JP6589352B2 - Biological information measuring device, sensor unit, and biological information measuring system - Google Patents

Description

  The technology described in this specification relates to a biological information measuring device, a sensor unit, and a biological information measuring system.

  A technique for measuring the heart rate and pulse of a human body using an electrocardiograph or a pulse meter is known. Many of electrocardiographs and pulse meters are “contact type” in which electrodes are directly attached to the skin of a human body for measurement. On the other hand, for example, a technique for measuring a heartbeat in a non-contact manner using a Doppler sensor is also known.

Special table 2009-501044 JP-A-4-3488741 JP 2003-275185 A

  When the heart rate is measured in a non-contact manner using the Doppler sensor, if the Doppler sensor moves relative to the human body due to a factor different from the heart rate, a noise component is added to the detection value of the Doppler sensor according to the movement. If a noise component is added to the detection value of the Doppler sensor, for example, the detection accuracy of the heartbeat decreases. The heartbeat and pulse are examples of biological information.

  In one aspect, one of the objects of the technology described in this specification is to improve the measurement accuracy of biological information such as a heartbeat.

  In one aspect, the biological information measurement device may include a Doppler sensor, a capacitance sensor, and a processor. The Doppler sensor receives reflected waves by irradiating a living body with radio waves. The capacitance sensor detects a change in capacitance between the living body and the living body. The processor may detect a signal component whose frequency changes according to the movement of the living body from the detection signal of the Doppler sensor, based on the change of the frequency component included in the detection signal of the capacitance sensor.

In one aspect, the sensor unit may include a Doppler sensor, a capacitance sensor, and a processor. The capacitance sensor may detect a change in capacitance in a spatial range that overlaps a spatial range in which the Doppler sensor can detect a change in distance. Processor, before based on the change of the frequency components included in the output of Kiseiden capacitive sensor, the output of the Doppler sensor may perform the operation of detecting the signal component that changes the frequency in accordance with the movement of the living body.

  Furthermore, in one aspect, the biological information measurement system may include a sensor unit and a processor. The sensor unit may include a Doppler sensor and a capacitance sensor. The processor receives the detection signal of the Doppler sensor and the detection signal of the capacitance sensor, and based on the change of the frequency component included in the detection signal of the capacitance sensor, from the detection signal of the Doppler sensor, You may detect the signal component from which a frequency changes according to the motion of the said biological body.

  As one aspect, the measurement accuracy of biological information can be improved.

It is a block diagram which shows an example of the sensing system which concerns on one Embodiment. It is a block diagram which shows the structural example of the non-contact vital sensor illustrated in FIG. FIG. 3 is a block diagram illustrating a configuration example of a Doppler sensor in the non-contact vital sensor illustrated in FIG. 2. FIG. 3 is a block diagram illustrating a configuration example of a capacitance sensor in the non-contact vital sensor illustrated in FIG. 2. FIG. 5 is a diagram illustrating an example of a time change of a detection signal of the capacitance sensor illustrated in FIG. 4. It is a block diagram which shows the structural example of the management system illustrated in FIG. It is a flowchart for demonstrating the operation example of the sensing system which concerns on 1st Example. It is a figure which shows an example of the detection signal of the Doppler sensor which concerns on 1st Example. It is a figure which shows an example of the frequency analysis result of the detection signal of the Doppler sensor illustrated in FIG. It is a figure which shows an example of the signal waveform corresponded to the respiration component isolate | separated from the signal waveform of the Doppler sensor value illustrated in FIG. It is a figure which shows an example of the signal waveform corresponded to the heartbeat component isolate | separated from the signal waveform of the Doppler sensor value illustrated in FIG. (A) is a figure which shows typically the detection axis | shaft of the inertial sensor illustrated in FIG. 2, (B) is a figure which shows typically the state which attached the inertial sensor illustrated in (A) to the human body. It is a figure which shows an example of the threshold value used for the threshold value determination process of FIG. It is a flowchart for demonstrating the operation example of the sensing system which concerns on 2nd Example. It is a figure which shows an example of the external appearance of the non-contact vital sensor illustrated in FIG. It is a figure which shows the example by which the jig | tool which can attach a harness was attached to the main body of the non-contact vital sensor illustrated in FIG. It is a figure which shows the example by which the harness was attached to the jig | tool of the non-contact vital sensor illustrated in FIG. It is a figure which shows the example by which the harness was attached to the jig | tool of the non-contact vital sensor illustrated in FIG. It is a figure which shows typically the example of an internal structure of the non-contact vital sensor illustrated to FIGS. It is a figure which shows an example of the frequency analysis result of the detection signal of a Doppler sensor for demonstrating the comparison process of the feature point of FIG. It is a figure which shows an example of the frequency analysis result of the detection signal of an inertial sensor for demonstrating the comparison process of the feature point of FIG. It is a figure which shows an example of the frequency analysis result of the detection signal of a capacitive sensor for demonstrating the comparison process of the feature point of FIG.

  Hereinafter, embodiments will be described with reference to the drawings. However, the embodiment described below is merely an example, and there is no intention to exclude various modifications and technical applications that are not explicitly described below. Various exemplary embodiments described below may be implemented in combination as appropriate. Note that, in the drawings used in the following embodiments, portions denoted by the same reference numerals represent the same or similar portions unless otherwise specified.

  FIG. 1 is a block diagram illustrating an example of a sensing system according to an embodiment. The sensing system 1 illustrated in FIG. 1 may include a non-contact vital sensor 2, a communication device 3, a network (NW) 4, and a management system 5, for example.

  The non-contact vital sensor 2 is capable of sensing non-contact information of a living body that is an example of a sensing target. The “biological information” may be abbreviated as “biological information” or may be paraphrased as “vital information”. “Sensing” may be rephrased as “detection” or “measurement”. Therefore, “sensing system 1” may be referred to as “biological information measurement system 1”.

  A non-limiting example of “vital information” is information indicating a heartbeat, respiration, movement, and the like of a living body. The “living body” may include an “organ” of the living body. “Heartbeat” may be regarded as information indicating the movement of “heart” which is an example of “organ”.

  The “movement” (also referred to as “position change”) of the living body may be abbreviated as “body movement” for convenience. The “body movement” is not limited to the movement during the activity of the living body, and may include the movement of the living body surface (for example, the skin) according to the heartbeat or breathing at the time of rest such as sleeping of the living body. .

  It may be considered that the movement of the living body surface occurs according to the movement of the organ of the living body. For example, the skin moves according to the heartbeat. In addition, the skin moves according to the expansion and contraction of the lungs accompanying breathing.

  The non-contact vital sensor 2 (simply abbreviated as “Vital sensor 2” or “Sensor 2”) exemplarily irradiates a sensing target with radio waves such as microwaves, and reflects and receives the sensing target. Based on the change of the reflected wave, the “movement” of the living body can be detected.

  For example, when the distance between the sensor 2 and the sensing target changes, the reflected wave changes due to the Doppler effect. The change in the reflected wave can be viewed as a change in one or both of the amplitude and frequency of the reflected wave.

  Here, it is assumed that the sensor 2 is attached to a human body that is an example of a living body. The attachment position of the sensor 2 to the human body may illustratively be the chest of the human body. In the sensor 2 attached to the human body, for example, the distance between the human skin and the heart changes as the human body beats. Therefore, the change according to the distance change appears in the reflected wave of the radio wave irradiated by the sensor 2. Based on the change in reflection, for example, the heartbeat and pulse of the human body can be measured.

  For example, the vital sensor 2 may be attached so as to contact the skin of the human body or may be attached to clothing of the human body. However, the attachment of the vital sensor 2 to the human body does not have to be strictly fixed (may be referred to as “restraint”).

  For example, the vital sensor 2 may be attached to the human body in such a manner that relative movement with respect to the human body can occur in any of the three-dimensional directions. As an example of such an attachment aspect, the aspect which accommodates the vital sensor 2 in the pocket of clothes is mentioned. The clothes pocket may be a chest pocket of a jacket such as a shirt as a non-limiting example.

  As another attachment mode, the vital sensor 2 may be attached to the human body using an attachment device such as a harness, as will be described later. For example, the vital sensor 2 may be attached to the human body with a harness from above the clothing. Even in such an attachment mode, if the movement between the clothing and the human body surface deviates, the vital sensor 2 may be moved relative to the human body.

  Next, the communication device 3 illustrated in FIG. 1 can transmit the sensing result of the vital sensor 2 to the management system 5 via the network 4, for example. Therefore, the communication device 3 may be connectable to the network 4 by wire or wireless.

  In other words, the communication device 3 may include a communication interface (IF) that supports one or both of wireless and wired communication. For example, a communication method based on LTE (Long Term Evolution) of 3GPP (3rd Generation Partnership Project) or LTE-Advanced may be applied to wireless communication of the communication device 3.

  Further, satellite communication may be applied to wireless communication of the communication device 3. When using satellite communication, the communication device 3 may be able to communicate with the management system 5 via the communication satellite without going through the network 4.

  The sensing result of the vital sensor 2 is not limited to vital information, and may include information indicating the calculation and determination results obtained based on the vital information. The sensing result may be referred to as “sensor information” for convenience.

  As illustrated in FIG. 1, the communication device 3 may be external to the vital sensor 2 or may be built in the vital sensor 2. The external communication device 3 attached to the vital sensor 2 may be, for example, a device carried by a person (also referred to as a “user”) to which the vital sensor 2 is attached.

  The communication device 3 carried by the user may illustratively be a mobile phone (a smart phone may be included), a notebook PC, a tablet PC, or the like. “PC” is an abbreviation for “personal computer”.

  For the connection between the vital sensor 2 and the communication device 3, a wired connection or a wireless connection may be applied. In other words, the vital sensor 2 may include a communication IF that supports communication by one or both of wireless and wired. For wireless connection, for example, “WiFi (Wireless Fidelity)” (registered trademark) or “Bluetooth” (registered trademark) may be used.

  For example, the network 4 may be a WAN (Wide Area Network), a LAN (Local Area Network), the Internet, or the like. The network 4 may include a radio access network. The radio access network may be a radio access network conforming to the above-described LTE or LTE-Advanced.

  The management system 5 receives the sensor information transmitted by the communication device 3 via the network 4 (or may be a communication satellite), and stores and manages the received sensor information. The management of sensor information may include making the sensor information into a database (DB). DB data may be referred to as “cloud data” or “big data”.

  Based on the received sensor information, the management system 5 can monitor body movements such as heartbeat, respiration, and movement of the user to whom the vital sensor 2 is attached. In addition, the management system 5 may use the monitoring result of the body movement for prediction of a change in the physical or mental state of the user.

  For example, the management system 5 may determine whether the life activity of the user is in a safe or stable state based on the monitoring result of the body movement. Further, the management system 5 may predict and determine whether the physical or mental state of the user may be different from the normal state according to the tendency of body movement.

  For example, the user's activity can be monitored in real time by the management system 5 during the activity of the user to whom the vital sensor 2 is attached or at rest such as during sleep. Further, based on the monitor result, the management system 5 can manage or predict the physical or mental health condition of the user.

  For example, the management system 5 can predict and determine whether or not the user has a sign of physical or mental illness based on vital information obtained when the user sleeps.

  The management system 5 may be realized by one or a plurality of servers, for example. In other words, the sensor information obtained by the vital sensor 2 may be managed by one server or distributedly managed by a plurality of servers in the management system 5. For example, the server may correspond to a cloud server provided in a cloud data center.

  Non-limiting examples of users to which the vital sensor 2 is attached include police officers (may include riot police), fire fighters, rescue crews, SDF crews, military crews, vehicle crews, factories Workers, athletes, etc. are listed. “Vehicle occupants” may include automobile drivers, airplane pilots and crews, ship captains and sailors, astronauts, and the like. In other words, the target to which the vital sensor 2 is attached may be a living body for which the management system 5 wants to manage the activity state, health state, and the like.

(Configuration example of non-contact vital sensor 2)
Next, a configuration example of the vital sensor 2 will be described with reference to FIG. As shown in FIG. 2, the vital sensor 2 may include, for example, a Doppler sensor 21, a capacitance sensor 22, a processor 24, a memory 25, and a communication interface (IF) 26. The vital sensor 2 may be additionally provided with an inertial sensor 23. The inertial sensor 23 may be optional. “Vital sensor 2” may be referred to as “sensor unit 2” or “biological information measuring device 2”.

  The Doppler sensor 21, the capacitance sensor 22, the processor 24, the memory 25, and the communication interface (IF) 26 may be connected to the bus 27 so as to be able to communicate with each other. If the inertial sensor 23 is provided, the inertial sensor 23 may also be connected to the bus 27.

  For example, the Doppler sensor 21 generates a beat signal by phase-detecting a radio wave transmitted toward a living body that is an example of a sensing target and a reflected wave of the transmission radio wave. A beat signal may be provided to the processor 24 as a detection signal of the Doppler sensor 21. The detection signal of the Doppler sensor 21 may be referred to as “detection value” or “Doppler sensor value” for convenience.

  For example, as illustrated in FIG. 3, the Doppler sensor 21 includes an antenna 211, a local oscillator (OSC) 212, an MCU (Micro Control Unit) 213, a detection circuit 214, an operational amplifier (OP) 215, and a battery 216. Good.

  The antenna 211 transmits the radio wave having the oscillation frequency generated by the OSC 212 to the conditioned space, and receives the radio wave (reflected wave) reflected by the user located in the conditioned space. In the example of FIG. 3, the antenna 211 is shared for transmission and reception, but may be individual for transmission and reception.

  For example, the OSC 212 oscillates according to the control of the MCU 213 and outputs a signal having a predetermined frequency (may be referred to as a “local signal” for convenience). The local signal is transmitted as a transmission radio wave from the antenna 211 and input to the detection circuit 214.

  The oscillation frequency of the OSC 212 (in other words, the frequency of the radio wave transmitted by the Doppler sensor 21) may be, for example, a frequency in the microwave band. For example, the microwave band may be a 2.4 GHz band or a 24 GHz band.

  These frequency bands are examples of frequency bands that are allowed to be used indoors by the Japanese Radio Law. A frequency band not subject to regulations of the Radio Law may be used for the transmission radio wave of the Doppler sensor 21.

  For example, the MCU 213 controls the oscillation operation of the OSC 212 in accordance with the control of the processor 24.

  The detection circuit 214 detects the phase of the reflected wave received by the antenna 211 and the local signal from the OSC 212 (in other words, transmission radio wave), and outputs a beat signal. The detection circuit 214 may be replaced with a mixer that mixes the transmission radio wave and the reflected wave. Mixing by the mixer may be regarded as equivalent to phase detection.

  Here, in the beat signal obtained by the detection circuit 214, an amplitude change and a frequency change appear due to the Doppler effect according to physical changes such as a heartbeat of the living body, breathing, and body movement.

  For example, the frequency and amplitude value of the beat signal tend to increase as the amount of physical change of the living body (in other words, the relative speed with respect to the Doppler sensor 21) increases. In other words, the beat signal includes information indicating changes in the heartbeat, respiration, and body movement of the living body.

  The operational amplifier 215 amplifies the beat signal output from the detection circuit 214. The amplified beat signal may be input to the processor 24, illustratively as a Doppler sensor value.

  The battery 216 illustratively supplies drive power to the MCU 213, the detection circuit 214, and the operational amplifier 215.

  Next, the capacitance sensor 22 illustrated in FIG. 2 exemplarily shows a change in the distance between the detection surface of the capacitance sensor 22 and a sensing target (for example, the human body surface) as a change in capacitance value. It is possible to detect.

  In relation to the Doppler sensor 21, the capacitance sensor 22 is arranged so as to detect a change in capacitance value in a spatial range that overlaps a spatial range in which radio waves are irradiated from the Doppler sensor 21 toward the sensing target. That's fine.

  In other words, the capacitance sensor 22 is arranged such that the detection axis capable of detecting a change in capacitance value is along the detection axis of the Doppler sensor 21 capable of detecting a change in distance using the Doppler effect. Good.

As shown in FIG. 4, the capacitance sensor 22 may include a detection electrode 221 exemplarily. The area of the detection surface of the detection electrode 221 is represented by S [m ² ], the distance (distance) between the detection surface of the detection electrode 221 and the sensing target (for example, human body) is represented by d [m], and the detection electrode 221 and the human body Is assumed to be uniformly filled with a dielectric (for example, air) having a dielectric constant ε. The detection electrode 221 and the human body are both examples of conductors.

  When a positive voltage (V) is applied to the detection electrode 221 with respect to the ground (GND), a positive charge (+ Q) is generated in the detection electrode 221, and an electric field is generated between the detection electrode 221 and GND. When a human body is present in the electric field, the human body is subjected to electrostatic induction and has a negative charge (-Q) opposite to the charge (+ Q) of the detection electrode 221 on the human body surface. Such a phenomenon may be referred to as “polarization”.

  Since the electric field becomes stronger as the human body approaches the detection electrode 221, the polarization increases. As the polarization increases, the positive charge (+ Q) of the detection electrode 221 increases due to the induction of the negative charge (−Q) generated in the human body.

  Here, the electric field (E) between the detection electrode 221 and the human body can be expressed by E = Q / εS according to Gauss's law. The voltage (V) between the detection electrode 221 and the human body can be expressed by V = Qd / εS.

  Since the capacitance value (C) can be expressed by C = Q / V, the distance d between the detection electrode 221 and the human body can be obtained by d = εSV / Q. Accordingly, as the human body approaches the detection electrode 221 (the interval d decreases), the charge Q increases and the capacitance value C increases. As the human body moves away from the detection electrode 221 (the interval d increases), the charge Q increases. It can be seen that the capacitance value C decreases with the decrease.

  In FIG. 4, “Cg” represents a virtual capacitance that the human body has with GND, and “Cp” and “Cq” respectively represent the virtual capacitance that the detection electrode 221 has with GND. Represents capacity. One of “Cp” and “Cq” may correspond to a capacitance serving as a reference for detection. The capacitance value C detected by the detection electrode 221 may be regarded as changing according to a change in series capacitance of the capacitance Cg and the capacitance Cp (or Cq).

  The change in the capacitance value C of the detection electrode 221 appears as a change in current generated in the detection electrode 221. For example, the current generated in the detection electrode 221 may be converted and amplified by the amplifier 222 into a voltage value corresponding to the current value.

  The output voltage value of the amplifier 222 may be input to the processor 24 as a detection signal of the capacitance sensor 22, for example. The detection signal of the capacitance sensor 22 may be referred to as “detection value” or “capacitance sensor value” for convenience.

  FIG. 5 shows an example of the time change of the detection signal of the capacitance sensor 22. The horizontal axis in FIG. 5 represents time (exemplarily “second”), and the vertical axis in FIG. 5 represents voltage (V). The capacitance sensor 22 may be a so-called “touch panel”. The “touch panel” may be a “liquid crystal panel”.

  The touch panel 22 is an example of an input device that can input information to, for example, the processor 24 of the vital sensor 2. In other words, the capacitance sensor 22 may combine the sensing function and the function as the touch panel 22 which is an example of the information input device.

  Next, the inertial sensor 23 illustrated in FIG. 2, which may be an option, can illustratively sense the “movement” of the vital sensor 2 itself. In other words, the inertial sensor 23 can sense the “movement” of the Doppler sensor 21 included in the vital sensor 2. The “movement” of the vital sensor 2 can occur, for example, according to the body movement of the living body to which the vital sensor 2 is attached.

  The inertial sensor 23 may be an acceleration sensor or a gyroscope. As the acceleration sensor, for example, any one of a piezoelectric sensor and a capacitance sensor may be applied. For the gyroscope, any of a rotary machine (coma) type, an optical type, and a vibration type sensor may be applied.

  The inertial sensor 23 may have one or a plurality of detection axes. For example, the inertial sensor 23 may be the inertial sensor 23 having two or three detection axes. The “movement” in the direction along the detection axis may be detected by the inertial sensor 23 as “acceleration”, for example.

  At least one detection axis (first detection axis) of the inertial sensor 23 is oriented in the directionality of the transmission radio wave of the Doppler sensor 21 (which may be referred to as “radio wave transmission direction” for convenience). It's okay. In other words, the inertial sensor 23 may be arranged and set so that the “movement” of the vital sensor 2 in the radio wave transmission direction of the Doppler sensor 21 can be detected.

  In the biaxial inertial sensor 23, the second detection axis may be orthogonal to the first detection axis, and in the triaxial inertial sensor 23, the second and third detection axes are respectively the first detection axes. The second detection axis and the third detection axis may be orthogonal to each other.

  A signal corresponding to the “movement” detected by the inertial sensor 23 may be input to the processor 24. The inertial sensor 23 only needs to be operating while the Doppler sensor 21 is operating. The detection signal of the inertial sensor 23 may be referred to as “detected value” or “inertial sensor value” for convenience.

  As will be described later, the inertial sensor value may be used as auxiliary information when detecting body movement (for example, heartbeat) of the living body based on the Doppler sensor value and the capacitance sensor value.

  Next, the processor 24 illustrated in FIG. 2 sends the Doppler sensor value and the capacitance sensor value (or the Doppler sensor value, the capacitance sensor value, and the inertial sensor value) to the management system 5 through the communication IF 26, for example. It is possible to send to.

  Alternatively, the processor 24 may detect information indicating body movement such as a heartbeat of the living body as vital information by calculation based on the above two (or three) sensor values. Instead of (or in addition to) each sensor value, the processor 24 may transmit the vital information as the calculation result or the information obtained in the calculation process to the management system 5 through the communication IF 26. .

  The processor 24 is an example of a computing device having computing power. The arithmetic device may be referred to as an arithmetic device or an arithmetic circuit. For example, a CPU (Central Processing Unit) or a DSP (Digital Signal Processor) may be applied to the processor 24 which is an example of an arithmetic device.

  The memory 25 is an example of a storage medium, and may be a RAM (Random Access Memory), a flash memory, or the like. The memory 25 may store programs and data used for the processor 24 to read and operate. The “program” may be referred to as “software” or “application”. The “data” may include data generated according to the operation of the processor 24.

  The communication IF 26 exemplarily provides a connection interface with the communication device 3 illustrated in FIG. 1 and enables communication with the management system 5 via the communication device 3. For example, the communication IF 26 may transmit sensor values, vital information, information obtained in the calculation process of vital information, and the like to the management system 5 via the communication device 3. Therefore, the communication IF 26 (or the communication IF 26 and the communication device 3) is an example of a transmission unit that transmits information to the management system 5 when paying attention to the transmission process.

  Some or all of the elements of the vital sensor 2 illustrated in FIG. 2 may be realized by an IC (Integrated Circuit), an LSI (Large Scale Integration), an FPGA (Field-Programmable Gate Array), or the like.

(Configuration example of the management system 5)
FIG. 6 is a block diagram illustrating a configuration example of the management system 5 illustrated in FIG. The management system 5 illustrated in FIG. 6 may include, for example, a processor 51, a memory 52, a storage device 53, a communication IF 54, and a peripheral IF 55. The processor 51, the memory 52, the storage device 53, the communication IF 54, and the peripheral IF 55 may be connected to the bus 56 so as to communicate with each other.

  The processor 51 is an example of a computing device having computing power. For example, a CPU or a DSP may be applied to the processor 51.

  The memory 52 is an example of a storage medium, and may be, for example, a RAM or a flash memory. The memory 52 may store programs and data used for the processor 51 to read and operate. The “data” may include data generated according to the operation of the processor 51.

  The storage device 53 is also an example of a storage medium, and may exemplarily be a hard disk drive (HDD), a solid state drive (SSD), or the like. Information transmitted by the vital sensor 2 via the communication device 3 may be stored in the storage device 53.

  Further, the storage device 53 may store programs and data used for the processor 51. The processor 51 may develop and use the program and data stored in the storage device 53 in the memory 52. Therefore, the memory 52 may be a “work memory” for the processor 51.

  The communication IF 54 exemplarily provides a connection interface with the network 4 and can communicate with the vital sensor 2 via the communication device 3 connected to the network 4.

  Communication between the communication IF 54 of the management system 5 and the vital sensor 2 via the network 4 and the communication device 3 may be one-way communication in the direction from the vital sensor 2 to the management system 5. In other words, the management system 5 only needs to be able to receive the information transmitted by the vital sensor 2, and may not control the operation of the vital sensor 2 by transmitting control information or the like to the vital sensor 2. However, bidirectional communication may be possible.

  The peripheral IF 55 is an interface for connecting a peripheral device to the management system 5 exemplarily. Peripheral devices may include an input device for inputting information to the management system 5 and an output device for outputting information obtained by the management system 5. The input device may include a keyboard, a mouse, a touch panel, and the like. The output device may include a display, a printer, and the like.

  By the way, as described above, the vital sensor 2 irradiates the human body with radio waves from the Doppler sensor 21 and detects a change in the reflected wave, for example, between the human body surface and the organ corresponding to the heartbeat of the human body. Distance change can be detected. It is possible to detect the heart rate and respiratory rate of the human body based on the distance change.

  Here, since the heart rate and breathing of the human body can limit the rough cycle in advance, in a stable state such as at rest, for example, by filtering the Doppler sensor value according to the limited cycle, some noise components can be canceled can do.

  However, when the human body is in an active state such as walking or running, both the vital sensor 2 and the human body move, so the distance between the vital sensor 2 and the human body surface is likely to change due to factors other than heartbeat and respiration.

  Therefore, a noise component resulting from human activity is added to the Doppler sensor value. In addition, since the movement of the human body during activity is not always constant and difficult to predict, it is difficult to quantify and cancel the noise component added to the Doppler sensor value.

  In the case of an electrocardiograph or pulse meter that is directly attached to and fixed on the human body surface (in other words, “skin”), the distance change between the human body surface and the electrocardiograph or pulse meter due to human activities is somewhat Since it can be suppressed, it is considered that the noise component can be reduced.

  However, when the vital sensor 2 is not fixed to the surface of the human body and the movement of the vital sensor 2 and the movement of the human body may not be synchronized, the noise component due to human activity becomes large, and the heartbeat of the active human body is increased. The detection accuracy of the number and respiration rate may decrease.

  As a non-limiting example, assume that the vital sensor 2 is not fixed to the surface of the human body but is placed in the chest pocket of the clothes to detect the heart rate and breathing state of the active human body.

  The movement of the human body and the movement of the clothes may not be synchronized depending on human activities such as swinging and raising / lowering the arm of the human body, walking and running, and the type and material of the clothes. Then, the distance between the human body and the vital sensor 2 placed in the chest pocket of the clothes (which may be referred to as “sensing distance” for convenience) varies depending on factors other than heartbeat and respiration. It may end up. Factors different from heartbeat and respiration may be referred to as “disturbance” or “asynchronous body movement” for convenience.

  In addition, the sensing distance may change depending on, for example, the movement of the great pectoral muscle of the human body. The pectoralis major muscle is used, for example, in daily human activities such as swinging arms and raising and lowering luggage during walking and running, and also in training and training such as push-ups and movements on the stomach.

  Thus, the sensing distance may change even when the human body is active using the great pectoral muscle. Furthermore, the amount of change in the sensing distance is likely to increase as the muscle mass of the greater pectoral muscle increases (in other words, the “thickness of the chest” increases).

  The change in sensing distance due to disturbance (asynchronous body motion) different from the heartbeat and breathing of the human body is the detection value (in other words, Doppler) of the distance change between the vital sensor 2 and the human body according to the heartbeat and breathing. It becomes a noise component for the sensor value.

  The noise component added to the Doppler sensor value due to such disturbance can be canceled or reduced by using the capacitance sensor 22 whose capacitance value detected in accordance with a change in the distance from the human body. For example, a change in the distance between the Doppler sensor 21 and a human body through clothing can be detected as a change in capacitance value.

  The capacitance sensor 22 cannot detect a change in distance itself, but can detect a change in capacitance value as a change in frequency of the capacitance sensor value. For example, the capacitance value tends to increase as the distance between the Doppler sensor 21 and the clothing or the distance between the clothing and the human body approaches, and conversely, the capacitance value decreases as the distance increases. There is a tendency.

  Therefore, when frequency analysis is performed on the capacitance sensor value, a frequency change corresponding to the change in the capacitance value appears, and a frequency component that shows a relatively large change compared to the other (referred to as “peak frequency” for convenience). Good) appears.

  The peak frequency may be regarded as indicating the timing (or period; the same applies hereinafter) when the capacitance value changes in the time domain. Based on the timing, it is possible to separate from the Doppler sensor value a signal component corresponding to heartbeat or respiration and a noise component caused by disturbance.

  For example, the Doppler sensor value and the capacitance sensor value are each subjected to FFT (Fast Fourier Transform) processing, and peak frequencies appearing in the respective FFT results (which may be referred to as “FFT peak frequency” for convenience) are compared. “FFT” may be replaced with “DFT (Discrete Fourier Transform)” (the same applies hereinafter).

  The FFT peak frequency of the Doppler sensor value where the peak frequency matches the FFT peak frequency of the capacitance sensor value is a frequency component in which a noise component due to asynchronous body motion corresponding to human body activity is added to the Doppler sensor value. You may determine that there is.

  On the other hand, the FFT peak frequency of the Doppler sensor value in which the FFT peak frequency of the capacitance sensor value does not match the peak frequency is a frequency component in which the change according to the heartbeat or respiration is dominant in the Doppler sensor value. You may judge.

  Thus, using the capacitance sensor value, in the Doppler sensor value, the frequency component in which the change according to the heartbeat or the respiration is dominant, and the noise component due to the disturbance (asynchronous body motion) according to the human body activity And can be separated.

  Therefore, it is possible to cancel (or may be referred to as “filtering”) or reduce a noise component caused by asynchronous body motion according to human body activity from the Doppler sensor value. Therefore, it is possible to improve the detection accuracy of the heart rate and the respiration rate by the vital sensor 2.

(Operation example)
Hereinafter, an operation example of the above-described sensing system will be described with reference to FIGS. Note that the operation example described below focuses on processing for canceling or reducing the noise component of the Doppler sensor value using the capacitance sensor value (may be collectively referred to as “noise canceling” for convenience). It is an example.

  In the following, the vital sensor 2 is provided with an inertial sensor 23, and the inertial sensor value is used as an auxiliary for “noise canceling”, and “noise canceling” is transmitted to the management system 5 (for example, the processor 51). An example to be implemented will be described. However, the inertial sensor value may not be essential for “noise canceling”, and “noise canceling” may be performed by the vital sensor 2 (for example, the processor 24).

(First embodiment)
FIG. 7 is a flowchart for explaining an operation example focusing on noise canceling according to the first embodiment.

  As illustrated in FIG. 7, the management system 5 receives the sensor information transmitted from the vital sensor 2 to the management system 5. The sensor information includes, for example, a Doppler sensor value, a capacitance sensor value, and an inertial sensor value. The sensor information is received by the communication IF 54 of the management system 5 and input to the processor 51 of the management system 5 (processing P11a, P11b, P11c).

  Note that, as illustrated in FIG. 7, the drive of the capacitance sensor 22 may be controlled by the processor 24 of the vital sensor 2. The processor 24 may functionally include a line driving unit 241 and a touch panel control unit 242.

  The line driving unit 241 may drive the touch panel by applying a voltage to electrodes (also referred to as “line electrodes”) arranged in a grid pattern on the touch panel, which is an example of the capacitance sensor 22. The touch panel control unit 242 may control driving of the touch panel 22 by the line driving unit 241. The line driving unit 241 and the touch panel control unit 242 may be implemented by the processor 24 reading and operating a program and data for driving and controlling the touch panel from the memory 25.

  In response to the reception of the Doppler sensor value, the processor 51 performs frequency analysis on the Doppler sensor value (processing P12a). FFT may be applied to the frequency analysis. For example, the Doppler sensor value is converted from a time-domain signal into a frequency-domain signal (which may be referred to as a “frequency signal” for convenience) by FFT.

  The processor 51 detects (may be referred to as “extraction”) the FFT peak frequency from the frequency signal of the Doppler sensor value (processing P13a). The FFT peak frequency of the Doppler sensor value is an example of a frequency component indicating a characteristic change according to heartbeat or respiration, and may be referred to as a “feature point” for convenience.

  A plurality of “feature points” of the Doppler sensor value may be detected per unit time. When a plurality of “feature points” are detected per unit time, the processor 51 may store the “feature points” in, for example, the memory 52 (or the storage device 53) (processing P14a). From the YES route, process P15a). The “unit time” may be a second unit or a minute unit. The “unit time” may be variable.

  On the other hand, if the number of detected “feature points” per unit time is one (NO in process P14a), the processor 51 bypasses the storage process P15a and performs the “feature point” comparison process. (Process P16a).

  The “feature point comparison process” may be, for example, a “feature point” comparison process obtained from each of the Doppler sensor value, the capacitance sensor value, and the inertial sensor value. In the “comparison process”, for example, information indicating a characteristic change corresponding to body movement, obtained from a capacitance sensor value or an inertial sensor value from information candidates indicating a characteristic change corresponding to heartbeat or respiration. Can be excluded. Therefore, the “comparison process” may be referred to as “filtering” for convenience.

  “Characteristic points” for the capacitance sensor value and the inertial sensor value may be obtained by frequency analysis in the same manner as the Doppler sensor value. For example, the processor 51 may calculate a capacitance value (processing P12b) in response to reception of the capacitance sensor value (processing P11b), and perform frequency analysis on the calculation result (processing P13b).

  You may apply FFT also to the frequency analysis of an electrostatic capacitance value. The processor 51 may extract a feature point corresponding to the peak frequency from the FFT result of the capacitance value (processing P14b). The feature point regarding the capacitance sensor value is an example of a frequency component indicating a characteristic change according to body movement different from heartbeat or respiration.

  Further, the processor 51 may calculate the amplitude of the inertial sensor value in response to the reception of the inertial sensor value (process P11c) (process P12c), and determine whether or not the calculation result is equal to or greater than the threshold (process). P13c).

  It may be considered that the larger the inertial sensor value, the greater the body movement, and the greater the noise component that is likely to be added to the Doppler sensor value. Therefore, the noise component added to the Doppler sensor value may be dominant in the noise component caused by the body movement indicated by the inertial sensor value, rather than the noise component caused by the body movement indicated by the capacitance sensor value. is there. Therefore, the noise component of the Doppler sensor value may be effectively canceled based on the inertial sensor value without using the capacitance sensor value.

  On the other hand, the noise component due to the body movement indicated by the capacitance sensor value is smaller than the noise component caused by the body movement indicated by the inertial sensor value when the amplitude of the inertial sensor value is small and the noise component added to the Doppler sensor value is May become more dominant. Therefore, the noise component of the Doppler sensor value may be effectively canceled based on the capacitance sensor value without using the inertial sensor value.

  The threshold value used for the determination process P13c may be set based on a criterion or policy that which of the capacitance sensor value and the inertial sensor value is more effective for noise canceling as described above. The threshold value may be stored and set in, for example, the memory 52 or the storage device 53 illustrated in FIG. A specific example of the threshold will be described later.

  If the amplitude of the inertial sensor value is greater than or equal to the threshold as a result of the threshold determination (YES in process P13c), the processor 51 may perform frequency analysis on the amplitude calculated from the inertial sensor value (process P14c). You may apply FFT also to the said frequency analysis.

  The processor 51 extracts a feature point corresponding to the peak frequency from the FFT result of the amplitude of the inertial sensor value (processing P15c). The feature point obtained from the inertial sensor value is an example of a frequency component indicating a characteristic change according to asynchronous body motion different from heartbeat and respiration.

  The processing for extracting the feature points from the Doppler sensor value, the capacitance sensor value, and the inertial sensor value described above may be executed in parallel by the processor 51. The processor 51 may compare the feature points of the sensor values and filter the noise component of the Doppler sensor value due to asynchronous body motion (processing P16a).

  For example, in the FFT result of the Doppler sensor value, the human heart rate component tends to have a peak frequency in a frequency range of about 0.7 Hz to 3 Hz. Further, in the FFT result of the Doppler sensor value, the respiratory component of the human body tends to have a peak frequency in a frequency range of about 0.1 Hz to 0.3 Hz.

  Therefore, the processor 51 compares the FFT peak frequency of the Doppler sensor value with the FFT peak frequency of one or both of the capacitance sensor value and the inertial sensor value in these frequency ranges.

  As a result of the comparison, the processor 51 determines that the frequency component having the same FFT peak frequency is a noise component corresponding to asynchronous body movement, not a change corresponding to heartbeat or respiration, and determines the noise component as a Doppler sensor value. It may be deleted from the FFT result. In this way, the processor 51 can narrow down the frequency component corresponding to the heartbeat component and the respiratory component in the FFT result of the Doppler sensor value.

  In addition, when the range which the frequency components of the sensing target such as the heart rate component and the respiratory component of the human body described above can be obtained in advance or can be easily predicted, the processor 51 performs the FFT target before the FFT described above. The signal may be filtered beforehand. For example, in the example of the frequency range described above, the processor 51 may perform low-pass filtering (LPF) on frequency components lower than 0.1 Hz before FFT.

  FIG. 8 shows an example of the temporal change of the Doppler sensor value, and FIG. 9 shows an example of the FFT result of the Doppler sensor value illustrated in FIG. As illustrated in FIG. 9, in the FFT result of the Doppler sensor value, a peak frequency corresponding to the respiratory component appears on the low frequency side, and a peak frequency corresponding to the heartbeat component appears on the high frequency side.

  The processor 51 may perform, for example, bandpass filtering (BPF) on the Doppler sensor value illustrated in FIG. 8 with the peak frequency appearing in the FFT result as the center frequency.

  As illustrated in FIGS. 10 and 11, the BPF can separate the signal waveform corresponding to the respiratory component and the signal waveform corresponding to the heartbeat component from the signal waveform of the Doppler sensor value illustrated in FIG. 8.

  In other words, the processor 51 detects, based on the change in the frequency component included in the capacitance sensor value, the signal component whose frequency changes according to the “movement” associated with the breathing or heartbeat of the human body from the Doppler sensor value. it can.

  A non-limiting example of filtering in the process P16a will be described with reference to FIGS. 20 shows an example of the frequency analysis result of the Doppler sensor value, FIG. 21 shows an example of the frequency analysis result of the inertial sensor value, and FIG. 22 shows an example of the frequency analysis result of the capacitance sensor value.

  In FIG. 20, the frequencies indicated by reference signs a1 to a5 represent FFT peak frequencies, which are examples of “feature points” for Doppler sensor values, and are obtained, for example, by the feature point extraction process P13a described above. For example, a1 <a2 <a3 <a4 <a5.

  Similarly, in FIG. 21, the frequencies indicated by reference numerals b1 to b3 represent FFT peak frequencies, which are examples of “feature points” for the inertial sensor value, and are obtained, for example, by the feature point extraction process P15c described above. For example, b1 <b2 <b3.

  In FIG. 22, the frequencies indicated by reference numerals c1 to c4 represent FFT peak frequencies that are examples of “feature points” for the capacitance sensor value, and are obtained, for example, by the feature point extraction process P14b described above. . For example, c1 <c2 <c3 <c4.

  In the comparison process P16a, for example, the “feature point” obtained for the Doppler sensor value is compared with the “feature point” obtained for the inertial sensor value. When a set of feature points a1 to a5 is represented by “A” and a set of feature points b1 to b3 is represented by “B”, in the comparison process P16a, it is included in the set A and not included in the set B (separately In other words, a feature point satisfying the relationship of “A \ B” is obtained.

  20 and 21, if a2 = b2 and a5 = b3, feature points a1, a3, and a4 for Doppler sensor values are candidates.

  Similarly, when the set of feature points c1 to c4 for the capacitance sensor value is represented by “C”, in the comparison process P16a, for example, it is included in the set A and not included in the set C (in other words, , A feature point satisfying the relationship of “A \ C” is obtained.

  In the example of FIGS. 20 and 22, if a2 = c2, a4 = c3, and a5 = c4, feature points a1 and a3 for Doppler sensor values are candidates.

  A feature point included in the set A and not included in the set B and a feature point included in the set A but not included in the set C are (A \ B) ∩ (A \ C), for example, feature points a1 and a3 are candidates.

  In the comparison process P16a, the BPF process is performed on the original signal waveform (see, for example, FIG. 8) of the Doppler sensor value based on the candidate feature points (in other words, the frequency) narrowed down as described above. Thereby, as illustrated in FIG. 10 and FIG. 11, the signal waveform corresponding to the respiratory component and the signal waveform corresponding to the heartbeat component can be separated.

  The processor 51 can calculate the heart rate and the respiration rate from the signal waveform obtained by the BPF process. For example, in the case of a heart rate, the processor 51 identifies a feature point (for example, an amplitude peak) of the signal waveform illustrated in FIG. 11 and obtains a time interval (for example, “second”) of the feature point.

  For example, the processor 51 can calculate the heart rate per minute by dividing 1 minute (= 60 seconds) by the obtained time interval (process P17a). Similarly, the respiration rate can be calculated by the processor 51.

  The heart rate and respiration rate calculated by the processor 51 may be appropriately output to an output device such as a display or a printer through the peripheral IF 55 (processing P18a).

  The process of calculating the capacitance value from the Doppler sensor value in process P12b is limited to the case where the amplitude of the inertial sensor value is determined to be less than the threshold value in the above-described threshold determination process P13c (in the case of NO). The setting may be enabled. Thereby, the power consumption of the processor 51 can be reduced.

  For example, if the amplitude of the inertial sensor value is less than the threshold, the processor 51 determines that the noise component due to the body movement indicated by the capacitance sensor value is dominant in the noise component added to the Doppler sensor value. It's okay.

  Therefore, the processor 51 may perform the above-described BPF while enabling noise canceling based on the capacitance sensor value. In other words, the processor 51 detects a noise component caused by body movement that cannot be detected from the change in the detection value of the inertial sensor 23 from the change in the detection value of the capacitance sensor 22 to detect the Doppler sensor value. You may cancel from

  Detecting a noise component caused by body movement that cannot be detected from a change in the detection value of the inertial sensor 23 from a change in the detection value of the capacitance sensor 22 increases the detection sensitivity of the asynchronous body movement. It may be understood that it corresponds to.

  In this way, the processor 51 adaptively changes the sensor value used for noise canceling of the Doppler sensor value to one of the capacitance sensor value and the inertial sensor value according to the magnitude of the change indicated by the inertial sensor value. You may control.

(Example of determination threshold for inertial sensor value)
Next, an example of the determination threshold used in the process P13c of FIG. 7 will be described with reference to FIGS. FIG. 12A shows an example of the detection axis of the inertial sensor 23, and FIG. 12B schematically shows how the inertial sensor 23 (in other words, “vital sensor 2”) is attached to the human body. It is a side view. FIG. 13 is a diagram illustrating a setting example of a determination threshold value according to the posture of the human body to which the vital sensor 2 is attached.

  An inertial sensor 23 illustrated in FIG. 12A is a three-axis inertial sensor having three detection axes (X, Y, and Z). The inertial sensor 23 illustratively has a size of width W [cm] × height H [cm] × thickness D [cm]. As a non-limiting example, W = 3 [cm], H = 5 [cm], and D = 1 [cm].

  A first detection axis X is set in the width (W) direction of the inertial sensor 23, a second detection axis Y is set in the height (H) direction, and a third detection axis Z in the thickness (D) direction. May be set. As illustrated in FIG. 12B, the inertial sensor 23 may be attached to the human body so that the surface formed by the detection axes X and Y is in a positional relationship parallel to the human body surface.

  For example, when the vital sensor 2 is accommodated in a chest pocket of a human clothes, the three detection axes X, Y, and Z with respect to the human body have an arrangement relationship as illustrated in FIG. In the arrangement relationship, the detection axis Z can detect a motion in a direction away from (or approach to) the human chest as an acceleration, for example.

  The detection axis Z may be oriented in the direction of directivity that the transmission radio wave of the Doppler sensor 21 has. The direction of directivity of the transmission radio wave of the Doppler sensor 21 may be regarded as corresponding to the direction of the detection axis of the Doppler sensor 21. Therefore, the inertial sensor 23 may have one of the detection axes oriented so that the acceleration in the detection axis direction of the Doppler sensor 21 can be detected.

  As illustrated in FIG. 13, the processor 51 may vary the determination threshold used for the determination process P <b> 13 c in FIG. 7 according to the detection values for the detection axes X, Y, and Z of the inertial sensor 23. FIG. 13 shows that the detection values (gravity acceleration [G]) of the detection axes X, Y, and Z can vary according to the posture of the human body. In other words, the posture of the human body can be determined based on the detection values of the detection axes X, Y, and Z.

  For example, as shown in FIG. 13, if the detected values for the detection axes X, Y, and Z are “0G, −1G, and 0G”, respectively, it may be determined that the human body is in the “standing position”. If “0G, −0.8G, 0.2G”, it may be determined that the human body is in the “middle waist” posture.

  Further, if the detection values for the detection axes X, Y, and Z are “0G, 0G, and −1G”, respectively, it is determined that the human body is in the “back-to-back” (so-called “backward”) posture. If it is “0G, 0G, 1G”, it may be determined that the human body is in the “prone position” (so-called “flank”) posture.

  Furthermore, if the detection values for the detection axes X, Y, and Z are “1G (or −1G), 0G, and 0G”, respectively, it may be determined that the human body is in the “side-down position”. If “± 0.4G, ± 1.8G, 0.7G”, it may be determined that the human body is in the “walking” posture.

  Note that the detection values for the detection axes X, Y, and Z shown in FIG. 13 are merely examples. Further, the posture that the human body can take is not limited to the example shown in FIG. Other postures not explicitly shown in FIG. 13 may be determined based on the detected values for the detection axes X, Y, and Z. A determination threshold value according to the difference in the posture of the human body may be given to the processor 51.

  For example, data illustrated in FIG. 13 (may be referred to as “determination threshold data” for convenience) may be stored in the memory 52 (see FIG. 6). The format of the determination threshold data stored in the memory 52 may be a table format or a list format, but is not limited to these formats.

  Based on the detection values (for example, amplitude values) for the detection axes X, Y, and Z of the inertial sensor 23, the processor 51 refers to the determination threshold data in the memory 52 and selectively reads out the corresponding determination threshold. You may use for the determination process P13c of FIG.

  As illustrated in FIG. 13, the determination threshold value corresponding to “middle hip position” compared to “standing position” may be set to a small value as an absolute value. Further, the determination threshold value corresponding to the “back position” compared to the “middle waist position” may be set to a smaller value as an absolute value.

  Furthermore, the determination threshold values corresponding to “prone position” and “side position” may be set as absolute values that are smaller than the determination threshold value of “standing position” and larger than the determination threshold value of “back position”. . In addition, the determination threshold corresponding to “walking” may be set to the same determination threshold as “standing”, for example.

  As the determination threshold value increases, the chance of being determined as “NO” in the process P13c of FIG. 7 increases, so that noise cancellation of the Doppler sensor value using the capacitance sensor value is more easily performed.

  In other words, changing the determination threshold to a large value may be regarded as equivalent to increasing the detection sensitivity of asynchronous body motion based on the detection value of the capacitance sensor 22. For example, it is considered that the “back-to-back position” is a posture that the human body often takes when resting, such as during sleep. Therefore, the detection threshold is set to a smaller value than other postures, and the detection sensitivity of asynchronous body movements May be lowered.

  On the other hand, in the postures of “middle hip position”, “prone position”, and “lateral position”, the signal component included in the Doppler sensor value depends on the signal component corresponding to heartbeat and breathing and other asynchronous body movements. It is considered difficult to distinguish which signal component is dominant. Note that the postures of “middle hip position”, “prone position”, and “lateral position” may be referred to as “intermediate posture” for convenience.

  Therefore, the processor 51 may increase the detection sensitivity of asynchronous body motion by changing the determination threshold value to a large value. Thereby, noise cancellation of the Doppler sensor value using the capacitance sensor value becomes easier to execute.

  Accordingly, it is possible to effectively cancel the noise component of the Doppler sensor value caused by asynchronous body movement (for example, movement of the great pectoral muscle) in the “intermediate posture”.

  Compared to the “intermediate posture”, the signal component included in the Doppler sensor value is more dependent on the signal component corresponding to the heartbeat and breathing and other asynchronous body movements in the “standing” and “walking” postures. It is considered difficult to distinguish which signal component is dominant.

  Therefore, the processor 51 may further increase the detection sensitivity of asynchronous body motion by changing the determination threshold value to a larger value. This makes it possible to effectively reduce the noise component of the Doppler sensor value caused by the asynchronous body movement even if there is asynchronous body movement (for example, movement of the great pectoral muscles) in the posture of “standing position” or “walking”. It becomes possible to cancel.

  Note that the determination threshold may be set separately for the detection axis of the inertial sensor 23. In this case, information (which may be referred to as “policy”) indicating which determination threshold value is given priority may be set in advance.

(Second embodiment)
In the first embodiment described above, in the comparison process P16a of FIG. 7, by comparing the “feature points”, the frequency component corresponding to the heartbeat and respiration and the noise component caused by the asynchronous body motion are separated, and the Doppler is separated. The example of canceling the noise component from the sensor value has been described.

  In the second embodiment, an example of separating the signal component corresponding to the heartbeat or respiration and the noise component caused by the asynchronous body motion, which are superimposed on the signal waveform of the Doppler sensor value, by “waveform separation” is shown in FIG. Will be described with reference to FIG.

  In FIG. 14, the feature point extraction processes P13a, P14b, and P15c for the Doppler sensor value, the capacitance sensor value, and the inertial sensor value described in FIG. 7 may be unnecessary. In FIG. 14, the feature point candidate determination process P14a for the Doppler sensor value described in FIG. 7 may be unnecessary. Furthermore, in FIG. 14, the comparison process P16a of FIG. 7 may be replaced with the waveform separation process P21.

  The waveform separation process P21 is an example of a process for separating a signal waveform corresponding to each signal component from a signal on which a plurality of signal components are superimposed (also referred to as “synthesis”) by frequency analysis. For example, the processor 51 responds to the heart rate and respiration included in the Doppler sensor value based on the frequency analysis result of the Doppler sensor value and the frequency analysis result of one or both of the capacitance sensor value and the inertial sensor value. The waveform corresponding to the signal component and the waveform corresponding to the noise component caused by the asynchronous body motion can be separated.

  The processor 51 can calculate the heart rate and the respiration rate in the same manner as in the first embodiment based on the signal component corresponding to the heart rate and respiration separated from the Doppler sensor value. The calculation result may be appropriately output to an output device such as a display or a printer through the peripheral IF 55.

  According to the second embodiment, as compared with the first embodiment, since it is not necessary to obtain a feature point from the frequency analysis result of each sensor value, the calculation processing speed is increased depending on the calculation capability of the processor 51. I can expect.

  In the first embodiment and the second embodiment described above, noise cancellation using the capacitance sensor value may not be performed depending on the inertial sensor value. However, the capacitance sensor value is the same as the inertial sensor value. Regardless, it may be a setting used for noise canceling.

(Appearance example of vital sensor 2)
An example of the external appearance of the vital sensor 2 is shown in FIGS. FIG. 15 shows an example of the appearance of the main body of the vital sensor 2. As illustrated in FIG. 16, a jig 201 that allows a harness to be attached may be attached to the vital sensor 2 body.

  For example, as schematically shown in FIGS. 17 and 18, the jig 201 has a space in which a belt-like harness 202 can be passed between the longitudinal end of the vital sensor 2 body and the jig 201. As such, it may be attached to the vital sensor 2 body. Screwing may be applied to the attachment of the jig 201 to the vital sensor 2 body.

  The harness 202 facilitates the attachment and fixing of the vital sensor 2 to, for example, a human body. However, the vital sensor 2 may be accommodated in a clothing pocket or the like without using the harness 202 as described above.

  For example, the vital sensor 2 with the jig 201 may be accommodated in a clothing pocket, or the vital sensor 2 body without the jig 201 may be accommodated in a clothing pocket. As schematically illustrated in FIG. 18, the vital sensor 2 may be attached to a human body such that a surface on which a liquid crystal panel, which is an example of the capacitance sensor 22, is opposed to the human body surface.

  In other words, vitals are applied to the human body so that the normal of the surface of the touch panel faces the direction along the detection axis Z of the inertial sensor 23 (which is also the detection axis of the Doppler sensor 21) illustrated in FIG. A sensor 2 may be attached.

  Note that, as schematically illustrated in FIG. 19, the Doppler sensor 21 may be disposed inside the vital sensor 2 main body in a space that avoids the space corresponding to the region where the touch panel 22 is provided.

  For example, the processor 24 (line driving unit 241 and touch panel control unit 242) illustrated in FIG. 7 may be arranged in a space corresponding to the area where the touch panel 22 is provided in the vital sensor 2 body. In this case, the Doppler sensor 21 may be arranged in a space that avoids the space occupied by the processor 24.

  Thereby, the radio wave transmitted from the antenna 211 of the Doppler sensor 21 can be prevented or suppressed from being absorbed by the touch panel 22 and the detection sensitivity of the Doppler sensor 21 being lowered. The inertial sensor 23 may also be arranged in a space that avoids the space occupied by the processor 24, as with the Doppler sensor 21. For example, the Doppler sensor 21 and the inertial sensor 23 may be arranged in a space that avoids the space occupied by the processor 24 in the set.

  As described above, according to the embodiment including the first and second examples described above, the Doppler sensor value is corrected according to the capacitance sensor value (or the capacitance sensor value and the inertial sensor value). The noise component of the Doppler sensor value can be canceled or reduced.

  For example, even if the human body and the vital sensor 2 attached to the human body move asynchronously according to the activity of the human body, the noise component caused by such asynchronous body movement is detected by the capacitance sensor 22. It can be detected and canceled or reduced from the Doppler sensor value.

  Since the detection value of the capacitance sensor 22 increases as it approaches the human body, a change in the distance between the capacitance sensor 22 and the human body can be detected by, for example, frequency analysis of the detection value. Therefore, it is possible to cancel or reduce the noise component caused by the asynchronous body motion added to the Doppler sensor value according to the detected distance change.

  Since the noise component caused by asynchronous body motion can be canceled or reduced from the Doppler sensor value, the detection accuracy such as the heart rate and the respiration rate based on the Doppler sensor value is improved. Therefore, even if a distance change occurs between the vital sensor 2 and the human body due to the activity of the human body, the heart rate, respiratory rate, etc. of the human body can be accurately detected.

  For example, assume that the vital sensor 2 is housed in a clothing pocket of the human body and is not fixed to the human body. When the human body performs an activity such as walking or running in such a situation, the vital sensor 2 (Doppler sensor 21) approaches or moves away from the human body surface according to the human body activity, and the movement is asynchronous with the heartbeat and breathing. "Is likely to occur in the Doppler sensor 21.

  Even if such an asynchronous “movement” occurs in the Doppler sensor 21 and a noise component is added to the Doppler sensor value, the asynchronous “movement” can be detected by the capacitance sensor 22 to cancel or reduce the noise component. . Therefore, it is possible to accurately detect the heart rate and respiratory rate of the active human body without contact.

(Other)
In the embodiments including the above-described examples, the “human body” is illustrated as an example of the “living body” to which the vital sensor 2 is attached. However, the “biological body” to which the vital sensor 2 is attached is an animal other than the human body. May be.

  For example, the animal may be a mammal, fish, bird or the like. As a non-limiting example, a vital sensor 2 may be attached to pets such as dogs and cats, or livestock such as cows and horses, and the health status of the pets and livestock may be managed by the management system 5.

1 Sensing system 2 Non-contact vital sensor 21 Doppler sensor 211 Antenna 212 Local oscillator (Oscillator, OSC)
213 Micro Control Unit (MCU)
214 Detection Circuit 215 Operational Amplifier (OP)
216 Battery 22 Capacitance sensor 221 Detection electrode 222 Amplifier 23 Inertial sensor 24 Processor 241 Line drive unit 242 Touch panel control unit 25 Memory 26 Communication interface (IF)
27 Bus 3 Communication device 4 Network 5 Management system 51 Processor 52 Memory 53 Storage device 54 Communication IF
55 Peripheral IF
56 Bath 201 Jig 202 Harness

Claims (7)

A Doppler sensor that irradiates a living body with radio waves and receives reflected waves;
A capacitance sensor that detects a change in capacitance between the living body and the living body;
A processor for detecting a signal component whose frequency changes according to the movement of the living body from the detection signal of the Doppler sensor, based on a change of the frequency component included in the detection signal of the capacitance sensor;
A biological information measuring device comprising: Detection of the signal component by the processor is:
The biological information measuring apparatus according to claim 1, comprising removing a frequency component corresponding to a change in the capacitance contained in the detection signal of the capacitance sensor from the detection signal of the Doppler sensor.   The biological information according to claim 1, wherein the capacitance sensor is arranged so as to be able to detect a change in the capacitance in a spatial range overlapping a spatial range irradiated with the radio wave from the Doppler sensor. measuring device. An inertial sensor capable of detecting movement of the biological information measuring device;
The processor is
The biological information measuring device according to claim 1, wherein the signal component is detected when an amplitude value of a detection signal of the inertial sensor is equal to or less than a threshold value. The inertial sensor has a plurality of detection axes,
The biological information measuring apparatus according to claim 4, wherein the processor varies the threshold based on an amplitude value of a detection signal obtained for each of the detection axes in accordance with the posture of the biological body. A Doppler sensor,
A capacitance sensor that detects a capacitance change in a spatial range that overlaps a spatial range in which the Doppler sensor can detect a distance change;
Based on the change of the frequency components included in the output of the previous Kiseiden capacitive sensor, the output of the Doppler sensor, and a processor for performing operations for detecting a signal component which changes its frequency in accordance with the movement of the living body,
With a sensor unit. A sensor unit having a Doppler sensor and a capacitance sensor;
The sensor unit receives the detection signal of the Doppler sensor and the detection signal of the capacitance sensor, and based on the change of the frequency component contained in the detection signal of the capacitance sensor, from the detection signal of the Doppler sensor, the sensor unit A processor for detecting a signal component whose frequency changes according to the movement of a living body to which
A biological information measurement system comprising:

Images