Disease onset prediction device, method and program

Abstract

This device (1) is provided with: an exercise sensor (31) for continuously detecting the motion of an exerciser involving intermittent exercise; and a heartbeat meter (32) for continuously measuring the exerciser's heartbeat. The device is further provided with an activity detection unit (12), a heartbeat monitoring unit (13), an activity monitoring unit (14), and a notification processing unit (15). The activity detection unit (12) detects, from the motion of the exerciser, an activity intensity, a high activity-intensity period, and a low activity-intensity period. The heartbeat monitoring unit (13) detects a declining trend in the exerciser's heart rate in every low activity-intensity period and monitors whether or not the present declining trend is lower as compared to a predetermined standard. In the case when the monitoring by the heartbeat monitoring unit is affirmed, the activity monitoring unit (14) monitors whether or not the activity intensity measured in the next high activity-intensity period is lower as compared with the activity intensities detected in the high activity-intensity periods up to the previous one. In the case when the monitoring by the activity monitoring unit is affirmed, the notification processing unit (15) forecasts an outbreak of disease. With this configuration, outbreak of disease in an exerciser during exercise is predicted with high accuracy.

Description

Technical Field

[0001] The present invention relates to a technique for predicting the occurrence of diseases during exercise or sports with intermittent activities by integrating medical knowledge into biological information obtained by sensing.

Background Art

[0002] As serious accidents during exercise or sports activities, sudden death from ventricular fibrillation and death due to heatstroke are known. These can occur not only in patients with medical diseases or the elderly, but also in healthy young people, and it is difficult to prevent them only by medical check-ups performed before the start of exercise or sports activities. In recent years, methods of monitoring the heart rate during exercise or sports activities using wearable sensors such as wristwatches and wearable types are known.

[0003] Patent Document 1 describes a heatstroke onset risk determination device that determines the heatstroke onset risk according to the difference between the pulse rate and the reference pulse rate by comparing the pulse rate with the reference pulse rate, which is the reference value, during outdoor work, notifies the determined onset risk to the user, and estimates the presence or absence of the user's evacuation behavior by comparing it with the activity intensity thereafter.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

[0006] Furthermore, while the disease risk determination device described in Patent Document 1 utilizes the user's pulse rate and activity level, the user's activity level is used to estimate whether the user took evasive action after being notified of the disease risk, and not to determine whether or not the risk occurred.

[0007] The present invention has been made in view of the above, and provides a disease onset prediction device, method, and program that predict the onset of disease from changes in heart rate and activity intensity during intermittent exercise. [Means for solving the problem]

[0008] The disease onset prediction device according to the present invention comprises an exercise sensor that continuously detects the movements of an exerciser with intermittent exercise, and a heart rate monitor that continuously measures the heart rate of the exerciser, and further comprises an activity detection means that detects activity intensity, high activity intensity period and low activity intensity period from the exerciser's movements detected by the exercise sensor, a first monitoring means that detects a decreasing trend in the exerciser's heart rate each time a low activity intensity period occurs and monitors whether the current decreasing trend is lower than a predetermined standard, a second monitoring means that, if the monitoring by the first monitoring means is confirmed, monitors whether the activity intensity detected in the next high activity intensity period is lower than the activity intensity detected in previous high activity intensity periods, and a notification means that, if the monitoring by the second monitoring means is confirmed, provides a disease onset prediction.

[0009] Furthermore, the disease onset prediction method according to the present invention uses an exercise sensor that continuously detects the movements of an exerciser with intermittent exercise and a heart rate monitor that continuously measures the heart rate of the exerciser, the activity detection means detects the activity intensity, high activity intensity period and low activity intensity period from the exerciser's movements detected by the exercise sensor, the first monitoring means detects the decreasing trend of the exerciser's heart rate each time a low activity intensity period occurs and monitors whether the current decreasing trend is lower than a predetermined standard, the second monitoring means, if the monitoring by the first monitoring means is confirmed, monitors whether the activity intensity detected in the next high activity intensity period is lower than the activity intensity detected in previous high activity intensity periods, and the notification means, if the monitoring by the second monitoring means is confirmed, makes a disease onset prediction.

[0010] Furthermore, the program according to the present invention enables a computer to function as a disease onset prediction device.

[0011] According to these inventions, the risk of disease occurrence in the first stage is managed by monitoring the decreasing trend of heart rate during periods of low activity intensity, and the risk of disease occurrence in the second stage is managed by monitoring whether sufficiently high activity intensity is obtained during periods of high activity intensity, in response to the risk of disease occurrence in the first stage. This makes it possible to predict fatigue, heatstroke, and cardiac activity with higher accuracy. Cardiac disease and heatstroke are thought to occur due to exercise with relatively high activity intensity for extended periods, and the present invention can predict such fatigued states. In this way, by integrating biological information obtained through sensing with medical knowledge to detect states that may lead to the occurrence of disease, it becomes possible to prevent the occurrence of disease. [Effects of the Invention]

[0012] According to the present invention, the occurrence of diseases in athletes during exercise can be predicted with high accuracy by integrating biological information and medical knowledge. [Brief explanation of the drawing]

[0013] [Figure 1]This is an overall configuration diagram showing one embodiment of the disease onset prediction device according to the present invention. [Figure 2] This flowchart shows the procedure for disease onset prediction processing I. [Figure 3] This is a flowchart showing the procedure for disease occurrence prediction processing II. [Figure 4] In the time chart showing the results of the experimental example, (A) represents activity intensity, (B) represents heart rate, (C) represents tympanic temperature, and (D) represents the heat index (WBGT: Wet Bulb Globe Temperature). [Modes for carrying out the invention]

[0014] Figure 1 is an overall configuration diagram showing one embodiment of a disease onset prediction device. In Figure 1, the disease onset prediction device 1 includes an information processing unit 10, which is preferably composed of a computer (processor). The information processing unit 10 is connected to a storage unit 101, and, if necessary, a display unit 21 and an operation unit 22 for displaying images. In addition, the information processing unit 10 is connected to various sensors for collecting biometric information of the exerciser, typically via short-range wireless communication means. In this embodiment, it includes an exercise sensor 31, a heart rate monitor 32, and, if necessary, a thermometer 33 for measuring the temperature of the exercise environment.

[0015] The memory unit 101 has a memory area for storing a processing program for disease occurrence prediction processing and various registration data referenced when executing the processing program, and a work area for temporarily storing data being processed (time-series data acquired from external sources, calculation processing data, etc.). The display unit 21 displays result information after information processing and displays confirmation of the content of operation input. The operation unit 22 is an input device equipped with a mouse and keyboard. The operation unit 22 may employ a so-called known touch panel, in which a transparent sheet made of pressure-sensitive elements is layered on the screen of the display unit 21, and instructions to the corresponding buttons are received by pressing on the displayed buttons or icons.

[0016] The motion sensor 31 can be an accelerometer or GPS (Global Positioning System) receiver attached to a part of the body or to an appropriate place on clothing. Alternatively, the motion sensor 31 may be a stationary video camera (imaging device) that captures images of the movement area of ​​the person moving, and the biological activity (movement) of the person being moved may be detected from the video camera's moving images. For example, a known skeleton detection algorithm (Open Pose) can be used. This method can detect, for example, the movement of the waist position by extracting feature points (joint positions: nodes) and feature directions (skeleton) from the moving images of each labeled player, even when there are multiple people moving.

[0017] A heart rate monitor 32 is a wearable sensor, such as a wristwatch or other bio-worn device, that monitors heart rate during exercise or sports activities.

[0018] Furthermore, the thermometer 33 detects the body temperature of the exerciser, and in this embodiment, an ear thermometer that is inserted into the exerciser's ear to measure the tympanic membrane temperature, which is said to be close to the brain temperature, can be used. Alternatively, the temperature of the exercise environment may be used instead of the exerciser's temperature as the thermometer 33. Depending on the temperature detected by the thermometer 33, a predetermined value α (see Figure 4(A)) of the monitoring standard, corresponding to the temperature characteristics of the heart rate, can be changed and set, enabling more accurate predictions.

[0019] In this embodiment, for example, the exercise sensor 31, heart rate monitor 32, and thermometer 33 are worn by the exerciser, while various configurations can be adopted for the arrangement of the information processing unit 10 (including the storage unit 101), display unit 21, and operation unit 22. In this embodiment, the information processing unit 10 is composed of a portable personal computer and is equipped with a display unit 21 and an operation unit 22, and will be described below as being operated by an administrator.

[0020] Therefore, in this embodiment, the information processing unit 10 receives biometric information as an index from various biometric information detection devices worn by a person during exercise, and the information processing unit 10 continuously executes a disease occurrence prediction process based on the received various biometric information and outputs it to the display unit 21. Note that the method of notifying the prediction may be, in addition to an image, for example, voice or a predetermined alarm. In addition to an image, particularly in the case of sound, a functional unit that wirelessly receives a prediction signal from the information processing unit 10 side and outputs it as an acoustic signal may be provided in any of the sensors on the person side. Other aspects will be described later.

[0021] In FIG. 1, the information processing unit 10 functions as a data acquisition unit 11, an activity detection unit 12, a heart rate monitoring unit 13, an activity monitoring unit 14, a notification processing unit 15, and a timer 16 by reading out a processing program stored in the storage unit 101 and executing it on the main memory (work area).

[0022] The data acquisition unit 11 continuously captures biometric signals from the motion sensor 31, the heart rate meter 32, and the thermometer 33 at a predetermined cycle. The activity intensity signal from the motion sensor 31 is preferably the magnitude of acceleration. The heart rate signal or heart rate number signal from the heart rate meter 32 is converted and output as the number of heartbeats per minute. The temperature signal from the thermometer 33 is captured at a relatively long cycle, for example, every few minutes, in this embodiment. For example, it is shown as an experimental example in the time charts of FIGS. 4(A) and (B).

[0023] The activity detection unit 12 executes a process of binarizing the activity intensity in the level direction from the activity intensity signal detected by the motion sensor 31. For example, using a predetermined level or a level around the median of the activity intensity according to the type of exercise as a threshold, the exercise period is divided into a high activity intensity period and a low activity intensity period. As illustrated in FIG. 4(A), it involves intermittent exercise in the time direction.

[0024] This embodiment predicts conditions that may lead to serious illnesses such as heart disease, heatstroke, and fatigue during sports activities, based on the time-series changes in activity level and heart rate during intermittent physical exercise with periods of relatively high and relatively low activity levels. Intermittent physical exercise refers to physical activity in exercise or sports where relatively high and relatively low activity levels (including activity and rest) alternate, rather than a constant level of activity. Examples include circuit training and many sports such as tennis, soccer, baseball, basketball, volleyball, and boxing.

[0025] The reason for dividing exercise into high-intensity and low-intensity periods is that while it is possible to monitor heart rate during exercise by electrically monitoring the electrocardiogram using electrodes attached to the body surface, or by sensing the pulse using pulse pressure or light, accurately monitoring the electrocardiogram or pulse during activity is difficult. Therefore, in this embodiment, as will be described later, the heart rate is accurately detected at relatively low activity levels during intermittent exercise.

[0026] The heart rate monitoring unit 13 extracts the heart rate acquired after a predetermined time has elapsed from the start of each low-activity period. The timer 16 measures this predetermined time. The predetermined time can be set appropriately depending on the type of exercise or sport; for example, if a rest period is set in advance during exercise, the heart rate can be set to the middle or end of the set time. By extracting the heart rate within the low-activity period, highly accurate biometric information can be obtained.

[0027] Furthermore, the heart rate monitoring unit 13 detects a decreasing trend in the exerciser's heart rate and monitors whether the current decreasing trend is lower than a predetermined standard. The heart rate itself may be used as the indicator for the decreasing trend. In this case, it monitors whether the heart rate extracted this time is greater than the value obtained by adding a predetermined value α to the heart rate up to the previous time, which is the predetermined standard. Here, the heart rate up to the previous time may be the heart rate extracted last time, the average of the heart rates extracted up to the previous time, or the first heart rate at the start of monitoring. Furthermore, the predetermined value α can be set to an appropriate value (as a dead zone) depending on the type of exercise or sport, for example, a value of "5" may be considered. In this case, if the predetermined standard is "100", the heart rate monitoring unit 13 will determine whether the current heart rate is greater than "105" (=100+5). This determination is positioned as the first stage of disease risk assessment.

[0028] If the monitoring judgment by the heart rate monitoring unit 13 is confirmed, the activity monitoring unit 14 then makes a judgment as the second stage of disease risk assessment based on activity intensity. The activity monitoring unit 14 monitors whether the activity intensity detected in the next high-activity period, immediately after the monitoring judgment by the heart rate monitoring unit 13 is confirmed, is lower than the activity intensity detected in previous high-activity periods. Specifically, the activity monitoring unit 14 monitors whether the activity intensity detected in the next high-activity period is lower than the activity intensity detected in previous high-activity periods by a predetermined difference β or more.

[0029] Here, the activity intensity detected during previous periods of high activity intensity may be the activity intensity detected in the previous period, its average value, the average of activity intensities detected up to the previous period, or the activity intensity from the first period, its average value. Alternatively, the peak value may be used instead of the average value. Furthermore, the predetermined difference β, which acts as a dead zone, may be set appropriately depending on the type of exercise or sport. A state in which the activity intensity decreases during exercise is detected as a second-stage risk state, where the risk increases further with continued exercise.

[0030] By applying different indicators to different time periods and evaluating risk in two stages in this way, it becomes possible to effectively and accurately prevent heatstroke, extreme fatigue, and other related issues before they occur.

[0031] If the activity monitoring unit 14 determines that the activity intensity detected in the next high-activity-intensity period is lower than the activity intensity detected in previous high-activity-intensity periods by a predetermined difference β or more, the notification processing unit 15 will issue a forecast notification, for example, to the display unit 21, indicating that the risk of disease occurrence is high.

[0032] Figure 2 is a flowchart showing the procedure for disease onset prediction processing I. First, when the processing program is started, heart rate (HR) data is acquired from the heart rate monitor 32 (step S1), and at the same time, activity intensity (PA) is calculated from the intensity signal acquired from the exercise sensor 31, and the high activity intensity period (TH) and low activity intensity period (TL) are detected (step S3).

[0033] Next, it is determined whether the activity signal has transitioned from a high activity intensity period TH to a low activity intensity period TL (step S5). If it is determined that the transition has occurred, a timer that measures a predetermined time T0 is turned ON (step S7). On the other hand, if it is determined that the transition has not occurred, it is then determined whether the process has ended (step S27). If it has ended, the process exits this flow; otherwise, the process returns to step S5 and the same process is repeated.

[0034] If the timer is turned on in step S7, it is then determined whether a predetermined time T0 has elapsed (step S9). If it has not yet elapsed, it is then determined whether the activity has transitioned from a low activity intensity period TL to a high activity intensity period TH (step S11). If it is determined that the transition has not occurred, the process returns to step S9 and the timer continues. On the other hand, if it is determined that the transition has occurred, the timer is reset (step S13) because the low activity intensity period TL was too short, and the process proceeds to step S27.

[0035] On the other hand, if a predetermined time T0 has elapsed in step S9, the heart rate HR is extracted (step S15), and then the timer is reset (step S17).

[0036] Next, it is determined whether the current heart rate HR1 is greater than the previous heart rate HR2 plus a predetermined value α (step S19). If this determination is affirmative, it is then determined whether a transition has occurred from a low activity intensity period TL to a high activity intensity period TH (step S21). After waiting for this transition, it is determined whether the activity intensity PA1 detected in the next high activity intensity period TH is lower than the activity intensity PA2 detected in the previous high activity intensity period TH by a predetermined difference β or more (step S23). If the determination in step S23 is affirmative, a prediction process to inform the user of the risk of disease occurrence is performed (step S25), and the process proceeds to step S27. On the other hand, if the determinations in steps S19 and S23 are negative, the process proceeds directly to step S27.

[0037] Next, Figure 3 is a flowchart showing the procedure for Disease Onset Prediction Processing II. While Disease Onset Prediction Processing I monitors the change in heart rate (HR) from the start of the low-activity intensity period (TL) to a predetermined time afterward, Disease Onset Prediction Processing II monitors the change in the difference between heart rate (HR1) at the start of the low-activity intensity period (TL) and heart rate (HR2) at a predetermined time afterward (i.e., a downward trend). Steps S37, S41, and S49 of the procedure for Disease Onset Prediction Processing I differ from those of Disease Onset Prediction Processing I, and these will be explained below.

[0038] Step S37 extracts the heart rate (HRb) when the activity transitions from the low-intensity activity period (TL) to the high-intensity activity period (TH), and turns on the timer.

[0039] Step S41 extracts the heart rate (HRa) after a predetermined time T0 has elapsed since the transition to the low activity intensity period (TL), calculates the difference between the two (HRb-HRa) and records it.

[0040] Step S49 monitors the decreasing trend in heart rate and determines whether the difference between the previous difference (HRb-HRa) and the current difference (HRb-HRa) is greater than a predetermined value γ. If this determination is affirmed, the decreasing trend in heart rate is considered lower than the standard, and the patient is judged to be in a risky state at Stage 1.

[0041] Figure 4 is a time chart showing the results of an experimental example, where (A) is activity intensity, (B) is heart rate, (C) is tympanic temperature, and (D) is the heat index (WBGT: Wet Bulb Globe Temperature). This experimental example involved university students as subjects and simulated a tennis match. As shown in Figure 4(A), the exercise took place from 10:00 to 12:30. The low-activity intensity period (the interval from time t2 to t1 in each session, see Figure 4(A)) corresponds to the set break (2 minutes after the end of each set) and the court change break (90 seconds).

[0042] Furthermore, a wearable sensor was used as the exercise sensor 31, and a DSP wireless ECG / HR logger (manufactured by Sports Sensing Co., Ltd.) was used as the heart rate monitor 32. In addition, in Figure 4, for reference, an ear thermometer MC-510 and Ken-on-kun Mimi (manufactured by Omron Corporation) were used for tympanic temperature, and the heat index (wet bulb globe temperature: WBGT) and subjective exercise intensity (RPE) were expressed on the Borg scale.

[0043] Intermittent exercise, consisting of high-intensity periods during exercise (t1-t2) and low-intensity periods during rest (t2-t1), is described later (Figure 4(A)).

[0044] Heart rate fluctuated wildly during the high-intensity period of exercise (t1-t2), but gradually decreased in a consistent downward trend as the activity transitioned to the low-intensity period (t2-t1), and then began to rise again when the rest period ended and activity resumed. It can be seen that the heart rate dropped to around "110" during rest. On the other hand, during the rest period around 11:20, it only dropped to "120" (see Bc in Figure 4(B), (difference>α)). At this point, a first-stage risk occurrence prediction was made.

[0045] Furthermore, compared to this point Bc, the activity intensity in the next interval (next time) was at least lower than the previous activity intensity, for example, the peak intensity, by a predetermined value of β or more (see Figure 4(A) for the Tc period, (β < difference)). This suggests a state where movement becomes sluggish due to fatigue, for example, and thus a prediction of the occurrence of the second stage risk was made. In the experiment, after the occurrence of the first stage risk, measures such as sufficiently cooling the body surface during rest periods allowed exercise to continue.

[0046] In Figures 4(C) and (D), the tympanic temperature during exercise was around 37°C, the subjective exercise intensity was approximately "15" to "19", and the WBGT was slightly above "31". The details of the subjective exercise intensity are shown in (Table 1).

[0047] [Table 1]

[0048] In this embodiment, the information processing unit 10 was described as a portable personal computer that can be managed by an administrator, but it may also be configured in a way that allows the exerciser to directly recognize the risk of disease development. For example, the heart rate monitor 32 may be equipped with the functions of the information processing unit 10, display unit 21, and sound unit.

[0049] Furthermore, in this embodiment, the timing of heart rate extraction was set to after a predetermined time has elapsed within the low-activity intensity period, but it is not limited to this, and for example, it may always be set to the end of the low-activity intensity period. In this case, a process can be performed to convert the extracted heart rate to a value for the predetermined time based on the time until the end of the low-activity intensity period and the pre-prepared heart rate decrease characteristics. With this method, the heart rate can be extracted each time, regardless of the length of the low-activity intensity period.

[0050] Furthermore, the exercises in this invention may include not only those for which known rules have been established, but also, for example, self-taught jogging, walking, and various other types of training and sports that involve intermittent exercise.

[0051] As described above, the disease occurrence prediction device according to the present invention preferably comprises an exercise sensor that continuously detects the movements of an exerciser with intermittent exercise, and a heart rate monitor that continuously measures the heart rate of the exerciser, and further comprises an activity detection means that detects activity intensity, high activity intensity period and low activity intensity period from the exerciser's movements detected by the exercise sensor, a first monitoring means that detects a decreasing trend in the exerciser's heart rate each time a low activity intensity period occurs and monitors whether the current decreasing trend is lower than a predetermined standard, a second monitoring means that, if the monitoring by the first monitoring means is confirmed, monitors whether the activity intensity detected in the next high activity intensity period is lower than the activity intensity detected in previous high activity intensity periods, and a notification means that, if the monitoring by the second monitoring means is confirmed, provides a disease occurrence forecast.

[0052] Furthermore, the disease onset prediction method according to the present invention preferably uses an exercise sensor that continuously detects the movements of an exerciser with intermittent exercise and a heart rate monitor that continuously measures the heart rate of the exerciser, wherein the activity detection means detects the activity intensity, high activity intensity period and low activity intensity period from the exerciser's movements detected by the exercise sensor, the first monitoring means detects the decreasing trend of the exerciser's heart rate each time a low activity intensity period occurs and monitors whether the current decreasing trend is lower than a predetermined standard, the second monitoring means, if the monitoring by the first monitoring means is confirmed, monitors whether the activity intensity detected in the next high activity intensity period is lower than the activity intensity detected in previous high activity intensity periods, and the notification means, if the monitoring by the second monitoring means is confirmed, provides a disease onset prediction.

[0053] Furthermore, it is preferable that the program according to the present invention allows a computer to function as a disease onset prediction device.

[0054] According to these inventions, the risk of disease occurrence in the first stage is managed by monitoring the decreasing trend of heart rate during periods of low activity intensity, and the risk of disease occurrence in the second stage is managed by monitoring whether sufficiently high activity intensity is obtained during periods of high activity intensity, in response to the risk of disease occurrence in the first stage. This makes it possible to predict fatigue, heatstroke, and cardiac activity with higher accuracy. Cardiac disease and heatstroke are thought to occur due to exercise with relatively high activity intensity for extended periods, and the present invention can predict such fatigued states. In this way, by integrating biological information obtained through sensing with medical knowledge to detect states that may lead to the occurrence of disease, it becomes possible to prevent the occurrence of disease.

[0055] Furthermore, it is preferable that the first monitoring means detects the heart rate as a decreasing trend at each predetermined time point in the low-activity intensity period, and monitors whether the current heart rate is greater than the predetermined standard, which is the heart rate up to the previous time point plus a predetermined value. With this configuration, the first monitoring means can perform the first stage of monitoring using the heart rate at each predetermined time point.

[0056] Furthermore, it is preferable that the heart rate from previous sessions includes the heart rate from at least one previous low-activity period. This configuration makes it easier to obtain the reference heart rate from previous sessions.

[0057] Furthermore, the heart rate mentioned above is preferably the heart rate during the initial low-intensity activity period. With this configuration, the reference heart rate is set to a value that is closer to the exerciser's resting state.

[0058] Furthermore, it is preferable that the first monitoring means detects a first heart rate at the start of each low-activity period, and a second heart rate at a predetermined time after the end of the low-activity period, and monitors whether the current difference, which is the difference between the current first and second heart rates as a trend of decreasing heart rate, is smaller than the predetermined standard, which is the difference between the previous first and second heart rates (the difference up to the previous time) minus a predetermined value. With this configuration, the change in the decrease in heart rate for each low-activity period is compared with the standard.

[0059] Furthermore, it is preferable that the first and second heart rates from the previous session include the heart rate from at least one previous low-activity period. This configuration makes it easier to obtain the first and second heart rates on the reference side.

[0060] Furthermore, it is preferable that the second monitoring means monitors whether the activity intensity detected in the next high-activity-intensity period is lower than the activity intensity detected in the previous high-activity-intensity periods by a predetermined difference or more. With this configuration, it is monitored whether the activity intensity in the high-activity-intensity period is lower than a predetermined difference or more, that is, whether a sufficiently high activity intensity is obtained.

[0061] Furthermore, the present invention preferably includes a thermometer for detecting the temperature of the exercise environment, and the first monitoring means preferably changes the predetermined standard according to the detected temperature. This configuration allows for more accurate prediction corresponding to the temperature characteristics of heart rate. Note that the temperature of the exercise environment may include the temperature around the exerciser and the exerciser's body temperature. [Explanation of Symbols]

[0062] 1. Disease Occurrence Prediction Device 10 Information Processing Section 101 Storage section 12 Activity detection unit 13. Heart rate monitoring unit (first monitoring means) 14. Activity Monitoring Unit (Second Monitoring Measure) 15. Notification Processing Unit (part of the notification means) 21 Display unit (part of the notification means) 31 Motion Sensors 32 Heart rate monitors 33 Thermometer

Claims

1. A motion sensor that continuously detects the movements of a person, including intermittent movements, A heart rate monitor that continuously measures the heart rate of the aforementioned athlete, Activity detection means for detecting activity intensity, high activity intensity period, and low activity intensity period from the movement of the person being moved detected by the aforementioned motion sensor, A first monitoring means detects the exerciser's heart rate each time a low-intensity activity period occurs and monitors whether the heart rate detected this time is greater than the heart rate detected last time plus a predetermined value α. If the monitoring by the first monitoring means is confirmed, a second monitoring means monitors whether the activity intensity detected in the next high-activity-intensity period is lower than the activity intensity detected in previous high-activity-intensity periods, A disease occurrence prediction device comprising a notification means that, if monitoring by the second monitoring means is confirmed, provides a forecast for the occurrence of at least one of heart disease and heatstroke.

2. The disease onset prediction device according to claim 1, wherein the first monitoring means detects the exerciser's heart rate a predetermined time after the start of each low-activity intensity period.

3. The disease onset prediction device according to claim 1, wherein the previously detected heart rate includes the heart rate detected during at least one previous period of low activity intensity.

4. Equipped with a thermometer to detect the temperature of the exercise environment, The disease onset prediction device during exercise according to claim 1, characterized in that the first monitoring means changes the predetermined value α according to the detected temperature.

5. A motion sensor that continuously detects the movements of a person, including intermittent movements, A heart rate monitor that continuously measures the heart rate of the aforementioned athlete, Activity detection means for detecting activity intensity, high activity intensity period, and low activity intensity period from the movement of the person being moved detected by the aforementioned motion sensor, A first monitoring means that, during each period of low activity intensity, detects a first heart rate at the start of the period and a second heart rate at a predetermined time after the start of the period, and monitors the relationship between the difference obtained by subtracting the second heart rate from the first heart rate detected in the previous period and the difference obtained by subtracting the second heart rate from the first heart rate detected in the current period, and whether the difference obtained by subtracting If the monitoring by the first monitoring means is confirmed, a second monitoring means monitors whether the activity intensity detected in the next high-activity-intensity period is lower than the activity intensity detected in previous high-activity-intensity periods, A disease occurrence prediction device comprising a notification means that, if monitoring by the second monitoring means is confirmed, provides a forecast for the occurrence of at least one of heart disease and heatstroke.

6. The disease onset prediction device according to claim 5, wherein the previously detected first and second heart rates include heart rates detected during at least one previous low-activity intensity period.

7. Using an exercise sensor that continuously detects the movements of an exerciser involving intermittent exercise, and a heart rate monitor that continuously measures the heart rate of the exerciser, The activity detection means detects the activity intensity, high activity intensity period, and low activity intensity period from the movement of the person detected by the motion sensor. The first monitoring means detects the exerciser's heart rate each time the low-intensity activity period occurs and monitors whether the heart rate detected this time is greater than the heart rate detected last time plus a predetermined value α. If the monitoring by the first monitoring means is confirmed, the second monitoring means monitors whether the activity intensity detected in the next high-activity-intensity period is lower than the activity intensity detected in previous high-activity-intensity periods. A disease occurrence prediction method in which a notification means predicts the occurrence of at least one of heart disease and heatstroke when monitoring by the second monitoring means is confirmed.

8. Using an exercise sensor that continuously detects the movements of an exerciser involving intermittent exercise, and a heart rate monitor that continuously measures the heart rate of the exerciser, The activity detection means detects the activity intensity, high activity intensity period, and low activity intensity period from the movement of the person detected by the motion sensor. The first monitoring means detects a first heart rate at the start of each low-activity intensity period and a second heart rate at a predetermined time after the start. It monitors the relationship between the difference obtained by subtracting the second heart rate from the first heart rate detected previously and the difference obtained by subtracting the second heart rate from the first heart rate detected currently, and whether the difference obtained by subtracting If the monitoring by the first monitoring means is confirmed, the second monitoring means monitors whether the activity intensity detected in the next high-activity-intensity period is lower than the activity intensity detected in previous high-activity-intensity periods. A disease occurrence prediction method in which a notification means predicts the occurrence of at least one of heart disease and heatstroke when monitoring by the second monitoring means is confirmed.

9. A program for causing a computer to function as a disease onset prediction device according to any one of claims 1 to 4.

10. A program for causing a computer to function as a disease onset prediction device according to claim 5 or 6.

Images