based on the heartbeat information calculated by the heartbeat detection unit 56.

The following description will focus on the differences of the fifth embodiment from the fourth embodiment.
FIG. 33 is a flowchart showing the flow of the biological state acquisition process in the arithmetic device according to Embodiment 5 of the present invention.
(S31: I signal and Q signal acquisition)
The I signal and the Q signal output from the IQ detector 20 in the biological state acquisition system are signals in which respiration, heartbeat, and body motion are all mixed. For this reason, the I signal and Q signal output from the IQ detector 20 are passed through the high-frequency bandpass filter 32 for detecting heartbeat and body motion, thereby extracting the heartbeat signal and body motion signal. This signal is converted into a digital signal by the AD converter 40 and input to the IQ signal acquisition means 51. The pass frequency band of the band pass filter 32 is set in advance. In this way, the IQ signal acquisition means 51 acquires IQ signals (heartbeat and body motion signals) sequentially output in time series from the AD converter 40 according to the movement of the human body surface.

(S32: Calculate time series data of norm of IQ vector)
The heartbeat detecting unit 56 expresses a point on the IQ plane of the acquired signal (a signal in which the heartbeat signal and the body motion signal are mixed) acquired by the IQ signal acquiring unit 51 as a vector from the origin (hereinafter, this vector). (The position vector of the acquired signal on the IQ plane is called the IQ vector). Then, the norm of the IQ vector (amplitude = square root of the sum of squares of I and Q) is calculated.

(S33: Heartbeat signal extraction)
Here, since the acquisition signal acquired by the IQ signal acquisition unit 51 is a signal in which the heartbeat signal and the body motion signal are mixed, the heartbeat detection unit 56 passes the low-pass filter (not shown) as described above. . As a result, a waveform of a heartbeat signal indicating pulsation due to a heartbeat is obtained.

FIG. 34 is a diagram showing an IQ signal output in a state where the heartbeat signal and the body motion signal are mixed, and shows the IQ signal after the band-pass filter. FIG. 35 is a diagram showing a vector norm waveform after the signal output of FIG. 34 is subjected to envelope processing and then low-pass filter processing. FIG. 35 also shows a reference (actual pulsation waveform) for reference.
As apparent from a comparison between FIG. 34 and FIG. 35, a waveform of a heartbeat signal synchronized with an actual pulsation waveform is obtained by passing through a low-pass filter (not shown).

  The heartbeat detecting means 56 detects a heartbeat signal corresponding to one heartbeat based on the periodic fluctuation of the waveform of the heartbeat signal (I signal and Q signal) after passing through the low-pass filter, and uses the heart rate in the unit period as heartbeat information. calculate. Hereinafter, specific processing of heartbeat detection will be described.

(S34: Calculating fluctuation range of heart rate during unit period and heart rate during certain period)
FIG. 36 is a diagram illustrating time-series data of the signal strength (amplitude = square root of square sum of each of I and Q) of the I signal and the Q signal after the low-pass filter processing. FIG. 36 corresponds to a partially enlarged view of FIG.
The heart rate detecting means 56 counts the heart rate from time series data (hereinafter referred to as amplitude time series data) of the signal intensity (amplitude = square root of sum of squares of I and Q) of the I signal and the Q signal after low pass filter processing. To do. As shown in FIG. 36, the period from the peak of the amplitude time series data to the next peak is counted as one beat.

  As a specific calculation process for counting the heart rate from the amplitude time series data, the method shown in FIGS. 37 to 39 can be used. In the method of FIG. 37, a constant value is subtracted from the amplitude time-series data, and the zero cross point of the data after subtraction is detected. One beat is counted for every three zero-cross points. As another method, the peak of amplitude time-series data may be extracted as shown in FIG. 38 and counted from one peak to the next peak as one beat. Further, as another method, as shown in FIG. 39, the bottom of the valley of the amplitude time series data is extracted, and the time from when one valley bottom appears until the next valley bottom appears is counted as one beat. Also good.

(Heart rate time series data generation)
The heart rate detecting means 56 calculates the heart rate for a unit period (for example, the past one minute) by the above method. The above heart rate is calculated over a certain period (for example, 3 minutes) to generate heart rate time-series data. The heart rate detecting means 56 calculates the fluctuation range (standard deviation) of the heart rate within a certain period. As described above, the heart rate detecting means 56 calculates the heart rate and the fluctuation range of the heart rate as the heart rate information.

(S35: Heart rate correction necessity determination / correction (if correction is required))
During sleep, when the pulsation of the body surface due to a heartbeat is complicated, for example, the body surface may pulsate in two stages in one beat. In this case, in the above method, one beat may be counted as a plurality of beats. In this case, the heart rate needs to be corrected. Therefore, in step S35, it is determined whether or not the heart rate needs to be corrected in order to determine whether the heart rate can be accurately counted. The details of the correction necessity determination process will be described later. Here, the description of the biological state acquisition process when it is determined that the heart rate can be accurately counted and correction is unnecessary is continued.

(S36: Autonomic nerve state determination)
The autonomic nerve state determination unit 54 </ b> A calculates an index for determining the state of the autonomic nerve based on the heartbeat information calculated by the heartbeat detection unit 56. This index may be the value of the heart rate per unit time or the fluctuation range of the heart rate over a certain period, or may be a value obtained by substituting each into a certain function, etc. , Take a large value. The state of the autonomic nerve can be determined by this index. For example, the index is compared with a preset threshold. If the index is larger than the threshold, it is determined that the sympathetic nerve is dominant, and if the index is less than the threshold, it is determined that the parasympathetic nerve is dominant. In addition, for example, the degree of sympathetic nerve activity may be determined.

(S37-S41: Sleep state determination)
The sleep state determination unit 55A determines the sleep state based on the heart rate information calculated by the heart rate detection unit 56. Next, the operation of the sleep state determination unit 55A will be described.

  It is known that the human heart rate during sleep changes in appearance depending on the sleep state, similar to the respiratory rate described above. In general, the heart rate during deep sleep is low and stable (the heart rate fluctuation range is small), and during shallow sleep, the heart rate is high and unstable (the heart rate fluctuation range is large). Moreover, it is the most unstable during REM sleep, and the heart rate fluctuation range is even larger. Therefore, the first heart rate threshold value, the second heart rate threshold value (<first heart rate threshold value), the first heart rate fluctuation range threshold value, and the second heart rate fluctuation range threshold value for determining REM sleep, shallow sleep, and deep sleep. (<First heart rate fluctuation range threshold) is set in advance, and the sleep state is determined by comparison with each threshold.

  That is, if the heart rate is equal to or greater than the first heart rate threshold and the heart rate variation is equal to or greater than the first heart rate variation, the REM sleep is determined (S37, S39). If the heart rate is less than the first heart rate threshold value and greater than or equal to the second heart rate threshold value, and the heart rate fluctuation width is less than the first heart rate fluctuation width threshold and greater than or equal to the second heart rate fluctuation width threshold value, it is determined to be shallow sleep (S38). , S40). If the heart rate is less than the second heart rate threshold value and the heart rate fluctuation range is less than the second heart rate fluctuation range threshold value, it is determined to be deep sleep (S38, S41).

  These threshold values may be set for each individual based on the sleep data obtained by providing a learning period and acquiring sleep data of one sleep cycle or more. The sleep data during the learning period may be analyzed based on a predetermined algorithm, and a threshold value may be determined and automatically set.

(Detailed description of determination of necessity of heart rate correction in step S35 in FIG. 33)
Hereinafter, details of the necessity determination of heart rate correction will be described. The case where the heart rate needs to be corrected corresponds to, for example, the case where the body surface pulsates in two stages in one beat as described above. FIG. 40 shows an example of time-series data of the displacement amount (average value) of the body surface when the movement of the body surface is complicated.

FIG. 40 is a diagram showing amplitude time-series data, and shows a heartbeat signal when the body surface pulses in two stages in one beat.
When the heart rate detection means 56 counts the heart rate from the amplitude time series data, the measurement data in FIG. 40 is counted as 2 beats as shown in FIGS. 41 to 43 correspond to the counting methods shown in FIGS. 37 to 39, respectively. The heart rate correction necessity determination algorithm is basically the same as the respiratory rate correction necessity determination algorithm of the fourth embodiment, and the heart rate correction necessity determination process will be briefly described below.

FIG. 44 is a flowchart showing the flow of the necessity determination process and the correction process (when correction is required) in FIG. 45 and 46 are diagrams showing a heart rate for each unit period and a frequency distribution of the heart rate. FIG. 45 shows a case where the heart rate count is normally performed, and FIG. The case where it is not broken is shown.
As can be seen from the comparison with FIGS. 45 and 46, when the heart rate count is normally performed, the shape is substantially normal distribution, whereas when it is not normally performed, a plurality of (here 2 Shape).

  Therefore, the heart rate detection means 56 calculates the frequency distribution of the heart rate (S51) and checks whether or not the distribution has a substantially normal distribution shape (S52). If the distribution is a substantially normal distribution shape, it is determined that normal heart rate calculation has been performed, and it is determined that correction is unnecessary (S53). On the other hand, if the distribution deviates from the substantially normal distribution shape, it is determined that normal heart rate calculation has not been performed, and it is determined that correction is necessary (S54). The same method as in the fourth embodiment can be used to determine whether or not the heart rate distribution has a substantially normal distribution shape.

  When it is determined that correction is required by the above algorithm (S55), the heart rate detecting means 56 estimates the number N of peaks of the heart rate distribution (for example, by maximum likelihood estimation) (S56). Then, based on the number N, the heart rate calculated in step S34 (see FIG. 33) is corrected over the past certain period (S57). Specifically, when the number of peaks is N, N beats are counted again as one beat. Thus, accurate heart rate information can be obtained.

  As described above, when it is determined in step S54 that correction is necessary, correction is performed. Then, based on the corrected heartbeat information, the state determination of the autonomic nerve in step S36 and the sleep state determination in steps S37 to S41 are performed.

  The arithmetic device 50 outputs the determination result of the state of the autonomic nerve and the sleep state determined as described above to an external device such as an air conditioner. On the device side that has received the determination result, device control according to the determination result is performed. A specific example of device control will be described in a later-described sixth embodiment.

  As described above, in the fifth embodiment, the norm of the IQ vector is calculated, and the heart rate is detected from the time series data of the norm. Therefore, the conventional high load processing such as frequency analysis is not required, and the heart rate can be calculated at high speed with low load. In addition, since a process with a high load is not required, the heart rate can be obtained using an inexpensive arithmetic device 50. In addition, the state of the autonomic nerve and the sleep state of the living body that are closely correlated with the heart rate can be determined at high speed. Since the state of the autonomic nerve and the sleep state are related to each other, the sleep state may be determined based on the index indicating the state of the autonomic nerve. By estimating the activity of the autonomic nerve accompanying the heartbeat, it becomes possible to determine the sleep state according to the physiological model.

  Further, according to the fifth embodiment, it is possible to detect a counting error that counts one heartbeat into a plurality of heartbeats. When a counting error is detected, the heart rate is corrected. Calculation accuracy can be improved. As a result, the determination accuracy of the state of the autonomic nerve and the sleep state can be improved.

  In addition, since the pulsation of the body surface due to the heartbeat moves fast and is complicated, the heartbeat signal may not be extracted by the method of the fourth embodiment. In other words, there are many points where the speed becomes zero other than the turning point of the heartbeat (the individual difference is also large). For this reason, it is difficult to detect the heartbeat with the method of the fourth embodiment, which is based on the fact that the speed becomes zero. On the other hand, in the method of the fifth embodiment, since the heartbeat is detected from the time series change of the signal intensity, the heartbeat can be easily detected.

  In the fifth embodiment, the case of detecting a heartbeat has been described. However, the present invention is not necessarily limited to a heartbeat, and can be detected by a similar method as long as changes such as respiration and pulse wave can be acquired as information by IQ detection. .

  Further, in the fifth embodiment, an example in which the state determination of the autonomic nerve and the sleep state determination are performed based on both the heart rate and the heart rate fluctuation range is shown, but either the heart rate or the heart rate fluctuation range is shown. The determination may be made based on the above. When both the heart rate and the heart rate fluctuation range are used, it is possible to determine with high accuracy by determining in a multidimensional space. Moreover, since the state of the autonomic nerve and the sleep state are related to each other, the sleep state may be determined based on the index indicating the state of the autonomic nerve.

Embodiment 6 FIG. (Body motion detection)
In the sixth embodiment, a biological state acquisition device that acquires body movements as the biological state will be described.

FIG. 47 is a block diagram showing a configuration of a biological state acquisition system according to Embodiment 6 of the present invention. 47, the same reference numerals are given to the same portions as those in the fifth embodiment shown in FIG.
In the biological state acquisition system 300 according to the sixth embodiment, the biological state acquisition unit 52 includes a body movement detection unit 57, an autonomic nerve state determination unit 54B, and a sleep state determination unit 55B, and other configurations are implemented. This is the same as the fifth embodiment. Hereinafter, the difference between the sixth embodiment and the fifth embodiment will be mainly described.
The body motion detecting unit 57 detects body motion such as turning over from the locus on the IQ plane of the acquired signals (I signal and Q signal) acquired by the IQ signal acquiring unit 51.

  The body motion detection means 57 expresses a point on the IQ plane of the acquisition signal (I signal and Q signal) acquired by the IQ signal acquisition means 51 as a vector from the origin (hereinafter referred to as this vector (acquisition on the IQ plane). Signal position vector) is called IQ vector). When body movement occurs, the amount of displacement of the body surface movement is larger than that of heartbeat and respiration, and the displacement time is short. Therefore, the norm of the IQ vector at the moment when the body motion occurs becomes a large value. The body motion detection means 57 performs body motion detection using this feature. That is, the body motion detection means 57 calculates the norm of the IQ vector for each sampling or the square of the norm, and integrates these values obtained in a unit period (for example, 5 seconds) (calculates integration or summation). Then, threshold determination is performed on the integrated value, and the presence or absence of body movement in the unit period is determined. In addition, the body motion detecting unit 57 calculates the number of body motions within a certain period (for example, 8 minutes) based on the result of the presence / absence determination of body motion.

  The autonomic nerve state determination unit 54 </ b> B determines the state of the autonomic nerve based on the number of body movements calculated by the body motion detection unit 57.

  The sleep state determination unit 55 </ b> B determines the sleep state based on the number of body motions calculated by the body motion detection unit 57.

FIG. 48 is a flowchart showing the flow of the biological state acquisition process in the arithmetic device according to Embodiment 6 of the present invention.
(S61: I signal and Q signal acquisition)
The I signal and the Q signal output from the IQ detector 20 in the biological state acquisition system are signals in which respiration, heartbeat, and body motion are all mixed. For this reason, the I signal and Q signal output from the IQ detector 20 are passed through the high-frequency bandpass filter 32 for detecting heartbeat and body motion, thereby extracting the heartbeat signal and body motion signal. This signal is converted into a digital signal by the AD converter 40 and input to the IQ signal acquisition means 51. The pass frequency band of the band pass filter 32 is set in advance. In this way, the IQ signal acquisition means 51 acquires IQ signals (heartbeat and body motion signals) sequentially output in time series from the AD converter 40 according to the movement of the human body surface.

(S62: Norm integration)
The body motion detection means 57 calculates the norm (= square root of the sum of squares of I and Q) of the IQ vector of the acquired signal (signal in which the heartbeat signal and the body motion signal are mixed) for each sampling time, and the unit period The integrated value of the norm (for example, 5 seconds) is calculated.

(S63-66: calculation of the number of body movements during a certain period)
The body motion detection means 57 compares the norm integrated value of the unit period with a preset threshold value (S63), and determines that there is body motion if the integrated value of the norm of the unit period is equal to or greater than the threshold value. The number of occurrences is counted (S64). If the integrated value of the norm of the unit period is less than the threshold value, it is determined that there is no body movement (S65). The processes from step S31 to S34 are performed for a certain period (for example, 8 minutes) (S66).

(S67: Autonomic nerve state determination)
When the certain period has elapsed, the autonomic nerve state determination unit 54B calculates an index for determining the state of the autonomic nerve based on the number of body movements within the certain period counted in steps S63 to S66. This index may be the value of the number of body movements itself, or may be a value obtained by substituting each into a certain function. Here, the larger the sympathetic nerve is, the larger the value is. With this index, the state of the autonomic nerve can be determined. For example, the index is compared with a preset threshold. If the index is larger than the threshold, it is determined that the sympathetic nerve is dominant, and if the index is less than the threshold, it is determined that the parasympathetic nerve is dominant. In addition, for example, the degree of sympathetic nerve activity may be determined.

(S68-S74: Sleep state determination)
Moreover, the sleep state determination means 55B determines a sleep state based on the number of body movements during a certain period. Next, the operation of the sleep state determination unit 55B will be described.

  It is known that aspects of human body movement during sleep change depending on the sleep state. Generally, the number of body movements during deep sleep and REM sleep is small, and the number of body movements increases as sleep becomes shallower, and the number of body movements increases most in the awake state. Therefore, the first body motion threshold, the second body motion threshold (<first body motion threshold), and the third body motion threshold (<first motion) for determining awakening, light sleep, REM sleep, and deep sleep, respectively. (2 body motion frequency threshold) is obtained and set in advance by experiments or the like, and the sleep state is determined by comparison with each threshold.

  That is, if the number of body motions during a certain period is equal to or greater than the first body motion number threshold value, it is determined that the user is awake (S68, S69). If the number of body motions in a certain period is less than the first body motion number threshold and greater than or equal to the second body motion number threshold, it is determined that the sleep is shallow (S70, S71). If the number of body motions in a certain period is less than the second body motion number threshold value and greater than or equal to the third body motion number threshold value, it is determined as REM sleep (S72, S73). If the number of body motions during a certain period is less than the third body motion number threshold, it is determined that the sleep is deep (S72, S74).

  These threshold values may be set for each individual based on the sleep data obtained by providing a learning period and acquiring sleep data of one sleep cycle or more. Further, sleep data during the learning period may be analyzed based on a predetermined algorithm, and a threshold value may be determined and automatically set.

  The arithmetic unit 50 outputs the determination result of the state of the autonomic nerve and the sleep state determined as described above to the air conditioner 1 as described in the first to third embodiments.

  As described above, in the sixth embodiment, the presence or absence of body movement is detected from the integrated value of the norm of the IQ vector. That is, the conventional high-load processing such as frequency analysis is not required, and it is possible to detect the presence / absence of body movement and calculate the number of body movements within a certain period of time with a low load. Further, since a process with a high load is not required, body motion information can be obtained using an inexpensive arithmetic device 50. In addition, the state of the autonomic nerve and the sleep state that are closely correlated with the body motion information can be determined at high speed. Since the state of the autonomic nerve and the sleep state are related to each other, the sleep state may be determined based on the index indicating the state of the autonomic nerve. By estimating the activity of the autonomic nerve accompanying the heartbeat, it becomes possible to determine the sleep state according to the physiological model.

Embodiment 7 FIG. (Combination of breathing, heart rate and body movement)
In Embodiments 4 to 6 described above, examples have been described in which the biological state acquisition unit 52 includes a respiration detection unit 53, a heartbeat detection unit 56, and a body motion detection unit 57, respectively. The biological state acquisition unit 52 in the biological state acquisition system of the seventh embodiment is configured to include all these detection units.

FIG. 49 is a block diagram showing a configuration of the biological state acquisition system according to Embodiment 7 of the present invention. In FIG. 49, the same components as those in the block diagrams of the fourth to sixth embodiments shown in FIGS. 16, 32, and 47 are denoted by the same reference numerals.
In the biological state acquisition system 400 of the seventh embodiment, the biological state acquisition unit 52 includes a respiration detection unit 53, a heartbeat detection unit 56, and a body movement detection unit 57 similar to those in the fourth to sixth embodiments. Furthermore, the biological state acquisition means 52 obtains the detection results (respiration rate of human (living body), respiratory cycle fluctuation (respiration rate fluctuation), heart rate, heart rate fluctuation, body motion number) of each detection means 53, 56, 57. An autonomic nerve state determination unit 54C that determines an autonomic nerve state in an appropriate combination is provided. In addition, the biological state acquisition unit 52 obtains the detection results (respiration rate of human (living body), respiratory cycle variation (respiration rate variation), heart rate, heart rate variation, body motion number) of the detection units 53, 56, and 57. A sleep state determination unit 55C that determines a human sleep state in an appropriate combination is provided. By combining these multiple detection results to determine the state of the autonomic nerve and the sleep state, it is more accurate than the method of determining the state of the autonomic nerve and the sleep state using only the heartbeat, respiration, and body movement. Judgment is possible. The following description will focus on the differences of the seventh embodiment from the fourth to sixth embodiments.

  FIG. 50 is a diagram showing the characteristics of body movement, breathing, and heart rate in each case of shallow sleep (including awakening), deep sleep, and REM sleep. It is the figure plotted to the series. As for body motion, the time series data of the norm of the IQ vector is also a diagram showing the same characteristics as in FIG. Since light sleep and awakening have the same characteristics in each of body movement, breathing, and heart rate, they are combined into one item. The body movement at the time of awakening has a feature that the movement of the body movement is larger and the number of occurrences is larger than that in the case of shallow sleep, and is indicated by a dotted line in the figure.

As is clear from FIG. 50, the body movement is larger and more frequently generated in shallow sleep (including awakening) than in deep sleep and REM sleep. For this reason, it can distinguish from the 1st state which is a shallow sleep, or the 2nd state which is either deep sleep or REM sleep by body motion information. In addition, as described in the sixth embodiment, it is possible to distinguish deep sleep and REM sleep only by the number of body movements, but since both sleep states have small movements and the number of occurrences is small. It is difficult to determine a sleep state with high accuracy. However, focusing on the respiration and heartbeat in FIG. 50, the respiration and heartbeat are stable in deep sleep and unstable in REM sleep. Therefore, deep sleep and REM sleep can be distinguished by further using at least one of respiration and heartbeat.
The computing device 50 according to the seventh embodiment determines the sleep state based on the above characteristics.

FIG. 51 is a flowchart showing the flow of the biological state acquisition process in the arithmetic device according to Embodiment 7 of the present invention.
(S81-S83: Acquisition of information on body movement, breathing, heart rate)
Each of the body motion detection unit 57, the respiration detection unit 53, and the heart rate detection unit 56 of the biological state acquisition unit 52 detects the body motion, respiration, and heart rate within a certain sleep state determination period (for example, 8 minutes). The operations of the body motion detection unit 57, the respiration detection unit 53, and the heart rate detection unit 56 are the same as those in the above embodiment. The body motion detection unit 57 calculates the number of body motions (S81), and the respiration detection unit 53 The fluctuation range of the respiratory rate and the respiratory cycle is calculated (S82). The heart rate detecting means 56 calculates a heart rate and a heart rate fluctuation range (S83).

(S84: Autonomic state determination)
Then, the autonomic nerve state determination unit 54C determines the state of the autonomic nerve based on various calculation results related to body movement, respiration, and heartbeat. That is, an index for determining the state of the autonomic nerve is calculated using a combination of the number of body movement occurrences, the number of breaths, the fluctuation range of the respiratory cycle, the heart rate, and the fluctuation range of the heart rate. This index may be a value obtained by substituting each into some function, and takes a larger value as the sympathetic nerve is more dominant. With this index, the state of the autonomic nerve can be determined. For example, the index is compared with a preset threshold. If the index is larger than the threshold, it is determined that the sympathetic nerve is dominant, and if the index is less than the threshold, it is determined that the parasympathetic nerve is dominant. In addition, for example, the degree of sympathetic nerve activity may be determined.

(S85-S91: Sleep state determination)
In addition, the sleep state determination unit 55C first determines whether the sleep state is the first state of awakening or shallow sleep, REM sleep or deep sleep based on the number of body movements for a certain period calculated by the body motion detection unit 57. The second state is distinguished. That is, the number of body motions is greater than or equal to the second body motion number threshold (see FIG. 48 of the sixth embodiment), and the integrated value of the norm of the IQ vector of the body motion signal is greater than the preset first body motion integrated value threshold. It is determined whether or not is larger (S85). If this determination is YES, the sleep state is determined to be the first state, and it can be determined that it is either awake or shallow sleep.

  In this case, subsequently, a determination is made to distinguish whether the sleep state is awake or shallow sleep. That is, the second body motion integration in which the number of body motions during a certain period is equal to or greater than the first body motion number threshold (see FIG. 48 of the sixth embodiment) and the norm integrated value of the IQ vector of the body motion signal is preset. When the value is greater than or equal to the value threshold (> first body motion integrated value threshold) (S86), it is determined that the user is awake (S87). On the other hand, if the determination in step S87 is NO, it is determined to be light sleep (S88).

  If the determination in step S85 is NO and the second state is determined, then the sleep state determination means 55C determines whether the sleep state is REM sleep or deep sleep. That is, it is determined whether at least one of respiration and heartbeat is stable or unstable. If it is determined to be unstable, it is determined to be REM sleep (S90), and if it is determined to be stable, it is determined to be deep sleep (S91). The determination of stable or unstable may be made based on the fluctuation rate of the respiratory rate and the respiratory cycle, or the heart rate and the fluctuation rate of the heart rate.

  As described above, in the seventh embodiment, the state of the autonomic nerve and the sleep state are determined by combining the calculation results of the respiration detection unit 53, the heartbeat detection unit 56, and the body motion detection unit 57. Thereby, it is possible to make a detailed determination with higher accuracy than the method of determining the state of the autonomic nerve and the sleep state using only the heartbeat, the respiration, and the body movement. That is, it becomes possible to improve the determination accuracy of deep sleep and REM sleep, which is difficult to determine with high accuracy only by the calculation result of the body motion detection unit 57 of the sixth embodiment. In the flowchart of FIG. 51, both respiration and heartbeat are detected, but at least one of them may be detected, and in step S89, it may be determined whether the detected side is stable or unstable.

By the way, the setting of each threshold value (first respiratory rate threshold value, second respiratory rate threshold value, first heart rate threshold value, etc.) used in the above embodiments 4 to 7 is combined with measurement data such as an electroencephalogram, for example, It can also be obtained in this way. That is, each threshold value is obtained by the following method based on a sleep state discrimination result based on measurement data of another device such as an electroencephalogram.
Learn a sleep state cycle from sleep onset to wake up by measuring a sleep state for several days, and the learning data in that learning period matches the sleep state discrimination result based on the measurement data of another device such as brain waves Each threshold is set as follows.

  The sleep state determination means 55 provides a learning period to learn the sleep state cycle from sleep onset to wake-up, and changes each threshold so that the sleep state cycle in the learning period approaches the basic sleep state cycle. May be. Specifically, for example, each threshold is adjusted so that the deep sleep time is 20 to 30% of the total time. Since the ratios of REM sleep, shallow sleep, and deep sleep change depending on the setting of the threshold, the threshold is determined so that the ratio of deep sleep becomes 20-30% of the whole while changing the threshold value little by little. It may be.

  Moreover, when determining a sleep state only by a body motion, when determining the threshold value for distinguishing REM sleep and deep sleep which are hard to distinguish only by a body motion, you may set as follows. For example, the threshold value is set so that the ratio of deep sleep is 30 to 40% (a value slightly higher than 20 to 30% of the total) with respect to the sum of deep sleep and REM sleep.

  In addition, as an example of changing each threshold so that the sleep state cycle in the learning period approaches the basic sleep state cycle, for example, it is also possible to set each threshold from the number of 90-minute sleep cycles repeated during sleep It is. The number of repetitions of the 90-minute sleep cycle is set to several times (for example, four times), and each threshold value is set so as to be the number of times. Moreover, you may determine from the ratio of deep sleep and the repetition frequency of a sleep cycle. Further, for example, when the tendency of the deep sleep ratio or the number of repetitions of the sleep cycle varies depending on the age, each threshold value may be set based on the tendency corresponding to the age.

Embodiment 8 FIG. (Autonomic nerve state and sleep state determination based on stability of locus on IQ plane)
In each of the above embodiments, the state of the autonomic nerve and the sleep state are determined based on respiration, heartbeat, and body movement. However, in Embodiment 8, the state of the autonomic nerve directly from the trajectory drawn on the IQ plane and A biological state acquisition system for determining a sleep state will be described.

FIG. 52 is a block diagram showing a configuration of the biological state acquisition system according to Embodiment 8 of the present invention. In FIG. 52, the same parts as those of the fourth embodiment shown in FIG.
In the biological state acquisition system 500 according to the eighth embodiment, the biological state acquisition unit 52 includes a stability calculation unit 58, an autonomic nerve state determination unit 54D, and a sleep state determination unit 55D. Further, the pass frequency band of the band pass filter 30 </ b> A is set in advance as a band suitable for the stability calculation processing in the stability calculation means 58. Other configurations are the same as those in the fourth embodiment. The following description will focus on the differences of the eighth embodiment from the fourth embodiment.
The stability calculation means 58 calculates the stability of the trajectory on the IQ plane of the acquisition signals (I signal and Q signal) acquired by the IQ signal acquisition means 51. A method for calculating the stability will be described later. Since the muscle activity is stable when the living body to be measured is in deep sleep, the acquired signal repeatedly draws substantially the same locus on the IQ plane as shown in FIG. On the other hand, in cases other than deep sleep, the acquired signal draws an unstable locus as shown in FIG. 54 on the IQ plane. Thus, the stability of the trajectory increases in the order of awakening, REM sleep, shallow sleep, and deep sleep, and is correlated with the sleep state. Therefore, the sleep state can be determined from the stability of the trajectory.

Hereinafter, a specific example of the stability calculation method will be described.
(Stability calculation method 1)
The IQ plane is expressed by a quantized finite plane composed of M × M pixels. The number of pixels used in the trajectory drawn by the measurement data on the IQ plane within a certain period is counted, and this reciprocal is used as an index of the stability of the trajectory. The number of pixels used on the quantized IQ plane increases according to the degree to which the locus is unstable. Accordingly, the stability index becomes a smaller value as it becomes unstable, and this is used as the stability index.

(Stability calculation method 2)
The similarity between the trajectory drawn on the IQ plane for a certain period and the trajectory drawn immediately after this is calculated, and this is used as an index of the stability of the trajectory. The degree of similarity uses mutual information, correlation coefficient, and the like.

  The sleep state determination unit 55D determines the sleep state by determining the threshold value of the stability index of the trajectory calculated by the stability calculation unit 58. The threshold values (first stability threshold value, second stability threshold value, and third stability threshold value) used for the determination may be stored in advance in the memory in the arithmetic device 50, or may be automatically set for each individual. Good. In the case of automatic setting, sleep data of one sleep cycle or more is acquired by providing a learning period, the distribution of the stability of the trajectory is estimated from the sleep data, and a threshold is appropriately set based on the distribution.

FIG. 55 is a flowchart showing the flow of the biological state acquisition process in the arithmetic device according to Embodiment 8 of the present invention.
(S101: I signal and Q signal acquisition)
Since the I signal and the Q signal output from the IQ detector 20 in the biological state acquisition system 500 are signals in which respiration, heartbeat, and body motion are all mixed, the stability is calculated by passing the signal through the bandpass filter 30A. A signal suitable for is extracted. This signal is converted into a digital signal by the AD converter 40 and input to the IQ signal acquisition means 51. As described above, the IQ signal acquisition unit 51 acquires the IQ signals sequentially output in time series from the AD converter 40 in accordance with the movement of the human body surface.

(S102: Plot measurement data for a certain period on the IQ plane)
The stability calculation means 58 plots the acquired signals (I signal and Q signal) obtained at each sampling time within a certain period on the IQ plane.

(S103: locus stability calculation)
The stability calculation means 58 calculates the stability of the trajectory drawn by the acquired signal for each sampling time plotted on the IQ plane. The stability of the trajectory itself may be used as an index for determining the state of the autonomic nerve. The state of the autonomic nerve can be determined by this index. For example, the index is compared with a preset threshold. If the index is larger than the threshold, it is determined that the sympathetic nerve is dominant, and if the index is less than the threshold, it is determined that the parasympathetic nerve is dominant. In addition, for example, the degree of sympathetic nerve activity may be determined.

(S104-S110: Sleep state determination)
The sleep state determination unit 55D includes the stability calculated by the stability calculation unit 58, a preset first stability threshold, a second stability threshold (<first stability threshold), and a third stability threshold (< The second stability threshold) is compared to determine the sleep state. That is, if the stability is equal to or higher than the first stability threshold, it is determined that the user is awake (S104, S105), and if the stability is lower than the first stability threshold and equal to or higher than the second stability threshold, it is determined that the sleep is REM (S106, S107). ). Further, if the stability is less than the second stability threshold and not less than the third stability threshold, it is determined as shallow sleep (S108, S109), and if it is less than the third stability threshold, it is determined as deep sleep (S108, S110).

  The arithmetic unit 50 outputs the determination result of the sleep state determined as described above to the air conditioner 1 as described in the first to third embodiments.

  As described above, in the eighth embodiment, the stability of the trajectory drawn on the IQ plane by the acquired signal within a certain period is used as an index, and the state of the autonomic nerve and the sleep state are determined based on this. For this reason, as in the case of the fourth to seventh embodiments, frequency analysis or the like is unnecessary, and the determination of the state of the autonomic nerve and the sleep state can be performed with low load and high speed. Further, the state of the autonomic nerve and the sleep state can be determined directly and simply from the stability of the trajectory without calculating the heart rate or the respiratory rate. Therefore, even when the movement of the body surface is complicated and erroneous calculation of the heart rate and the respiration rate cannot be avoided, it is possible to determine the state of the autonomic nerve and the sleep state without being affected by it.

  In addition to the method of determining the threshold value numerically based on the stability of the trajectory, the following method may be used. Of the trajectories drawn on the IQ plane, a trajectory pattern drawn on the IQ plane within a certain period is stored in advance in the storage device 60 for each sleep state (or for each autonomic nerve state). Then, the sleep state is determined by comparing the trajectory pattern held in the storage device 60 with the trajectory pattern based on the measurement data (IQ signal) and searching for the most similar one. Even when the trajectories of the same degree as the stability index are drawn numerically, the sleep state can be classified in more detail according to the shape of the trajectory by determining the pattern in this way. Moreover, if the locus pattern for verification is prepared for each age, sex, and further for each user, the sleep state can be determined with higher accuracy. Similarly, the state of the autonomic nerves can be determined in advance, for example, by storing in advance a trajectory pattern corresponding to each state such as sympathetic nerve dominance and parasympathetic nerve dominance. Become.

  Further, by comparing the trajectory based on the acquired signal with the trajectory data corresponding to the sleep state of each user, it is possible to identify who the user is sleeping. For this reason, as will be described later in Embodiment 6, when the output (sleep state) of the biological state acquisition device is used to control a device (for example, an air conditioner), device control tailored to the user is possible. It becomes.

  By the way, the sleep state determination means of the biological state acquisition systems of Embodiments 4 to 8 described above can also determine a person's wake-up state (first waking state). Generally, it is said that it is easy to wake up before and after REM sleep, and can wake up comfortably when getting up after REM sleep. For this reason, if a person is awakened by detecting a wake-up state and driving a device having an alarm function at that timing, for example, a comfortable wake-up can be provided.

Hereinafter, a method for determining the wake-up state will be briefly described.
A state in which at least one of three conditions is satisfied after a certain period of time from the start of falling asleep, after a REM sleep after repeating a predetermined number of sleep cycles, or when the REM sleep time is a certain time or more. Are determined to be in the wake-up state (the state of waking up first). During sleep from sleep onset to wake up, the REM sleep state is entered multiple times, but the duration of each REM sleep gradually increases toward wake up. Therefore, on the condition that the REM sleep time is a certain time or more, it can be determined that the REM sleep condition that satisfies the condition is in the wake-up state. Each condition for determining the wake-up state may be set based on a learning function.

  Moreover, in the case of the apparatus which determines a sleep state using the locus | trajectory pattern on IQ plane like Embodiment 8, it is also possible to determine a wake-up state as follows. A learning period is provided, sleep data including at least from bedtime to waking up is collected, and a trajectory on the IQ plane that appears a certain period before waking up is acquired. Then, the trajectory data is held as wake-up sign data, and the timing at which measurement data that matches (similar) the wake-up sign data is obtained is determined as the wake-up state.

  As described above, the wake-up state obtained as described above can be used for controlling a device having a wake-up function, for example. Specifically, control for prompting awakening, such as gradually increasing the illuminance in the room or generating a sound, may be performed so that the user wakes up while in the wake-up state. Note that if control for forcibly waking up at the wake-up time is performed, a comfortable awakening cannot be obtained if the human sleep state is not a wake-up state. Therefore, control for prompting the user to wake up is performed when the user wakes up before the wake-up time. This makes it possible to wake up comfortably and to wake up before the set time, so that time can also be used effectively.

  In addition to the alarm clock and the air conditioner as described above, the device having the biological state acquisition function is a device having a function of stimulating five senses such as lighting and aroma function, and an AV device such as a TV or a music player. It may be an air conditioner such as a hot water bottle, a humidifier, a dehumidifier, or an air purifier.

  Moreover, you may comprise the apparatus which performs the following controls as an apparatus which provides comfortable sleep environment. When falling asleep, sound and light are gradually lowered over a certain period of time (for example, 30 minutes) to induce a sleep rhythm. During sleep, the ambient noise is reduced by functions such as noise cancellation. Since the illuminance has the effect of controlling the biological rhythm, which is a necessary condition for getting up, the illuminance of light is increased over a certain time (for example, 30 minutes) before the wake-up time. The sound is adjusted to increase during the wake-up time and can be controlled to an unpleasant volume by setting. In addition, a human body detecting means is provided and adjusted so that light does not directly hit a human. The adjusting method may be any method such as lens wrinkles, actuators, and ON / OFF of a plurality of LEDs. In the human body detection, the antenna of the Doppler radar sensor 10 may be directed toward the person, and the LED may be directed to a position different from that direction.

  In the case of hot water bottles, the temperature is gradually lowered and raised again when getting up. It is also possible to warm to a level that does not sweat with a temperature and humidity sensor.

  Further, since there is a low probability of waking up by external noise such as sound, light, and temperature change during deep sleep, control that causes noise as a device may be performed during deep sleep. For example, an air conditioner can perform a cleaning function, a wind direction change, a ventilation fan drive, and the like.

  In each of the above embodiments, the description has been made assuming that the sensing is always performed by the Doppler radar sensor 10. However, the Doppler radar sensor 10 is stopped at regular intervals to save power, and the sleep state is determined. An interval may be provided. In this case, power consumption can be reduced as compared with the case where sensing is always performed. However, when the stop time is long, the sleep state determination is hindered. Since there is almost no body movement in the deep sleep state and it is difficult to get up, the probability of transition to the next sleep state is low. For this reason, if the stop time is set longer in the deep sleep state and the sensing interval is made longer, it is possible to save power without hindering the determination of the sleep state. Moreover, when deep sleep time continues more than predetermined time, you may make it the system of stopping a sensing temporarily. By shortening the sensing time, further energy saving can be performed. In shallow sleep, on the contrary, the sensing interval is shortened so that the sleep state determination is not hindered. When such control is performed, the energization time is shortened, which is effective in extending the life.

  The biological state information acquired by the biological state acquisition device 50 can be used as a trigger for starting operation in addition to the sleep state determination. For example, breathing may be intentionally stopped for a few seconds, and this may be detected by the biological state acquisition device 50 and, for example, a sleep timer may be started. When the breathing is intentionally stopped, the biological state acquisition device 50 continues to detect the heartbeat but cannot detect only the breathing. Therefore, the biological state acquisition device 50 can recognize this state as intentional respiratory stop. Further, it is also possible to dare to take a large deep breath from normal breathing, detect the change in breathing as a trigger with the biological state acquisition device 50, and perform the device operation.

In Embodiments 4 to 8 described above, the Doppler radar sensor is mainly used for description. However, any sensor that can detect IQ may be used, and sleep state determination may be performed by any sensor that can obtain biological state information. Any type sensor can be used.
In addition, the above-described biological state acquisition device 50 can be incorporated into a device that requires grasping of a relaxed state or grasping of a healthy state, taking advantage of the fact that biological state information can be accurately obtained even in a state other than a sleeping state. For example, if it is incorporated in a system for elderly care, it can acquire biological state information in a non-contact manner so as not to be a burden on the user who needs care, such as health management, abnormality detection (for example, apnea syndrome, It can be used for respiratory failure). In addition, the air conditioner 1 may be automatically changed according to the biological state information so that the health of the air conditioner 1 does not affect health (for example, when breathing is disturbed, the windshield mode is set). Etc.).

  Moreover, it is also possible to detect abnormalities of a pet (life and death, rough breathing (disease or heat stroke)) by using the biological state acquisition device 50 for home pets and acquiring the biological state of the pet. In addition, it is possible to detect an outpatient or the like by operating the biological state acquisition device 50 when there is an answering machine.

  Since there is a difference in the respiratory / heartbeat frequency and the amplitude (integrated amount) of body motion between animals and humans, it is possible to identify whether the living body to be measured is an animal or a human from these measured values. For this reason, a sensor capable of acquiring biological state information such as a Doppler radar sensor is provided in the outdoor unit, and only when it is determined that the person is a human being, it is used for outdoor security such as driving a warning function effective for security such as a fan, buzzer, and LED. It is also possible. In the case where a sensor capable of acquiring biological state information is arranged in the outdoor unit, it may be arranged as a separate type device, and in this case, the same effect is obtained.

  DESCRIPTION OF SYMBOLS 1 Air conditioner, 2 Air conditioning means, 3 Temperature detection means, 4 Arithmetic unit, 4a Air conditioning control means, 5 Remote control, 10 Doppler radar sensor, 20 IQ detector, 30 band pass filter, 30A band pass filter, 31 band pass filter , 32 band pass filter, 40 AD converter, 50 arithmetic unit (biological state acquisition device), 51 IQ signal acquisition unit, 52 biological state acquisition unit, 53 respiratory detection unit, 54 autonomic state determination unit, 54A autonomic state determination Means, 54B autonomic state determination means, 54C autonomic state determination means, 54D autonomic state determination means, 55 sleep state determination means, 55A sleep state determination means, 55B sleep state determination means, 55C sleep state determination means, 55D sleep state Determination means, 56 Heart rate detection means, 57 Body movement Detection means, 58 Stability calculation means, 60 Storage device, 100 Sleep state acquisition system (biological state acquisition system), 101 server, 101a Control means, 102 device, 103 user terminal, 200 biological state acquisition system, 300 biological state acquisition system , 400 biological state acquisition system, 500 biological state acquisition system, 600 air conditioner, 610 air conditioning means, 620 arithmetic unit.

Claims (17)

Air-conditioning means for air-conditioning the indoor space;
A sleep state acquisition system for determining a sleep state of a sleeper;
Control means for determining the set temperature of the air conditioning means according to the sleep state determined by the sleep state acquisition system and the operation mode, and controlling the air conditioning means so that the temperature of the indoor space becomes the set temperature. ,
The control means includes
When the sleeper starts sleeping, if the operation mode is cooling operation, the set temperature is gradually lowered to a temperature lower than the initial set temperature, and then the set temperature is set to the initial setting when the sleeper goes into deep sleep. When the temperature is set higher than the temperature, and after transitioning to deep sleep through the deep sleep, the set temperature is continued for a predetermined time while in deep sleep, and the process of lowering the set temperature to the predetermined temperature after the predetermined time elapses is shallow. An air conditioner characterized by repeating the setting of raising the set temperature from the current level during deep sleep and shallow sleep when sleeping and moving from deep sleep to deep sleep . If the就 Nesha has started sleep, if the heating operation is operation mode, the set temperature, gradually lowered to a temperature below the initial set temperature, and then, if the sleeping person has entered a deep sleep, than the current set temperature 2. The air conditioner according to claim 1, wherein the air conditioner is set to a temperature lower than. 3. The air conditioner according to claim 2 , wherein the control unit continues the set temperature at the set temperature at the time of deep sleep after entering the first deep sleep during the heating operation until entering the wake-up stage. . The air conditioner according to any one of claims 1 to 3, wherein the control unit sets the set temperature to a temperature that exceeds the initial set temperature when entering the wake-up stage. The said control means raises preset temperature in the range which does not exceed initial preset temperature, if it enters into the wake-up stage when the energy-saving setup is made at the time of heating operation, The claim 2 or Claim characterized by the above-mentioned. 3. The air conditioner according to 3 . The control means updates the set temperature to the initial set temperature when going into deep sleep after taking deep sleep during heating operation , and sets the same temperature as the previous deep sleep when going into deep sleep again The air conditioner according to any one of claims 2 , 4 and 5 , wherein the setting is repeated during deep sleep and light sleep. Wherein, after lowering gradually the set temperature after sleep onset during the heating operation to a temperature below the initial set temperature, claim, characterized in that lower than the current forcibly set temperature predetermined time elapses 2 The air conditioner according to any one of claims 4 and 5 . It has means for detecting the amount of clothes of a sleeping person and means for detecting the temperature in the bed, and the control means changes the set temperature based on the detection result of each detection means. The air conditioner according to any one of claims 1 to 7 . The air conditioner according to any one of claims 1 to 8 , wherein the control means performs control for generating noise in the air conditioner while the sleeping person is in deep sleep. The sleep state acquisition system has a detection unit that detects biological state information of a sleeping person, and the control unit identifies the sleeping person from the biological state information detected by the detection unit, and serves as air conditioning control data for the individual sleeping person. The air conditioner according to any one of claims 1 to 9 , wherein the air conditioning unit is controlled based on air conditioning control data registered in advance. The sleep state acquisition system includes a detection unit that detects biological state information of a sleeping person, and the detection unit performs detection at regular intervals, and the length of the regular interval is deep sleep when deep sleep is in progress. The air conditioner according to any one of claims 1 to 9 , wherein the air conditioner is longer than the inside. The sleep state acquisition system has a detection unit that detects biological state information of a sleeping person, and the control unit determines a comfort level based on the biological state information detected by the detection unit, and the comfort level is high. switching the air-conditioning control energy saving setting, the air according to any one of claims 1 to 9 wherein the comfort after switching to the energy saving setting and cancels the energy-saving setting when lowered Harmony machine. The sleep state acquisition system has a detection unit that detects biological state information of a sleeping person, and determines a sleep state from the biological state information detected by the detection unit. The detection unit is physically operated from an air conditioner. The air conditioner and the separation device can be communicated with each other by a communication method capable of specifying the position of each other, and the control means reflects the position of the separation device. Control is performed, The air conditioner as described in any one of Claim 1 thru | or 9 characterized by the above-mentioned. The sleep state acquisition system includes a detection unit that detects biological state information of a sleeping person, and determines a sleep state from the biological state information detected by the detection unit, and the control means includes the detection unit. The air conditioner according to any one of claims 1 to 9 , wherein a predetermined change in the detected biological state information is used as an operation trigger for the air conditioner. Communication means for connecting to a server that manages air conditioning control data via a network, the control means downloads air conditioning control data for controlling the air conditioning means from the server, and downloads the air conditioning control data to the downloaded air conditioning control data based air conditioner according to any one of claims 1 to 1 4, characterized in that running the air-conditioning control. A plurality of air conditioners according to any one of claims 1 to 15 , and a server connected to the air conditioner via a network,
The air conditioner stores air conditioning control data that specifies a set temperature for each sleep state according to an operation mode;
User-operable input means for changing the set temperature of the air conditioning control data stored in the storage means and creating new air conditioning control data;
Transmission means for transmitting the air conditioning control data created by the user to the server via a network and making the data public on the network;
The control means of the air conditioner downloads air conditioning control data published on the network in response to a request from a user, executes air conditioning control based on the downloaded air conditioning control data,
The server
A plurality of air conditioning control data transmitted from the air conditioner is disclosed on the network, and a vote of an evaluation result by a plurality of users for the air conditioning control based on the disclosed air conditioning control data is accepted via the network, and the vote result is displayed. An air conditioning system characterized by having control means that aggregate, rank, and disclose. The control unit of the air conditioner acquires sleep information including a sleep cycle of a sleeper when performing air conditioning control based on the downloaded air conditioning control data by the sleep state acquisition system and transmits the sleep information to the server,
The control means of the server calculates an average of sleep comfort based on sleep information from a plurality of users who downloaded the air conditioning control data for each air conditioning control data, and publishes it according to the ranking result. The air conditioning system according to claim 16 .

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