Information processing device, information processing method, and information processing program

Abstract

To obtain an information processing device, information processing method, and information processing program capable of supporting the improvement of physical function.SOLUTION: An information processing device 10 acquires biological information measured about a subject and a load amount applied to a body of the subject by exercise that affects the biological information performed within a predetermined period up to a present time, predicts prediction biological information, which is biological information at predetermined prediction timing after the present time, on the basis of the biological information and the load amount, and creates a plan related to exercise necessary to match the biological information that may be measured at the prediction timing with the prediction biological information.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] BACKGROUND ART Conventionally, techniques for supporting rehabilitation performed to improve decline in physical function due to trauma, aging, brain dysfunction, and the like are known.

[0003] For example, Patent Document 1 discloses analyzing changes in a person's physical function based on time-series changes in the person's physical condition and suggesting improvements to the physical function. Patent Document 2, for example, discloses determining a user's condition based on sensor data including the user's biometric information, predicting whether the user will undergo rehabilitation in the future, and presenting rehabilitation support information. Patent Document 3, for example, discloses comparing transitions in bioindicator values ​​indicating a subject's condition and transitions in treatment procedures performed by the subject with past cases, and predicting when a target bioindicator value will be reached. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. WO2019 / 187099 [Patent Document 2] International Publication No. WO2020 / 234957 [Patent Document 3] Japanese Patent Publication No. 2020-187550 Summary of the Invention [Problem to be solved by the invention]

[0005] In rehabilitation for improving physical function, multiple exercise trainings with different types and intensities may be combined, but the degree of effectiveness of each type of exercise training is thought to vary depending on the state of the subject's physical function. For example, in rehabilitation for running ability, starting with low-intensity walking training in the initial stage and then performing higher-intensity jogging and sprinting training as physical function improves may more effectively improve running ability. Also, for example, performing high-intensity training in the early stage of rehabilitation may impose too much strain on the body, which may actually worsen physical function.

[0006] Therefore, for example, in the early stages of rehabilitation, it may be more effective to focus on the number of steps rather than the walking speed while in the later stages, it may be more effective to focus on the walking speed rather than the number of steps. Thus, there is a need for a technology that can support the improvement of physical function by planning the content of exercise training in rehabilitation taking into account the effects of various types of exercise training that vary depending on the state of the subject's physical function.

[0007] The present disclosure provides an information processing device, an information processing system, an information processing method, and an information processing program that can support improvement of physical functions. [Means for solving the problem]

[0008] A first aspect of the present disclosure is an information processing device comprising at least one processor, which acquires biometric information measured on a subject and the amount of stress placed on the subject's body by exercises that affect the biometric information performed within a predetermined period up to the present time, predicts predicted biometric information, which is biometric information at a predetermined predicted timing after the present time, based on the biometric information and the amount of stress, and creates a plan for exercises required to match the biometric information that can be measured at the predicted timing with the predicted biometric information.

[0009] In the above aspect, the processor may derive a required load, which is the amount of load placed on the subject's body that is necessary to match the biometric information that can be measured at the predicted timing with the predicted biometric information, and create an exercise plan that satisfies the required load.

[0010] In the above aspect, the processor may derive the required load using a pre-trained model that takes biological information and predicted biological information as input and has the required load as output.

[0011] In the above aspect, the processor may accept a specification of the prediction timing.

[0012] In the above aspect, the processor may accept the specification of a fluctuation target for the biometric information, and based on the acquired biometric information and load amount, predict the expiration time of the period required for the biometric information to achieve the fluctuation target, and use the expiration time as the prediction timing.

[0013] In the above aspect, the processor may create a plan that specifies a schedule of exercises to be performed in order to match the biological information that can be measured at the predicted timing with the predicted biological information.

[0014] In the above aspect, the processor may receive specification of at least one of the dates and times when the subject can exercise and the dates and times when the subject cannot exercise, and create a plan in accordance with the specified dates and times.

[0015] In the above aspect, the processor may create a plan that determines the type of exercise to be performed from among a plurality of types of exercise each having a different load.

[0016] In the above aspect, the processor may acquire physical information indicating the physical level of the subject, and create a plan that specifies performing a predetermined type of exercise in accordance with the physical information.

[0017] In the above aspect, the processor may acquire multiple pieces of motion information indicating each of multiple types of motion amounts measured in accordance with the subject's motion, acquire weights derived in advance corresponding to the types of motion information, and derive the load amount based on the multiple pieces of motion information and the weights.

[0018] In the above aspect, the exercise information may indicate at least one of the number of steps, walking speed, electromyogram, and flexion angle and flexion speed of the subject's joint.

[0019] In the above aspect, the physical information may indicate a level of improvement when improving the physical function of the subject.

[0020] A second aspect of the present disclosure is an information processing method that includes a process of acquiring biometric information measured on a subject and the amount of stress placed on the subject's body due to exercise that affects the biometric information performed within a predetermined period up to the present time, predicting predicted biometric information, which is biometric information at a predetermined predicted timing after the present time, based on the biometric information and the amount of stress, and creating a plan for exercise required to match the biometric information that can be measured at the predicted timing with the predicted biometric information.

[0021] A third aspect of the present disclosure is an information processing program that causes a computer to execute a process of acquiring biometric information measured on a subject and the amount of stress placed on the subject's body due to exercise that affects the biometric information performed within a predetermined period up to the present time, predicting predicted biometric information, which is biometric information at a predetermined predicted timing after the present time, based on the biometric information and the amount of stress, and creating a plan for exercise required to match the biometric information that can be measured at the predicted timing with the predicted biometric information. [Effects of the Invention]

[0022] According to the above aspects, the information processing device, information processing method, and information processing program of the present disclosure can support improvement of physical functions. [Brief explanation of the drawings]

[0023] [Figure 1] FIG. 1 is a schematic configuration diagram of an information processing system. [Figure 2] FIG. 2 is a block diagram illustrating an example of a hardware configuration of an information processing device. [Figure 3] FIG. 2 is a block diagram illustrating an example of a functional configuration of an information processing device. [Figure 4] FIG. 10 is a diagram for explaining a method for deriving weights. [Figure 5] FIG. 10 is a diagram for explaining a method for deriving a load amount. [Figure 6] FIG. 10 is a correlation diagram showing an example of the correlation between biological information and a total load. [Figure 7] FIG. 10 is a diagram for explaining a method for predicting biological information. [Figure 8] FIG. 10 is a diagram illustrating an example of a screen displayed on a display. [Figure 9] 10 is a flowchart illustrating an example of a weight derivation process. [Figure 10] 10 is a flowchart illustrating an example of a load derivation process. [Figure 11] 10 is a flowchart illustrating an example of a prediction process. [Figure 12] 10 is a flowchart illustrating an example of a planning process. DETAILED DESCRIPTION OF THE INVENTION

[0024] Hereinafter, examples of embodiments for carrying out the technology of the present disclosure will be described in detail with reference to the drawings.

[0025] An example of the configuration of an information processing system 1 according to this embodiment will be described with reference to Fig. 1. As shown in Fig. 1, the information processing system 1 includes an information processing device 10, at least one exercise information measurement device 12, at least one biological information measurement device 14, and a learning information DB (DataBase) 16. The information processing device 10 and the exercise information measurement device 12, the information processing device 10 and the biological information measurement device 14, and the information processing device 10 and the learning information DB 16 are capable of communicating with each other via wired or wireless communication.

[0026] The exercise information measurement device 12 has a function of measuring exercise information indicating the amount of exercise measured in response to the exercise of the subject. Here, the exercise information indicates the amount of exercise measured over time, and may be, for example, information indicating at least one of the number of steps, walking speed, electromyogram, and the flexion angle and flexion speed of the subject's joints. In these cases, the exercise information measurement device 12 may be, for example, a wearable device such as a smartwatch equipped with a sensor capable of detecting exercise, such as a pedometer, an electromyogram, and an acceleration sensor. Furthermore, a combination of these devices may also be used.

[0027] The biological information measuring device 14 has a function of measuring biological information of the subject. For example, the biological information may be information indicating at least one of body temperature, heart rate, electrocardiogram, electromyogram, blood pressure, arterial oxygen saturation (SpO2), blood glucose level, and lipid level. In these cases, the biological information measuring device 14 may be, for example, a thermometer, a heart rate monitor, a blood glucose self-monitoring device, or a wearable device such as a smart watch equipped with a sensor that measures biological information such as heart rate and arterial oxygen saturation.

[0028] Furthermore, for example, the biological information may be information indicating at least one of a medical image captured by a medical imaging device and a feature amount analyzable from the medical image. Examples of medical imaging devices include X-ray imaging, CT (Computed Tomography), MRI (Magnetic Resonance Imaging), ultrasound imaging, endoscopic imaging, fundus photography, and PET (Positron Emission Tomography). By using these medical imaging devices as the biological information measuring device 14, medical images can be obtained as biological information. The feature amount is, for example, information indicating an abnormal shadow such as a lesion and the volume, major axis, minor axis, pixel value, etc. of a structure to be diagnosed.

[0029] Furthermore, for example, the biological information may be information indicating the results of at least one of a hematological test, an infectious disease test, a biochemical test, an immunological test, a genetic test, a bacterial test, and a urinalysis. A hematological test is a test that obtains, for example, white blood cell count, red blood cell count, and hemoglobin concentration as test results. A biochemical test is a test that obtains, for example, various indicators related to enzymes, proteins, sugars, lipids, and electrolytes as test results. An infectious disease test is a test that obtains, for example, the presence or absence of various infectious diseases such as influenza infection and COVID-19 infection as test results.

[0030] An immunological test is a test that obtains, as test results, the detection results of substances specific to, for example, tumor markers, hormones, and allergies. A genetic test is a test that obtains, as test results, genetic information related to constitutions and diseases, for example, by analyzing DNA (Deoxyribonucleic Acid). A bacterial test is a test that obtains, as test results, the type and amount of bacteria present inside and on the body. A urinalysis is a test that obtains, as test results, for example, urinary sugar, urinary protein, and urinary occult blood. When these various test results are used as biological information, the biological information measuring device 14 can be, for example, a known analyzer that analyzes blood, urine, and the like as specimens.

[0031] The learning information DB 16 is a database that stores learning exercise information and learning biological information. The learning information DB 16 is realized by a storage medium such as a hard disk drive (HDD), a solid state drive (SSD), or a flash memory.

[0032] The learning motion information and learning biological information are the same types of information as the above-mentioned motion information and biological information, respectively, and are performance data measured over time in rehabilitation conducted in the past. That is, the learning motion information and learning biological information are information measured over time from the start of rehabilitation (e.g., immediately after treatment) to the end (e.g., when recovery is complete) of rehabilitation. Note that the learning motion information and learning biological information may be information measured from a subject other than the subject currently being processed.

[0033] In rehabilitation for improving physical function, multiple exercise trainings with different types and intensities may be combined, but the degree of effectiveness of each type of exercise training is thought to vary depending on the state of the subject's physical function. For example, in rehabilitation for running ability, starting with low-intensity walking training in the initial stage and then performing higher-intensity jogging and sprinting training as physical function improves may more effectively improve running ability. Furthermore, for example, performing high-intensity training in the initial stage of rehabilitation may impose too much strain on the subject, which may actually worsen physical function.

[0034] Therefore, for example, in the early stages of rehabilitation, it may be more effective to focus on the number of steps rather than the walking speed while in the later stages, it may be more effective to focus on the walking speed rather than the number of steps. Thus, there is a need for a technology that can support the improvement of physical function by planning the content of exercise training in rehabilitation taking into account the effects of various types of exercise training that vary depending on the state of the subject's physical function.

[0035] Therefore, the information processing device 10 according to this embodiment plans the contents of exercise training in rehabilitation taking into consideration the state of the subject's physical functions. In addition, during this process, the amount of load applied to the subject's body is calculated taking into consideration the state of the subject's physical functions. The detailed configuration of the information processing device 10 will be described below.

[0036] First, an example of the hardware configuration of an information processing device 10 according to this embodiment will be described with reference to FIG. 2. As shown in FIG. 2, the information processing device 10 includes a CPU (Central Processing Unit) 21, a non-volatile storage unit 22, and a memory 23 serving as a temporary storage area. The information processing device 10 also includes a display 24 such as a liquid crystal display, an input unit 25 such as a keyboard, a mouse, and buttons, and a network I / F (Interface) 26 for wired or wireless communication with the exercise information measurement device 12, the biological information measurement device 14, and an external network (not shown). The CPU 21, the storage unit 22, the memory 23, the display 24, the input unit 25, and the network I / F 26 are connected via a bus 28 such as a system bus and a control bus so that various information can be exchanged between them. Examples of the information processing device 10 include a personal computer, a server computer, a tablet terminal, a smartphone, and a wearable terminal.

[0037] The storage unit 22 is realized by a storage medium such as a hard disk drive (HDD), a solid state drive (SSD), or a flash memory. The storage unit 22 stores an information processing program 27 for the information processing device 10. The CPU 21 reads the information processing program 27 from the storage unit 22, loads it into the memory 23, and executes the loaded information processing program 27. The CPU 21 is an example of a processor of the present disclosure.

[0038] Next, an example of the functional configuration of the information processing device 10 according to this embodiment will be described with reference to Fig. 3. As shown in Fig. 4, the information processing device 10 includes an acquisition unit 30, a derivation unit 32, a prediction unit 34, a planning unit 36, and a control unit 38. The CPU 21 executes the information processing program 27, thereby functioning as the acquisition unit 30, the derivation unit 32, the prediction unit 34, the planning unit 36, and the control unit 38.

[0039] In the following explanation, we assume that the subject is a patient with knee osteoarthritis and is undergoing rehabilitation after treatment (so-called regenerative medicine) in which mesenchymal stem cells are transplanted into the knee cartilage defect to regenerate the cartilage. Furthermore, we use information indicating three types of exercise quantity: number of steps, walking speed, and knee flexion angle, as multiple pieces of exercise information, and information indicating the volume of the knee cartilage defect analyzed from MRI images as biometric information.

[0040] [Weight derivation] First, with reference to FIG. 4, a method for deriving weights for deriving the amount of load imposed on the subject's body by exercise will be described. Weights are derived for each type of training exercise information based on the training exercise information. In FIG. 4, the number of steps Xa1, walking speed Xa2, and bending angle Xa3 are each indicated by a solid line. Weights W1, W2, and W3 for the number of steps Xa1, walking speed Xa2, and bending angle Xa3 are each indicated by a dashed-dotted line. The partial load Ya1 for the number of steps Xa1 is indicated by a long dashed line, the partial load Ya2 for the walking speed Xa2 is indicated by a short dashed line, and the partial load Ya3 for the bending angle Xa3 is indicated by a dotted line. The load La is indicated by a thick solid line.

[0041] The acquiring unit 30 acquires multiple pieces of learning exercise information measured over time from the learning information DB 16. Specifically, the acquiring unit 30 acquires learning exercise information previously measured from subjects who have a disease similar to that of a subject for whom the content of exercise training in this rehabilitation is being planned.

[0042] It is preferable that the acquisition unit 30 acquires representative learning exercise information generated based on multiple pieces of learning exercise information previously measured from multiple subjects who have the same disease as the subject for whom the content of the current exercise training is being planned. For example, it is preferable that the acquisition unit 30 acquires learning exercise information for each of the multiple subjects stored in the learning information DB 16, and generates representative values ​​such as the average and median as learning exercise information to be used in subsequent processing.

[0043] The derivation unit 32 calculates a weight Wi(t a ) is derived by the derivation unit 32. a A specific method for deriving () will be explained using the following calculation formulas (1) to (4). Yai(t a )=Wi(t a )×Xai(t a ) × Ci …(1) La(t a )=ΣYai(t a ) …(2) ∫La(t a )dt a =K …(3) dLa(t a ) / dt a =M …(4) Note that i is an integer of 2 or more, and in the following description, i is set to 1 to 3. K and M are positive constants.

[0044] Yai(t a ) is the measurement time t a Learning motion information Xai(t a ) represents the amount of partial load applied to the subject's body depending on the amount of exercise indicated by Ci. Ci is a conversion coefficient for converting various pieces of learning exercise information Xai, such as the number of steps, walking speed, and bending angle, into partial load Yai (i.e., to make the units consistent), and is predetermined for each type of learning exercise information and stored in advance in, for example, storage unit 22.

[0045] Wi(t a) is the learning motion information Xai(t a ) at the measurement time t a As mentioned above, in rehabilitation, the degree of effect of exercise training with different types and intensities is considered to vary depending on the state of the subject's physical function. For example, at the measurement time t in the early stage of rehabilitation, a 1 and the measurement time point t a Even if the same exercise training is performed at the measurement time t a 1 and t a It is considered that the partial load applied to the subject's body may differ in each of the two. In other words, Xa1(t a 1) and Xa1(t a 2) are consistent, Ya1(t a 1) and Ya1(t a 2) do not necessarily coincide. Therefore, in equation (1), the weight Wi(t a ) over time, the partial load Yai(t a ) can also be varied over time.

[0046] La(t a ) is the measurement time t a Partial load Yai(t a ) at the measurement time t a represents the overall load on the subject's body due to the amount of exercise indicated by the various pieces of exercise information for learning Xa1 to Xa3 in the above. a ) is set to satisfy equations (3) and (4). Equation (3) is a ) satisfies the predetermined constant K. Equation (4) means that the time integral of La(t a ) satisfies a predetermined constant M, that is, La(t a ) is represented by a straight line with a slope of M. By satisfying equations (3) and (4), the total load from the start to the end of rehabilitation can be secured, while the load can be increased as rehabilitation progresses.

[0047] The derivation unit 32 calculates La(t a ) satisfies equations (3) and (4), various partial loadings Yai(t a ) is determined. The derivation unit 32 then determines the partial load amount Yai(t a ), learning exercise information Xai(t a ) and Ci, the weight Wi(t a ) is derived.

[0048] In Figure 4, various partial loadings Yai(t a ) is illustrated as a monotonically increasing straight line, but is not limited to this. For example, a ) may decrease in part or in whole over time, or may be a curve.

[0049] The derivation unit 32 may also use machine learning to derive the weights. For example, the input may be various types of training motion information Xai(t a ) and the output is the various partial loads Yai(t a ) is pre-trained to obtain the partial load Yai(t a ) may be derived. For example, the partial load Yai(t a ) is the input, and the output is the weight Wi(t a ) may be used.

[0050] [Derivation of loading amount] Next, with reference to FIG. 5, a method for deriving the amount of load imposed on the subject's body due to exercise will be described. The amount of load is derived based on exercise information and weights. In FIG. 5, the number of steps Xr1, walking speed Xr2, and bending angle Xr3 are each indicated by solid lines. Weights W1, W2, and W3 for the number of steps Xr1, walking speed Xr2, and bending angle Xr3 are each indicated by dashed-dotted lines. The partial load Yr1 for the number of steps Xr1 is indicated by a long dashed line, the partial load Yr2 for the walking speed Xr2 by a short dashed line, and the partial load Yr3 for the bending angle Xr3 by a dotted line. The load Lr is indicated by a thin solid line, and the reference load La (see FIG. 4) is indicated by a thick solid line.

[0051] The acquisition unit 30 acquires from the exercise information measurement device 12 a plurality of pieces of exercise information Xri(t r For example, the acquisition unit 30 acquires the number of steps Xr1, walking speed Xr2, and bending angle Xr3 during a period P from the start of rehabilitation to the present time.

[0052] The acquisition unit 30 also uses a weight Wi(t r Specifically, the acquisition unit 30 acquires the measurement time t r The measurement time t of the learning motion information corresponding to a The weights Wi(t a ) as weights Wi(t r ) and the weight Wi(t a ) is the training motion information Xai(t a ) at the measurement time t a Therefore, the weight Wi(t r ) also at the measurement time t r It fluctuates over time depending on the

[0053] The acquisition unit 30 acquires the motion information at the measurement time t r The weight Wi(t r) is obtained. For example, the weights Wi(t r ) may be omitted.

[0054] The derivation unit 32 calculates the motion information Xri(t r ) and weights Wi(t r ) and the load Lr(t r ) is derived. r ) is expressed by the following calculation formulas (5) and (6). Yri(t r )=Wi(t r )×Xri(t r ) × Ci …(5) Lr(t r )=ΣYri(t r ) …(6) Note that i is an integer of 2 or more, and in the following description, i ranges from 1 to 3.

[0055] Yri(t r ) is the measurement time t r represents the partial load applied to the subject's body by the amount of exercise indicated by the exercise information Xri(t) in the time domain. Ci is a conversion coefficient similar to that in the above-mentioned equation (1). The derivation unit 32 calculates the partial load by converting the multiple pieces of exercise information Xri(t) and weights Wi(t r ) based on equation (5), Yri(t r ) is derived.

[0056] Lr(t r ) is the measurement time t r Partial load Yri(t r ) at the measurement time t r Various motion information Xri(t r The derivation unit 32 calculates Yri(t r ) based on equation (6), Lr(t r ) is derived. In this way, the loading Lr(t r) is the weight that varies over time, Wi(t r ) is taken into account and is derived to reflect the state of the subject's physical function.

[0057] In reality, various types of motion information are measured individually, and the measurement time t r Therefore, the derivation unit 32 may derive various types of motion information measured within a predetermined period (for example, seven days) at the same measurement time t r That is, the derivation unit 32 may consider the plurality of pieces of exercise information Xri(t r ) based on the loading Lr(t r ) may be derived.

[0058] [Prediction of biological information at the predicted timing] Next, a method for predicting biological information at a predicted timing (hereinafter referred to as "predicted biological information") will be described with reference to FIGS.

[0059] The acquisition unit 30 acquires biological information measured on the subject from the biological information measurement device 14. The acquisition unit 30 also acquires the amount of stress placed on the subject's body by exercises that have been performed within a predetermined period up to the present time and that affect the biological information. For example, the acquisition unit 30 uses the derivation unit 32 to derive exercise information Xri(t r ) and weights Wi(t r ) and the loading Lr(t r ) to get the

[0060] By the way, the biological information is the total load from the start to the end of rehabilitation (i.e., the load Lr(t r ) time integral ∫Lr(t r )dt r) improves or worsens depending on the type of biometric information. Fig. 6 is a correlation diagram showing an example of the correlation between the biometric information and the total load. The correlation between the biometric information and the total load is predetermined for each type of biometric information and is stored in advance in the storage unit 22, for example.

[0061] As an example of biological information, in the case of knee cartilage defect volume, as shown in Figure 6, it is known that excessive or insufficient total load increases (worsens) the defect volume, whereas appropriate total load decreases (improves). In cartilage regeneration using mesenchymal stem cell transplantation, mesenchymal stem cells are transplanted into the cartilage defect. The transplanted mesenchymal stem cells differentiate to produce chondroblasts, which then produce chondrocytes, which then produce cartilage (specifically, a collagen and proteoglycan matrix). This process repairs the cartilage defect. To promote the differentiation and proliferation of these cells and promote cartilage defect repair, rehabilitation is necessary. It is particularly important to perform rehabilitation with the amount of exercise appropriately controlled according to the state of repair. Insufficient rehabilitation does not promote sufficient repair, while excessive rehabilitation can destroy and reduce the number of cells in the repair process.

[0062] On the other hand, there may be a time lag before the effects of exercise training in rehabilitation are reflected in biological information. Therefore, the prediction unit 34 predicts predicted biological information at a predetermined prediction timing after the current time based on the biological information and the load acquired by the acquisition unit 30. Figure 7 shows the actual measured value Zr(t r ) and the load Lr(t r ) and the predicted value Zrp(t r ) and predicted load Lrp(t r ) and FIG.

[0063] Specifically, the prediction unit 34 may search for past cases similar to the current biological information and total load using the learning information DB 16, and may predict the predicted biological information by reusing the learning biological information from the past cases. Alternatively, the prediction unit 34 may predict the predicted biological information using a trained model such as CNN that is trained in advance to use the biological information and load as input and the predicted biological information as output.

[0064] The predicted timing may be, for example, a timing after a predetermined period (for example, one month) has elapsed from the present time, or may be a timing arbitrarily set by the user via the input unit 25. That is, the prediction unit 34 may accept a prediction timing designated by the user.

[0065] For example, the prediction unit 34 may receive a specified variation target for the biological information, and may set the timing at which the biological information can achieve the variation target (e.g., the shortest timing or a standard timing) as the prediction timing. Specifically, the prediction unit 34 may predict the expiration time of the period required for the biological information to achieve the variation target based on the biological information and the load amount acquired by the acquisition unit 30, and set the expiration time as the prediction timing. For example, the prediction unit 34 may search for past cases similar to the biological information and the load amount up to the current time using the learning information DB 16, and may predict the expiration time by identifying the time at which the learning biological information achieved the variation target in the past case. The variation target for the biological information may be specified numerically, for example, a 20% reduction in the volume of the knee cartilage defect.

[0066] [Exercise training plan] Next, a method for planning exercise training in rehabilitation will be described with reference to Figures 7 and 8. As described above, there may be a time lag before the effects of exercise training in rehabilitation are reflected in biological information. Therefore, in order to make biological information that can be measured at a predicted timing coincide with predicted biological information, it is necessary to carry out exercise training taking the time lag into consideration.

[0067] The planning unit 36 ​​creates a plan for the exercise required to match the biological information that can be measured at the predicted timing with the predicted biological information predicted by the prediction unit 34. Fig. 8 is an example of a screen D1 displayed on the display 24, which includes the exercise training planned by the planning unit 36.

[0068] Specifically, the planning unit 36 ​​derives a required load, which is a load applied to the body of the subject that is required to make the biological information that can be measured at the predicted timing coincide with the predicted biological information. For example, the planning unit 36 ​​calculates the required load, which is a load applied to the body of the subject that is required to make the biological information that can be measured at the predicted timing coincide with the predicted biological information. r ) and loading Lr(t r ), the planning unit 36 ​​searches for similar past cases using the learning information DB 16. In addition, the planning unit 36 ​​searches for similar past cases using the current biometric information Zr(t r ) corresponding to the learning biometric information Za(t a ) is the predicted biological information Zrp(t r ) corresponding to the learning biometric information Zap(t a ) the total load ∫La(t a )dt a Then, the planning unit 36 ​​determines the determined total load ∫La(t a )dt a and the total load ∫Lr(t r )dt r The difference between and is the required load ∫Lrp(t r )dt r It is derived as:

[0069] For example, the planning unit 36 ​​may derive the required load using a trained model such as a CNN that has been trained in advance to use the current biological information Zr and predicted biological information Zrp as input and the required load as output.

[0070] The planner 36 also creates a plan for exercise that satisfies the calculated required load. Specifically, as shown in FIG. 8, the planner 36 creates a plan that determines the type of exercise to be performed from among multiple types of exercise, each with a different load. For example, different types of exercise training are effective in the early and later stages of rehabilitation. Therefore, the planner 36 creates a plan that determines the content of exercise training appropriate for changing the current biological information Zr to the predicted biological information Zrp. The load for each exercise training is stored in advance in, for example, the storage unit 22.

[0071] 8, the planning unit 36 ​​creates a plan that determines a schedule for performing exercises required to match the biological information that can be measured at the predicted timing with the predicted biological information Zrp. For example, the planning unit 36 ​​may create a plan taking into consideration the convenience of the subject and the convenience of the rehabilitation institution. Specifically, the planning unit 36 ​​may receive a date and time specification for at least one of a date and time when the subject can perform exercise and a date and time when the subject cannot perform exercise, and create a plan in accordance with the received date and time specification.

[0072] The control unit 38 controls the display 24 to display a screen D1 including the rehabilitation exercise training plan created by the planning unit 36.

[0073] Next, the operation of the information processing device 10 according to this embodiment will be described with reference to Figures 9 to 12. In the information processing device 10, the CPU 21 executes the information processing program 27, thereby executing the weight derivation process, load derivation process, prediction process, and planning process shown in Figures 9 to 12. The processes may be executed consecutively in the order of weight derivation process, load derivation process, prediction process, and planning process when a command to start execution is given by the user via the input unit 25, for example, or only the designated process may be executed individually.

[0074] First, the weight derivation process will be described with reference to Fig. 9. In step S10, the acquisition unit 30 acquires multiple pieces of learning exercise information from the learning information DB 16. In step S12, the derivation unit 32 derives a weight for each piece of learning exercise information based on the multiple pieces of learning exercise information acquired in step S10, and then ends this weight derivation process.

[0075] Next, the load derivation process will be described with reference to Fig. 10. In step S20, the acquisition unit 30 acquires multiple pieces of motion information from the motion information measurement device 12. In step S22, the acquisition unit 30 acquires weights (i.e., the weights derived in step S12) that have been derived in advance corresponding to the types of motion information acquired in step S20. In step S24, the derivation unit 32 derives the load applied to the subject's body based on the multiple pieces of motion information acquired in step S20 and the weights acquired in step S22, and this load derivation process ends.

[0076] Next, the prediction process of predicted biological information at the prediction timing will be described with reference to Fig. 11. In step S30, the acquisition unit 30 acquires biological information from the biological information measurement device 14. The acquisition unit 30 also acquires the amount of stress applied to the subject's body (i.e., the amount of stress derived in step S24). In step S32, the prediction unit 34 predicts predicted biological information at a predetermined prediction timing after the current time based on the biological information and the amount of stress acquired in step S30, and then ends this prediction process.

[0077] Next, the planning process for exercise training in rehabilitation will be described with reference to Fig. 12. In step S40, the planning unit 36 ​​derives a required load for matching biological information that can be measured at the predicted timing with predicted biological information (i.e., predicted biological information predicted in step S32). In step S42, the planning unit 36 ​​creates a plan for exercise training that satisfies the required load derived in step S40, and ends this planning process.

[0078] As described above, the information processing device 10 according to one embodiment of the present disclosure acquires a plurality of pieces of exercise information indicating each of a plurality of types of exercise amounts measured according to the exercise of the subject, acquires a weight derived in advance based on the exercise information for each type of exercise information, and derives the amount of load on the subject's body based on the plurality of pieces of exercise information and the weight.

[0079] That is, the information processing device 10 according to this embodiment can derive the amount of stress according to the state of the subject's physical function by using weights derived based on exercise information. Therefore, the amount of stress imposed on the subject's body by exercise training up to the present time and the amount of stress to be imposed on the subject's body by exercise training to be performed in the future can be derived as an index that takes into account the state of the subject's physical function, thereby supporting improvement of physical function.

[0080] In addition, an information processing device 10 according to another aspect of the present disclosure acquires biometric information measured on a subject and the amount of stress placed on the subject's body by exercises that affect the biometric information performed within a predetermined period up to the present time, and predicts predicted biometric information, which is biometric information at a predetermined predicted timing after the present time, based on the biometric information and the amount of stress, and creates a plan for exercises required to match the biometric information that can be measured at the predicted timing with the predicted biometric information.

[0081] That is, the information processing device 10 according to the present embodiment can predict predicted biological information at a predicted timing based on biological information and load up to the present time, and can plan exercise training by setting the predicted biological information as a goal, thereby supporting improvement of physical function. Furthermore, by using the load derived in consideration of the state of the subject's physical function, a plan can be created taking into account the effects of various types of exercise training that vary depending on the state of the subject's physical function, thereby further supporting improvement of physical function.

[0082] In the above embodiment, the weighting is varied depending on the time axis from the start to the end of rehabilitation to derive the load amount corresponding to the state of the subject's physical function, but this is not limiting. More precisely, it is preferable that the acquisition unit 30 acquires physical information indicating the subject's physical level, and the derivation unit 32 varies the weighting depending on the physical information to derive the load amount reflecting the physical level (the subject's physical function).

[0083] In this case, it is assumed that physical information indicating the physical level of the subject at the time of measurement may be added to the learning motion information and learning biological information stored in the learning information DB 16. The derivation unit 32 may derive the weight based on the learning motion information to which physical information corresponding to the physical information added to the motion information acquired from the motion information measurement device 12 is added. In other words, the derivation unit 32 may derive the weight based on the learning motion information measured from a subject having a physical level equivalent to that of the subject currently being processed.

[0084] The physical level may indicate, for example, the level of improvement when improving the subject's physical function, which is expressed by objective assessments such as maximum walking speed and range of motion of joints, and subjective assessments such as the degree of pain caused by exercise.

[0085] The planning unit 36 ​​may also create a plan that specifies the execution of a predetermined type of exercise depending on physical information indicating the physical level of the subject. For example, the planning unit 36 ​​may vary the type of exercise training to be performed depending on the range of motion of the knee joint of a patient with knee osteoarthritis.

[0086] Furthermore, in the above embodiment, the derivation unit 32 may derive the load amount applied to a part of the subject's body, such as the knee joint or hip joint. For example, exercise training effective for rehabilitation differs between the knee joint and the hip joint. Therefore, for example, the derivation unit 32 may derive the load amount applied to the knee joint and the load amount applied to the hip joint using different types of motion information and weights, respectively.

[0087] Furthermore, although the above embodiment describes support for rehabilitation performed to improve decline in physical function in the treatment of knee osteoarthritis, the technology of the present disclosure can be applied to other cases. For example, the technology of the present disclosure may be applied to support for rehabilitation performed to improve decline in physical function due to trauma, aging, brain dysfunction, etc. For example, to suppress decline in intellectual functions such as comprehension, judgment, and logic, as in dementia, rehabilitation may be applied to include not only physical exercise training but also intellectual function training such as calculation and puzzles. In this case, the load may be derived, for example, using the accuracy rate of calculations and the time required to complete a puzzle. Furthermore, the technology may be applied to support, for example, muscle training performed to improve the physical function of healthy individuals. In other words, the above "physical level" is not limited to the improvement level when improving the subject's physical function, but may also be the subject's muscle strength level, etc. Furthermore, the types and combinations of multiple pieces of motion information and biological information used in processing may be appropriately changed depending on each case.

[0088] Furthermore, in the above embodiment, the following various processors can be used as the hardware structure of processing units that perform various processes, such as the acquisition unit 30, derivation unit 32, prediction unit 34, planning unit 36, and control unit 38. As described above, the various processors include a CPU, which is a general-purpose processor that executes software (programs) and functions as various processing units, as well as dedicated electrical circuits that are processors having a circuit configuration specifically designed to perform specific processes, such as a programmable logic device (PLD), a processor whose circuit configuration can be changed after manufacture, such as an FPGA (Field Programmable Gate Array), and an ASIC (Application Specific Integrated Circuit).

[0089] A single processing unit may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, multiple processing units may be configured with a single processor.

[0090] Examples of configuring multiple processing units with a single processor include, first, a form in which one processor is configured with a combination of one or more CPUs and software, and this processor functions as multiple processing units, as typified by computers such as client and server. Second, a form in which a processor is used to realize the functions of the entire system including multiple processing units with a single IC (Integrated Circuit) chip, as typified by systems on chips (SoCs). In this way, various processing units are configured using one or more of the above-mentioned various processors as a hardware structure.

[0091] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements.

[0092] In the above embodiment, the information processing program 27 is pre-stored (installed) in the storage unit 22, but this is not limiting. The information processing program 27 may be provided in a form recorded on a recording medium such as a CD-ROM (Compact Disc Read Only Memory), a DVD-ROM (Digital Versatile Disc Read Only Memory), or a USB (Universal Serial Bus) memory, or may be provided in a form stored in a large-scale database on the cloud. The information processing program 27 may also be downloaded from an external device via a network. Furthermore, the technology of the present disclosure extends to a storage medium that non-temporarily stores an information processing program, in addition to the information processing program.

[0093] The technology of the present disclosure can also be achieved by appropriately combining the above-described exemplary embodiments. The above-described description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure and are merely examples of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or new elements may be substituted from the above-described description and illustrations, within the scope of the gist of the technology of the present disclosure. [Explanation of symbols]

[0094] 1. Information Processing Systems 10. Information processing equipment 12 Exercise information measurement device 14 Biological information measuring device 16 Learning Information Database 21 CPU 22 Memory section 23 Memory 24 displays 25 Input section 26 Network I / F 27 Information Processing Program 28 Bus 30 Acquisition Department 32 Derivation part 34 Prediction Department 36 Planning Department 38 Control Unit D1 screen

Claims

1. at least one processor; The processor: Acquire biometric information relating to the subject's physical function that is the subject of rehabilitation, and the amount of stress applied to the subject's body by exercises that have been performed within a predetermined period up to the present time and that have affected the biometric information; calculating target biological information, which is the biological information at a predetermined predicted timing after the present time, based on the biological information and the load; A plan for exercise training in rehabilitation necessary to match the biological information that can be measured at the predicted timing with the target biological information is created. Information processing device.

2. The processor: deriving a required load, which is a load applied to the body of the subject that is necessary to make the biological information that can be measured at the predicted timing coincide with the target biological information; Create a plan for the exercise training that satisfies the required load The information processing device according to claim 1 .

3. The processor: The required load is derived using a trained model that has been trained in advance to set the biological information and the target biological information as inputs and the required load as output. The information processing device according to claim 2 .

4. The processor: Accept the specification of the predicted timing The information processing device according to any one of claims 1 to 3.

5. The processor: Accepting a designation of a variation target related to the biological information; Based on the acquired biological information and the load, a time when the period necessary for the biological information to achieve the fluctuation target will expire is predicted, and the time when the period expires is set as the predicted timing. The information processing device according to any one of claims 1 to 3.

6. The processor: The plan is created, which defines a schedule for the exercise training required to match the biological information that can be measured at the predicted timing with the target biological information. The information processing device according to any one of claims 1 to 5.

7. The processor: Accepting designation of at least one of a date and time when the subject can perform the exercise training and a date and time when the subject cannot perform the exercise training; Create the plan in accordance with the specified date and time The information processing device according to claim 6 .

8. The processor: The plan is created, which determines the type of exercise training to be performed from among a plurality of types of exercise training each having a different load. The information processing device according to any one of claims 1 to 7.

9. The processor: acquiring physical information indicating a physical level of the subject; The plan is created, which specifies that a predetermined type of exercise training is to be performed in accordance with the physical information. The information processing device according to claim 8 .

10. The processor: acquiring a plurality of pieces of exercise information indicating a plurality of types of exercise amounts measured in response to the exercise of the subject; Obtaining a weight derived in advance corresponding to the type of motion information; Deriving the load amount based on the plurality of pieces of motion information and the weights. The information processing device according to any one of claims 1 to 9.

11. The movement information indicates at least one of the number of steps, walking speed, electromyogram, and flexion angle and flexion speed of the subject's joint. The information processing device according to claim 10.

12. The physical information indicates a level of improvement when improving the physical function of the subject. The information processing device according to claim 9 .

13. Acquire biometric information relating to the subject's physical function that is the subject of rehabilitation, and the amount of stress applied to the subject's body by exercises that have been performed within a predetermined period up to the present time and that have affected the biometric information; calculating target biological information, which is the biological information at a predetermined predicted timing after the present time, based on the biological information and the load; A plan for exercise training in rehabilitation necessary to match the biological information that can be measured at the predicted timing with the target biological information is created. An information processing method in which processing is performed by an information processing device.

14. Acquire biometric information relating to the subject's physical function that is the subject of rehabilitation, and the amount of stress applied to the subject's body by exercises that have been performed within a predetermined period up to the present time and that have affected the biometric information; calculating target biological information, which is the biological information at a predetermined predicted timing after the present time, based on the biological information and the load; A plan for exercise training in rehabilitation necessary to match the biological information that can be measured at the predicted timing with the target biological information is created. An information processing program that causes a computer to execute a process.

Images