Electronic device, algorithm selection method, and recording medium

Abstract

The present invention provides an electronic device, an algorithm selection method, and a recording medium. The electronic device comprises: a biometric detection value acquisition unit that acquires biometric detection values used to calculate biometric information of a wearer of the electronic device; and a processing unit that estimates a change trend in information associated with the wearer's actions and, based on the estimated change trend in the information associated with the actions, selects an algorithm for calculating the biometric information based on the biometric detection values.

Description

Technical Field

[0001] The present invention relates to an electronic device, an algorithm selection method and a recording medium. Background Art

[0002] In recent years, electronic devices that can be worn on the body and use optical sensors and other sensors to measure biological information such as pulse rate have been developed. Such electronic devices can easily measure the biological information of the user (wearer). On the other hand, during periods of intense exercise, the values obtained from the sensors (sensor values) are prone to contain noise components, sometimes resulting in reduced accuracy of the measured biological information. To address this problem, for example, Japanese Patent Application Laid-Open No. 2017-148312 discloses a sensor information processing device that changes the algorithm for calculating biological information based on sensor values in accordance with the user's exercise state.

[0003] In conventional technology, such as that disclosed in Japanese Patent Application Laid-Open No. 2017-148312, the user's motion state is detected and the pulse detection algorithm is modified based on the properties of the sensor values estimated from the motion state, thereby suppressing the noise components contained in the sensor values. However, in reality, the properties of the sensor values do not change completely synchronously with changes in the user's motion state. There is often a temporal deviation between the timing of changes in the properties of the sensor values and the timing of changes in the motion state. However, conventional technology does not take this temporal deviation into account. Summary of the Invention

[0004] An electronic device according to one embodiment of the present invention comprises: a biological detection value acquisition unit that acquires biological detection values for calculating biological information of a wearer of the electronic device; and a processing unit that estimates a trend of change in information associated with the content of an action of the wearer and selects an algorithm for calculating the biological information based on the biological detection values, based on the estimated trend of change in the information associated with the content of the action.

[0005] An algorithm selection method according to an embodiment of the present invention is an algorithm selection method in an electronic device, the electronic device comprising a biometric detection value acquisition unit and a processing unit for acquiring biometric detection values for calculating biometric information of a wearer, wherein the processing unit estimates a tendency of change in information associated with an action content of the wearer, and selects an algorithm for calculating the biometric information based on the biometric detection values based on the estimated tendency of change in the information associated with the action content.

[0006] A recording medium according to one embodiment of the present invention is a non-transitory computer-readable recording medium recording a program executable by a processing unit of an electronic device having a biometric detection value acquisition unit and a processing unit, wherein the biometric detection value acquisition unit acquires biometric detection values for calculating biometric information of a wearer, wherein the processing unit estimates a change trend of information associated with the content of the wearer's action in accordance with the program, and selects an algorithm for calculating the biometric information based on the biometric detection values based on the estimated change trend of the information associated with the content of the action. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 This is a block diagram showing an example of a functional configuration of an electronic device according to an embodiment.

[0008] Figure 2 This is a diagram showing an example of the appearance of an electronic device as viewed from the front.

[0009] Figure 3 This is a diagram showing an example of the appearance of an electronic device as viewed from the back.

[0010] Figure 4 This is a diagram showing an example of the appearance of an electronic device having a pulse rate display unit that displays the pulse rate with a pointer.

[0011] Figure 5 This is a diagram showing an example of the appearance of an electronic device having a pulse rate display unit that displays the pulse rate in a graph.

[0012] Figure 6 This is an example of a flowchart of the pulse rate display process according to the embodiment.

[0013] Figure 7 This is the first part of an example of a flowchart of the algorithm selection process according to the embodiment.

[0014] Figure 8 This is the second part of an example of a flowchart of the algorithm selection process according to the embodiment.

[0015] Figure 9 This is the third part of an example of a flowchart of the algorithm selection process according to the embodiment.

[0016] Figure 10 This is the fourth part of an example of a flowchart of the algorithm selection process according to the embodiment.

[0017] Figure 11 This is the fifth part of an example of a flowchart of the algorithm selection process according to the embodiment.

[0018] Figure 12 This is a diagram showing an example of changes in pulse rate.

[0019] Figure 13 This is an example of a flowchart of the archive creation process according to the embodiment.

[0020] Figure 14 It is a diagram showing a display example of the pulse rate display section displaying a plurality of pulse rate candidates.

[0021] Figure 15 This is an example of a flowchart of the pulse rate correction process according to the embodiment.

[0022] Figure 16 It is a diagram showing a display example on the pulse rate display unit when the pulse rate correction process is performed. DETAILED DESCRIPTION

[0023] The electronic device and the like according to the embodiment will be described with reference to the accompanying drawings. In the drawings, the same or corresponding parts are denoted by the same reference numerals.

[0024] (Implementation Method)

[0025] The electronic device according to the embodiment is a wristwatch-type device capable of measuring the user's pulse rate when worn on the user's wrist, such as a smartwatch.

[0026] like Figure 1 As shown, the electronic device 100 of the embodiment includes: a processing unit 110, a storage unit 120, a biological detection value acquisition unit 130, a motion detection unit 131, a display unit 140, an operation unit 150, an output unit 155, a timer unit 160, a communication unit 170, and a position acquisition unit 180.

[0027] The processing unit 110 is composed of a processor such as a CPU (Central Processing Unit). The processing unit 110 executes pulse rate display processing, etc., described below, using programs stored in the storage unit 120. The processing unit 110 supports multithreading and can execute multiple processes in parallel.

[0028] The storage unit 120 stores programs and necessary data executed by the processing unit 110. The storage unit 120 may include, but is not limited to, RAM (Random Access Memory), ROM (Read Only Memory), or flash memory. Alternatively, the storage unit 120 may be located within the processing unit 110.

[0029] The biological detection value acquisition unit 130 includes an LED (Light Emitting Diode) and a PD (Photodiode) as pulse wave sensors. The PD receives light emitted from the LED and reflected from the body, detecting the pulse wave based on the temporal changes in the intensity of the received light. The processing unit 110 obtains the value (AD value) obtained by performing analog-to-digital conversion of the received light intensity from the PD as the biological detection value and calculates the pulse rate based on the temporal changes in the AD value.

[0030] The motion detection unit 131 includes an acceleration sensor 132, a gyroscope sensor 133, and a tilt sensor 134, and obtains detection values (motion detection values) from each sensor. However, as long as the motion detection unit 131 includes at least one sensor (e.g., the acceleration sensor 132) for detecting the user's motion state, it is not necessary to include other sensors. In addition, in order to detect the user's motion state, the motion detection unit 131 may also include sensors other than the acceleration sensor 132, the gyroscope sensor 133, and the tilt sensor 134 (e.g., a geomagnetic sensor, a pressure sensor, etc.). For example, by using a pressure sensor to detect the change in altitude, it is also possible to detect whether the user is climbing or descending a slope.

[0031] The acceleration sensor 132 is a three-axis acceleration sensor that detects motion in three orthogonal directions. For example, when a user wearing the electronic device 100 moves, the processing unit 110 can obtain from the acceleration sensor 132 the direction and degree of acceleration of the user's movement.

[0032] The gyro sensor 133 is an angular velocity sensor that detects the angular velocity of rotation. For example, when a user wearing the electronic device 100 rotates their body, the processing unit 110 can obtain from the gyro sensor 133 information about the direction and angular velocity of the rotation.

[0033] The tilt sensor 134 measures the tilt angle of an object based on gravity. For example, when the electronic device 100 is tilted, the processing unit 110 can detect the tilt of the electronic device 100 through the tilt sensor 134.

[0034] The display unit 140 includes display devices such as physical hands, a liquid crystal display, and an organic EL (Electro-Luminescence) display. The display unit 140 displays the pulse rate measured by the biological detection value acquisition unit 130 and the time measured by the timing unit 160. Alternatively, the display unit 140 may include an analog time display using physical hands (second hand, minute hand, hour hand), a date wheel, a motor driver, a motor, and a gear train mechanism. Furthermore, the display unit 140 may not be a physical analog time display, but may instead display the analog time by displaying an image of hands on a display device such as a liquid crystal display.

[0035] The operating unit 150 is a user interface such as a knob or pushbutton switch, and receives user input. The processing unit 110 can determine the user's input based on the detection results of the knob rotation or switch pressing status of the operating unit 150. Furthermore, if the electronic device 100 includes a touch panel integrated with the display unit 140, this touch panel also serves as the operating unit 150, receiving user click operations, etc.

[0036] The output unit 155 includes a speaker and outputs audio announcements and sound effects. Alternatively, the electronic device 100 may include an LED (light emitting unit) or a vibrator (vibration unit) as the output unit 155 instead of or in addition to the speaker.

[0037] The timing unit 160 measures the time displayed on the display unit 140 by the electronic device 100. Furthermore, the timing unit 160 also functions as a timer for measuring a specified time. Furthermore, the timing unit 160 may be implemented as software that changes the value stored at a predetermined address in the storage unit 120 at predetermined intervals (e.g., 1 second), or as dedicated hardware. Furthermore, the timing unit 160 may be provided within the processing unit 110.

[0038] Communication unit 170 is a communication interface for electronic device 100 to communicate data with external devices (e.g., smartphones, tablets, personal computers (PCs), other smartwatches, etc.) or to obtain information from the Internet. Communication unit 170 may include, for example, but is not limited to, a wireless communication interface for communicating via Bluetooth (registered trademark) or a wireless LAN (Local Area Network).

[0039] The location acquisition unit 180 receives satellite signals transmitted from GPS (Global Positioning System) satellites to acquire the current location of the electronic device 100. Since the location acquisition unit 180 cannot receive satellite signals indoors, the processing unit 110 can also determine whether the current location is outdoor or indoor based on whether the location acquisition unit 180 can receive satellite signals.

[0040] like Figure 2 As shown, the electronic device 100 has an hour hand 141, a minute hand 142, a second hand 143, a date wheel 144, and a pulse rate display 145 on the front as a display unit 140. The electronic device 100 displays the time using the hour hand 141, minute hand 142, and second hand 143, displays the date using the date wheel 144, and displays the user's pulse rate using the pulse rate display 145.

[0041] In addition, if Figure 2 As shown, the electronic device 100 has a handle 151 and button switches 152 and 153 on the side to accept user operations. Figure 3 As shown, the electronic device 100 includes a biological detection value acquisition unit 130 on the back surface.

[0042] The electronic device 100 calculates the user's pulse rate using a pulse rate measurement algorithm based on the AD value obtained by the biometric detection value acquisition unit 130. Here, the AD value is obtained from the biometric detection value acquisition unit 130 (pulse wave sensor) and is therefore also referred to as a biometric detection value (sensor value). In this embodiment, four pulse rate measurement algorithms are provided, corresponding to the pulse rate's changing trends: an algorithm for low trends, an algorithm for rising trends, an algorithm for high trends, and an algorithm for falling trends.

[0043] In any pulse rate measurement algorithm, the processing unit 110 basically calculates the pulse rate per minute based on the temporal increase or decrease in the AD value obtained from the biological detection value acquisition unit 130, and then calculates the pulse rate based on the moving average of this pulse rate. Because the temporal increase or decrease in the AD value includes noise components due to body movement, etc., when calculating the pulse rate per minute, the processing unit 110 performs frequency analysis (such as Fourier transform) on the AD value waveform to obtain the frequency component. This frequency component also includes components other than the pulse rate, such as noise components due to body movement, etc., and higher harmonic components. Therefore, the processing unit 110 cannot usually uniquely determine the pulse rate.

[0044] Therefore, the pulse rate measurement algorithm calculates pulse rate candidates along with their likelihoods (accuracy of the pulse rate) at predetermined time intervals (e.g., 1-second intervals). Furthermore, at these time intervals, the pulse rate with the highest likelihood is displayed on pulse rate display 145. For example, if the pulse rate measurement algorithm calculates three pulse rate candidates, with candidate 1 being 60 bpm (beats per minute) at a likelihood of 50%, candidate 2 being 90 bpm at a likelihood of 40%, and candidate 3 being 30 bpm at a likelihood of 10%, pulse rate display 145 will display 60 as the pulse rate.

[0045] In addition, the display of the pulse rate in the pulse rate display section 145 is not limited to digital display. Figure 4 As shown, the electronic device 100 may include a pulse rate display unit 145 that displays the pulse rate in analog form using a pointer 146. By displaying the pulse rate using the pointer 146, the user can visually grasp the magnitude of the pulse rate by the angle of the pointer 146 even if the user cannot recognize the numerals.

[0046] In addition, the display of the pulse rate in the pulse rate display section 145 is not limited to digital display or analog display. Figure 5 As shown, electronic device 100 may also display the pulse rate as a graph on pulse rate display unit 145. This graphical display allows the user to easily understand temporal changes in the pulse rate. Furthermore, electronic device 100 may transmit pulse rate information to another device, such as a smartphone or PC, via communication unit 170, and have the pulse rate displayed as a graph on that other device.

[0047] Next, refer to Figure 6 The pulse rate display process, which displays the pulse rate on electronic device 100, will now be described. However, the pulse rate measurement algorithm used to calculate the pulse rate in this process is selected during the algorithm selection process described later. Therefore, the algorithm selection process must be executed in parallel with the pulse rate display process. For example, when a user instructs electronic device 100 to display the pulse rate via operating unit 150, the pulse rate display process and the algorithm selection process begin. Alternatively, the pulse rate display process and the algorithm selection process may also begin in parallel with other processes when electronic device 100 is booted up.

[0048] When the pulse rate display process begins, the processing unit 110 first determines whether a pulse rate measurement algorithm has been selected through the algorithm selection process (step S101). If a pulse rate measurement algorithm has not been selected (step S101: No), the process returns to step S101. However, as will be described later, the low-level trend algorithm is immediately selected as the pulse rate measurement algorithm when the algorithm selection process is initiated. Therefore, the determination in step S101 is usually immediately Yes.

[0049] If the pulse rate measurement algorithm is selected (step S101: Yes), the processing unit 110 illuminates the LED of the biological detection value acquisition unit 130 (step S102). Light emitted from the LED and reflected by the living body is received by the PD of the biological detection value acquisition unit 130, and the processing unit 110 converts the received light intensity at the PD using an AD converter to obtain an AD value (step S103).

[0050] Next, the processing unit 110 calculates the user's pulse rate candidates and their likelihoods based on the AD value using the currently selected pulse rate measurement algorithm (step S104). The processing unit 110 then selects the pulse rate to be displayed from the candidate pulse rates based on the calculated likelihoods (step S105). Typically, in step S104, the processing unit 110 selects the pulse rate with the highest likelihood.

[0051] Then, the processing unit 110 displays the pulse rate selected in step S105 on the pulse rate display unit 145 (step S106 ), and returns to step S102 .

[0052] In the algorithm selection process, which is executed in parallel with the pulse rate display process described above, processing unit 110 estimates the user's action content (exercise state), estimates the trend of change in information (pulse rate) associated with the estimated action content based on the estimated action content, and selects an algorithm (pulse rate measurement algorithm) based on the estimated trend of change for calculating the biological information (pulse rate) from the biological detection value (AD value). Specifically, processing unit 110 selects the most appropriate pulse rate measurement algorithm from four types: a low-trend algorithm, an increasing-trend algorithm, a high-trend algorithm, and a decreasing-trend algorithm, depending on the trend of change in the pulse rate. By selecting a pulse rate measurement algorithm from these four types, processing unit 110 can improve pulse rate measurement accuracy.

[0053] The low-trend algorithm is selected when the pulse rate is estimated to be stabilizing at a relatively low value. In this algorithm, processing unit 110 outputs the pulse rate as a moving average of the pulse rate per minute calculated from the AD value over a first reference time period (a relatively long period, such as 10 seconds). This algorithm is generally selected when the user is resting or in normal circumstances (not exercising). By using a relatively long moving average, the low-trend algorithm reduces the effects of noise and other factors, allowing for a more accurate output of the user's pulse rate when not exercising.

[0054] The rising trend algorithm is selected when the pulse rate is estimated to be on an upward trend. In this algorithm, processing unit 110 outputs the moving average of the pulse rate per minute calculated from the AD value over a second reference time period (a relatively short period, such as 5 seconds) as the pulse rate. In the rising trend algorithm, by setting the moving average time to a relatively short period, the algorithm improves tracking of rising pulse rates.

[0055] Furthermore, in the algorithm for an upward trend, processing unit 110 also calculates a moving average of the pulse rate per minute, calculated from the AD value, within the first reference time period. While this algorithm is selected, the pulse rate is expected to be on an upward trend. Therefore, if the pulse rate based on the moving average within the second reference time period indicates a downward trend, the pulse rate is output as the moving average within the first reference time period, thereby slowing the decline. This minimizes the pulse rate from being affected by noise and other factors.

[0056] The high-trend algorithm is selected when the pulse rate is estimated to be maintaining a relatively high value. In this algorithm, processing unit 110 outputs the moving average of the pulse rate per minute, calculated based on the AD value, over the second reference time period as the pulse rate. This algorithm uses a relatively short moving average time to improve tracking of pulse rate fluctuations. This is because, when the user is not exercising, the pulse rate may increase further due to changes in exercise intensity, or it may decrease.

[0057] The downward trend algorithm is selected when the pulse rate is estimated to be declining. In this algorithm, processing unit 110 outputs the moving average of the pulse rate per minute, calculated from the AD value, over the second reference time period as the pulse rate. In the downward trend algorithm, the time used for moving average is relatively short, thereby improving tracking of a decrease in the pulse rate.

[0058] Furthermore, in the algorithm for a downward trend, processing unit 110 also calculates a moving average of the pulse rate per minute, calculated from the AD value, within the first reference time period. While this algorithm is selected, the pulse rate is expected to be on a downward trend. Therefore, if the pulse rate based on the moving average within the second reference time period shows an upward trend, the pulse rate is output as the moving average within the first reference time period, slowing the increase. This minimizes the pulse rate from increasing due to noise and other factors.

[0059] Reference Figures 7 to 11 The algorithm selection process is described below, which is a process for selecting a pulse rate measurement algorithm by the electronic device 100. This process, like the pulse rate display algorithm described above, is started in response to a user instruction or when the electronic device 100 is turned on.

[0060] When the algorithm selection process begins, the processing unit 110 first estimates the pulse rate change trend as a "low trend" (step S201) and selects the low trend algorithm as the pulse rate measurement algorithm (step S202). This process starts the pulse rate measurement based on the pulse rate display process described above.

[0061] During the algorithm selection process, the processing unit 110 estimates the value of the pulse rate's changing tendency (the estimated value of the pulse rate's changing tendency) as "low trend," "increasing trend," "high trend," or "down trend." However, there is a high probability that the user's normal pulse rate changing tendency is "low trend." Therefore, in steps S201 and S202, the processing unit 110 sets the initial value of the estimated value of the pulse rate's changing tendency to "low trend" and selects the algorithm for the low trend as the initial setting for the pulse rate measurement algorithm. However, the pulse rate changing tendency at that point in time may not actually be "low trend." However, the processing unit 110 subsequently (in the loop after step S204) repeatedly estimates the user's behavior to estimate the pulse rate's changing tendency. Therefore, even if the pulse rate changing tendency is initially estimated incorrectly, the processing unit 110 can gradually make a correct estimate.

[0062] Furthermore, when actually encoding this processing, the change tendency itself need not be directly treated as a value. For example, the change tendencies of "low trend," "increasing trend," "high trend," and "decreasing trend" can be represented by integer values of "1," "2," "3," and "4," respectively. When the processing unit 110 uses these values to substitute the result of the pulse rate change tendency (the estimated value of the change tendency) into the variable V, in step S201, the processing unit 110 substitutes "1" as the initial value into the variable V. In steps S222, S242, S245, S262, S265, S282, and S285, described later, the processing unit 110 substitutes "2," "4," "3," "4," "2," "2," and "1," respectively, into the variable V.

[0063] In addition, if it is assumed that the user wears the electronic device 100 for a relatively long time (for example, one day), the processing unit 110 may also perform a process (step S203) to determine whether the pulse rate is stable at a relatively low value after step S202. In this case, if the pulse rate is not stable at a relatively low value, the processing unit 110 waits in step S203 until it stabilizes at a low value. This is because if the user wears the electronic device 100 for a long time, the processing unit 110 can automatically obtain the pulse rate when it stabilizes at a relatively low value based on the pulse rate measured during this period. In addition, during the standby period in step S203, in order to quiet the user, the processing unit 110 may also display a message such as "Please be quiet" on the display unit 140 or output a sound from the output unit 155.

[0064] Next, the processing unit 110 estimates the content of the user's behavior (step S204). The method by which the processing unit 110 estimates the content of the user's behavior in step S204 is arbitrary, and examples thereof include estimation based on the detection results of the motion detection unit 131, estimation based on the user's past behavior patterns (behavior history), estimation based on the user's registered behavior plan, and estimation using the position acquisition unit 180. Furthermore, the processing unit 110 may combine multiple of these estimation methods to estimate the content of the user's behavior.

[0065] Estimation based on the detection results of the motion detection unit 131 refers to a method of estimating the content of the user's behavior based on the detection values (motion detection values) of the sensors included in the motion detection unit 131. For example, the processing unit 110 uses a known behavior estimation method based on machine learning or other means to estimate the user's current behavior (e.g., standing still, walking, brisk walking, slow running (light jogging), fast running (sprinting), cycling, fitness, etc.) based on the detection values of the motion detection unit 131. By estimating the behavior based on the motion detection values, the processing unit 110 can not only estimate the user's behavior content in real time, but also select the sensors used for behavior estimation as needed, thereby improving the accuracy of the behavior estimation.

[0066] Estimation based on a user's past behavior patterns (action history) refers to a method in which the behavior content estimated by the processing unit 110 in the past is stored in the storage unit 120 along with date and time information as an action history, and the action history is used to estimate the user's behavior content. For example, the processing unit 110 uses the current time (or day of the week) as a key to extract the behavior content of the same time period (on the same day of the week) from the action history, and infers that the user should currently be performing the extracted behavior content. When using this method, the storage unit 120 includes an action history storage unit that stores the user's action history. By making inferences based on the action history, the processing unit 110 can effectively utilize past estimation results.

[0067] Estimation based on the action plan registered by the user refers to a method of estimating the user's action content using information about the action plan that the user has pre-registered in the storage unit 120. For example, if the user has registered an action plan such as "light running from 8 p.m. to 9 p.m. on weekdays" as an action plan, the processing unit 110 extracts the action content of the same time period (on the same day of the week) from the action plan using the information of the current time (or day of the week) as a keyword, and infers that the user should currently perform the extracted action content. When using this method, the storage unit 120 has an action plan storage unit that stores the user's action plan. By making an inference based on the action plan, the processing unit 110 can accurately infer the action when the user performs the action according to the action plan registered by the user.

[0068] Estimation using location acquisition unit 180 refers to a method for estimating the user's behavior based on the location information acquired by location acquisition unit 180. For example, if location acquisition unit 180 detects that the user has entered an outdoor location, processing unit 110 may infer that the user has begun exercising. Alternatively, if the location acquired by location acquisition unit 180 is located near a facility related to exercise, the user's behavior may be inferred as being exercised. By using location acquisition unit 180 for estimation, processing unit 110 can infer appropriate behavior based on the user's current location.

[0069] return Figure 7 The processing unit 110 determines whether the low-level trend algorithm is selected as the pulse rate measurement algorithm (step S205). If the low-level trend algorithm is selected (step S205; yes), the process proceeds to Figure 8 The processing unit 110 determines whether "exercise start" was estimated as the result of the estimation of the user's action content in step S204 (step S221). Here, "exercise start" does not need to distinguish the type of exercise. If it is estimated that a certain type of exercise has started, the determination in step S221 is yes. However, if the estimated action content is "stationary" or "walking", it is not considered as exercise, and the determination in step S221 is no.

[0070] If the user's action content is not estimated as "exercise start" (step S221; No), the process returns to Figure 7 In step S204, the processing unit 110 again estimates the content of the user's behavior.

[0071] If the user's action content is estimated as "exercise start" (step S221; yes), the processing unit 110 estimates the pulse rate change trend as "increasing trend" (step S222). Then, the processing unit 110 selects the increasing trend algorithm as the pulse rate measurement algorithm (step S223) and returns to Figure 7Step S204.

[0072] On the other hand, Figure 7 In step S205, if the low-trend algorithm is not selected as the pulse rate measurement algorithm (step S205; No), the processing unit 110 determines whether the rising-trend algorithm is selected as the pulse rate measurement algorithm (step S206). If the rising-trend algorithm is selected (step S206; Yes), the process proceeds to step S206. Figure 9 The processing unit 110 determines whether the user's action content was estimated as "exercise completed" in step S204 (step S241). Here, "exercise completed" does not need to distinguish between different types of exercise. If a certain type of exercise is estimated to have ended (for example, if the action content estimated this time is "standing still" or "walking"), the determination in step S241 is yes.

[0073] If the result of the estimation of the user's action content indicates that "exercise is finished" (step S241; yes), the processing unit 110 estimates the pulse rate change trend as a "downward trend" (step S242). Then, the processing unit 110 selects the downward trend algorithm as the pulse rate measurement algorithm (step S243) and returns to Figure 7 Step S204.

[0074] On the other hand, if the user's action content is not estimated as "exercise completed" in step S241 (step S241: No), processing unit 110 determines whether the pulse rate is stable at a relatively high value (step S244). Specifically, if the pulse rate calculated in the pulse rate display processing executed in parallel is above a high reference value (e.g., 100 bpm) and the pulse rate fluctuation is below a reference fluctuation value (e.g., ±5 bpm / minute), processing unit 110 determines that the pulse rate is stable at a relatively high value.

[0075] If the pulse rate is not stable at a relatively high value (step S244; No), the processing unit 110 returns to Figure 7 Step S204.

[0076] If the pulse rate is stable at a relatively high value (step S244; yes), the processing unit 110 estimates the pulse rate change trend as a "high trend" (step S245). Then, the processing unit 110 selects the high trend algorithm as the pulse rate measurement algorithm (step S246) and returns to Figure 7 Step S204.

[0077] On the other hand, Figure 7In step S206, if the rising trend algorithm is not selected as the pulse rate measurement algorithm (step S206; No), the processing unit 110 determines whether the high trend algorithm is selected as the pulse rate measurement algorithm (step S207). If the high trend algorithm is selected (step S207; Yes), the process proceeds to Figure 10 The processing unit 110 determines whether “exercise completion” is estimated as the result of the estimation of the user's action content in step S204 (step S261 ).

[0078] If the result of the estimation of the user's action content indicates that "exercise is finished" (step S261; yes), the processing unit 110 estimates the pulse rate change trend as a "downward trend" (step S262). Then, the processing unit 110 selects the downward trend algorithm as the pulse rate measurement algorithm (step S263) and returns to Figure 7 Step S204.

[0079] On the other hand, if the determination in step S261 does not indicate "exercise completion" as a result of the user's action content estimation (step S261: No), the processing unit 110 determines whether an increase in exercise intensity was estimated as a result of the user's action content estimation in step S204 (step S264). Specifically, an increase in exercise intensity is determined to be estimated when the moving speed detected by the motion detection unit 131 increases by a reference increase ratio (e.g., 10%), when an increase in exercise intensity can be estimated based on the estimated action content (e.g., a change from "low-speed running" to "high-speed running"), or when the change in altitude detected by the pressure sensor increases by a reference altitude change amount (e.g., 20%).

[0080] If the increase in exercise intensity is not estimated (step S264; No), the processing unit 110 returns to Figure 7 Step S204.

[0081] If the increase in exercise intensity is estimated (step S264; yes), the processing unit 110 estimates the pulse rate change trend as an "increasing trend" (step S265). Then, the processing unit 110 selects the increasing trend algorithm as the pulse rate measurement algorithm (step S266) and returns to Figure 7 Step S204.

[0082] On the other hand, Figure 7 In step S207, if the high trend algorithm is not selected as the pulse rate measurement algorithm (step S207; No), the processing unit 110 determines whether the downward trend algorithm is selected as the pulse rate measurement algorithm (step S208). If the downward trend algorithm is selected (step S208; Yes), the process proceeds to step S208. Figure 11The processing unit 110 determines whether “exercise start” is estimated as the result of estimating the content of the user's action in step S204 (step S281).

[0083] If the user's action content is estimated as "exercise start" (step S281; yes), the processing unit 110 estimates the pulse rate change trend as "increasing trend" (step S282). Then, the processing unit 110 selects the increasing trend algorithm as the pulse rate measurement algorithm (step S283) and returns to Figure 7 Step S204.

[0084] On the other hand, if the user's action content is not estimated as "exercise start" in step S281 (step S281: No), processing unit 110 determines whether the pulse rate is stable at a relatively low value (step S284). Specifically, if the pulse rate calculated in the pulse rate display processing executed in parallel is below a low reference value (e.g., 100 bpm) and the pulse rate fluctuation is below a reference fluctuation value (e.g., ±5 bpm / minute), processing unit 110 determines that the pulse rate is stable at a relatively low value.

[0085] If the pulse rate is not stable at a relatively low value (step S284; No), the processing unit 110 returns to Figure 7 Step S204.

[0086] If the pulse rate is stable at a relatively low value (step S284; yes), the processing unit 110 estimates the pulse rate change trend as a "low trend" (step S285). Then, the processing unit 110 selects the low trend algorithm as the pulse rate measurement algorithm (step S286) and returns to Figure 7 Step S204.

[0087] On the other hand, Figure 7 In step S208, if the algorithm for downward trend is not selected as the pulse rate measurement algorithm (step S208; No), the process returns to step S204.

[0088] Through the pulse rate display process and algorithm selection process described above, electronic device 100 estimates the changing trend of the user's pulse rate and selects a pulse rate measurement algorithm that is appropriate for the estimated changing trend. This allows the selection of an appropriate algorithm before the pulse rate actually changes (including the timing of the pulse rate change). Furthermore, by measuring the pulse rate using the selected algorithm, pulse rate changes can be appropriately tracked, thereby improving the accuracy of pulse rate measurements. Furthermore, during the algorithm selection process, the changing trend of the pulse rate can be appropriately estimated by estimating the user's actions.

[0089] For example, when a user's pulse count is Figure 12 In the case of changes such as those shown by solid line 301, the pulse rate measurement algorithm is selected as follows: a low-trend algorithm is selected in time zone tz1; an upward-trend algorithm is selected in time zone tz2; a high-trend algorithm is selected in time zone tz3; a downward-trend algorithm is selected in time zone tz4; and a low-trend algorithm is selected in time zone tz5. Therefore, by using a relatively long moving average in time zones tz1 and tz5, a stable pulse rate with minimal error can be measured. Furthermore, by using a relatively short moving average in time zones tz2, tz3, and tz4, changes in the pulse rate can be tracked. Furthermore, by using an algorithm that easily tracks rising pulse rates in time zone tz2 and an algorithm that easily tracks falling pulse rates in time zone tz4, pulse rate measurement with minimal error can be achieved.

[0090] Furthermore, in the algorithm selection process described above, in step S204, processing unit 110 estimates the user's behavior and estimates the pulse rate change trend based on the estimation result. However, processing unit 110 may estimate the pulse rate change trend without estimating the user's behavior based on the motion detection value obtained by motion detection unit 131, the action plan stored in the action plan storage unit, the position obtained by the position acquisition unit, and the like.

[0091] In addition, in the above-mentioned algorithm selection process, it can also be considered that the fact that the processing unit 110 selects any one of the algorithms for low tendency, rising tendency, high tendency, and falling tendency in steps S202, S223, S243, S246, S263, S266, S283, and S286 itself indicates that the processing unit 110 estimates the changing tendency of the pulse rate as "low tendency", "rising tendency", "high tendency", and "falling tendency", respectively. Therefore, the processing unit 110 may not perform the processing of steps S201, S222, S242, S245, S262, S265, S282, and S285.

[0092] (Variation 1)

[0093] In the above-described embodiment, the moving average of the pulse rate over a relatively short period of time is typically output as the pulse rate in both the upward trend algorithm and the downward trend algorithm. If the calculated value is determined to be an abnormal value, the moving average of the pulse rate over a relatively long period of time is output as the pulse rate. Furthermore, the calculated value is determined to be an abnormal value when the pulse rate indicates a downward trend in the upward trend algorithm and when the pulse rate indicates an upward trend in the downward trend algorithm. However, the determination of an abnormal value is not limited to the above-described determination; it may also be determined based on previously accumulated pulse rate data. As a first variation, an embodiment in which a user profile of the pulse rate is created to determine abnormal values will be described.

[0094] In variant example 1, the processing unit 110 accumulates the calculated pulse rate in the storage unit 120. If the accumulation reaches a certain level (for example, the amount of the past 10 exercises), a user profile is created based on the previously accumulated pulse rate data, and values that are inconsistent with the created user profile are determined to be abnormal values.

[0095] Reference Figure 13 The following describes a profile creation process in which the processing unit 110 creates a user profile. This process may be started in response to a user instruction or may be started in parallel with other processes when the electronic device 100 is activated.

[0096] When the profile creation process begins, the processing unit 110 first stores the pulse rate calculated in the pulse rate display process described above in the storage unit 120 according to the user's activity content estimated in the algorithm selection process (step S301). For example, if the user "walks briskly" for 10 minutes, "runs at a low speed" for 5 minutes, and "walks briskly" for 3 minutes, the storage unit 120 will store the pulse rate data for the first 10 minutes as the "first brisk walking" activity content, the pulse rate data for the next 5 minutes as the "first slow running" activity content, and the pulse rate data for the next 3 minutes as the "second brisk walking" activity content.

[0097] Next, the processing unit 110 determines whether the amount of data accumulated in step S301 is less than a reference minimum accumulation amount (e.g., 10 times, when continuous action content (e.g., "brisk walking") for a reference accumulation time (e.g., 1 minute) or longer is counted as one time) (step S302). If the amount of accumulated data is less than the reference minimum accumulation amount (step S302: Yes), the processing unit 110 returns to step S301 and continues accumulating pulse counts.

[0098] If the accumulated data amount is equal to or greater than the reference minimum accumulated amount (step S302 ; No), the processing unit 110 selects one action content (eg, “brisk walking”) corresponding to the pulse rate data accumulated in the storage unit 120 (step S303 ).

[0099] Then, the processing unit 110 extracts multiple pulse rate data corresponding to the selected action content in a time series according to a reference time unit (for example, 1 second), and records the maximum and minimum values of the pulse rate at each time and the maximum and minimum values of the pulse rate change rate at each time as the upper and lower limits of the pulse rate and the pulse rate change rate at that time in the storage unit 120 (step S304).

[0100] For example, suppose the pulse rate per second from the start of the "first brisk walking" (0 second) to 2 seconds later is 60, 61, 63, the pulse rate per second from the start of the "second brisk walking" (0 second) to 2 seconds later is 70, 69, 72, and the pulse rate per second from the start of the "third brisk walking" (0 second) to 2 seconds later is 71, 65, 60. In addition, if the pulse rate change rate at time (t) is defined as "the pulse rate at time (t+1) - the pulse rate at time (t)", then the pulse rate change rate per second from the start of the "first brisk walking" (0 seconds) to 1 second later is 1, 2, the pulse rate change rate per second from the start of the "second brisk walking" (0 seconds) to 1 second later is -1, 3, and the pulse rate change rate per second from the start of the "third brisk walking" (0 seconds) to 1 second later is -6, -5.

[0101] Then, in step S304, the processing unit 110 records 71 (the pulse rate of the third brisk walking) as the upper limit of the pulse rate at the start (0 seconds), records 60 (the pulse rate of the first brisk walking) as the lower limit, records 1 (the pulse rate of the first brisk walking) as the upper limit of the pulse rate change rate, records -6 (the pulse rate of the third brisk walking) as the lower limit, records 69 (the pulse rate of the second brisk walking) as the upper limit of the pulse rate 1 second later, records 61 (the pulse rate of the first brisk walking) as the lower limit, records 3 (the pulse rate of the second brisk walking) as the upper limit of the pulse rate change rate, and records -5 (the pulse rate of the third brisk walking) as the lower limit.

[0102] In step S304 , the pulse rate and the upper and lower limits of the pulse rate change rate at each time of the action content selected in step S303 are recorded in the storage unit 120 in this manner.

[0103] Next, processing unit 110 determines whether all action items corresponding to the pulse rate data stored in storage unit 120 have been selected in step S303 executed so far (step S305). If not (step S305: No), the process returns to step S303. Thus, the upper and lower limits of the pulse rate and pulse rate change rate at each time for each action item are recorded in storage unit 120. These upper and lower limits of the pulse rate and pulse rate change rate at each time for each action item constitute the user profile.

[0104] When all the action contents corresponding to the pulse rate data stored in the storage unit 120 have been selected (step S305; Yes), the profile creation process ends.

[0105] Furthermore, in the rising trend algorithm, even if the pulse rate calculated using the moving average over the second reference time (relatively short time) exceeds the upper limit of the user profile created by the above-mentioned profile creation process, or if the pulse rate change rate exceeds the upper limit of the user profile, the processing unit 110 outputs the moving average over the first reference time (relatively long time) as the pulse rate. This prevents the pulse rate from rising too sharply.

[0106] Furthermore, in the decreasing trend algorithm, processing unit 110 outputs the moving average over the first reference time (relatively long time) as the pulse rate even if the pulse rate calculated by the moving average over the second reference time (relatively short time) is lower than the lower limit of the user profile created by the above-mentioned profile creation process, or if the pulse rate change rate is lower than the lower limit of the user profile. This prevents the pulse rate from dropping too sharply.

[0107] In Modification 1, as described above, by using the user's past pulse rate data (user profile), the accuracy of the pulse rate can be further improved.

[0108] (Variation 2)

[0109] In the above-described embodiment and Modification 1, processing unit 110 regards the pulse rate with the highest likelihood among the calculated pulse rates as the correct pulse rate and displays it on display unit 140. However, in reality, the pulse rate with the highest likelihood is not necessarily the correct pulse rate. As Modification 2, an embodiment will be described in which the pulse rate calculated by processing unit 110 can be corrected by a reference device or a user that can output an accurate pulse rate.

[0110] In the above embodiment, for example Figure 5 As shown in FIG. 1 , only the pulse rate with the maximum likelihood is displayed in the pulse rate display unit 145. In contrast, the electronic device 100 of the second modification example is as follows. Figure 14 As shown in FIG, the pulse rate whose likelihood is not the highest is also displayed on the pulse rate display section 145. Figure 14 As an example, a graph of pulse rates with a likelihood of 50% is shown by solid line 312, a graph of pulse rates with a likelihood of 40% is shown by dotted line 311, and a graph of pulse rates with a likelihood of 10% is shown by dotted line 313. In this case, in the above-described embodiment other than Modification 2, only solid line 312 is displayed as a graph of pulse rates on pulse rate display unit 145.

[0111] Furthermore, in this example, it is assumed that after time t1, the pulse rate indicated by solid line 312 is incorrect, while the pulse rate indicated by dotted line 311 is correct. In this case, at time t1, for example, the user notifies electronic device 100 that the correct pulse rate is indicated by dotted line 311. As a result, electronic device 100 according to Modification 2 can display a more accurate pulse rate.

[0112] Reference Figure 15 The pulse rate correction process for performing such pulse rate correction will be described. This pulse rate correction process begins when the user instructs electronic device 100 to execute the pulse rate correction process via operation unit 150. However, executing the pulse rate correction process requires executing the pulse rate display process and algorithm selection process described above in parallel. Therefore, if these processes are not already executed, the pulse rate display process and algorithm selection process are started before the pulse rate correction process begins, and then the pulse rate correction process begins.

[0113] When the pulse rate correction process begins, the processing unit 110 obtains the pulse rate candidates and their likelihoods calculated in the pulse rate display process executed in parallel (step S401). The processing unit 110 then displays all of the obtained pulse rate candidates on the pulse rate display unit 145 (step S402). However, if there are a large number of candidates, the processing unit 110 may display a predetermined number (e.g., up to the top three digits) in descending order of likelihood. Furthermore, to clarify the magnitude relationship of the likelihoods, the processing unit 110 may display the pulse rate candidate with the highest likelihood as a solid line and the remaining pulse rate candidates as dotted lines.

[0114] Next, the processing unit 110 determines whether pulse rate correction is necessary (step S403). For example, if pulse rate correction is instructed by a user operation (e.g., if a pulse rate graph other than the maximum likelihood is clicked on the pulse rate display unit 145), or if the error between the pulse rate and the reference device is greater than a reference error (e.g., 10 bpm), pulse rate correction is determined to be necessary.

[0115] If it is not determined that correction is necessary (step S403; No), the process returns to step S401. If it is determined that correction is necessary (step S403; Yes), the processing unit 110 records the difference between the pulse rate requiring correction and the correct pulse rate (correction amplitude) and the likelihood of the correct pulse rate in the storage unit 120 (step S404).

[0116] Next, processing unit 110 obtains the pulse rate candidates and their likelihoods calculated in the parallel pulse rate display process, similar to step S401 (step S405). Processing unit 110 then determines whether any of the obtained pulse rate candidates matches the likelihood and correction width recorded in step S404 (step S406). "Consistent with the likelihood and correction width" here means that the likelihood error is within a reference likelihood error (e.g., 10%) and the correction width error is within a reference correction width error (e.g., 10%).

[0117] As an example, assuming both the reference likelihood error and the reference correction width error are set to 10%, for example, in step S403, it is determined that a pulse rate of 60 bpm with a likelihood of 50% requires correction. The correct pulse rate at this time is 90 bpm, and its likelihood is 40%. In this case, in step S404, "Likelihood 40%, correction width 30 bpm" is recorded. Then, in step S405, a pulse rate of 62 bpm with a likelihood of 52%, a pulse rate of 91 bpm with a likelihood of 42%, and a pulse rate of 35 bpm with a likelihood of 6% are obtained as candidate pulse rates. Then, in step S406, processing unit 110 determines whether there are pulse rate candidates with a likelihood within the reference likelihood error range (36% to 44%) and a difference from the maximum likelihood pulse rate (62 bpm) by a correction width within the reference correction width error range (27 bpm to 33 bpm). In this example, there is a "pulse rate of 91 bpm with a likelihood of 42%" that satisfies this condition, and therefore the determination in step 406 is yes.

[0118] If the pulse rate candidates acquired in step S405 include one that matches the likelihood and correction width recorded in step S404 (step S406: Yes), the processing unit 110 determines that the pulse rate that matches the conditions in step S406 is a correct value and displays the pulse rate on the pulse rate display unit 145 (step S407). For example, the pulse rate determined to be a correct value is displayed with a solid line, and the other pulse rates are displayed with a dotted line.

[0119] On the other hand, if there is no candidate that matches the likelihood and correction width recorded in step S404 (step S406: No), the processing unit 110 determines that the pulse rate with the highest likelihood among the pulse rates obtained in step S405 is the correct value, and displays the pulse rate on the pulse rate display unit 145 (step S408). For example, the pulse rate with the highest likelihood is displayed with a solid line, and the other pulse rates are displayed with a dotted line.

[0120] Next, the processing unit 110 determines whether to terminate the calibration process (step S409). For example, if the user indicates the termination of the process through an operation, the calibration process is terminated. Alternatively, if the user's activity, as estimated during the concurrently executed algorithm selection process, changes (e.g., from "running" to "standing still"), the processing unit 110 may determine that "the exercise for measuring pulse rate has ended" and terminate the calibration process.

[0121] If it is determined that the correction process is not to be terminated (step S409; No), the process returns to step S405. If it is determined that the correction process is to be terminated (step S409; Yes), the processing unit 110 terminates the pulse rate correction process.

[0122] In obtaining the above Figure 14 In the case of the pulse rate shown in FIG. 3 , the pulse rate correction process is executed. When the electronic device 100 is instructed to need correction at time t1 (the correct pulse rate is not the solid line 312 but the dotted line 311), after time t1, Figure 16 As shown, in Figure 14 The pulse rate represented by the dotted line 311 is represented by the solid line 322. Figure 14 and Figure 16 In the example, after time t2, the correction error is considerably smaller than the value at time t1. Therefore, the pulse number with a likelihood of 40% does not conform to the condition of step S406. After time t2, the solid line 312 (at Figure 16 The values (solid line 323) are shown as correct values.

[0123] In reality, the likelihood of the pulse rate varies each time the pulse rate is calculated, and in many cases it is not possible to draw a graph by connecting pulse rates of the same likelihood with a line. However, for easier understanding, Figure 14 、 Figure 16 , a graph connecting pulse rates with the same likelihood is shown in FIG. Furthermore, in the pulse rate correction process, whether or not to perform correction can be determined based on whether the conditions of step S406 are met (it is not necessary to have pulse rates with the same likelihood). Therefore, even in situations where a graph based on pulse rates with the same likelihood cannot be drawn, correction can be performed without any problems.

[0124] In the second modification, by performing the pulse rate correction process, even if the initially calculated pulse rate is erroneous, a more accurate pulse rate can be displayed after correction.

[0125] (Variation 3)

[0126] In the above-described embodiment and variations, processing unit 110 selects a pulse rate measurement algorithm from four algorithms. However, the selected algorithms are not limited to four. For example, the selected algorithms may be limited to two: a low-level trend algorithm and a high-level trend algorithm. As Variation 3, an embodiment in which a pulse rate measurement algorithm is selected from these two algorithms will be described.

[0127] In Modification 3, processing unit 110 normally selects the low-trend algorithm as the pulse rate measurement algorithm. Then, when it is estimated that the user has started exercising, processing unit 110 estimates that the pulse rate is rising and selects the high-trend algorithm. Then, when it is estimated that the user has finished exercising, processing unit 110 estimates that the pulse rate is falling and selects the low-trend algorithm.

[0128] As described above, the low-trend algorithm outputs the moving average of the pulse rate over a relatively long period of time as the pulse rate. Therefore, the frequency of obtaining biological detection values from the biological detection value acquisition unit 130 can be reduced compared to when other algorithms are selected. This can also reduce the number of times the LED in the biological detection value acquisition unit 130 is illuminated, thereby reducing power consumption. On the other hand, the high-trend algorithm, the rising-trend algorithm, and the falling-trend algorithm require obtaining the moving average of the pulse rate over a relatively short period of time, so the frequency of obtaining biological detection values is higher than that of the low-trend algorithm, which tends to increase power consumption.

[0129] In Modification 3, by reducing the number of algorithms to two, the load of the algorithm selection process can be reduced, and the frequency of selecting the low-order trending algorithm can be increased (compared to the case of selecting from four algorithms), thereby reducing the power consumption of electronic device 100.

[0130] (Variation 4)

[0131] Furthermore, the pulse rate measurement algorithm is not limited to one that corresponds to the pulse rate change trend. It is also conceivable that the trend of the biometric detection value obtained by the biometric detection value acquisition unit 130 may vary depending on the type of exercise. Therefore, a pulse rate measurement algorithm corresponding to the type of exercise may be prepared. The processing unit 110 estimates the user's exercise type during the algorithm selection process and selects an algorithm corresponding to the estimated exercise. For example, an algorithm for running, an algorithm for cycling, an algorithm for swimming, an algorithm for mountain climbing, and the like are conceivable.

[0132] In addition, it is assumed that the tendency of the biological detection value in the biological detection value acquisition unit 130 during swimming also varies depending on the swimming style. Therefore, an algorithm for use in freestyle swimming, an algorithm for use in breaststroke, an algorithm for use in backstroke, an algorithm for use in butterfly stroke, etc. can also be prepared. The processing unit 110 also estimates the swimming style during swimming and selects an algorithm suitable for the estimated swimming style.

[0133] (Variant 5)

[0134] In the pulse rate correction process described above, multiple pulse rate candidates calculated by the currently selected pulse rate measurement algorithm (e.g., the low-trend algorithm) are corrected to the correct pulse rate based on likelihood and correction amplitude. However, the pulse rate candidates are not limited to those calculated by the single pulse rate measurement algorithm. Pulse rates calculated by other pulse rate measurement algorithms (e.g., the rising-trend algorithm, the high-trend algorithm, and the falling-trend algorithm, if the low-trend algorithm is selected) are also included in the pulse rate candidates, allowing the user to select the correct value (or alternatively, the correct value can be selected by comparing it with the pulse rate of a reference device). This also allows for correction of the timing of pulse rate measurement algorithm selection.

[0135] In addition, in the embodiment described above, the electronic device 100 estimates the trend of change in the pulse rate based on the estimated content of the user's behavior and calculates the pulse rate using a pulse rate measurement algorithm corresponding to the estimated trend of change. However, the information calculated by the electronic device 100 is not limited to the pulse rate. For example, because the biological detection value acquisition unit 130 includes a sensor for measuring blood pressure, the electronic device 100 can also measure the user's blood pressure. However, the trend of change in blood pressure can also be estimated based on the content of the user's behavior (for example, a decreasing trend when resting, an increasing trend during exercise, etc.). Therefore, the electronic device 100 can also measure the user's blood pressure using an algorithm corresponding to the trend of change.

[0136] Furthermore, the electronic device 100 increases or decreases the number of sensors included in the biological detection value acquisition unit 130 as needed, in addition to blood pressure, and measures arbitrary biological information obtained from the biological detection value acquisition unit 130 using an algorithm corresponding to the change tendency.

[0137] While pulse rate, blood pressure, and other information calculated based on the information (biometric values) from the biometric value acquisition unit 130 are considered biometric information, various other information that can be calculated based on this information (e.g., stress level, vascular age) are also considered biometric information. Furthermore, the electronic device 100 may also measure any of these biometric information using an algorithm tailored to their changing trends.

[0138] Furthermore, the electronic device 100 does not necessarily need to output the biological information in the form of display on the display unit. The electronic device 100 may output the biological information in the form of sound, for example.

[0139] Furthermore, the electronic device 100 can also be implemented as a wearable computer that can be worn on the user's body, or as a computer such as a smartphone, tablet, or PC that can obtain biological detection values detected by sensors worn on the user's body. Specifically, in the above embodiment, a program for the pulse rate display process and the like executed by the electronic device 100 is pre-stored in the storage unit 120. However, the program can also be stored and distributed on a computer-readable recording medium such as a floppy disk, CD-ROM (Compact Disc Read Only Memory), DVD (Digital Versatile Disc), MO (Magneto-Optical Disc), memory card, or USB memory, and then read and installed in a computer, thereby forming a computer capable of executing the above-mentioned processes.

[0140] Furthermore, the program can be superimposed on a carrier wave and applied via a communication medium such as the Internet. For example, the program can be posted and distributed on a bulletin board (BBS) on a communication network. Furthermore, the program can be activated and executed like other application programs under the control of an OS (Operating System), thereby enabling the execution of the above-mentioned processes.

[0141] In addition, the processing unit 110 may be composed of any single processor such as a single processor, a multi-processor, or a multi-core processor, or may be composed of any processor combined with a processing circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field-Programmable Gate Array).

[0142] While the processing unit 110 has been described as supporting multithreaded processing, this is not limited to multi-core parallel processing. Even if the processing unit 110 utilizes a single core, it can execute various processes in parallel through software-based time-sharing, for example, by periodically performing algorithm selection processing during the pulse rate display process. Furthermore, the processing unit 110 does not need to support multithreaded processing; for example, it can execute various processes by performing algorithm selection processing at the end of each cycle of the pulse rate display process.

[0143] While preferred embodiments of the present invention have been described above, the present invention is not limited to the specific embodiments described above, and the present invention encompasses inventions described in the scope of claims and their equivalents.

Claims

1. An electronic device, characterized in that: have: a biological detection value acquisition unit that acquires a biological detection value for calculating biological information of a wearer of the electronic device; as well as Processing Department, The processing unit estimates the wearer's action content from a plurality of action contents using a predetermined method, estimates a change tendency of the biometric information based on the estimated action content, and selects an algorithm for calculating the biometric information based on the biometric detection value based on the estimated change tendency of the biometric information. In the case where an algorithm for an upward trend is selected as the algorithm, if the estimated action content is the end of exercise, the processing unit estimates the change trend of the biological information as a downward trend and selects the algorithm for a downward trend as the algorithm. If the biological information is above a high-level reference value and its change is below a reference change value, the processing unit estimates the change trend of the biological information as a high-level trend and selects the algorithm for a high-level trend as the algorithm.

2. An electronic device, characterized in that: have: a biological detection value acquisition unit that acquires a biological detection value for calculating biological information of a wearer of the electronic device; as well as Processing Department, The processing unit estimates the wearer's action content from a plurality of action contents using a predetermined method, estimates a change tendency of the biometric information based on the estimated action content, and selects an algorithm for calculating the biometric information based on the biometric detection value based on the estimated change tendency of the biometric information. In the case where an algorithm for a downward trend is selected as the algorithm, if the estimated action content is the start of exercise, the processing unit estimates the trend of change of the biological information as an upward trend and selects the algorithm for an upward trend as the algorithm. If the biological information is below a low-order baseline value and its change is below a baseline change value, the processing unit estimates the trend of change of the biological information as a low-order trend and selects the algorithm for a low-order trend as the algorithm.

3. The electronic device according to claim 1 or 2, characterized in that: When the low-trend algorithm is selected as the algorithm, if the estimated action content is the start of exercise, the processing unit estimates the change trend of the biological information as an increasing trend and selects the increasing trend algorithm as the algorithm.

4. The electronic device according to claim 1 or 2, characterized in that: When the high-trend algorithm is selected as the algorithm, if the action content is estimated to be the end of exercise, the processing unit estimates the change trend of the biological information to be a downward trend and selects the downward trend algorithm as the algorithm. If it is judged based on the estimated action content that the exercise intensity is increasing, the processing unit estimates the change trend of the biological information to be an upward trend and selects the upward trend algorithm as the algorithm.

5. The electronic device according to claim 1 or 2, characterized in that: The electronic device further comprises: a motion detection unit that obtains a motion detection value related to the wearer's motion state; and an action history storage unit that stores the wearer's past actions as action history, The processing unit estimates the content of the wearer's behavior based on the motion detection value obtained by the motion detection unit and the behavior history stored in the behavior history storage unit, and stores the estimated behavior content in the behavior history storage unit.

6. The electronic device according to claim 1 or 2, characterized in that: The electronic device further comprises: a motion detection unit that obtains a motion detection value related to the wearer's motion state, The processing unit estimates a change tendency of the wearer's biological information based on the motion detection value obtained by the motion detection unit.

7. An algorithm selection method in an electronic device comprising a biological detection value acquisition unit and a processing unit for acquiring biological detection values for calculating biological information of a wearer, characterized in that: The processing unit estimates the wearer's action content from a plurality of action contents using a predetermined method, estimates a change tendency of the biometric information based on the estimated action content, and selects an algorithm for calculating the biometric information based on the biometric detection value based on the estimated change tendency of the biometric information. In the case where an algorithm for an upward trend is selected as the algorithm, if the estimated action content is the end of exercise, the processing unit estimates the change trend of the biological information as a downward trend and selects the algorithm for a downward trend as the algorithm. If the biological information is above a high-level reference value and its change is below a reference change value, the processing unit estimates the change trend of the biological information as a high-level trend and selects the algorithm for a high-level trend as the algorithm.

8. An algorithm selection method in an electronic device comprising a biological detection value acquisition unit and a processing unit for acquiring biological detection values for calculating biological information of a wearer, characterized in that: The processing unit estimates the wearer's action content from a plurality of action contents using a predetermined method, estimates a change tendency of the biometric information based on the estimated action content, and selects an algorithm for calculating the biometric information based on the biometric detection value based on the estimated change tendency of the biometric information. In the case where an algorithm for a downward trend is selected as the algorithm, if the estimated action content is the start of exercise, the processing unit estimates the trend of change of the biological information as an upward trend and selects the algorithm for an upward trend as the algorithm. If the biological information is below a low-order baseline value and its change is below a baseline change value, the processing unit estimates the trend of change of the biological information as a low-order trend and selects the algorithm for a low-order trend as the algorithm.

9. The algorithm selection method according to claim 7 or 8, characterized in that: When the low-trend algorithm is selected as the algorithm, if the estimated action content is the start of exercise, the processing unit estimates the change trend of the biological information as an increasing trend and selects the increasing trend algorithm as the algorithm.

10. The algorithm selection method according to claim 7 or 8, characterized in that: When the high-trend algorithm is selected as the algorithm, if the action content is estimated to be the end of exercise, the processing unit estimates the change trend of the biological information to be a downward trend and selects the downward trend algorithm as the algorithm. If it is judged based on the estimated action content that the exercise intensity is increasing, the processing unit estimates the change trend of the biological information to be an upward trend and selects the upward trend algorithm as the algorithm.

11. A non-transitory computer-readable recording medium recording a program executable by a processing unit of an electronic device having a biological detection value acquisition unit and a processing unit, wherein the biological detection value acquisition unit acquires biological detection values for calculating biological information of a wearer, wherein: The processing unit estimates the wearer's action content from a plurality of action contents using a predetermined method according to the program, estimates the change tendency of the biometric information based on the estimated action content, and selects an algorithm for calculating the biometric information based on the biometric detection value based on the estimated change tendency of the biometric information. In the case where an algorithm for an upward trend is selected as the algorithm, if the estimated action content is the end of exercise, the processing unit estimates the change trend of the biological information as a downward trend and selects the algorithm for a downward trend as the algorithm. If the biological information is above a high-level reference value and its change is below a reference change value, the processing unit estimates the change trend of the biological information as a high-level trend and selects the algorithm for a high-level trend as the algorithm.

12. A non-transitory computer-readable recording medium recording a program executable by a processing unit of an electronic device having a biological detection value acquisition unit and a processing unit, wherein the biological detection value acquisition unit acquires biological detection values for calculating biological information of a wearer, wherein: The processing unit estimates the wearer's action content from a plurality of action contents using a predetermined method according to the program, estimates the change tendency of the biometric information based on the estimated action content, and selects an algorithm for calculating the biometric information based on the biometric detection value based on the estimated change tendency of the biometric information. In the case where an algorithm for a downward trend is selected as the algorithm, if the estimated action content is the start of exercise, the processing unit estimates the trend of change of the biological information as an upward trend and selects the algorithm for an upward trend as the algorithm. If the biological information is below a low-order baseline value and its change is below a baseline change value, the processing unit estimates the trend of change of the biological information as a low-order trend and selects the algorithm for a low-order trend as the algorithm.

13. The recording medium according to claim 11 or 12, wherein When the low-trend algorithm is selected as the algorithm, if the estimated action content is the start of exercise, the processing unit estimates the change trend of the biological information as an increasing trend and selects the increasing trend algorithm as the algorithm.

14. The recording medium according to claim 11 or 12, wherein When the high-trend algorithm is selected as the algorithm, if the action content is estimated to be the end of exercise, the processing unit estimates the change trend of the biological information to be a downward trend and selects the downward trend algorithm as the algorithm. If it is judged based on the estimated action content that the exercise intensity is increasing, the processing unit estimates the change trend of the biological information to be an upward trend and selects the upward trend algorithm as the algorithm.

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