JP6707886B2 - Measuring device and electronic equipment - Google Patents

Description

The present disclosure relates to a measuring device that measures an electrodermal activity (EDA), and an electronic device including such a measuring device.

The electrodermal activity is a change in electric potential or impedance in the skin, and changes in accordance with mental sweating accompanying expression of human emotion such as fear, awakening, cognition, and stress. For example, Non-Patent Document 1 discloses a study in which such an electrodermal activity is measured in a living environment to estimate a human emotion.

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 that a measuring device for measuring biological signals such as electrodermal activity can measure for a long time in daily life. In particular, when a wearable device, for example, is used as such a measuring device, miniaturization is generally desired, so that the capacity of the battery that can be mounted becomes small. Therefore, such a measuring device is desired to have low power consumption.

The present disclosure has been made in view of the above problems, and an object thereof is to provide a measuring device and an electronic device that can reduce power consumption.

The measuring device of the present disclosure includes a first measuring unit, a control unit, and a threshold value generating 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 the conductance value. The control unit obtains an average value of the conductance value in the first period and selects one of the plurality of operation modes based on the comparison result of the average value and the threshold value. The threshold value generation unit generates a threshold value based on a plurality of conductance values. The threshold value generation unit calculates a low frequency component and a high frequency component of conductance based on a plurality of conductance values, and calculates a threshold value based on the low frequency component and a high frequency component.

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 the conductance value. The control unit obtains an average value of the conductance value in the first period and selects one of the plurality of operation modes based on the comparison result of the average value and the threshold value. The threshold value generation unit generates a threshold value based on a plurality of conductance values. The processing unit performs a predetermined process using a plurality of conductance values. The threshold value generation unit calculates a low frequency component and a high frequency component of conductance based on a plurality of conductance values, and calculates a threshold value based on the low frequency component and a high frequency component.

In the measuring device and the electronic device of the present disclosure, the conductance value is measured by the first measuring 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 the plurality of conductance values measured by the first measuring unit.

According to the measuring 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 measuring unit is based on a comparison result between the average value of the conductance values and the threshold value. Since power consumption is determined, power consumption can be reduced. Note that the effects described here are not necessarily limited, and any effects described in the present disclosure may be present.

It is a block diagram showing an example of 1 composition of a measuring device concerning one embodiment of this indication. FIG. 6 is an explanatory diagram illustrating an operation example of a learning data generation unit illustrated in FIG. 1. FIG. 8 is another explanatory diagram illustrating an operation example of the learning data generation unit illustrated in FIG. 1. 6 is a flowchart illustrating an operation example of a learning data generation unit illustrated in FIG. 1. FIG. 3 is an explanatory diagram illustrating an operation example of a learning data analysis unit illustrated in FIG. 1. 6 is a flowchart showing an operation example of the measurement control unit shown in FIG. 1. FIG. 3 is a timing waveform chart showing an operation example of the measuring apparatus shown in FIG. 1. It is a perspective view showing the appearance composition of the wristwatch to which the embodiment is applied. It is a block diagram showing an example of 1 composition of a measuring device concerning 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. Application example

<1. Embodiment>
[Example of configuration]
FIG. 1 illustrates a configuration example of a measuring device 1 according to an embodiment. The measuring device 1 is a device for measuring electrodermal activity of a user. The measurement device 1 includes a sweat 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 perspiration sensor 20 measures skin conductance. The perspiration 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 surface of the user's skin. The detection unit 22 is configured to include, for example, a current amplification circuit and a Wheatstone bridge circuit.

The electrode 23 electrically connects the perspiration sensor 20 to 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, or sole of the foot. As the electrode 23, for example, an Ag/AgCl electrode or a metal electrode such as stainless steel can be used. Further, the electrode 23 may be configured by using a disposable electrode.

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

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

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. As the AD conversion unit 26, for example, an integrated circuit chip can be used. 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 perspiration sensor 20 has the AD conversion unit 26, but the invention is not limited to this. For example, the control unit 30 may have the AD conversion unit 26 instead. Good.

With this configuration, the perspiration sensor 20 generates the signal Gmeas indicating the time change of the conductance value based on the measured value. Then, the sweat sensor 20 supplies the signal Gmeas to the control unit 30 and the processing unit 5 via the bus 9.

The perspiration 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 sweat sensor 20 has a normal operation mode M1 and a low operation mode. 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 conversion unit 26 is set to, for example, 128 Hz in the normal operation mode M1. The AD conversion unit 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, once a second for 100 milliseconds, for example. In the low power consumption operation mode M2, the sampling frequency of the AD converter 26 is set to, for example, 1 Hz so as to be synchronized with these operations. Accordingly, the AD conversion unit 26 samples the output signal of the filter unit 25 while 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 perspiration 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 their operations. As a result, the power consumption of the sweat sensor 20 can be significantly reduced.

The sensor 6 has, 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, a pressure sensor, a humidity sensor, and an illuminance sensor. .. Then, the sensor 6 supplies the detection result of 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 device 1 can identify the place where the user is, and can switch the operation mode depending on the place. Specifically, for example, when the user is in the office, the perspiration 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 skin electrical activity. taking measurement. Then, the measuring device 1 generates learning data DS based on the measurement result of the electrodermal activity when the user is in the office, and generates a threshold Gth based on the learning data DS, as described later. , This threshold Gth is used to determine the operation mode. Further, when the user is in a place other than the office, the operation mode of the sweat sensor 20 is set to the stop mode M3. As a result, the measuring apparatus 1 can reduce power consumption.

Similarly, for example, by providing the pulse sensor, the measuring apparatus 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 device 1 can detect whether the user is resting and switch the operation mode based on the detection result. Further, for example, by using a technique of detecting the position of the user indoors by combining a geomagnetic sensor and an acceleration sensor, the measuring device 1 detects whether the user is in the living room or the bedroom, and the detection result. The operation mode can be switched based on Further, for example, by providing an air temperature sensor, an air pressure sensor, and a humidity sensor, the measuring device 1 can measure the electrodermal activity according to the air temperature, air pressure, and humidity. Further, for example, by providing the illuminance sensor, the user's activity time zone can be detected, so that the measurement apparatus 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 has 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 the signal Gtonic, which is the low-frequency component (baseline), and the signal Gphasic, which is the high-frequency component, based on the signal Gmeas. Then, the learning data generation unit 31 generates the learning data DS based on the signal Gtonic and the signal Gphasic.

2A and 2B show an example of the waveform of electrodermal activity that can be acquired by the perspiration 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 the signal Gtonic, which is the low-frequency component, and the signal Gphasic, which is the high-frequency component, based on the signal Gmeas. Then, the learning data generation unit 31 obtains the average value Vt of the signal Gtonic in the 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 a pulse of a predetermined condition appears in the signal Gphasic (pulse detection number Np) in the period PA. Specifically, for example, in FIG. 2C, the learning data generating 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 pulse detection number Np in each period PA. In this way, the learning data generation unit 31 generates the learning data DS.

FIG. 3 illustrates a configuration example of the learning data DS. In this example, the average value Vt1 and the pulse detection number Np1 are obtained in the period PA1 (FIG. 2), and the average value Vt2 and the pulse detection number Np2 are obtained in the period PA2, and the average value Vt3 is obtained. The number of times of pulse detection Np3 is obtained in the period PA3. As described above, the learning data DS includes a plurality of data sets each including the average value Vt and the pulse detection number Np. The learning data generation unit 31 generates such learning data DS. Then, 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 the 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 perspiration sensor 20 based on the signal Gmeas and the threshold value Gth and instructing the operation mode to the perspiration sensor 20 via the bus 9. is doing.

The processing unit 5 estimates the emotion of the user based on the signal Gmeas supplied from the perspiration sensor 20 and the detection result supplied from the sensor 6, and performs a predetermined process 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 measuring device 1 is applied.

Here, the sweat sensor 20 corresponds to a specific but not limitative example of “first measuring section” in one embodiment of the disclosure. The measurement control unit 33 corresponds to a specific but not limitative example of “control unit” in one embodiment of the present disclosure. The learning data generation unit 31 and the learning data analysis unit 32 correspond to a specific but not limitative example of “threshold generation unit” in one embodiment of the present disclosure.

[Operation and action]
Subsequently, the operation and action of the measuring apparatus 1 according to the present embodiment will be described.

(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 generator 21 generates a voltage used in the detector 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 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 secured 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 perspiration 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 the learning data DS based on the signal Gmeas. The learning data analysis unit 32 generates the threshold value Gth based on the learning data DS. The measurement control unit 33 controls the measurement operation of the perspiration sensor 20 and the sensor 6. In addition, the measurement control unit 33 determines the operation mode of the perspiration sensor 20 based on the signal Gmeas and the threshold value Gth, and instructs the operation mode to the perspiration sensor 20 via the bus 9.

Then, the processing unit 5 estimates the emotion of the user based on the signal Gmeas and the detection result of the sensor 6, and performs a predetermined process according to the estimation result.

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

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

First, the learning data generation unit 31 starts the 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 steps S1 to S4, the learning data generation unit 31 obtains the number of times a pulse of a predetermined condition appears in the signal Gphasic (pulse detection number Np) in the period from the start of the timer T1 to the expiration thereof. Specifically, in the signal Gphasic (FIG. 2C) of the signal Gphasic indicating the high frequency component of the signal Gmeas, the learning data generation unit 31 has a pulse width shorter than a predetermined width and a pulse height higher than a predetermined height. It is confirmed whether or not a pulse larger than the above appears (step S2), and when such a pulse appears (“Y” in step S2), the pulse detection number Np is incremented (step S3). The learning data generation unit 31 obtains the pulse detection number Np by repeating this operation until the timer T1 expires.

When the timer T1 ends in step S4 (“Y” in step S4), the period PA started in step S1 ends, and thus the learning data generation unit 31 causes the low frequency of the signal Gmeas in the period PA. The 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 pulse detection number Np (step S6). As a result, the average value Vt and the pulse detection number 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 number Np (step S8), and returns to step S1.

In this way, the learning data generation unit 31 generates the learning data DS including the number of sets of the average value Vt and the number of pulse detections Np, which is the number of learning data CNT.

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

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

The learning data analysis unit 32 generates the threshold value 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 the points having the pulse detection number Np larger than the predetermined threshold value NpTh. The learning data analysis unit 32 obtains the 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, and for example, the coefficient a may be "1" or a value larger than "1". The coefficient a may be set higher in order to enhance the effect of reducing power consumption. The learning data analysis unit 32 thus 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 perspiration sensor 20 and the sensor 6. Further, the measurement control unit 33 determines which of the normal operation mode M1 and the low power consumption operation mode M2 the sweat sensor 20 should operate on the basis of the signal Gmeas and the threshold value Gth. The operation of determining the operation mode of the sweat sensor 20 will be described in detail below.

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. The operation will be described in detail below.

First, the measurement control unit 33 confirms whether or not the number of data of the learning data DS is sufficient (step S11). Specifically, the measurement control unit 33 confirms whether or not the data number of the learning data DS is sufficient based on the learning data number CNT in the flow shown in FIG. When the learning data number CNT is small and the learning data DS has an insufficient data number (“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) and the process returns to step S11. Then, the measurement control unit 33 repeats steps S11 and S12 until the learning data number CNT exceeds a predetermined value and the learning data DS has a sufficient data number.

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

Next, the measurement control unit 33 starts the timer T3 (step S14). The timer T2 expires in 1 second in this example. Then, the measurement control unit 33 confirms whether or not the timer T3 has expired (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.

When 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 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 of steps S14 to S17 are repeated until the timer T2 expires. As a result, in this example, the signal Gmeas obtained by the sampling operation every one second is accumulated in the data buffer of the measurement control unit 33. Since the measuring apparatus 1 performs the measurement each time the timer T3 expires, the signal Gmeas every one second is accumulated regardless of whether the operation mode is the normal operation mode M1 or the low power consumption operation mode M2. To be done.

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

Next, the measurement control unit 33 confirms 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 perspiration sensor 20 to the normal operation mode M1 (step S20), and the average value. When Gavg is not larger than the threshold value Gth ("N" in step S19), the measurement control unit 33 sets the operation mode of the perspiration 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, where (A) shows the signal Gmeas and (B) shows the average value Gavg. In this example, the measurement control unit 33 obtains the average value Gavg every 60 seconds based on the signal Gmeas. Then, the measurement control unit 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 is higher than the threshold value Gth during the period from timing t1 to t2. Therefore, during this period, the sweat 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 the timing t2 to t3. Therefore, during this period, the sweat sensor 20 operates in the low power consumption operation mode M2.

As described above, in the measuring apparatus 1, the power consumption can be reduced by providing the 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 multifunctional. In the case where such an electronic device is equipped with a function of estimating the emotion of the user, it is desired that the measuring device for measuring the electrodermal activity can operate for a long time by being driven by a battery. In other words, such a measuring device is desired to have low power consumption. Since the measuring apparatus 1 is provided with the low power consumption operation mode M2 in addition to the normal operation mode M1, the power consumption can be reduced. As a result, the measuring device 1 can operate for a long time, and is a device suitable for estimating the emotion of the user.

Particularly, 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 emotion of the user is high, and when the average value Gavg is low, the emotion of the user is calm. Therefore, when the average value Gavg is high, the normal operation mode M1 is operated, and when the average value Gavg is low, the low power consumption operation mode M2 is operated, whereby the power consumption can be efficiently reduced.

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 the electrodermal activity has a large individual difference, it is difficult to uniquely determine the threshold Gth applicable to all users. In the measuring device 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. As a result, in the measuring apparatus 1, the threshold value Gth desired for each user can be set, so that the power consumption can be effectively reduced.

[effect]
As described above, in the present embodiment, the operation mode is determined by comparing the average value Gavg of the signal Gmeas with the threshold value Gth. Therefore, it is possible to reduce power consumption while maintaining analysis accuracy. You can

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

<2. Application example>
Next, an application example of the measuring device described in the above embodiment will be described.

FIG. 8 shows an appearance of a wrist watch to which the measuring device of the above-mentioned embodiment is applied. This wristwatch has, for example, a dial 110 and a band portion 120. The wristwatch is equipped with the measuring device according to the above-described embodiments.

The measuring device according to the above-described embodiments and the like can be applied to various things that the user wears, such as a wristband, glasses, and a ring, in addition to such a wristwatch. It is possible to configure a wearable terminal that can analyze the.

Although the present technology has been described above 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-described embodiment, the measurement control unit 33 has instructed the perspiration sensor 20 about the operation mode via the bus 9, but the present invention is not limited to this. Instead of this, a dedicated signal line may be used to instruct the operation mode, for example, as in the measuring apparatus 1A shown in FIG. The measuring device 1A includes a control unit 30A and a perspiration sensor 20A. The control unit 30A has a measurement control unit 33A. The measurement control unit 33A uses the signal MSEL to instruct the perspiration sensor 20A in the operation mode.

It should be noted that the effects described in the present specification are merely examples and are not limited, and may have other effects.

Note that the present technology may be configured as below.

(1) 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 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 of the average value and a threshold value,
A threshold value generating unit that generates the threshold value based on a plurality of the conductance values.
(2) The threshold value 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 value 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 of a predetermined condition appears in the high frequency component and a low frequency component average value of the low frequency component in each of the plurality of second periods. Is generated and the threshold value is calculated based on the plurality of sets of data.
(4) The first measuring unit intermittently measures the conductance value at a first frequency in the first operation mode, and in the second operation mode, the conductance value is the first conductance value. Measured intermittently at a second frequency lower than
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 device according to any one of (1) to (3) above.
(5) A second measurement unit further including any one or more of a pulse sensor, an acceleration sensor, a gyro sensor, a geomagnetic sensor, a GPS sensor, a temperature sensor, a pressure sensor, a humidity sensor, and an illuminance sensor,
The said threshold generation part is a measuring apparatus in any one of said (1) to (4) which produces|generates the said threshold value also based on the measurement result by the said 2nd measurement part.
(6) The said conductance value is a user's skin conductance value, The measuring device in any one of said (1) to (5).
(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 that 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 value generation unit that generates the threshold value based on a plurality of the conductance values; and a processing unit that performs a predetermined process using the plurality of the conductance values.

1, 1A... Measuring device, 5... Processing part, 6... Sensor, 9... Bath, 20, 20A... Sweating sensor, 21... Voltage generation part, 22... Detection part, 23... Electrode, 24... Amplification part, 25... Filter Part, 26... AD conversion part, 30, 30A... control part, 31... learning data generation part, 32... learning data analysis part, 33, 33A... measurement control part, DS... learning data, CNT... learning data number, 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... Pulse detection number, NpTh... Threshold value, PA... Period, T1 to T3... Timer, Vt... Average value.

Claims (6)

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 that 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;
A threshold value generation unit that generates the threshold value based on a plurality of the conductance values ,
The threshold value generation unit calculates a low frequency component and a high frequency component of conductance based on a plurality of the conductance values, and calculates the threshold value based on the low frequency component and the high frequency component . The threshold generation 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 the plurality of second periods. , calculates the threshold based on the set data of multiple
The measuring device according to claim 1 . The first measurement unit intermittently measures the conductance value at a first frequency in the first operation mode, and sets the conductance value from the first frequency in the second operation mode. Also 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 device according to claim 1 or 2 . A second measurement unit including any one or more of a pulse sensor, an acceleration sensor, a gyro sensor, a geomagnetic sensor, a GPS sensor, a temperature sensor, a pressure sensor, a humidity sensor, and an illuminance sensor,
The measurement device according to any one of claims 1 to 3 , wherein the threshold value generation unit generates the threshold value also based on a measurement result obtained by the second measurement unit. The measurement 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 that 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 of the average value and a threshold value;
A threshold value generation unit that generates the threshold value based on a plurality of the conductance values; and a processing unit that performs a predetermined process using the plurality of the conductance values ,
The electronic device which calculates a low frequency component and a high frequency component of conductance based on a plurality of the conductance values, and calculates the threshold based on the low frequency component and the high frequency component .

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