Health diagnosis device, wearable device, health diagnosis system, model determination device, and program

Abstract

A physical condition diagnosis device (1) is provided with an information acquisition unit (11), an activity body temperature estimation unit (12), and a physical condition diagnosis unit (13). The information acquisition unit (11) acquires biological information including temperature information of at least one site of a target organism to be subjected to physical condition diagnosis, and variation factor information including activity information indicating activity of the target organism that varies the temperature of the site. The activity body temperature estimation unit (12) finds, from the biological information and the variation factor information, activity body temperature data indicating temperature variation of the site due to the activity. The physical condition diagnosis unit (13) finds a physical condition index, indicating a physical condition of the target organism, on the basis of the activity body temperature data.

Description

Technical Field

[0001] The present disclosure relates to a physical condition diagnosis device, a wearable device, a physical condition diagnosis system, a model determination device ,oh and a program.

Background Art

[0002] As an example of physical condition management, technologies for predicting physical conditions based on biological information of a target organism have been developed. As an example of this type of technology, a biological rhythm prediction device that predicts the ultradian rhythm waveform of biological information such as a user's body temperature and heart rate index is disclosed in Patent Document 1. The biological rhythm prediction device disclosed in Patent Document 1 reduces the influence based on influencing factors from biological signals with an ultradian rhythm period, and predicts the ultradian rhythm waveform at the time when the planned action is to be performed in consideration of the user's planned actions.

[0003] As another example, a physical condition monitoring device that evaluates the abnormal state of the human body from biological information such as blood pressure, pulse, and body temperature is disclosed in Patent Document 2. The physical condition monitoring device disclosed in Patent Document 2 corrects a reference value according to the mental activity state or physical activity state of the measured person, and determines the presence or absence of an abnormality in the human body based on a comparison between the measured value of the biological information and the corrected reference value.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] The biorhythm prediction device disclosed in Patent Document 1 can predict the ultradian rhythm waveform at the time when a user's planned activities will occur, taking into account the user's planned activities, but it cannot diagnose the subject's physical condition.

[0006] The health monitoring device disclosed in Patent Document 2 determines the presence or absence of abnormalities in the human body based on a comparison of measured biological information, such as body temperature, with a corrected reference value. Therefore, if an abnormality in the human body becomes the dominant factor in fluctuations in body temperature and causes a large fluctuation in body temperature, the health monitoring device disclosed in Patent Document 2 can diagnose the abnormality from the fluctuations in body temperature. In other words, it is difficult for the health monitoring device disclosed in Patent Document 2 to detect internal abnormalities that do not become the dominant factor in fluctuations in body temperature, making it difficult to accurately diagnose the health condition of the target organism.

[0007] This disclosure is made in view of the circumstances described above, and describes a health diagnosis device, wearable device, health diagnosis system, and model determination device that accurately diagnose the health condition of a target organism. ,oh The purpose is to provide a program. [Means for solving the problem]

[0008] To achieve the above objective, the health diagnosis device of this disclosure comprises an information acquisition unit, an activity body temperature estimation unit, and a health diagnosis unit. The information acquisition unit acquires biological information including temperature information of at least one part of the target organism to be diagnosed, and fluctuation factor information including activity information indicating the activity of the target organism that causes fluctuations in the temperature of the part. The activity body temperature estimation unit acquires biological information 、 Information on factors causing fluctuations Furthermore, based on the heat transfer pathways within the target organism, which are determined according to the heat production and heat dissipation within the target organism. , caused by the activity Organs within the target organism The system collects activity body temperature data that shows temperature fluctuations. The health diagnosis department then uses the activity body temperature data to determine health indicators that show the health condition of the target organism. [Effects of the Invention]

[0009] The health diagnosis device described herein determines health indicators of the target organism based on activity body temperature data, which shows temperature fluctuations in parts of the target organism caused by its activity, thereby enabling accurate diagnosis of the target organism's health. [Brief explanation of the drawing]

[0010] [Figure 1] Block diagram of the health condition diagnosis system according to Embodiment 1 [Figure 2] A diagram showing the hardware configuration of the health condition diagnostic device according to Embodiment 1. [Figure 3] A flowchart showing an example of the operation of the health condition diagnosis process performed by the health condition diagnosis device according to Embodiment 1. [Figure 4] Timing chart showing an example of temperature information, activity information, and activity body temperature data in Embodiment 1 [Figure 5] This figure shows an example of the display of diagnostic results by the health condition diagnostic device according to Embodiment 1. [Figure 6] Block diagram of the health condition diagnosis system according to Embodiment 2 [Figure 7] Timing chart showing an example of temperature information and activity information in Embodiment 2 [Figure 8] A flowchart illustrating an example of the operation of the activity body temperature data calculation process performed by the health condition diagnostic device according to Embodiment 2. [Figure 9] Timing chart showing an example of activity body temperature data in Embodiment 2 [Figure 10] A diagram showing an example of a heat transfer pathway within the target organism in Embodiment 2. [Figure 11] This figure shows an example of the display of diagnostic results by the health condition diagnostic device according to Embodiment 2. [Figure 12] Block diagram of the health condition diagnosis system according to Embodiment 3 [Figure 13] This figure shows an example of the display of diagnostic results by the health condition diagnostic device according to Embodiment 3. [Figure 14] Block diagram of the first modified example of the physical condition diagnosis system according to the embodiment. [Figure 15]Block diagram of the second modification of the physical condition diagnosis system according to the embodiment [Figure 16] Block diagram of the third modification of the physical condition diagnosis system according to the embodiment [Figure 17] Block diagram of the fourth modification of the physical condition diagnosis system according to the embodiment [Figure 18] Diagram showing a modification of the hardware configuration of the physical condition diagnosis apparatus according to the embodiment

Mode for Carrying Out the Invention

[0011] Hereinafter, a physical condition diagnosis apparatus, a wearable device, a physical condition diagnosis system, a model determination apparatus, a physical condition diagnosis method, and a program according to an embodiment of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or equivalent parts are denoted by the same reference numerals.

[0012] (Embodiment 1) A physical condition diagnosis apparatus for diagnosing the physical condition of organisms such as humans and animals, and a physical condition diagnosis system including the physical condition diagnosis apparatus will be described in Embodiment 1. The organism to be diagnosed is referred to as the target organism. The physical condition diagnosis system 100 shown in FIG. 1 includes a temperature sensor 51 that measures the temperature of at least one part of the target organism, an activity sensor 52 that generates an activity index indicating numerically the activity of the target organism that varies the temperature of the part, a physical condition diagnosis apparatus 1 that obtains a physical condition index indicating the physical condition of the target organism from the measurement result of the temperature sensor 51 and the activity index generated by the activity sensor 52, and an output device 53 that outputs the diagnosis result of the physical condition diagnosis apparatus 1 including the physical condition index.

[0013] The temperature sensor 51 is installed inside or outside the body of the target organism and measures the temperature of at least one part of the target organism. The temperature of the part of the target organism is the temperature of any part that can be measured or estimated, and is not limited to the temperature of the body surface, but includes internal body temperature, such as the temperature of organs or core body temperature. The temperature sensor 51 includes, for example, a temperature sensor attached to the armpit, calf, or upper arm of the target organism for health diagnosis, a temperature sensor installed inside the stomach of livestock that is the target of health diagnosis, or an infrared camera or thermograph installed at a distance from the target organism. The temperature sensor attached to the target organism is a thermistor, a resistance thermometer, etc. In Embodiment 1, the temperature sensor 51 is a temperature sensor attached to the armpit of the target organism for health diagnosis.

[0014] The activity sensor 52 generates an activity index that numerically represents the activities of the target organism, such as eating, exercising, sleeping, and excreting. The activity sensor 52 includes, for example, a wearable device that measures the target organism's heart rate, movement speed, etc., a camera capable of photographing the target organism, and a position sensor capable of acquiring the target organism's location. In Embodiment 1, the activity sensor 52 is a position sensor that detects whether the target organism for health diagnosis is in the location where it would normally eat. When the position sensor detects that the target organism is in that location, it can be assumed that the target organism is eating.

[0015] The physical condition diagnostic device 1 includes an information acquisition unit 11 that acquires biological information including temperature information of at least one part of the target organism and fluctuation factor information including activity information indicating the activity of the target organism that causes fluctuations in the temperature of the part; an activity temperature estimation unit 12 that determines activity temperature data indicating temperature fluctuations of the part caused by the activity of the target organism; and a physical condition diagnostic unit 13 that determines a physical condition index indicating the physical condition of the target organism based on the activity temperature data.

[0016] The information acquisition unit 11 acquires the measurement result from the temperature sensor 51 as temperature information and acquires the activity index generated by the activity sensor 52 as activity information. The information acquisition unit 11 sends biological information including temperature information to the activity body temperature estimation unit 12, and sends fluctuating factor information including activity information to the activity body temperature estimation unit 12 and the physical condition diagnosis unit 13.

[0017] The activity body temperature estimation unit 12 obtains activity body temperature data, which shows the temperature fluctuations of parts of the target organism caused by the organism's activity, from biological information and information on factors causing fluctuations. As an example, the activity body temperature estimation unit 12 obtains activity body temperature data that shows the temperature fluctuations of the stomach during eating.

[0018] The physical condition diagnosis unit 13 has a waveform processing unit 21 that diagnoses the physical condition of the target organism by performing waveform processing on activity body temperature data.

[0019] The waveform processing unit 21 determines a health condition index based on at least one of the phase characteristics, amplitude, frequency spectrum, and waveform of the activity body temperature data.

[0020] The output device 53 receives health indicators from the health diagnosis unit 13. The output device 53 outputs at least one of the following: visual information and audio information indicating the received health indicators, and control signals to external devices corresponding to the health indicators.

[0021] Figure 2 shows the hardware configuration of the health diagnosis device 1 having the above configuration. The health diagnosis device 1 comprises a processor 61, a memory 62, and an interface 63. The processor 61, memory 62, and interface 63 are connected to each other by a bus 60. The functions of each part of the health diagnosis device 1 are realized by software, firmware, or a combination of software and firmware. The software and firmware are written as programs and stored in the memory 62. The functions of each part described above are realized by the processor 61 reading and executing the programs stored in the memory 62. In other words, the memory 62 stores programs for executing the processing of each part of the health diagnosis device 1.

[0022] Memory 62 includes, for example, non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read-Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable and Programmable Read-Only Memory), magnetic disks, flexible disks, optical disks, CDs (Compact Discs), MiniDiscs, DVDs (Digital Versatile Discs), etc.

[0023] The health diagnosis device 1 is connected to the temperature sensor 51, the activity sensor 52, and the output device 53 via interface 63. Interface 63 has one or more interface modules conforming to standards, depending on the connection destination.

[0024] When the health diagnosis device 1 having the above configuration is started, it repeats the health diagnosis process shown in Figure 3 at predetermined intervals. The information acquisition unit 11 of the health diagnosis device 1 acquires biological information including temperature information, which is the measurement result of the temperature sensor 51, and acquires fluctuation factor information including activity information, which includes activity indicators generated by the activity sensor 52 (step S11).

[0025] An example of temperature information included in the biological information acquired by the information acquisition unit 11 is shown in Graph A of Figure 4. In Graph A of Figure 4, the horizontal axis represents time, and the vertical axis represents axillary temperature (unit: degrees Celsius). An example of activity information included in the variable factor information acquired by the information acquisition unit 11 is shown in Graph B of Figure 4. In Graph B of Figure 4, the horizontal axis represents time, and the vertical axis represents the activity index generated by the activity sensor 52. The activity index is indicated by either an H (High) level or an L (Low) level. The activity index generated by the activity sensor 52 is at an H level when the organism being examined is located in a place where it would normally eat, and at an L level when the organism is not located in a place where it would normally eat. In this case, if the activity index acquired by the information acquisition unit 11 from the activity sensor 52 is at an H level, the organism can be considered to be eating, as shown in Graph B of Figure 4. If the activity index acquired by the information acquisition unit 11 from the activity sensor 52 is at an L level, the organism can be considered not to be eating.

[0026] The information acquisition unit 11 sends the above-mentioned biological information and fluctuation factor information to the activity body temperature estimation unit 12, and sends the fluctuation factor information to the physical condition diagnosis unit 13.

[0027] As shown in Figure 3, the activity body temperature estimation unit 12 obtains activity body temperature data showing temperature fluctuations at sites caused by the activity of the target organism from biological information and fluctuation factor information (step S12). The activity of the target organism includes the mental and physical activities of the target organism. The correspondence between the activity of the target organism and the sites whose temperature fluctuates in response to that activity is obtained from biological knowledge. For example, from biological knowledge, the activity of the target organism is associated with organs where heat production or heat dissipation occurs as a result of that activity.

[0028] In Embodiment 1, the activity body temperature estimation unit 12 determines the stomach temperature fluctuation of the target organism during the period when the activity information included in the fluctuation factor information indicates that the target organism is eating. The activity body temperature estimation unit 12 extracts fluctuation components from the temperature information during the period when the activity index shown in Figure 4 is at the H level and the target organism is eating, specifically, the period from time T0 to time T1 and the period from time T2 to time T3. During the period when the target organism is eating, the stomach temperature rises due to digestive activity. Therefore, the fluctuation components obtained from the temperature information during the period when the target organism is eating correspond to the stomach temperature fluctuation.

[0029] As described above, the activity body temperature estimation unit 12 extracts the temperature fluctuation component while the activity index is at the H level, thereby obtaining activity body temperature data showing the stomach temperature fluctuation when the target organism is eating, as shown in graph C of Figure 4. In graph C of Figure 4, the horizontal axis represents time, and the vertical axis represents the stomach temperature fluctuation (unit: degrees). The activity body temperature estimation unit 12 sends the obtained activity body temperature data to the physical condition diagnosis unit 13.

[0030] As shown in Figure 3, the health diagnosis unit 13 of the health diagnosis device 1 determines health indicators of the target organism based on the activity temperature data obtained from the activity temperature estimation unit 12 and the activity information obtained from the information acquisition unit 11 (step S13). The health diagnosis unit 13 diagnoses the health of the target organism by analyzing a single organ or multiple organs that contribute to a specific function. In detail, the health diagnosis unit 13 determines health indicators that indicate at least one of the following: whether the target organism is in good health, whether the target organism has a disease, and the health level of the target organism expressed numerically. In Embodiment 1, the health diagnosis unit 13 determines the health level of the stomach and the metabolism of the stomach as health indicators.

[0031] As shown in Figure 1, the waveform processing unit 21 of the physical condition diagnosis unit 13 acquires activity information from the information acquisition unit 11 and activity body temperature estimation unit 12. The waveform processing unit 21 performs an FFT (Fast Fourier Transform) on the activity body temperature data to generate frequency domain data and determines whether the frequency corresponding to the peak value of the frequency domain data is within the target range. The target range can be determined from biological knowledge. If the frequency corresponding to the peak value is within the target range, the digestive function and digestive organs can be considered healthy. If the frequency corresponding to the peak value is not within the target range, it can be considered that there is a possibility of disease in the digestive function and digestive organs.

[0032] In Embodiment 1, the physical condition diagnosis unit 13 determines the stomach health level from the ratio of the frequency corresponding to the peak value of the frequency domain data to the ideal value, which is the median of the target range. The stomach health level is highest when the frequency corresponding to the peak value of the frequency domain data matches the ideal value, and decreases as the deviation between the frequency corresponding to the peak value of the frequency domain data and the ideal value increases.

[0033] Organ metabolism is closely related to heat production, and heat production can be estimated from the temperature fluctuations of the organs. Therefore, the physical condition diagnosis unit 13 determines the metabolic fluctuations of the stomach from the temperature fluctuations of the stomach indicated by the activity body temperature data. The physical condition diagnosis unit 13 transmits the diagnosis results, which include the health of the stomach and physical condition indicators showing the metabolic fluctuations of the stomach, to the output device 53. When the processing in step S13 of Figure 3 is completed, the physical condition diagnosis device 1 terminates the physical condition diagnosis process. The physical condition diagnosis device 1 repeats the above process at predetermined intervals.

[0034] The output device 53 outputs at least one of the following: visual information and audio information indicating health indicators included in the received diagnostic results, and control signals to external devices corresponding to the health indicators. For example, as shown in Figure 5, the output device 53 is a display device having a screen 54 on which the diagnostic results are displayed. The display area 54a of the screen 54 displays the stomach health status included in the diagnostic results, and the display area 54b of the screen 54 displays the metabolic changes of the stomach included in the diagnostic results.

[0035] As described above, the health condition diagnostic device 1 according to Embodiment 1 obtains health indicators of the target organism from activity body temperature data, which shows temperature fluctuations of body parts caused by the activity of the target organism. Therefore, health diagnosis becomes possible by obtaining health indicators of the target organism from temperature fluctuations of body parts before an abnormality inside the target organism becomes the dominant factor in temperature fluctuations and causes large fluctuations in body temperature. Accordingly, the health condition diagnostic device 1 according to Embodiment 1 can accurately diagnose the health of the target organism.

[0036] (Embodiment 2) The method for determining health indicators is not limited to the examples described above; health indicators may also be determined from biological information including temperature information from multiple body parts, and from multiple types of activity information. The health diagnosis device 2 according to Embodiment 2 shown in Figure 6 determines health indicators indicating the health of the target organism from the measurement results of multiple temperature sensors 51a, 51b and activity indicators generated by multiple activity sensors 52a, 52b.

[0037] The physical condition diagnosis unit 13 of the physical condition diagnosis device 2 includes, in addition to the waveform processing unit 21, a matching processing unit 22 that determines physical condition indicators by performing pattern matching processing between activity body temperature data and predetermined pattern data, and a statistical processing unit 23 that determines other physical condition indicators of the target organism based on the history of activity body temperature data and at least one of the combinations of physical condition indicators determined by the waveform processing unit 21 and the matching processing unit 22.

[0038] The hardware configuration of the health condition diagnostic device 2 according to Embodiment 2 is the same as the hardware configuration of the health condition diagnostic device 1 according to Embodiment 1. However, the health condition diagnostic device 2 is connected to temperature sensors 51a, 51b, activity sensors 52a, 52b, and output device 53 via interface 63.

[0039] The information acquisition unit 11 of the physical condition diagnostic device 2 acquires the measurement results of temperature sensors 51a and 51b as temperature information. Temperature sensor 51a is a temperature sensor attached to the armpit of the target organism, similar to the temperature sensor 51 in Embodiment 1. Temperature sensor 51b is a temperature sensor attached to the surface of the calf of the target organism.

[0040] The information acquisition unit 11 acquires activity indicators generated by activity sensors 52a and 52b, and type information indicating the type of activity associated with activity sensors 52a and 52b, as activity information. Activity sensor 52a includes a camera that photographs the contents of the meal for health diagnosis and a device that estimates calorie intake from the photographed meal contents. Activity sensor 52a outputs the estimated calorie intake as an activity indicator, and outputs a signal as type information that is H level indicating that the organism being diagnosed for health diagnosis is eating if the estimated calorie intake is above the threshold intake, and L level indicating that the organism is not eating if the estimated calorie intake is below the threshold intake.

[0041] The activity sensor 52b is a speed sensor attached to a target organism to measure its speed. For example, the activity sensor 52b is a wearable device worn by an organism undergoing health checks, and measures the organism's speed. The activity sensor 52b outputs a signal as type information that is H level, indicating that the organism is walking, if the measured speed of the organism is within the target walking speed range where the organism can be considered to be walking, and L level, indicating that the organism is not walking, if the measured speed of the organism is outside the target walking speed range. The activity sensor 52b outputs the measured speed of the organism as an activity index.

[0042] When the health condition diagnostic device 2 having the above configuration is started, it repeats the health condition diagnostic process shown in Figure 3 at predetermined intervals, similar to Embodiment 1. The information acquisition unit 11 of the health condition diagnostic device 2 acquires biological information including temperature information, which is the measurement result of temperature sensors 51a and 51b, and acquires fluctuation factor information including type information and activity information including activity indicators generated by activity sensors 52a and 52b. The information acquisition unit 11 sends the above temperature information and activity information to the activity body temperature estimation unit 12 and the health condition diagnostic unit 13.

[0043] Examples of temperature information included in the biological information acquired by the information acquisition unit 11 are shown in Graphs A and B of Figure 7. Graph A shows the temperature information output by the temperature sensor 51a. The horizontal axis of Graph A represents time, and the vertical axis represents the axillary temperature (unit: degrees Celsius). Graph B shows the temperature information output by the temperature sensor 51b. The horizontal axis of Graph B represents time, and the vertical axis represents the surface temperature of the calf (unit: degrees Celsius).

[0044] Examples of activity information included in the variable factor information acquired by the information acquisition unit 11 are shown in Graphs C and D of Figure 7. Graph C shows the type information output by the activity sensor 52a. The horizontal axis of Graph C represents time, and the vertical axis represents the H / L level. Graph D shows the activity index output by the activity sensor 52a. The horizontal axis of Graph D represents time, and the vertical axis represents calorie intake (unit: kcal).

[0045] Examples of activity information included in the variable factor information acquired by the information acquisition unit 11 are shown in graphs E and F of Figure 7. Graph E shows the type information output by the activity sensor 52b. The horizontal axis of graph E represents time, and the vertical axis represents the H / L level. Graph F shows the activity index output by the activity sensor 52b. The horizontal axis of graph F represents time, and the vertical axis represents the speed of the target organism (unit: m / min).

[0046] The activity body temperature estimation unit 12 obtains activity body temperature data, which indicates temperature fluctuations at sites caused by the activity of the target organism, from biological information and fluctuation factor information. Specifically, the activity body temperature estimation unit 12 obtains activity body temperature data by the activity body temperature data calculation process shown in Figure 8. Specifically, the activity body temperature estimation unit 12 performs the activity body temperature data calculation process shown in Figure 8 each time it obtains temperature information and activity information from the information acquisition unit 11.

[0047] The activity body temperature estimation unit 12 performs an FFT on each of the temperature information acquired from the temperature sensors 51a and 51b to generate frequency domain data, and extracts time domain periodic fluctuation data by taking out the frequency components corresponding to the periodic fluctuations of the target organism and performing an IFFT (Inverse Fast Fourier Transform) on them (step S21).

[0048] For example, the activity body temperature estimation unit 12 extracts frequency components corresponding to periodic fluctuations such as circadian and ultradian from frequency domain data obtained by performing an FFT on the axillary temperature data indicated by the temperature information acquired from temperature sensor 51a and the calf surface temperature data indicated by the temperature information acquired from temperature sensor 51b. An example of activity body temperature data showing periodic fluctuations is shown in Graph A of Figure 9. The horizontal axis of Graph A represents time, and the vertical axis represents temperature fluctuation (unit: degrees).

[0049] For example, since the human circadian rhythm is approximately 24 hours, frequency components corresponding to the circadian rhythm can be obtained by extracting frequency components corresponding to a time-domain period of 23 hours or more and 25 hours or less from frequency domain data. Therefore, the activity body temperature estimation unit 12 can extract frequency components corresponding to the circadian rhythm by filtering the frequency domain data obtained by performing an FFT on the axillary temperature data indicated by the temperature information acquired from the temperature sensor 51a, for example, using an LPF (Low Pass Filter). The activity body temperature estimation unit 12 obtains time-domain periodic fluctuation data by performing an IFFT on the extracted frequency components.

[0050] As shown in Figure 8, the activity body temperature estimation unit 12 obtains temperature information from temperature sensors 51a and 51b with the periodic fluctuation component removed (step S22). Specifically, the activity body temperature estimation unit 12 filters the frequency domain data generated in step S21 using an HPF (High Pass Filter) to remove the periodic fluctuation component corresponding to circadian rhythms.

[0051] The activity body temperature estimation unit 12 calculates activity body temperature data from the data obtained in step S22, according to the activity information acquired from the activity sensors 52a and 52b (step S23).

[0052] As shown in Figure 10, the activity body temperature estimation unit 12 estimates the stomach temperature of the target organism based on the heat transfer pathways within the target organism, using the axillary temperature data from which periodic fluctuation components have been removed in step S22 and the calf surface temperature data from which periodic fluctuation components have been removed in step S22. Within the target organism's body, heat is transferred through heat production, such as heat generation from the digestive organs during eating and heat generation from muscles during exercise, and heat dissipation, such as sweating. Based on these heat transfer pathways within the body due to heat production and heat dissipation, the activity body temperature estimation unit 12 estimates the stomach temperature of the target organism.

[0053] In the example in Figure 10, heat is transferred from heat sources 91 and 92 to body parts 81 and 82. Heat source 91 is, for example, the stomach, which generates heat through activity during feeding of the organism. Heat source 92 is, for example, the calf muscle, which generates heat during exercise of the organism. Body parts 81 and 82 are parts of the organism's body and are the locations measured by temperature sensors 51a and 51b. Body part 81 is, for example, the armpit. Body part 82 is, for example, the surface of the calf.

[0054] In Figure 10, the amount of heat transferred is represented by the thickness of the arrows. The amount of heat transferred from heat source 91 to part 81 is greater than the amount of heat transferred from heat source 91 to part 82. The amount of heat transferred from heat source 92 to part 82 is greater than the amount of heat transferred from heat source 92 to part 81. In this case, the temperature change ΔT that occurs at part 81 due to heat production at heat sources 91 and 92 can be expressed as a1 * temperature difference Δt1 between part 81 and heat source 91 + a2 * temperature difference Δt2 between part 81 and heat source 92. a1 and a2 are predetermined coefficients, and since part 81 is more strongly influenced by heat source 91, a1 > a2 holds true. For example, a1 = 0.6 and a2 = 0.1. Based on the above formula, it is possible to estimate the temperatures of heat sources 91 and 92 from the temperatures measured at parts 81 and 82.

[0055] The activity body temperature estimation unit 12 estimates the stomach temperature of the target organism based on the heat transfer pathway described above, using the axillary temperature data from which the periodic fluctuation component has been removed in step S22 of Figure 8, and the calf surface temperature data from which the periodic fluctuation component has been removed in step S22. Based on the estimated stomach temperature, the activity body temperature estimation unit 12 obtains activity body temperature data that shows the stomach temperature fluctuations during the period when the type information generated by the activity sensor 52a is at the H level.

[0056] An example of activity body temperature data showing fluctuations in stomach temperature is shown in Graph B of Figure 9. In Graph B, the horizontal axis represents time, and the vertical axis represents temperature fluctuations (in degrees Celsius).

[0057] Furthermore, the activity body temperature estimation unit 12 uses the surface temperature data of the calf from which the periodic fluctuation component has been removed in step S22 to determine the temperature fluctuation of the calf muscle during the period in which the type information included in the activity information acquired from the activity sensor 52b is at the H level.

[0058] An example of activity-based body temperature data showing temperature fluctuations in the calf muscles is shown in Graph C of Figure 9. In Graph C, the horizontal axis represents time, and the vertical axis represents temperature fluctuations (in degrees Celsius).

[0059] As described above, the activity body temperature estimation unit 12 obtains activity body temperature data for each body part, sends the obtained activity body temperature data to the physical condition diagnosis unit 13, and terminates the activity body temperature data calculation process.

[0060] The waveform processing unit 21 of the physical condition diagnosis unit 13 determines the health of the stomach and the metabolic changes of the stomach, similar to the first embodiment. The waveform processing unit 21 acquires temperature information from the information acquisition unit 11 and outputs the temperature information, for example, the axillary temperature of the target organism, along with the diagnosis results including the health of the stomach and the metabolic changes of the stomach, to the statistical processing unit 23 and the output device 53.

[0061] The matching processing unit 22 diagnoses the physical condition of the target organism by performing pattern matching processing between activity body temperature data, which shows temperature fluctuations in body parts caused by the organism's movement, and predetermined pattern data. Specifically, the matching processing unit 22 performs pattern matching processing between activity body temperature data, which shows temperature fluctuations of the calf muscles obtained by the activity body temperature estimation unit 12, and predetermined pattern data showing temperature fluctuations during convulsions.

[0062] The matching processing unit 22 determines the degree of cramping as a physical condition index based on the degree of agreement between the activity body temperature data, which shows the temperature fluctuations of the calf muscles, and the predetermined pattern data. If the degree of agreement between the activity body temperature data, which shows the temperature fluctuations of the calf muscles, and the pattern data is high, the degree of cramping is high. If the degree of agreement between the activity body temperature data, which shows the temperature fluctuations of the calf muscles, and the pattern data is sufficiently low, the degree of cramping is low, and it can be considered that no cramping occurred. The matching processing unit 22 outputs the diagnostic results, including the physical condition index indicating the degree of cramping, to the statistical processing unit 23 and the output device 53.

[0063] The statistical processing unit 23 acquires activity body temperature data from the activity body temperature estimation unit 12 and obtains diagnostic results, including health indicators, from the waveform processing unit 21 and the matching processing unit 22. Based on the activity body temperature data history and at least one of the combinations of health indicators obtained by the waveform processing unit 21 and the matching processing unit 22, the statistical processing unit 23 determines other health indicators.

[0064] The statistical processing unit 23 generates a history of activity body temperature data by storing the activity body temperature data each time it is acquired from the activity body temperature estimation unit 12. By performing statistical processing on the history of activity body temperature data, the statistical processing unit 23 can determine whether there are problematic patterns, such as whether the stomach temperature rises significantly during digestion on a regular basis, and can obtain a health indicator that shows whether or not there is a chronic stomach disease.

[0065] The statistical processing unit 23 determines other health indicators, such as health indicators indicating the health status and condition of the target organism, based on the combination of health indicators obtained by the waveform processing unit 21 and the matching processing unit 22. The statistical processing unit 23 outputs the diagnostic results, including the determined health indicators, to the output device 53.

[0066] For example, the statistical processing unit 23 determines that the target organism is in good physical condition and healthy when the stomach health level determined by the waveform processing unit 21 is above the first threshold and the degree of spasms determined by the matching processing unit 22 is below the second threshold. The statistical processing unit 23 determines that the target organism is in poor physical condition and unhealthy when the stomach health level determined by the waveform processing unit 21 is below the first threshold, or the degree of spasms determined by the matching processing unit 22 is higher than the second threshold. The first threshold is, for example, the median of the range of possible values ​​for health level. The second threshold is, for example, the median of the range of possible values ​​for the degree of spasms.

[0067] Furthermore, the statistical processing unit 23 determines the health status of the target organism based on the physical condition indicators obtained by the waveform processing unit 21 and the matching processing unit 22. The health status is an index that reaches its maximum value when the stomach health status obtained by the waveform processing unit 21 is at the upper limit and the degree of convulsions obtained by the matching processing unit 22 is at the lower limit, and reaches its minimum value when the stomach health status obtained by the waveform processing unit 21 is at the lower limit and the degree of convulsions obtained by the matching processing unit 22 is at the upper limit.

[0068] The output device 53 outputs at least one of the following: visual information and audio information indicating the diagnostic results received from the physical condition diagnosis unit 13, and control signals to external devices corresponding to the diagnostic results. For example, as shown in Figure 11, the output device 53 is a display device having a screen 54 on which the diagnostic results are displayed. Display area 54a of the screen 54 displays the stomach health status included in the diagnostic results of the waveform processing unit 21, and display area 54b of the screen 54 displays the metabolic fluctuations of the stomach included in the diagnostic results of the waveform processing unit 21. Display area 54c of the screen 54 displays temperature information included in the diagnostic results of the waveform processing unit 21, specifically, the axillary temperature.

[0069] Display area 54d of screen 54 displays the degree of convulsions included in the diagnosis results of the matching processing unit 22. Display area 54e of screen 54 displays the judgment of whether the target organism included in the diagnosis results of the statistical processing unit 23 is healthy or not. Display area 54f of screen 54 displays the health status of the target organism included in the diagnosis results of the statistical processing unit 23.

[0070] As described above, the waveform processing unit 21 and matching processing unit 22 of the physical condition diagnosis unit 13 of the physical condition diagnosis device 2 according to Embodiment 2 each determine physical condition indicators. The statistical processing unit 23 of the physical condition diagnosis device 2 determines other physical condition indicators based on past active body temperature and at least one of the combinations of physical condition indicators determined by the waveform processing unit 21 and the matching processing unit 22. The physical condition diagnosis device 2 can diagnose the physical condition of the target organism by determining multiple physical condition indicators. For this reason, the physical condition diagnosis device 2 can accurately diagnose the physical condition of the target organism.

[0071] The statistical processing unit 23 can diagnose the physical condition based on past diagnostic results, thus preventing it from misdiagnosing the physical condition of the target organism based on temporary fluctuations in the measurement results of the temperature sensors 51a, 51b or the activity sensors 52a, 52b.

[0072] (Embodiment 3) The method for determining health indicators is not limited to the examples described above. Health indicators may also be determined by applying activity body temperature data, which is obtained based on fluctuating factor information including environmental information that indicates the state of the environment surrounding the target organism in addition to activity information, to a learning model. The health diagnosis device 3 according to Embodiment 3 shown in Figure 12 determines health indicators that indicate the health of the target organism from the measurement results of multiple temperature sensors 51a, 51b, activity indicators generated by multiple activity sensors 52a, 52b, 52c, and measurement results of the environmental sensor 55.

[0073] The physical condition diagnosis device 3, in addition to the configuration of the physical condition diagnosis device 2 according to Embodiment 2, further includes a learning unit 14 that obtains a physical condition model, which is a model for deriving physical condition indicators from activity body temperature data and fluctuation factor information. The physical condition diagnosis unit 13 includes a waveform processing unit 21, a matching processing unit 22, a statistical processing unit 23, and a model diagnosis unit 24 that obtains physical condition indicators of the target organism by applying the activity body temperature data and fluctuation factor information to the physical condition model obtained by the learning unit 14.

[0074] The hardware configuration of the health diagnosis device 3 according to Embodiment 3 is the same as the hardware configuration of the health diagnosis device 1 according to Embodiment 1. However, the health diagnosis device 3 is connected to temperature sensors 51a, 51b, activity sensors 52a, 52b, 52c, environmental sensor 55, and output device 53 via interface 63.

[0075] The information acquisition unit 11 of the physical condition diagnosis device 3 acquires the measurement results of the temperature sensors 51a and 51b as temperature information, similar to the second embodiment. The information acquisition unit 11 also acquires activity indicators generated by the activity sensors 52a and 52b, and type information indicating the type of activity associated with the activity sensors 52a and 52b, as activity information, similar to the second embodiment. The information acquisition unit 11 also acquires activity indicators indicating the measured amount of water consumed from the activity sensor 52c, which is attached to a bottle used by the organism being diagnosed when it drinks water and measures the amount of water consumed.

[0076] The information acquisition unit 11 acquires measurement results of physical quantities indicating the environmental conditions around the target organism from the environmental sensor 55 as environmental information. The environmental sensor 55 is a sensor that measures brightness, temperature, humidity, odor, gas concentration, soil moisture, atmospheric pressure, radiation dose, magnetism, weather, dust, infrared radiation, etc. In Embodiment 3, the environmental sensor 55 measures temperature and humidity.

[0077] In Embodiment 3, the temperature sensors 51a, 51b, activity sensors 52a, 52b, and environmental sensor 55 are implemented as a wearable device 56 that can be worn by the target organism. For example, the target organism wears a wearable device 56 such as a headset or glasses-type device equipped with the temperature sensors 51a, 51b, activity sensors 52a, 52b, and environmental sensor 55, and the information acquisition unit 11 acquires temperature information, activity information, and environmental information from the communication unit 57 of the wearable device 56. More specifically, the communication unit 57 sends the temperature information acquired from the temperature sensors 51a, 51b, the activity information acquired from the activity sensors 52a, 52b, and the environmental information acquired from the environmental sensor 55 to the information acquisition unit 11 of the health condition diagnosis device 3.

[0078] In addition to calculating activity body temperature data, the activity body temperature estimation unit 12 also estimates the temperature of any part of the target organism. For example, the activity body temperature estimation unit 12 estimates the brain temperature of the target organism based on temperature information, activity information, and environmental information, specifically, axillary temperature and calf surface temperature, calorie intake, movement speed and water intake, and air temperature and humidity. The activity body temperature estimation unit 12 sends the activity body temperature data and estimated data showing the estimated temperature to the health condition diagnosis unit 13.

[0079] The waveform processing unit 21 of the physical condition diagnosis unit 13 determines the health of the stomach and the metabolic changes of the stomach, similar to the first embodiment. The waveform processing unit 21 acquires temperature information from the information acquisition unit 11 and acquires active body temperature data and estimated data from the active body temperature estimation unit 12. The waveform processing unit 21 outputs the diagnostic results, including temperature information (e.g., axillary temperature information of the target organism), stomach health, metabolic changes of the stomach, and estimated data (e.g., estimated brain temperature), to the statistical processing unit 23 and the output device 53.

[0080] The matching processing unit 22 acquires temperature information from the information acquisition unit 11 and activity body temperature estimation unit 12. The matching processing unit 22 determines the degree of convulsions, similar to Embodiment 2. The matching processing unit 22 also determines the risk of dehydration by performing pattern matching processing on the data pattern corresponding to the temperature and humidity included in the environmental information and the axillary temperature pattern included in the temperature information. The matching processing unit 22 adjusts the risk of dehydration based on at least one of the exercise intensity estimated from the speed of the target organism indicated by the activity sensor 52b, and the amount of water the target organism drinks, obtained from the activity sensor 52c. For example, high-intensity exercise in a high-temperature environment increases the risk of dehydration, while drinking water in a low-temperature environment reduces the risk of dehydration.

[0081] Furthermore, the matching processing unit 22 determines the presence or absence of heatstroke symptoms by performing pattern matching on data patterns corresponding to temperature and humidity included in the environmental information and axillary temperature patterns included in the temperature information. The matching processing unit 22 determines the heatstroke risk from the dehydration risk, axillary temperature included in the temperature information, and the amount of water consumed by the target organism obtained from the activity sensor 52c. The matching processing unit 22 transmits the diagnostic results, including the water consumption data, dehydration risk, presence or absence of heatstroke symptoms, and physical condition indicators showing the heatstroke risk, to the statistical processing unit 23 and the output device 53.

[0082] The learning unit 14 acquires information on factors causing fluctuations from the information acquisition unit 11 and acquires activity body temperature data from the activity body temperature estimation unit 12. The learning unit 14 acquires physical condition indicators from the waveform processing unit 21 and the matching processing unit 22, respectively. The learning unit 14 learns the correspondence between the activity body temperature data, information on factors causing fluctuations, and physical condition indicators.

[0083] The learning unit 14 obtains a health condition model, which is a neural network model for deriving health indicators of the target organism from activity body temperature data and information on factors causing fluctuations. Specifically, the learning unit 14 learns first training data, which includes activity body temperature data, information on factors causing fluctuations, and health indicators obtained by the waveform processing unit 21. The learning unit 14 takes the activity body temperature data and information on factors causing fluctuations as input values ​​and the health indicators obtained by the waveform processing unit 21, specifically stomach health and stomach metabolic fluctuations, as output values, and generates a first health condition model, which is a neural network model having an input layer, a hidden layer, and an output layer. The first health condition model is implemented using, for example, an RNN (Recurrent Neural Network), an LSTM (Long Short-Term Memory), or a general-purpose artificial intelligence model. Based on the first training data, the learning unit 14 adjusts the weights between the input layer and the hidden layer, the weights between the hidden layers, and the weights between the hidden layer and the output layer of the first health condition model.

[0084] The learning unit 14 learns second learning data, which includes activity body temperature data, information on factors causing fluctuations, and physical condition indicators obtained by the matching processing unit 22. The learning unit 14 uses the activity body temperature data and information on factors causing fluctuations as input values, and the physical condition indicators obtained by the matching processing unit 22, specifically the degree of convulsions, the presence or absence of heatstroke symptoms, and the risk of heatstroke, as output values, and generates a second physical condition model, which is a neural network model having an input layer, a hidden layer, and an output layer. Based on the second learning data, the learning unit 14 adjusts the weights between the input layer and the hidden layer, the weights between the hidden layers, and the weights between the hidden layer and the output layer of the second physical condition model.

[0085] The learning unit 14 learns third learning data, which includes activity body temperature data, information on factors causing fluctuations, at least one of the physical condition indicators obtained by the waveform processing unit 21 and the matching processing unit 22, and physical condition indicators obtained by the statistical processing unit 23. The learning unit 14 takes the activity body temperature data, information on factors causing fluctuations, and at least one of the physical condition indicators obtained by the waveform processing unit 21 and the matching processing unit 22 as input values, and outputs the physical condition indicators obtained by the statistical processing unit 23, specifically, the state of physical condition and health level, and generates a third physical condition model, which is a neural network model having an input layer, a hidden layer, and an output layer. Based on the third learning data, the learning unit 14 adjusts the weights between the input layer and the hidden layer, the weights between the hidden layers, and the weights between the hidden layer and the output layer of the third physical condition model.

[0086] The learning unit 14 sends the health condition model, which includes at least one of the first health condition model, the second health condition model, and the third health condition model obtained, to the model diagnosis unit 24.

[0087] The model diagnostic unit 24 obtains health indicators for the target organism by applying the information on factors causing fluctuations obtained from the information acquisition unit 11 and the activity temperature data obtained from the activity temperature estimation unit 12 to the health condition model obtained from the learning unit 14. Specifically, the model diagnostic unit 24 obtains health indicators, specifically stomach health and gastric metabolic fluctuations, by applying the activity temperature data and the information on factors causing fluctuations to the first health condition model.

[0088] The model diagnostic unit 24 applies activity body temperature data and information on factors causing fluctuations to a second physical condition model to obtain physical condition indicators, specifically the degree of convulsions, the presence or absence of heatstroke symptoms, and the risk of heatstroke.

[0089] The model diagnostic unit 24 applies activity body temperature data, fluctuation factor information, and physical condition indicators obtained by the waveform processing unit 21 and matching processing unit 22 to a third physical condition model to obtain physical condition indicators, specifically, the level of physical condition and health status.

[0090] The output device 53 outputs at least one of the following: visual information and audio information indicating the diagnostic results received from the physical condition diagnosis unit 13, and control signals to external devices corresponding to the diagnostic results. For example, as shown in Figure 13, the output device 53 is a display device having a screen 54 on which the diagnostic results are displayed. Similar to Embodiment 1, the display area 54a of the screen 54 displays the stomach health status included in the diagnostic results of the waveform processing unit 21, and the display area 54b of the screen 54 displays the metabolic changes of the stomach included in the diagnostic results of the waveform processing unit 21.

[0091] Similar to Embodiment 2, the display area 54c of screen 54 displays temperature information included in the diagnosis results of the waveform processing unit 21, specifically, the axillary temperature. The display area 54d of screen 54 displays the degree of convulsions included in the diagnosis results of the matching processing unit 22. The display area 54e of screen 54 displays the judgment of whether the target organism is healthy or not, included in the diagnosis results of the statistical processing unit 23. The display area 54f of screen 54 displays the health status of the target organism, included in the diagnosis results of the statistical processing unit 23.

[0092] Display area 54g of screen 54 displays whether or not there are symptoms of heatstroke included in the diagnosis results of the matching processing unit 22. Display area 54h of screen 54 displays the heatstroke risk included in the diagnosis results of the matching processing unit 22. Display area 54i of screen 54 displays the amount of water consumed included in the diagnosis results of the matching processing unit 22. Display area 54j of screen 54 displays the estimated brain temperature data included in the diagnosis results of the waveform processing unit 21.

[0093] As described above, the model diagnostic unit 24 of the physical condition diagnostic device 3 according to Embodiment 3 obtains other physical condition indicators by applying activity body temperature data and fluctuation factor information, or activity body temperature data, fluctuation factor information, and physical condition indicators, to a physical condition model which is a neural network model. This makes it possible to diagnose the physical condition of the target organism with high accuracy.

[0094] This disclosure is not limited to the embodiments described above. The embodiments can be combined in any way. For example, the physical condition diagnostic device 1 according to Embodiment 1 and the physical condition diagnostic device 2 according to Embodiment 2 may include a learning unit 14, similar to Embodiment 3.

[0095] The health diagnosis device may acquire a health model from an external device. The health diagnosis system 101 shown in Figure 14 includes temperature sensors 51a, 51b, activity sensors 52a, 52b, 52c, an environmental sensor 55, a health diagnosis device 4, an output device 53, and a model determination device 31 for determining a health model used for health diagnosis. The configuration of the health diagnosis device 4 is the same as that of the health diagnosis device 3, with the learning unit 14 removed. The model determination device 31 has a health model determination unit 32 that performs the same processing as the learning unit 14 in the health diagnosis device 3 according to Embodiment 3.

[0096] The physical condition model determination unit 32 acquires fluctuation factor information from the information acquisition unit 11, acquires activity body temperature data from the activity body temperature estimation unit 12, and acquires physical condition indicators from the waveform processing unit 21, matching processing unit 22, and statistical processing unit 23 of the physical condition diagnosis unit 13. Similar to the learning unit 14 of the physical condition diagnosis device 3 according to Embodiment 3, the physical condition model determination unit 32 learns the correspondence between activity body temperature data, fluctuation factor information, and physical condition indicators that indicate the physical condition of the target organism, and obtains a physical condition model for deriving physical condition indicators from the activity body temperature data and fluctuation factor information. The physical condition model determination unit 32 sends the obtained physical condition model to the model diagnosis unit 24 of the physical condition diagnosis unit 13.

[0097] The model diagnostic unit 24 obtains a health condition model from the health condition model determination unit 32. Similar to Embodiment 3, the model diagnostic unit 24 obtains a health condition index for the target organism by applying the fluctuating factor information obtained from the information acquisition unit 11 and the activity body temperature data obtained from the activity body temperature estimation unit 12 to the health condition model obtained from the health condition model determination unit 32.

[0098] The model determination device 31 may, in addition to the physical condition model, determine a temperature model for deriving activity body temperature data from biological information and fluctuating factor information. The physical condition diagnosis system 101 shown in Figure 15 comprises temperature sensors 51a, 51b, activity sensors 52a, 52b, 52c, an environmental sensor 55, a physical condition diagnosis device 5, an output device 53, and a model determination device 31 for determining a physical condition model used for physical condition diagnosis and a temperature model for deriving activity body temperature data. The configuration of the physical condition diagnosis device 5 is the same as that of the physical condition diagnosis device 3 with the learning unit 14 removed.

[0099] The temperature model determination unit 33 acquires biological information and fluctuation factor information from the information acquisition unit 11 and activity body temperature data from the activity body temperature estimation unit 12. The temperature model determination unit 33 learns the correspondence between biological information, fluctuation factor information, and activity body temperature data. The temperature model determination unit 33 finds a temperature model, which is a neural network model that takes biological information and fluctuation factor information as inputs and outputs activity body temperature data. Specifically, the temperature model determination unit 33 learns temperature learning data consisting of biological information, fluctuation factor information, and activity body temperature data, and generates a temperature model, which is a neural network model having an input layer, a hidden layer, and an output layer, with biological information and fluctuation factor information as input values ​​and activity body temperature data as output values. Based on the temperature learning data, the temperature model determination unit 33 adjusts the weights between the input layer and the hidden layer, the weights between the hidden layers, and the weights between the hidden layer and the output layer of the temperature model. The temperature model determination unit 33 sends the obtained temperature model to the activity body temperature estimation unit 12.

[0100] The activity body temperature estimation unit 12 obtains a temperature model from the temperature model determination unit 33. The activity body temperature estimation unit 12 obtains activity body temperature data by applying the biological information and fluctuation factor information obtained from the information acquisition unit 11 to the temperature model obtained from the temperature model determination unit 33. The activity body temperature estimation unit 12 sends the obtained activity body temperature data to the physical condition diagnosis unit 13.

[0101] The learning unit 14 of the physical condition diagnostic device 3 may, similar to the temperature model determination unit 33, determine a temperature model and obtain activity body temperature data based on the determined temperature model. In this case, the activity body temperature estimation unit 12 obtains activity body temperature data by applying the biological information and fluctuation factor information obtained from the information acquisition unit 11 to the temperature model obtained from the learning unit 14.

[0102] The temperature model may also be a neural network model that takes biological information, information on variable factors, and environmental information as input and outputs active body temperature data.

[0103] The target organisms are any organism, including humans, livestock, and pets. The activities of the target organisms are not limited to those that promote heat production, such as eating and exercise, but also include those that promote body temperature reduction, such as sleeping and meditation, as long as they cause temperature fluctuations in the target organism's body parts.

[0104] The information acquisition unit 11 may acquire biological information from the data input to the physical condition diagnosis device 1-5. For example, the information acquisition unit 11 may acquire information on the blood glucose level of the target organism as biological information. Fluctuations in blood glucose levels occurring in cycles shorter than 8 hours can be considered to be fluctuations caused by meals and digestion. In this case, the physical condition diagnosis unit 13 can determine, for example, whether or not there is a problem with nutrient absorption capacity from fluctuations in blood glucose levels and fluctuations in active body temperature corresponding to stomach temperature.

[0105] The information acquisition unit 11 may acquire activity information from the data input to the physical condition diagnosis device 1-5. For example, the information acquisition unit 11 may acquire type information indicating the activity status from the target organism's planned behavior.

[0106] The information acquisition unit 11 may acquire measurement results from temperature sensors located near arteries and temperature sensors located near veins. Temperature changes occurring upstream of blood flow appear as a phase difference between the measurement result from the temperature sensor located near arteries and the measurement result from the temperature sensor located near veins. At this time, the activity body temperature estimation unit 12 may obtain activity body temperature data from this phase difference. More specifically, the activity body temperature estimation unit 12 can obtain temperature information that is free from the influence of blood flow by removing the component of activity body temperature data obtained from the phase difference from the temperature information.

[0107] The information acquisition unit 11 may acquire an index indicating exercise intensity as activity information. If the target organism is healthy, the amplitude of the activity body temperature data will increase as the exercise intensity increases, and the amplitude of the activity body temperature data will decrease as the exercise intensity decreases. In this case, the physical condition diagnosis unit 13 may determine the physical condition index of the target organism, for example, its health level, based on whether or not the exercise intensity and the amplitude of the activity body temperature data are correlated. More specifically, the physical condition diagnosis unit 13 may determine the health level of the target organism based on the phase difference, frequency spectrum shift, amplitude divergence, etc., between the waveform data of the index indicating exercise intensity and the waveform data of the activity body temperature data.

[0108] The method by which the activity body temperature estimation unit 12 obtains activity body temperature data is not limited to the example described above. For example, the activity body temperature estimation unit 12 may compare the axillary temperature with a waveform pattern determined according to temperature fluctuations during meals, and extract the axillary temperature waveform that matches the waveform pattern as activity body temperature data.

[0109] The activity body temperature estimation unit 12 may extract frequency components corresponding to the period of activity from the frequency domain data, and perform an IFFT on the extracted frequency components to obtain time domain activity body temperature data.

[0110] The activity body temperature estimation unit 12 may remove the periodic fluctuation component by subtracting a reference waveform corresponding to the periodic fluctuation component from the waveform data indicated by the temperature information. As another example, the activity body temperature estimation unit 12 may obtain the periodic fluctuation component corresponding to the circadian from the axillary temperature using the cosine method, which determines the parameters of an approximate cosine wave using the least squares method.

[0111] The activity body temperature estimation unit 12 may calculate activity body temperature data from the variation from a reference value, such as the most recent measurement or the average value of measurements over a recent period, specifically, the variation in axillary temperature from the reference value.

[0112] The heat transfer pathway shown in Figure 10 is just one example, and the active body temperature estimation unit 12 may estimate the active body temperature based on factors such as the fact that the influence of heat exchange with the outside is dominant in parts of the target organism's body close to the surface, the influence of heat exchange with adjacent organs is dominant, and the degree to which the upstream side closer to the heart is influenced changes depending on the diameter of the blood vessels.

[0113] The physical condition diagnosis unit 13 is not limited to the examples described above. For example, the physical condition diagnosis unit 13 may acquire multiple activity body temperature data sets in which at least one of the target body part and fluctuation factors is different, and diagnose the physical condition of the target organism in the time domain from the correlation of the waveform data of the multiple activity body temperature data sets.

[0114] The physical condition diagnosis unit 13 may output a diagnosis result that includes at least one of the following: biological information, fluctuating factor information including activity information and environmental information, and activity body temperature data, and a physical condition index. For example, if the physical condition index indicating liver function obtained by the waveform processing unit 21 indicates a decrease in liver function, the physical condition diagnosis unit 13 may output a diagnosis result that includes the physical condition index indicating liver function, activity body temperature data of organs such as the liver and bladder, and estimated data indicating the temperature of organs such as the liver and bladder.

[0115] The waveform processing unit 21 may diagnose the physical condition of the target organism based on frequency domain data obtained by performing an IFFT on the activity body temperature data, based on whether the frequency component with the peak value is within the reference range, whether a peak value exists, whether the amplitude of the defined frequency component is within the target range, etc.

[0116] The waveform processing unit 21 may diagnose that an abnormality has occurred in the organ corresponding to the activity if the amplitude of the waveform data of the activity body temperature data is not within the target amplitude range based on the identification information corresponding to the activity body temperature data.

[0117] The waveform processing unit 21 can diagnose the health condition of the target organism based on activity temperature data for a fixed period from 0:00 to 24:00, a period corresponding to activity information, or a period from one rising edge to the next rising edge of the activity temperature waveform data, or any other arbitrarily determined period.

[0118] The waveform processing unit 21 may acquire multiple types of active body temperature data and determine health indicators of the target organism based on at least one of the phase, amplitude, frequency spectrum, and waveform of the waveform data for each active body temperature data. In this case, the waveform processing unit 21 may output a diagnostic result including multiple health indicators to the statistical processing unit 23, and the statistical processing unit 23 may determine other health indicators of the target organism based on the multiple health indicators determined by the waveform processing unit 21.

[0119] As an example, the waveform processing unit 21 may determine a health indicator of the target organism based on the frequency spectra of the activity body temperature data related to eating and the activity body temperature data related to excretion. In this case, the statistical processing unit 23 can determine a health indicator from the health indicator related to eating and the health indicator related to excretion that shows the health of organs related to both eating and excretion, and organs related to only one of them.

[0120] The matching processing unit 22 may determine a physical condition index from the activity body temperature data and fluctuation factor information. For example, the matching processing unit 22 may determine the degree of the cramp by performing pattern matching processing between the activity body temperature data showing temperature fluctuations of the calf muscles obtained by the activity body temperature estimation unit 12 and the defined pattern data showing temperature fluctuations during a cramp, and may also determine the cause of the cramp from the fluctuation factor information. For example, if the fluctuation factor information indicates that no exercise has been performed in the recent period, the cause of the cramp can be considered to be stiffness due to lack of exercise. As another example, if the fluctuation factor information indicates that exercise has been performed in the recent period, the cause of the cramp can be considered to be dehydration. The matching processing unit 22 outputs the diagnostic results, including the physical condition index indicating the degree of the cramp and the cause of the cramp, to the output device 53.

[0121] The statistical processing unit 23 may estimate the disease the target organism is suffering from by combining health indicators related to each organ, based on the combination of ailments appearing in each organ.

[0122] The statistical processing unit 23 may combine activity body temperature data and information on factors causing fluctuations to determine a health condition index. For example, if the activity body temperature data for a recent period shows an upward trend, and the information on factors causing fluctuations indicates that bowel movements have not occurred during that recent period, the statistical processing unit 23 may determine a health condition index indicating constipation.

[0123] The method for generating a health condition model by the learning unit 14 is not limited to the example described above. The learning unit 14 may learn by associating activity body temperature information, activity information, health indicators obtained by the matching processing unit 22, and health indicators obtained by the waveform processing unit 21, and obtain a first health condition model that takes activity body temperature information, activity information, and health indicators obtained by the matching processing unit 22 as input and outputs health indicators obtained by the waveform processing unit 21.

[0124] The learning unit 14 may generate a health condition model and a temperature model by performing supervised learning based on multidimensional function fitting. Supervised learning means that by providing a large amount of training data, which is a dataset of inputs and results, to the learning device, the learning device learns the features in the large dataset and generates a model that estimates the result from the input.

[0125] Similarly, the physical condition model determination unit 32 and the temperature model determination unit 33 may generate a physical condition model and a temperature model, respectively, by performing supervised learning based on multidimensional function fitting.

[0126] The diagnostic results sent from the health diagnosis unit 13 to the output device 53 may include, in addition to the health indicators obtained by each part of the health diagnosis unit 13, at least one of biological information and variable factor information, or activity body temperature data. The variable factor information may include both activity information and environmental information, or only one of them. The activity information included in the variable factor information may include both type information and activity indicators, or only one of them.

[0127] The statistical processing unit 23 stores health indicators each time they are obtained from the waveform processing unit 21 and the matching processing unit 22, and may determine other health indicators for the target organism from previously obtained health indicators. For example, the statistical processing unit 23 can determine that there is a possibility of PTSD if the degree of convulsions obtained by the matching processing unit 22 is high throughout the year regardless of the season, and the frequency of high convulsion severity is above a threshold frequency. As another example, the statistical processing unit 23 can determine that there is a possibility of cold sensitivity rather than PTSD if the timing of high convulsion severity obtained by the matching processing unit 22 is concentrated in winter.

[0128] The configuration of the health condition diagnosis unit 13 is not limited to the example described above, and is arbitrary as long as it can determine the health condition indicators of the target organism. For example, the health condition diagnosis unit 13 may have only a matching processing unit 22. As another example, the health condition diagnosis unit 13 may have both a matching processing unit 22 and a model diagnosis unit 24.

[0129] The output device 53 may output control signals to external devices according to the diagnostic results. For example, when the target organism is livestock, the output device 53 outputs control signals according to the diagnostic results to external devices that regulate the environment of the livestock barn, such as air conditioners, blowers, and feeding devices.

[0130] As another example, when the target organism is a human being exercising using exercise equipment, the output device 53 outputs a control signal to the exercise equipment corresponding to the physical condition indicators included in the diagnostic results. For example, if the diagnostic results indicate that the health level, which is one example of a physical condition indicator, is declining, the output device 53 transmits a control signal to the exercise equipment to reduce the exercise load.

[0131] As another example, when the target organism is a human, the output device 53 outputs a control signal corresponding to the diagnosis result to the ordering device that orders pharmaceuticals, supplements, etc. For example, if the diagnosis result indicates a possible disease, the health diagnosis unit 13 sends a control signal to the ordering device instructing it to order pharmaceuticals, supplements, etc. corresponding to the disease.

[0132] The implementation examples of the health condition diagnostic systems 100 and 101 are not limited to those described above; any implementation that can diagnose the health condition of the target organism is acceptable. For example, part or all of the health condition diagnostic device 3 shown in Figure 12 may be mounted on a wearable device 56 together with temperature sensors 51a and 51b, activity sensors 52a and 52b, and an environmental sensor 55. Similarly, part or all of the health condition diagnostic devices 1, 2, 4, and 5 may also be mounted on a wearable device 56.

[0133] The wearable device 56 may send and receive information with the health diagnosis device 3. More specifically, the wearable device 56 may acquire health indicators from the health diagnosis unit 13. The communication unit 57 of the wearable device 56 shown in Figure 16 acquires health indicators from the health diagnosis unit 13 of the health diagnosis device 3. The wearable device 56 has an equipment control unit 58 that controls the exercise equipment used by the target organism according to the health indicators received by the communication unit 57.

[0134] In detail, the communication unit 57 receives health indicators from at least one of the waveform processing unit 21, matching processing unit 22, statistical processing unit 23, and model diagnostic unit 24 of the health diagnosis unit 13. The communication unit 57 sends the received health indicators to the equipment control unit 58. The equipment control unit 58 controls the exercise equipment for the target organism exercising using the exercise equipment based on the exercise load corresponding to the health indicators obtained by the health diagnosis unit 13 of the health diagnosis device 3 and received by the communication unit 57. For example, if the health level, which is one example of a health indicator received by the communication unit 57, decreases, the equipment control unit 58 sends a control signal to the exercise equipment to reduce the exercise load.

[0135] As another example, the wearable device 56 shown in Figure 17 has an output unit 59 that outputs information about exercise load corresponding to physical condition indicators received by the communication unit 57 via screen display, audio, etc. For example, the output unit 59 suggests a lower exercise load when health deteriorates and a higher exercise load when health improves. Exercise load can be, for example, running pace, gradient, weight of exercise equipment, etc.

[0136] As another example, the wearable device 56 shown in Figures 16 and 17 may acquire diagnostic results from the output device 53. Alternatively, the output device 53 may be implemented on the wearable device 56.

[0137] The hardware configuration and flowchart described above are examples and can be changed and modified as needed. The central part that performs control processing, which has a processor 61, memory 62, and interface 63, can be implemented using a normal computer system, not a dedicated system. For example, the health diagnosis device 1-5 that performs the above processing may be configured by distributing a computer-readable recording medium (flexible disk, CD-ROM (Compact Disc-Read Only Memory), DVD-ROM (Digital Versatile Disc-Read Only Memory), etc.) containing a computer program for performing the above operations, and installing the computer program on a computer. Alternatively, the health diagnosis device 1-5 may be configured by storing the computer program on a storage device of a server device on a communication network and downloading it from a normal computer system.

[0138] If the functions of the health diagnosis device 1-5 are realized through a division of labor between the OS (Operating System) and the application program, or through collaboration between the OS and the application program, then only the application program portion may be stored on the recording medium or storage device.

[0139] It is also possible to superimpose a computer program onto a carrier wave and distribute it via a communication network. For example, the computer program could be posted on a bulletin board system (BBS) on a communication network and distributed via the network. Then, the aforementioned processing could be performed by launching this computer program and executing it under the control of the OS, just like any other application program.

[0140] The hardware configuration of the health diagnosis device 1-5 is not limited to the example described above. The health diagnosis device 1-5 may be implemented with a processing circuit 64, as shown in Figure 18. The processing circuit 64 is connected to the temperature sensor 51, activity sensor 52, and output device 53, etc., via an interface circuit 65. If the processing circuit 64 is dedicated hardware, it may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof. Each part of the health diagnosis device 1-5 may be implemented with an individual processing circuit 64, or each part of the health diagnosis device 1-5 may be implemented with a common processing circuit 64.

[0141] Some of the functions of the physical condition diagnostic device 1-5 may be implemented by dedicated hardware, while other parts may be implemented by software or firmware. For example, the information acquisition unit 11 may be implemented by the processing circuit 64 shown in Figure 18, and the activity body temperature estimation unit 12 and the physical condition diagnostic unit 13 may be implemented by the processor 61 shown in Figure 2 reading and executing a program stored in the memory 62. The various aspects of this disclosure are summarized below as an appendix. (Note 1) An information acquisition unit that acquires biological information including temperature information of at least one part of the target organism subject to health diagnosis, and fluctuation factor information including activity information indicating the activity of the target organism that causes the temperature of the said part to fluctuate, An activity body temperature estimation unit obtains activity body temperature data indicating temperature fluctuations in organs within the target organism caused by the activity, based on the aforementioned biological information, the fluctuation factor information, and the heat transfer pathways within the target organism determined according to the heat production and heat dissipation within the target organism's body; A health condition diagnosis unit that determines health indicators showing the health condition of the target organism based on the activity body temperature data, A health diagnostic device equipped with the following features. (Note 2) The information acquisition unit acquires the activity information, including an activity index that shows the activity of the target organism in numerical terms. The health diagnosis device described in Appendix 1. (Note 3) The information acquisition unit acquires the activity information, which includes type information indicating the type of activity of the target organism. A health condition diagnostic device as described in Appendix 1 or 2. (Note 4) The information acquisition unit acquires the information on factors causing fluctuations, which includes the activity information and environmental information indicating the environmental conditions around the target organism. A health diagnostic device as described in any of the appendices 1 to 3. (Note 5) The activity body temperature estimation unit obtains a temperature model for deriving the activity body temperature data from the biological information and the fluctuation factor information, and applies the biological information and fluctuation factor information obtained by the information acquisition unit to the temperature model to obtain the activity body temperature data that shows the temperature fluctuation of the body part caused by the activity indicated by the activity information included in the fluctuation factor information. A health diagnostic device as described in any of the appendices 1 to 4. (Note 6) The activity body temperature estimation unit obtains a plurality of activity body temperature data from the biological information and the fluctuation factor information, wherein at least one of the corresponding body part and activity is different from each other. A health diagnostic device as described in any of the appendices 1 to 5. (Note 7) The health condition diagnosis unit determines the health condition index based on a plurality of activity body temperature data. The health assessment device described in Appendix 6. (Note 8) The physical condition diagnosis unit obtains the physical condition index, which indicates at least one of the following: whether the target organism is in good physical condition, whether the target organism has any diseases, and the health level of the target organism, which is expressed numerically. A health diagnostic device as described in any of the notes 1 to 7. (Note 9) The health condition diagnosis unit determines the health condition index based on at least one of the biological information and the variable factor information, as well as the activity body temperature data. A health diagnostic device as described in any of the appendices 1 to 8. (Note 10) The physical condition diagnosis unit has a waveform processing unit that determines the physical condition index based on at least one of the phase characteristics, amplitude, frequency spectrum, and waveform of the waveform data of the activity body temperature data. A health diagnostic device as described in any of the notes 1 to 9. (Note 11) The physical condition diagnosis unit includes a matching processing unit that determines the physical condition index by performing pattern matching between a pattern determined according to the physical condition of the target organism and the activity body temperature data. A health condition diagnostic device as described in any of the appendices 1 to 10. (Note 12) The physical condition diagnosis unit has a matching processing unit that determines the physical condition index by performing pattern matching between a pattern determined according to the physical condition of the target organism and the activity body temperature data. The physical condition diagnosis unit includes a statistical processing unit that determines other physical condition indicators based on the history of the activity body temperature data and at least one of the combinations of physical condition indicators obtained by the waveform processing unit and the matching processing unit. The health assessment device described in Appendix 10. (Note 13) The health diagnosis unit outputs a health diagnosis result for the target organism, which includes at least one of the biological information, the variable factor information, and the activity body temperature data, and the health index. A health condition diagnostic device as described in any of the appendices 1 to 12. (Note 14) The physical condition diagnosis unit further comprises a model diagnosis unit which obtains a physical condition model for deriving the physical condition index from the activity body temperature data and the fluctuation factor information, and applies the activity body temperature data obtained by the activity body temperature estimation unit and the fluctuation factor information obtained by the information acquisition unit to the physical condition model to determine the physical condition index of the target organism. A health condition diagnostic device as described in any of the appendices 1 to 13. (Note 15) A model derived from the correspondence between the activity body temperature data, the information on factors causing fluctuations, and the health condition indicator, further comprising a learning unit for determining a health condition model for deriving the health condition indicator from the activity body temperature data and the information on factors causing fluctuations. A health diagnostic device as described in any of the appendices 1 through 14. (Note 16) A wearable device that can be worn by an organism subject to health assessment, and which transmits and receives information with a health assessment device described in any of the appendices 1 to 15, A temperature sensor that measures the temperature of at least one part of the target organism, An activity sensor that generates an activity index that numerically indicates the activity of the target organism that causes the temperature of the aforementioned part to fluctuate, A communication unit transmits the temperature sensor's measurement value and the activity index generated by the activity sensor to the health diagnosis device, and receives the health index obtained by the health diagnosis unit of the health diagnosis device from the health diagnosis device. With respect to the target organism exercising using exercise equipment, the device control unit controls the exercise equipment based on the exercise load corresponding to the physical condition indicators obtained by the physical condition diagnosis unit of the physical condition diagnosis device and received by the communication unit, A wearable device equipped with [features / equipment]. (Note 17) A wearable device that can be worn by an organism subject to health assessment, and which transmits and receives information with a health assessment device described in any of the appendices 1 to 15, A temperature sensor that measures the temperature of at least one part of the target organism, An activity sensor that generates an activity index that numerically indicates the activity of the target organism that causes the temperature of the aforementioned part to fluctuate, A communication unit transmits the temperature sensor's measurement value and the activity index generated by the activity sensor to the health diagnosis device, and receives the health index obtained by the health diagnosis unit of the health diagnosis device from the health diagnosis device. The device includes an output unit that outputs information about exercise load corresponding to the physical condition indicators obtained by the physical condition diagnosis unit and received by the communication unit for the target organism that is exercising, A wearable device equipped with [features / equipment]. (Note 18) A temperature sensor that measures the temperature of at least one part of the organism being examined, A health condition diagnostic device as described in any of Appendix 1 to 15, The information acquisition unit of the physical condition diagnostic device acquires the measurement result of the temperature sensor as temperature information. A health checkup system. (Note 19) The system further comprises an activity sensor that generates an activity index that numerically indicates the activity of the target organism that causes the temperature of the aforementioned part to fluctuate. The information acquisition unit of the physical condition diagnostic device acquires activity information including type information indicating the type of activity associated with the activity sensor and the activity index generated by the activity sensor. The health assessment system described in Appendix 18. (Note 20) The system further includes an environmental sensor that measures physical quantities indicating the environmental conditions surrounding the target organism. The information acquisition unit of the physical condition diagnostic device acquires the information on factors causing fluctuations, including the measurement results of the environmental sensor. The health assessment system described in Appendix 18 or 19. (Note 21) The health diagnosis device further includes an output device that acquires the health indicators determined by the health diagnosis unit, and outputs at least one of visual information and audio information indicating the acquired health indicators, as well as control signals to external devices corresponding to the health indicators. A health checkup system as described in any of the appendices 18 to 20. (Note 22) A health condition diagnostic device described in any of Appendix 1 to 15, A wearable device as described in Appendix 16 or 17, comprising: The information acquisition unit of the physical condition diagnostic device acquires the measurement result of the temperature sensor of the wearable device as temperature information, and acquires activity information including type information indicating the type of activity associated with the activity sensor of the wearable device and the activity index generated by the activity sensor. A health checkup system. (Note 23) A temperature model determination unit learns the correspondence between biological information including temperature information of at least one part of a target organism subject to health diagnosis, fluctuation factor information including activity information indicating the activity of the target organism that causes the temperature to fluctuate at the said part, and activity body temperature data indicating the temperature fluctuation of organs in the target organism's body caused by the said activity, and obtains a temperature model for deriving the activity body temperature data from the biological information and the fluctuation factor information. A model determination device equipped with the following features. (Note 24) The system further includes a health model determination unit that learns the correspondence between the activity body temperature data, the information on factors causing fluctuations, and health indicators that show the health condition of the target organism, and obtains a health model for deriving the health indicators from the activity body temperature data and the information on factors causing fluctuations. The model determination device described in Appendix 23. (Note 25) The system acquires biological information including temperature information for at least one part of the target organism subject to health diagnosis, and information on factors causing fluctuations, including activity information indicating the activity of the target organism that causes fluctuations in the temperature of the said part. Based on the aforementioned biological information, the information on factors causing fluctuations, and the heat transfer pathways within the target organism determined according to the heat production and heat dissipation within the target organism, activity body temperature data showing temperature fluctuations in the organs within the target organism caused by the activity is obtained. Based on the aforementioned activity body temperature data, a health indicator showing the health condition of the target organism is determined. Methods for diagnosing one's health condition. (Note 26) Computers, An information acquisition unit that acquires biological information including temperature information of at least one part of a target organism subject to health diagnosis, and information on factors causing fluctuations including activity information indicating the activity of the target organism that causes fluctuations in the temperature of said part. An activity body temperature estimation unit obtains activity body temperature data indicating temperature fluctuations in organs within the target organism caused by the activity, based on the aforementioned biological information, the fluctuation factor information, and the heat transfer pathway within the target organism determined according to the heat production and heat dissipation within the target organism's body, and A health diagnosis unit that determines health indicators showing the health condition of the target organism based on the activity body temperature data. A program that makes it function as such.

[0142] This disclosure allows for various embodiments and modifications without departing from the broad spirit and scope of this disclosure. Furthermore, the embodiments described above are for illustrative purposes only and do not limit the scope of this disclosure. In other words, the scope of this disclosure is indicated by the claims, not by the embodiments. Various modifications made within the scope of the claims and the equivalent significance of the disclosure are considered to be within the scope of this disclosure. [Explanation of Symbols]

[0143] 1,2,3,4,5 Health condition diagnosis device, 11 Information acquisition unit, 12 Activity body temperature estimation unit, 13 Health condition diagnosis unit, 14 Learning unit, 21 Waveform processing unit, 22 Matching processing unit, 23 Statistical processing unit, 24 Model diagnosis unit, 31 Model determination device, 32 Health condition model determination unit, 33 Temperature model determination unit, 51,51a,51b Temperature sensor, 52,52a,52b,52c Activity sensor, 53 Output device, 54 Screen, 54a,54b,54c,54d,54e,54f,54g,54h,54i,54j Display area, 55 Environmental sensor, 56 Wearable device, 57 Communication unit, 58 Equipment control unit, 59 Output unit, 60 Bus, 61 Processor, 62 Memory, 63 Interface, 64 Processing circuit, 65 Interface circuit, 81,82 Body part, 91, 92 Heat source, 100, 101 Health condition diagnosis system.

Claims

1. An information acquisition unit that acquires biological information including temperature information of at least one part of the target organism subject to health diagnosis, and fluctuation factor information including activity information indicating the activity of the target organism that causes the temperature of the said part to fluctuate, An activity body temperature estimation unit obtains activity body temperature data indicating temperature fluctuations in organs within the target organism caused by the activity, based on the aforementioned biological information, the fluctuation factor information, and the heat transfer pathways within the target organism determined according to the heat production and heat dissipation within the target organism's body; A health condition diagnosis unit that determines health indicators showing the health condition of the target organism based on the activity body temperature data, A health diagnostic device equipped with the following features.

2. The information acquisition unit acquires the activity information, including an activity index that shows the activity of the target organism in numerical terms. A health condition diagnostic device according to claim 1.

3. The information acquisition unit acquires the activity information, which includes type information indicating the type of activity of the target organism. A health condition diagnostic device according to claim 1 or 2.

4. The information acquisition unit acquires the information on factors causing fluctuations, which includes the activity information and environmental information indicating the environmental conditions around the target organism. A health condition diagnostic device according to claim 1 or 2.

5. The activity body temperature estimation unit obtains a temperature model for deriving the activity body temperature data from the biological information and the fluctuation factor information, and applies the biological information and fluctuation factor information obtained by the information acquisition unit to the temperature model to obtain the activity body temperature data that shows the temperature fluctuation of the body part caused by the activity indicated by the activity information included in the fluctuation factor information. A health condition diagnostic device according to claim 1 or 2.

6. The activity body temperature estimation unit obtains a plurality of activity body temperature data from the biological information and the fluctuation factor information, wherein at least one of the corresponding body part and activity is different from each other. A health condition diagnostic device according to claim 1 or 2.

7. The health condition diagnosis unit determines the health condition index based on a plurality of activity body temperature data. The health condition diagnostic device according to claim 6.

8. The physical condition diagnosis unit obtains the physical condition index, which indicates at least one of the following: whether the target organism is in good physical condition, whether the target organism has any diseases, and the health level of the target organism, which is expressed numerically. A health condition diagnostic device according to claim 1 or 2.

9. The health condition diagnosis unit determines the health condition index based on at least one of the biological information and the variable factor information, as well as the activity body temperature data. A health condition diagnostic device according to claim 1 or 2.

10. The physical condition diagnosis unit has a waveform processing unit that determines the physical condition index based on at least one of the phase characteristics, amplitude, frequency spectrum, and waveform of the waveform data of the activity body temperature data. A health condition diagnostic device according to claim 1 or 2.

11. The physical condition diagnosis unit includes a matching processing unit that determines the physical condition index by performing pattern matching between a pattern determined according to the physical condition of the target organism and the activity body temperature data. A health condition diagnostic device according to claim 1 or 2.

12. The physical condition diagnosis unit has a matching processing unit that determines the physical condition index by performing pattern matching between a pattern determined according to the physical condition of the target organism and the activity body temperature data. The physical condition diagnosis unit includes a statistical processing unit that determines other physical condition indicators based on the history of the activity body temperature data and at least one of the combinations of physical condition indicators obtained by the waveform processing unit and the matching processing unit. The health condition diagnostic device according to claim 10.

13. The health condition diagnosis unit outputs a health condition diagnosis result for the target organism, which includes at least one of the biological information, the fluctuating factor information, and the activity body temperature data, and the health condition index. A health condition diagnostic device according to claim 1 or 2.

14. The physical condition diagnosis unit further comprises a model diagnosis unit which obtains a physical condition model for deriving the physical condition index from the activity body temperature data and the fluctuation factor information, and applies the activity body temperature data obtained by the activity body temperature estimation unit and the fluctuation factor information obtained by the information acquisition unit to the physical condition model to determine the physical condition index of the target organism. A health condition diagnostic device according to claim 1 or 2.

15. A model derived from the correspondence between the activity body temperature data, the information on factors causing fluctuations, and the health condition indicator, further comprising a learning unit for determining a health condition model for deriving the health condition indicator from the activity body temperature data and the information on factors causing fluctuations. A health condition diagnostic device according to claim 1 or 2.

16. A wearable device that can be worn by a target organism subject to health diagnosis, and which transmits and receives information with the health diagnosis device described in claim 1, A temperature sensor that measures the temperature of at least one part of the target organism, An activity sensor that generates an activity index that numerically indicates the activity of the target organism that causes the temperature of the aforementioned part to fluctuate, A communication unit transmits the temperature sensor's measurement value and the activity index generated by the activity sensor to the health diagnosis device, and receives the health index obtained by the health diagnosis unit of the health diagnosis device from the health diagnosis device. With respect to the target organism exercising using exercise equipment, the device control unit controls the exercise equipment based on the exercise load corresponding to the physical condition indicators obtained by the physical condition diagnosis unit of the physical condition diagnosis device and received by the communication unit, A wearable device equipped with [a specific feature / ability].

17. A wearable device that can be worn by a target organism subject to health diagnosis, and which transmits and receives information with the health diagnosis device described in claim 1 or 2, A temperature sensor that measures the temperature of at least one part of the target organism, An activity sensor that generates an activity index that numerically indicates the activity of the target organism that causes the temperature of the aforementioned part to fluctuate, A communication unit transmits the temperature sensor's measurement value and the activity index generated by the activity sensor to the health diagnosis device, and receives the health index obtained by the health diagnosis unit of the health diagnosis device from the health diagnosis device. The device includes an output unit that outputs information about exercise load corresponding to the physical condition indicators obtained by the physical condition diagnosis unit and received by the communication unit for the target organism that is exercising, A wearable device equipped with [a specific feature / ability].

18. A temperature sensor that measures the temperature of at least one part of the organism being examined, A physical condition diagnostic device according to claim 1 or 2, comprising: The information acquisition unit of the physical condition diagnostic device acquires the measurement result of the temperature sensor as temperature information. A health checkup system.

19. The system further comprises an activity sensor that generates an activity index that numerically indicates the activity of the target organism that causes the temperature of the aforementioned part to fluctuate. The information acquisition unit of the physical condition diagnostic device acquires activity information including type information indicating the type of activity associated with the activity sensor and the activity index generated by the activity sensor. The health condition diagnosis system according to claim 18.

20. The system further includes an environmental sensor that measures physical quantities indicating the environmental conditions surrounding the target organism. The information acquisition unit of the physical condition diagnostic device acquires the information on factors causing fluctuations, including the measurement results of the environmental sensor. The health condition diagnosis system according to claim 18.

21. The health diagnosis device further includes an output device that acquires the health indicators determined by the health diagnosis unit, and outputs at least one of visual information and audio information indicating the acquired health indicators, as well as control signals to external devices corresponding to the health indicators. The health condition diagnosis system according to claim 18.

22. A health condition diagnostic device according to claim 1, A wearable device according to claim 16, The information acquisition unit of the physical condition diagnostic device acquires the measurement result of the temperature sensor of the wearable device as temperature information, and acquires activity information including type information indicating the type of activity associated with the activity sensor of the wearable device and the activity index generated by the activity sensor. A health checkup system.

23. A temperature model determination unit learns the correspondence between biological information including temperature information of at least one part of a target organism subject to health diagnosis, fluctuation factor information including activity information indicating the activity of the target organism that causes the temperature of the said part to fluctuate, and activity body temperature data indicating the temperature fluctuation of organs in the target organism's body caused by the said activity, and determines a temperature model for deriving the activity body temperature data from the biological information and the fluctuation factor information. A model determination device equipped with the following features.

24. The system further includes a health model determination unit that learns the correspondence between the activity body temperature data, the information on factors causing fluctuations, and health indicators that show the health condition of the target organism, and obtains a health model for deriving the health indicators from the activity body temperature data and the information on factors causing fluctuations. The model determination device according to claim 23.

25. Computers, An information acquisition unit that acquires biological information including temperature information of at least one part of a target organism subject to health diagnosis, and information on fluctuation factors including activity information indicating the activity of the target organism that causes the temperature of the said part to fluctuate. An activity body temperature estimation unit obtains activity body temperature data indicating temperature fluctuations in organs within the target organism caused by the activity, based on the aforementioned biological information, the fluctuation factor information, and the heat transfer pathway within the target organism determined according to the heat production and heat dissipation within the target organism's body, and A health diagnosis unit that determines health indicators showing the health condition of the target organism based on the activity body temperature data. A program that makes it function as such.

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