JP2017140199A - Measurement device and electronic apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain a measurement device which can be reduced in power consumption.SOLUTION: A measurement device in the present disclosure comprises a first measuring unit that has a plurality of operation modes including a first operation mode and a second operation mode which are different in operation frequency from each other and measures a conductance value; a control unit that determines an average of conductance values in a first period, and selects one of the plurality of operation modes on the basis of a comparison result between the average value and a threshold value; and a threshold value generation unit that generates a threshold value on the basis of a plurality of conductance values.SELECTED DRAWING: Figure 4

Description

  The present disclosure relates to a measurement apparatus that measures electrodermal activity (EDA), and an electronic apparatus that includes such a measurement apparatus.

  Electrocutaneous activity is a potential or impedance variation in the skin and varies in response to mental sweating associated with expression of human emotions such as fear, arousal, cognition, and stress. For example, Non-Patent Document 1 discloses a study for estimating human emotion by measuring such electrodermal activity in a living environment.

Cornelia Kappeler-Setz et al., "Towards long term monitoring of electrodermal activity in daily life", Personal and Ubiquitous Computing Volume 17, Issue 2, pp261-271.

  It is desirable to be able to measure for a long time in daily life by a measuring device that measures biological signals such as electrodermal activity. In particular, when a wearable device is used as such a measuring device, for example, since downsizing is generally desired, the capacity of the battery that can be mounted is reduced. Therefore, such a measuring apparatus is desired to have low power consumption.

  The present disclosure has been made in view of such problems, and an object of the present disclosure is to provide a measurement apparatus and an electronic apparatus that can reduce power consumption.

  The measurement device of the present disclosure includes a first measurement unit, a control unit, and a threshold value generation unit. The first measurement unit has a plurality of operation modes including a first operation mode and a second operation mode having different operation frequencies, and measures a conductance value. The control unit obtains an average value of conductance values in the first period, and selects one of a plurality of operation modes based on a comparison result between the average value and a threshold value. The threshold value generation unit generates a threshold value based on a plurality of conductance values.

  The electronic device of the present disclosure includes a first measurement unit, a control unit, a threshold value generation unit, and a processing unit. The first measurement unit has a plurality of operation modes including a first operation mode and a second operation mode having different operation frequencies, and measures a conductance value. The control unit obtains an average value of conductance values in the first period, and selects one of a plurality of operation modes based on a comparison result between the average value and a threshold value. The threshold value generation unit generates a threshold value based on a plurality of conductance values. The processing unit performs predetermined processing using a plurality of conductance values.

  In the measurement device and the electronic apparatus of the present disclosure, the conductance value is measured by the first measurement unit having a plurality of operation modes. Then, the operation mode of the first measurement unit is determined based on the comparison result between the average conductance value and the threshold value. This threshold value is generated based on a plurality of conductance values measured by the first measuring unit.

  According to the measurement device and the electronic apparatus of the present disclosure, a threshold value is generated based on a plurality of conductance values, and the operation mode of the first measurement unit is determined based on a comparison result between the average value of the conductance values and the threshold value. Therefore, the power consumption can be reduced. In addition, the effect described here is not necessarily limited, and there may be any effect described in the present disclosure.

It is a block diagram showing the example of 1 composition of the measuring device concerning one embodiment of this indication. It is explanatory drawing showing the example of 1 operation | movement of the learning data generation part shown in FIG. FIG. 10 is another explanatory diagram illustrating an operation example of the learning data generation unit illustrated in FIG. 1. It is a flowchart showing the example of 1 operation | movement of the learning data generation part shown in FIG. It is explanatory drawing showing the example of 1 operation | movement of the learning data analysis part shown in FIG. 3 is a flowchart illustrating an operation example of a measurement control unit illustrated in FIG. 1. FIG. 3 is a timing waveform diagram illustrating an operation example of the measurement apparatus illustrated in FIG. 1. It is a perspective view showing the appearance composition of a wristwatch to which an embodiment is applied. It is a block diagram showing the example of 1 structure of the measuring device which concerns on a modification.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. The description will be given in the following order.
1. Embodiment 2. FIG. Application examples

<1. Embodiment>
[Configuration example]
FIG. 1 shows a configuration example of a measuring apparatus 1 according to an embodiment. The measuring device 1 is a device that measures a user's electrodermal activity. The measuring device 1 includes a sweating sensor 20, a sensor 6, a control unit 30, and a processing unit 5. These blocks are connected to each other via a bus 9.

  The sweat sensor 20 measures skin conductance. The sweat sensor 20 includes a voltage generation unit 21, a detection unit 22, an electrode 23, an amplification unit 24, a filter unit 25, and an AD (Analog / Digital) conversion unit 26.

  The voltage generation unit 21 generates a voltage used in the detection unit 22 and a voltage applied to the user's skin.

  The detection unit 22 applies a voltage to the user's skin via the electrode 23 and detects a weak current flowing on the user's skin surface. The detection unit 22 includes, for example, a current amplification circuit, a Wheatstone bridge circuit, and the like.

  The electrode 23 electrically connects the sweat sensor 20 and the user's skin. The electrode 23 is brought into contact with a place where mental sweating is likely to occur, such as a user's finger, palm, wrist, and sole of the foot. As the electrode 23, for example, a metal electrode such as an Ag / AgCl electrode or stainless steel can be used. Moreover, you may comprise the electrode 23 using a disposable electrode.

  The amplifying unit 24 amplifies the output signal from the detecting unit 22 to a level at which a sufficient dynamic range and resolution can be secured in the AD conversion unit 26 in the subsequent stage.

  The filter unit 25 reduces noise included in the input signal, and includes a low-pass filter, for example. The cut-off frequency of the low-pass filter is set to a frequency sufficiently higher than the frequency (several hertz or less) of the signal corresponding to mental sweating.

The AD conversion unit 26 samples the output signal of the filter unit 25 at a predetermined sampling frequency, and converts the output signal (analog signal) into a digital value. For example, an integrated circuit chip can be used as the AD conversion unit 26. In particular, when the bus 9 is, for example, an I ² C bus, a chip having an interface for the I ² C bus can be used. In this example, the sweat sensor 20 has the AD conversion unit 26. However, the present invention is not limited to this. For example, the control unit 30 has the AD conversion unit 26. Also good.

  With this configuration, the sweat sensor 20 generates a signal Gmeas indicating a change in conductance value over time based on the measured value. The sweat sensor 20 supplies the signal Gmeas to the control unit 30 and the processing unit 5 through the bus 9.

  The sweat sensor 20 has a normal operation mode M1, a low power consumption operation mode M2, and a stop mode M3. Based on an instruction from the control unit 30 performed via the bus 9, the perspiration sensor 20 It operates in the power consumption operation mode M2 and the stop mode M3.

  In the normal operation mode M1, the voltage generation unit 21, the detection unit 22, the amplification unit 24, and the filter unit 25 always operate. The sampling frequency of the AD converter 26 is set to, for example, 128 Hz in the normal operation mode M1. The AD converter 26 operates only during sampling, and operates in a standby state during other periods.

  In the low power consumption operation mode M2, the voltage generation unit 21, the detection unit 22, the amplification unit 24, and the filter unit 25 operate intermittently. Specifically, the voltage generation unit 21, the detection unit 22, the amplification unit 24, and the filter unit 25 operate, for example, at a rate of once per second for 100 milliseconds, for example. In the low power consumption operation mode M2, the sampling frequency of the AD conversion unit 26 is set to 1 Hz, for example, so as to be synchronized with these operations. Thus, the AD conversion unit 26 samples the output signal of the filter unit 25 when the voltage generation unit 21, the detection unit 22, the amplification unit 24, and the filter unit 25 are operating. As a result, in the low power consumption operation mode M2, the power consumption in the sweat sensor 20 can be reduced to about 1/10 in this example as compared with the normal operation mode M1.

  In the stop mode M3, the voltage generation unit 21, the detection unit 22, the amplification unit 24, the filter unit 25, and the AD conversion unit 26 stop operating. Thereby, the power consumption in the perspiration sensor 20 can be significantly reduced.

  The sensor 6 includes, for example, at least one of a pulse sensor, an acceleration sensor, a gyro sensor, a GPS (Global Positioning System) sensor, a geomagnetic sensor, a temperature sensor, an atmospheric pressure sensor, a humidity sensor, and an illuminance sensor. . Then, the sensor 6 supplies the detection result by the sensor to the control unit 30 and the processing unit 5 via the bus 9.

  For example, by providing a GPS sensor, the measuring apparatus 1 can specify the location where the user is located, and can switch the operation mode depending on the location. Specifically, for example, when the user is in the office, the sweat sensor 20 operates in the normal operation mode M1 or the low power consumption operation mode M2 based on an instruction from the control unit 30, thereby performing the electrodermal activity. taking measurement. Then, the measuring apparatus 1 generates learning data DS based on the measurement result of the electrodermal activity when the user is at the office, as described later, and generates a threshold value Gth based on the learning data DS. The operation mode is determined using this threshold value Gth. When the user is outside the office, the operation mode of the sweat sensor 20 is set to the stop mode M3. Thereby, in the measuring apparatus 1, power consumption can be reduced.

  Similarly, for example, by providing a pulse sensor, the measuring device 1 can measure the electrodermal activity when the pulse of the user is low. Further, for example, by providing an acceleration sensor or a gyro sensor, the measuring apparatus 1 can detect whether the user is at rest and can switch the operation mode based on the detection result. Further, for example, using a technique for detecting the position of a user indoors by combining a geomagnetic sensor and an acceleration sensor, the measurement apparatus 1 detects whether the user is in the living room or in the bedroom, and the detection result. The operation mode can be switched based on In addition, for example, by providing an air temperature sensor, an atmospheric pressure sensor, and a humidity sensor, the measuring device 1 can measure electrodermal activity according to the air temperature, atmospheric pressure, and humidity. In addition, for example, by providing an illuminance sensor, it is possible to detect the user's activity time zone, and thus the measuring device 1 can switch the operation mode according to the user's activity time zone.

  The control unit 30 controls the operation of the measuring device 1. The control unit 30 is configured using a CPU (Central Processing Unit), a memory, or the like in an electronic device such as a so-called wearable device to which the measurement device 1 is applied. The control unit 30 includes a learning data generation unit 31, a learning data analysis unit 32, and a measurement control unit 33.

  The learning data generation unit 31 generates learning data DS based on the signal Gmeas. Specifically, the learning data generation unit 31 first obtains a signal Gtonic that is a low frequency component (baseline) and a signal Gphasic that is a high frequency component based on the signal Gmeas. The learning data generation unit 31 generates learning data DS based on the signal Gtonic and the signal Gphasic.

  FIG. 2 shows an example of the waveform of the electrical skin activity that can be acquired by the sweat sensor 20, where (A) shows the waveform of the signal Gmeas, (B) shows the waveform of the signal Gtonic, and (C) shows the signal. The waveform of Gphasic is shown. The learning data generation unit 31 obtains a signal Gtonic that is a low frequency component and a signal Gphasic that is a high frequency component based on the signal Gmeas. Then, for example, the learning data generation unit 31 obtains the average value Vt of the signal Gtonic in a period PA (PA1, PA2, PA3,...) Having a predetermined time width (for example, 60 seconds). Further, the learning data generation unit 31 obtains the number of times that a pulse having a predetermined condition appears in the signal Gphasic in the period PA (pulse detection number Np). Specifically, for example, in FIG. 2C, the learning data generation unit 31 counts the number of pulses having a pulse width shorter than a predetermined width and a pulse height larger than a predetermined height in the signal Gphasic. The learning data generation unit 31 sets a plurality of periods PA and obtains the average value Vt and the number of pulse detections Np in each period PA. In this way, the learning data generation unit 31 generates the learning data DS.

  FIG. 3 shows a configuration example of the learning data DS. In this example, the average value Vt1 and the number of pulse detections Np1 are obtained in the period PA1 (FIG. 2), the average value Vt2 and the number of pulse detections Np2 are obtained in the period PA2, and the average value Vt3 The number of pulse detections Np3 is obtained in the period PA3. As described above, the learning data DS includes a plurality of sets of data including the average value Vt and the number of pulse detection times Np. The learning data generation unit 31 generates such learning data DS. The learning data generation unit 31 supplies the learning data DS to the learning data analysis unit 32.

  The learning data analysis unit 32 (FIG. 1) generates a threshold value Gth based on the learning data DS. Then, the learning data analysis unit 32 supplies the generated threshold value Gth to the measurement control unit 33.

  The measurement control unit 33 controls the measurement operation of the sweat sensor 20 and the sensor 6. The measurement control unit 33 also has a function of determining the operation mode of the sweat sensor 20 based on the signal Gmeas and the threshold value Gth and instructing the sweat sensor 20 through the bus 9. doing.

  The processing unit 5 estimates the user's emotion based on the signal Gmeas supplied from the sweat sensor 20 and the detection result supplied from the sensor 6, and performs predetermined processing according to the estimation result. The processing unit 5 is configured using a CPU, a memory, or the like in an electronic device such as a so-called wearable device to which the measurement device 1 is applied.

  Here, the perspiration sensor 20 corresponds to a specific example of “first measurement unit” in the present disclosure. The measurement control unit 33 corresponds to a specific example of “control unit” in the present disclosure. The learning data generation unit 31 and the learning data analysis unit 32 correspond to a specific example of “threshold generation unit” in the present disclosure.

[Operation and Action]
Then, operation | movement and an effect | action of the measuring apparatus 1 of this Embodiment are demonstrated.

(Overview of overall operation)
First, with reference to FIG. 1, an outline of the overall operation of the measuring apparatus 1 will be described. In the sweat sensor 20, the voltage generation unit 21 generates a voltage used in the detection unit 22 and a voltage applied to the user's skin. The detection unit 22 applies a voltage to the user's skin through the electrode 23 and detects a weak current flowing on the user's skin surface. The amplification unit 24 amplifies the output signal from the detection unit 22 to a level at which a sufficient dynamic range and resolution can be ensured in the AD conversion unit 26 in the subsequent stage. The filter unit 25 reduces noise included in the input signal. The AD conversion unit 26 samples the output signal of the filter unit 25 at a predetermined sampling frequency, and converts the output signal (analog signal) into a digital value. In this way, the sweat sensor 20 generates the signal Gmeas indicating the time change of the conductance value, and supplies the signal Gmeas to the control unit 30 and the processing unit 5 via the bus 9. Similarly, the sensor 6 supplies the detection result to the control unit 30 and the processing unit 5 via the bus 9.

  In the control unit 30, the learning data generation unit 31 generates learning data DS based on the signal Gmeas. The learning data analysis unit 32 generates a threshold value Gth based on the learning data DS. The measurement control unit 33 controls the measurement operation of the sweat sensor 20 and the sensor 6. Further, the measurement control unit 33 determines the operation mode of the sweat sensor 20 based on the signal Gmeas and the threshold value Gth and instructs the sweat sensor 20 via the bus 9.

  And the process part 5 estimates a user's emotion based on the detection result in the signal Gmeas and the sensor 6, and performs the predetermined | prescribed process according to the estimation result.

(Detailed operation of learning data generation unit 31)
The learning data generation unit 31 generates learning data DS based on the signal Gmeas, for example, when the user starts using the measuring device 1 or when the sweat sensor 20 operates in the normal operation mode M1. Hereinafter, the operation of the learning data generation unit 31 will be described in detail.

  FIG. 4 illustrates an operation example of the learning data generation unit 31. The learning data generation unit 31 obtains a signal Gtonic that is a low frequency component and a signal Gphasic that is a high frequency component based on the signal Gmeas. The learning data generation unit 31 generates learning data DS based on the signal Gtonic and the signal Gphasic. This operation will be described in detail below.

  First, the learning data generation unit 31 starts a timer T1 (step S1). In this example, the timer T1 is a timer that expires in 60 seconds. That is, the period related to the timer T1 corresponds to the period PA in FIG. In step S1 to step S4, the learning data generation unit 31 obtains the number of times that a pulse with a predetermined condition appears in the signal Gphasic (pulse detection number Np) in the period from when the timer T1 starts until it expires. Specifically, the learning data generation unit 31 has a pulse width shorter than a predetermined width and a pulse height of a predetermined value in the signal Gphasic (FIG. 2C) indicating the high-frequency component of the signal Gmeas. It is confirmed whether or not a larger pulse appears (step S2). If such a pulse appears ("Y" in step S2), the number of pulse detection Np is incremented (step S3). The learning data generation unit 31 obtains the pulse detection count Np by repeating this operation until the timer T1 expires.

  In step S4, when the timer T1 has expired (“Y” in step S4), the period PA started in step S1 has ended, so the learning data generation unit 31 performs the low-frequency operation of the signal Gmeas in this period PA. An average value Vt of the signal Gtonic indicating the component is calculated (step S5).

  Next, the learning data generation unit 31 records the average value Vt and the number of pulse detections Np (step S6). Thereby, the average value Vt and the number of pulse detection times Np are added to the learning data DS.

  Then, the learning data generation unit 31 increments the learning data number CNT (step S7), resets the pulse detection count Np (step S8), and returns to step S1.

  In this way, the learning data generating unit 31 generates learning data DS that includes a set of the average value Vt and the number of pulse detections Np by the number of learning data CNT.

(Detailed operation of learning data analysis unit 32)
The learning data analysis unit 32 generates a threshold value Gth based on the learning data DS. Hereinafter, the operation of the learning data analysis unit 32 will be described in detail.

  FIG. 5 is a plot of the average value Vt and the number of pulse detections Np included in the learning data DS. Specifically, for example, a set of the average value Vt1 and the number of pulse detections Np1 (FIG. 3) is plotted as one point, a set of the average value Vt2 and the number of pulse detections Np2 is plotted as one point, and the average value Vt3 A set of the number of pulse detection times Np3 is plotted as one point. In this example, the points are divided into two groups G1, G2. The group G1 is a group corresponding to a state where the user's emotion is calm, for example, and the group G2 is a group corresponding to a state where the user's emotion is high, for example.

  The learning data analysis unit 32 generates a threshold Gth based on such learning data DS. Specifically, the learning data analysis unit 32 first searches for a point P1 having the lowest average value Vt among points having a pulse detection count Np larger than a predetermined threshold value NpTh. The learning data analysis unit 32 obtains a threshold value Gth by multiplying the average value Vt of the point P1 by a predetermined coefficient a. This coefficient a can be set to a value larger than “0” and smaller than “1”, for example. However, the present invention is not limited to this. For example, the coefficient a may be “1” or may be a value larger than “1”. In order to increase the power consumption reduction effect, the coefficient a may be set higher. In this way, the learning data analysis unit 32 generates the threshold value Gth based on the learning data DS.

(Detailed operation of the measurement control unit 33)
The measurement control unit 33 controls the measurement operation of the sweat sensor 20 and the sensor 6. Further, the measurement control unit 33 determines whether the perspiration sensor 20 should operate in the normal operation mode M1 or the low power consumption operation mode M2 based on the signal Gmeas and the threshold value Gth. Below, the operation | movement which determines the operation mode of the perspiration sensor 20 is demonstrated in detail.

  FIG. 6 illustrates an operation example of the measurement control unit 33. The measurement control unit 33 determines the operation mode of the sweat sensor 20 by comparing the average value of the signal Gmeas with the threshold value Gth. This operation will be described in detail below.

  First, the measurement control unit 33 confirms whether or not the number of learning data DS is sufficient (step S11). Specifically, the measurement control unit 33 confirms whether or not the number of learning data DS is sufficient based on the number of learning data CNT in the flow shown in FIG. When the number of learning data CNT is small and the number of learning data DS is not sufficient (“N” in step S11), the measurement control unit 33 sets the operation mode of the sweat sensor 20 to the normal operation mode M1 ( Step S12), the process returns to step S11. And the measurement control part 33 repeats step S11, S12 until the learning data number CNT exceeds predetermined value and the data number of learning data DS becomes sufficient.

  In step S11, when the number of learning data DS is sufficient (“Y” in step S11), the measurement control unit 33 starts a timer T2 (step S13). In this example, the timer T2 expires in 60 seconds.

  Next, the measurement control unit 33 starts a timer T3 (step S14). In this example, the timer T2 ends in 1 second. And the measurement control part 33 confirms whether the timer T3 was complete | finished (step S15). If the timer T3 has not expired ("N" in step S15), the process returns to step S15, and this step S15 is repeated until the timer T3 expires.

  If the timer T3 expires in step S15 (“Y” in step S15), the measurement control unit 33 accumulates the signal Gmeas in the data buffer of the measurement control unit 33 (step S16).

  Next, the measurement control unit 33 confirms whether or not the timer T2 has expired (step S17). If the timer T2 has not expired ("N" in step S17), the process returns to step S14, and the operations in steps S14 to S17 are repeated until the timer T2 ends. Thereby, in this example, the signal Gmeas obtained by the sampling operation every second is accumulated in the data buffer of the measurement control unit 33. Since the measuring apparatus 1 performs measurement every time the timer T3 expires, the signal Gmeas is accumulated every second regardless of whether the operation mode is the normal operation mode M1 or the low power consumption operation mode M2. Is done.

  In step S17, when the timer T2 expires (“Y” in step S17), the measurement control unit 33 calculates the average value Gavg based on the signal Gmeas stored in the data buffer of the measurement control unit 33. (Step S18).

  Next, the measurement controller 33 checks whether or not the average value Gavg is larger than the threshold value Gth (Gavg> Gth) (step S19). When the average value Gavg is larger than the threshold value Gth (“Y” in step S19), the measurement control unit 33 sets the operation mode of the sweat sensor 20 to the normal operation mode M1 (step S20). If Gavg is not greater than the threshold value Gth (“N” in step S19), the measurement control unit 33 sets the operation mode of the sweat sensor 20 to the low power consumption operation mode M2 (step S21).

  Then, the measurement control unit 33 clears the data buffer (step S22) and returns to step S13.

  FIG. 7 shows an operation example of the measuring apparatus 1, (A) shows the signal Gmeas, and (B) shows the average value Gavg. Based on the signal Gmeas, the measurement control unit 33 obtains an average value Gavg every 60 seconds in this example. And the measurement control part 33 determines the operation mode of the perspiration sensor 20 by comparing the average value Gavg and the threshold value Gth. In this example, the average value Gavg exceeds the threshold value Gth during the period from the timing t1 to t2. Therefore, during this period, the perspiration sensor 20 operates in the normal operation mode M1. Further, the average value Gavg is lower than the threshold value Gth during the period from timing t2 to t3. Therefore, during this period, the sweat sensor 20 operates in the low power consumption operation mode M2.

  As described above, the measurement apparatus 1 can reduce power consumption by providing two operation modes (the normal operation mode M1 and the low power consumption operation mode M2). That is, in recent years, wearable electronic devices and portable electronic devices have become more multifunctional. In such an electronic device, when a function for estimating a user's emotion is installed, it is desirable that a measuring device for measuring electrodermal activity can operate for a long time by battery driving. In other words, such a measuring device is desired to have low power consumption. In the measuring apparatus 1, since the low power consumption operation mode M2 is provided in addition to the normal operation mode M1, the power consumption can be reduced. As a result, the measurement apparatus 1 can operate for a long time and is an apparatus suitable for estimating the emotion of the user.

  In particular, in the measuring apparatus 1, since the operation mode is determined by comparing the average value Gavg of the signal Gmeas with the threshold value Gth, the power consumption can be reduced. That is, when the average value Gavg is high, for example, the user's emotion is high, and when the average value Gavg is low, for example, the user's emotion is calm. Therefore, when the average value Gavg is high, the operation can be performed in the normal operation mode M1, and when the average value Gavg is low, the operation can be performed in the low power consumption operation mode M2, thereby efficiently reducing the power consumption.

  Moreover, in the measuring apparatus 1, since the threshold value Gth is obtained by learning, the power consumption can be effectively reduced. That is, in general, since there are large individual differences in electrodermal activity, it is difficult to uniquely determine a threshold value Gth applicable to all users. In the measuring apparatus 1, the learning data DS is generated based on the signal Gmeas in the normal operation mode M1, and the threshold value Gth is generated based on the learning data DS. Thereby, in the measuring apparatus 1, since the threshold value Gth desirable for each user can be set, respectively, power consumption can be reduced effectively.

[effect]
As described above, in this embodiment, the operation mode is determined by comparing the average value Gavg of the signal Gmeas with the threshold value Gth, so that the power consumption can be reduced while maintaining the analysis accuracy. Can do.

  In the present embodiment, since the threshold value Gth is obtained by learning, power consumption can be effectively reduced.

<2. Application example>
Next, application examples of the measurement apparatus described in the above embodiment will be described.

  FIG. 8 shows the appearance of a wristwatch to which the measurement apparatus of the above embodiment is applied. This wristwatch has, for example, a dial face 110 and a band part 120. The wristwatch is equipped with the measuring device according to the above-described embodiment and the like.

  The measuring apparatus according to the above-described embodiment can be applied to various things worn by the user such as wristbands, glasses and rings in addition to such wristwatches. Can be constructed.

  Although the present technology has been described with reference to some embodiments and application examples to electronic devices, the present technology is not limited to these embodiments and the like, and various modifications are possible.

  For example, in the above embodiment, the measurement control unit 33 has instructed the operation mode to the sweating sensor 20 via the bus 9, but the present invention is not limited to this. Instead of this, for example, as in the measurement apparatus 1A shown in FIG. 9, the operation mode may be instructed using a dedicated signal line. The measurement apparatus 1A includes a control unit 30A and a sweat sensor 20A. The control unit 30A has a measurement control unit 33A. The measurement control unit 33A uses the signal MSEL to instruct an operation mode to the sweat sensor 20A.

  In addition, the effect described in this specification is an illustration to the last, and is not limited, Moreover, there may exist another effect.

  In addition, this technique can be set as the following structures.

(1) a first measurement unit that has a plurality of operation modes including a first operation mode and a second operation mode that have different operation frequencies, and that measures a conductance value;
A control unit that obtains an average value of the conductance values in a first period and selects one operation mode of the plurality of operation modes based on a comparison result between the average value and a threshold;
And a threshold generation unit that generates the threshold based on a plurality of conductance values.
(2) The threshold generation unit calculates a low frequency component and a high frequency component of conductance based on the plurality of conductance values, and calculates the threshold based on the low frequency component and the high frequency component. The measuring apparatus according to (1) above.
(3) The threshold value calculation unit includes set data including the number of times a pulse having a predetermined condition appears in the high frequency component and the low frequency component average value of the low frequency component in each of the plurality of second periods And the threshold value is calculated based on the plurality of sets of data. The measuring apparatus according to (2).
(4) In the first operation mode, the first measurement unit intermittently measures the conductance value at a first frequency, and in the second operation mode, calculates the conductance value in the first operation mode. Measured intermittently at a second frequency lower than the frequency of
The control unit selects the first operation mode when the average value is larger than the threshold value, and selects the second operation mode when the average value is smaller than the threshold value. The measuring apparatus according to any one of (1) to (3).
(5) a second measuring unit including at least one of a pulse sensor, an acceleration sensor, a gyro sensor, a geomagnetic sensor, a GPS sensor, a temperature sensor, an atmospheric pressure sensor, a humidity sensor, and an illuminance sensor;
The measurement apparatus according to any one of (1) to (4), wherein the threshold value generation unit generates the threshold value based also on a measurement result by the second measurement unit.
(6) The measurement device according to any one of (1) to (5), wherein the conductance value is a skin conductance value of a user.
(7) a first measurement unit that has a plurality of operation modes including a first operation mode and a second operation mode having different operation frequencies, and measures a conductance value;
A control unit that obtains an average value of the conductance values in a first period, and selects one of the plurality of operation modes based on a comparison result between the average value and a threshold value;
An electronic device comprising: a threshold generation unit that generates the threshold based on a plurality of conductance values; and a processing unit that performs a predetermined process using the plurality of conductance values.

  DESCRIPTION OF SYMBOLS 1,1A ... Measuring apparatus, 5 ... Processing part, 6 ... Sensor, 9 ... Bus, 20, 20A ... Sweat sensor, 21 ... Voltage generation part, 22 ... Detection part, 23 ... Electrode, 24 ... Amplification part, 25 ... Filter , 26 ... AD conversion unit, 30, 30A ... control unit, 31 ... learning data generation unit, 32 ... learning data analysis unit, 33, 33A ... measurement control unit, DS ... learning data, CNT ... number of learning data, Gavg ... Average value, Gmeas, Gphasic, Gtonic ... signal, Gth ... threshold, G1, G2 ... group, M1 ... normal operation mode, M2 ... low power consumption operation mode, M3 ... stop mode, Np ... number of pulse detections, NpTh ... Threshold value, PA ... period, T1-T3 ... timer, Vt ... average value.

Claims (7)

A first measurement unit that has a plurality of operation modes including a first operation mode and a second operation mode having different operation frequencies, and measures a conductance value;
A control unit that obtains an average value of the conductance values in a first period, and selects one of the plurality of operation modes based on a comparison result between the average value and a threshold value;
And a threshold generation unit that generates the threshold based on a plurality of conductance values. The threshold generation unit calculates a low frequency component and a high frequency component of conductance based on the plurality of conductance values, and calculates the threshold based on the low frequency component and the high frequency component. The measuring device described in 1. The threshold value calculation unit generates set data including the number of times a pulse of a predetermined condition appears in the high frequency component and the low frequency component average value of the low frequency component in each of a plurality of second periods. The measurement device according to claim 2, wherein the threshold value is calculated based on the plurality of the set data. The first measurement unit intermittently measures the conductance value at a first frequency in the first operation mode, and determines the conductance value from the first frequency in the second operation mode. Is measured intermittently at a low second frequency,
The control unit selects the first operation mode when the average value is larger than the threshold value, and selects the second operation mode when the average value is smaller than the threshold value. The measuring apparatus according to claim 1. A second measurement unit including at least one of a pulse sensor, an acceleration sensor, a gyro sensor, a geomagnetic sensor, a GPS sensor, a temperature sensor, an atmospheric pressure sensor, a humidity sensor, and an illuminance sensor;
The measurement device according to claim 1, wherein the threshold value generation unit generates the threshold value based also on a measurement result by the second measurement unit. The measuring device according to claim 1, wherein the conductance value is a skin conductance value of a user. A first measurement unit that has a plurality of operation modes including a first operation mode and a second operation mode having different operation frequencies, and measures a conductance value;
A control unit that obtains an average value of the conductance values in a first period, and selects one of the plurality of operation modes based on a comparison result between the average value and a threshold value;
An electronic device comprising: a threshold generation unit that generates the threshold based on a plurality of conductance values; and a processing unit that performs a predetermined process using the plurality of conductance values.

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