Exercise support device, exercise support method, exercise support system, and program

Abstract

An exercise support device (3) comprises an information processing unit (31) that supports the setting of a subsequent exercise menu intended for improvements toward a target body on the basis of prior body movement information for a subject as detected by a movement sensor (2). The information processing unit (31) comprises an activity amount processing unit (312) that, on the basis of the body movement information for the subject, calculates respective activity amounts and step counts for selected segments from among segments segmented by activity intensity, an image display processing unit (313) that, for each selected segment, outputs distribution information for activity amounts and step counts pre-acquired from a target person group to a display unit (32), and an image display processing unit (313) that outputs the activity amounts and step counts calculated by the activity amount processing unit (312) to the display unit (32). The present invention thereby appropriately supports the setting of a subsequent exercise menu intended for improvements toward a target body on the basis of prior body movement information for a subject as detected by a movement sensor.

Description

[Technical Field]

[0001] The present invention relates to a technology for assisting in setting an exercise menu to be continuously carried out for functional recovery or ability improvement (hereinafter referred to as improvement) in rehabilitation or sports. [Background technology]

[0002] In recent years, standards for physical activity (PA) aimed at extending healthy lifespan have been proposed, and the assessment of PA has become increasingly important (Non-Patent Documents 1, 2). Furthermore, there has been significant progress in measuring PA using acceleration sensors, such as wearable sensor devices (WSDs). There has been a report of measuring PA using wearable sensor devices in thousands of healthy individuals (Non-Patent Document 3), and the scope of their use is expected to expand in the future. Meanwhile, in the treatment of patients with knee joint diseases, patient-centered assessments such as the Knee Injury and Osteoarthritis Outcome Score (KOOS), the IKDC subjective score, and the Lysholm score have become increasingly important.

[0003] Patent Document 1 describes an activity meter that calculates a composite angular velocity, a vertical component angular velocity, and a horizontal component angular velocity from detected acceleration data in three axial directions, and then calculates activity intensity METs (Metabolic Equivalents) and determines the type of physical activity from these. Patent Document 2 describes a rehabilitation support device that includes an activity amount measuring unit that measures the activity amount of the paralyzed upper limb based on detection signals from an acceleration sensor, and a display unit that displays an image showing the activity amount of the paralyzed upper limb and a target activity amount value, and that displays a message to that effect on the display unit when a predicted value of the activity amount within a predetermined time is predicted not to reach the target activity amount value. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 4992595 [Patent Document 2] Japanese Patent Publication No. 2020-39566 [Non-patent literature]

[0005] [Non-Patent Document 1] IM Lee, EJ Shiroma. Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges. Br J Sports Med 2014; 48: 197-201. [Non-patent document 2] KL Piercy, RP Troiano, RM Ballard et al. The Physical Activity Guidelines for Americans. JAMA 2018; 320: 2020-2028. [Non-patent document 3] EJ. Shiroma, PS. Freedson, SG. Trost et al. Patterns of Accelerometer-Assessed Sedentary Behavior in Older Women. JAMA 2013; 310: 2562-2563. Summary of the Invention [Problem to be solved by the invention]

[0006] The patient-centered assessment described above is thought to have great advantages if it enables quantitative assessment of ADL (Activities of Daily Living) and sports activity levels by measuring the patient's physical activity (PA) using a wearable sensor device (WSD). However, as it stands, there are limitations in terms of quantitation, objectivity, and reproducibility.

[0007] Furthermore, Patent Documents 1 and 2 do not describe any technology that uses the quantitative and qualitative characteristics of each category and between categories divided according to the intensity of physical activity to assist in setting exercise menus for functional recovery through rehabilitation or improvement of sports ability.

[0008] The present invention has been made in consideration of the above, and proposes an exercise support device, an exercise support method, an exercise support system, and a program that provide support when setting the next exercise menu to improve the subject's body to a target level, based on the subject's prior body movement information detected by a motion sensor. [Means for solving the problem]

[0009] The exercise support device according to the present invention includes an information processing unit that supports the setting of a next exercise menu for improving a target body shape based on prior physical movement information of a subject detected by a movement sensor, and the information processing unit includes a measurement information processing means, a goal information processing means, and an output processing means. The measurement information processing means calculates, from the subject's physical movement information, an activity amount and a number of steps for each selected category of activity intensity. The goal information processing means outputs distribution information of the activity amount and the number of steps obtained in advance from a target group of subjects for each selected category. The output processing means outputs the activity amount and the number of steps calculated by the measurement information processing means to the output unit.

[0010] An exercise support system according to the present invention includes the exercise support device and a movement sensor that detects prior body movement information of the subject and transmits the detection result to the information processing unit.

[0011] Furthermore, the exercise support method of the present invention is an exercise support method that supports the setting of a next exercise menu for improving to a target body shape based on prior physical movement information of a subject detected by a movement sensor, and includes the steps of: calculating, from the subject's physical movement information, the amount of activity and the number of steps for each selected category of activity intensity; outputting distribution information of the amount of activity and the number of steps obtained in advance from a target group of subjects for each selected category; and outputting the amount of activity and the number of steps calculated in the calculating step to the output unit.

[0012] Furthermore, the program of the present invention is a program that uses a computer to assist in setting the next exercise menu for improving the subject's body to a target level, based on the subject's prior physical movement information detected by a movement sensor, and causes the computer to execute the following steps: calculating the activity level and number of steps for each selected category from each category into which activity intensity is divided, based on the subject's physical movement information; outputting distribution information of the activity level and number of steps previously obtained from the target group for each selected category to an output unit; and outputting the activity level and number of steps calculated in the calculation step to the output unit.

[0013] According to these inventions, the activity amount and number of steps for each selected category of activity intensity are calculated from the subject's prior body movement information, while distribution information of the activity amount and number of steps previously obtained from a target group is output to an output unit for each selected category, and the calculated activity amount and number of steps for the subject are output to the output unit. Therefore, since it is possible to recognize the distribution information of the target group, it is possible to preferably provide support when setting the next exercise menu for improving the target body from the subject's prior body movement information detected by the motion sensor. In the above, the subject and target group are assumed to be a group of healthy individuals if the subject is a patient with a leg disease, such as a knee joint disease, or a sports injury, and if the subject is an athlete, the target group is typically assumed to be a group of top athletes in that field. [Effects of the Invention]

[0014] According to the present invention, support for setting the next exercise menu for improving to a target physical condition is provided more accurately. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a block diagram showing an embodiment of an exercise support system according to the present invention. [Figure 2] 10 is a diagram showing the relationship between activity intensity and category, activity content of the category, and subcategory, and also showing the relationship with other evaluations. [Figure 3] Each activity intensity category is plotted on a coordinate system of activity level and number of steps, showing the distribution of the experimental results of activity level and number of steps for healthy and patient groups. (A) shows the SED category with the lowest activity intensity, and (B) shows the LPA category with the next lowest intensity. [Figure 4] This shows the distribution of activity levels and step counts in a healthy and patient group, based on experimental results. (A) shows the MVPA category with high activity intensity, (B) shows the lower MPA category obtained by subdividing the MVPA category, and (C) shows the higher VPA category obtained by subdividing the MVPA category. [Figure 5] This shows the distribution of experimental results on activity levels and step counts for healthy and patient groups, and is a graph of long-term MVPA, which indicates the amount of activity sustained over a specified period of time in the MVPA category. [Figure 6] This is a chart showing the amount of activity and the average number of steps (bottom row) of the healthy group and patient group in each category in this experiment, as well as each correlation value p. [Figure 7] FIG. 10 is a sample diagram illustrating an example of a patient's rehabilitation history. [Figure 8] 10 is a flowchart showing an example of measurement data processing executed by a CPU of a control unit. [Figure 9] 10 is a flowchart showing an example of rehabilitation assistance processing executed by a CPU of a control unit. DETAILED DESCRIPTION OF THE INVENTION

[0016] 1 is a block diagram showing an embodiment of an exercise support system 1 according to the present invention. The exercise support system 1 includes a motion sensor 2 and an information processing device 3, and in this embodiment, information communication between the two is possible via wired or wireless communication means.

[0017] The motion sensor 2 is attached to the human body to detect human body motion, particularly the acceleration of the motion. In this embodiment, a triaxial acceleration sensor 21 is employed, which is integrated with a measurement processing unit 22. Capacitive or piezoelectric acceleration sensors can be used as the acceleration sensor, and various acceleration sensors based on other detection principles may also be used. The motion sensor 2 is equipped with a mounting fixture such as a clip or fastener (not shown), and is attached to a central part of the human body, preferably in an appropriate location around the waist, via the mounting fixture. The motion sensor 2 can be attached to the waist so that the triaxial acceleration sensors face up and down, and in the front-to-back and left-to-right directions relative to the human body, and each directional component is detected from the acceleration sensor corresponding to each axis.

[0018] The motion sensor 2 is equipped with a power switch (not shown), and operates to perform detection while the switch is on. In the embodiment of rehabilitation support for knee joint diseases (exercise support for rehabilitation) described below, the motion sensor operates for a predetermined time, for example, about 10 hours, between waking up and going to bed, to detect daily movements of the human body as well as rehabilitation exercises (described later). The predetermined time may be set by specifying a start time and an end time, or it may be set to a flexible period, such as literally from waking up to going to bed. Alternatively, data may be collected for a longer period along with time information, and data for the required period may be selectively acquired during data acquisition.

[0019] For example, the same technology as that described in Patent Document 1 can be adopted as the configuration and function of the three-axis acceleration sensor 21 and the measurement processing unit 22. Briefly explained below, the measurement processing unit 22 has a processor (CPU) and executes a measurement program stored in a storage unit (not shown) to function as an activity intensity measurement unit 221, a step count measurement unit 222, and a clock unit 223 that measures the time or a required period of time.

[0020] The activity intensity measurement unit 221 captures acceleration data detected by the triaxial acceleration sensor 21 at a predetermined cycle of, for example, several tens of hertz, and measures activity intensity from the captured time-series acceleration data. For example, it calculates a composite acceleration S, a vertical component acceleration Sv, and a horizontal component acceleration Sh from the triaxial acceleration data, and uses these as judgment conditions to calculate activity intensity (METs: Metabolic equivalents) from the composite acceleration using, for example, the level of the composite acceleration S and the ratio of the component accelerations Sv and Sh, and also classifies the type of physical activity, for example, into daily activity, exercise, or rest. The activity intensity measurement unit 221 also calculates activity intensity METs from the detected acceleration as data for each unit time (for example, one minute).

[0021] The step counting unit 222 counts the number of times that the detected acceleration of the vertical direction component exceeds a predetermined threshold, and regards this as the cumulative number of steps within the detection period.

[0022] The information processing device 3 includes a control unit 31 configured with a processor (CPU). Connected to the control unit 31 are a display unit 32 that displays images, an operation unit 33 that receives operation instructions from the outside, a measurement data storage unit 34, and a storage unit 35. The operation unit 33 may be configured with a touch panel in which a transparent pressure-sensitive panel element is superimposed on the display unit 32.

[0023] The measurement data storage unit 34 stores data measured by the motion sensor 2 and imported into the information processing device 3 for each patient ID. The storage unit 35 has a healthy group data storage unit 351, a rehabilitation success case data storage unit 352, and a control program data storage unit 353. The healthy group data refers to activity details detected during the same period as the patient (in the above example, the 10 hours from waking up to going to bed), and details will be described later. In addition to the above memory areas, the storage unit 35 also has a work area (main memory unit) for executing information processing.

[0024] The control unit 31 reads the control program from the control program data storage unit 353 into a work area and executes it, thereby functioning as a measurement data acquisition unit 311, an activity amount processing unit 312, an image display processing unit 313, and an input processing unit 314 that processes reception of various information input via the operation unit 33 and acquisition of the information into the storage unit.

[0025] In response to a measurement data import instruction from the information processing device 3, the measurement data import unit 311 imports into the measurement data storage unit 34 the measurement data of the patient's physical activity and number of steps measured in advance, for example, at a specified time last time, by the motion sensor 2.

[0026] The activity amount processing unit 312 performs processing to classify (category) the acquired measurement data of the patient into predetermined sections according to the activity intensity, and to calculate the activity amount Ex for each section.

[0027] Figure 2 shows the relationship between activity intensity and categories, activity content and subcategories within each category, and separate assessments. In Figure 2, physical activity (PA) is classified into multiple categories based on activity intensity (METs): SED (Sedentary: 1-1.5 METs), which corresponds to sitting and lying down; LPA (Light Physical Activity: 1.6-2.9 METs), which corresponds to activities of daily living; MPA (Moderate Physical Activity: 3.0-5.9 METs), which corresponds to light sports; and VPA (Vigorous Physical Activity: ≥6.0 METs), which corresponds to vigorous sports. Furthermore, as a guideline for health promotion, a separate assessment called Long-Bout MVPA, which measures the amount of activity (Ex) included in MVPA for 10 or more consecutive minutes, has been adopted.

[0028] The activity amount processing unit 312 calculates the activity amount Ex by accumulating the activity intensity METs data corresponding to each category over a unit time, for example, one minute. The activity amount processing unit 312 also calculates the activity amount Ex for long-term MVPA, which is used for a separate evaluation. The calculation results are stored in the measurement data storage unit 34 as needed.

[0029] Next, a physical activity experiment was conducted on a group of healthy individuals (healthy group) and a group of patients (patient group), and the results were analyzed quantitatively and qualitatively. The analysis was performed using the well-known Student's t test and Pearson's correlation coefficient to (1) compare the patient group and healthy group in terms of activity level Ex and number of steps in each activity intensity METs category, and (2) examine the relationship between activity level Ex and number of steps within each category. In this analysis, the significance level for the P value of the correlation evaluation was set at the commonly used 5%.

[0030] Figures 3 to 5 show the distribution of experimental results for the healthy and patient groups regarding activity level Ex and step counts, plotted on a coordinate system of activity level Ex and step counts for each activity intensity METs category. Figure 3(A) shows the lowest activity intensity category SED, and Figure 3(B) shows the next lowest activity intensity category LPA. Figure 4(A) shows the high activity intensity category MVPA, Figure 4(B) shows the lower category MPA, which is a subdivision of the MVPA category, and Figure 4(C) shows the higher intensity category VPA. Figure 5 shows the long-out MVPA category within the MVPA category.

[0031] The experimental data shown in Figures 3 to 5 and 6 include patients with knee joint disease. The subjects of this experiment were 23 outpatients at Osaka University Hospital (patient group: 10 men, 13 women, aged 22 to 81 (average age 51)) and 28 healthy individuals with no history of knee joint disease (healthy group: 12 men, 16 women, aged 18 to 65 (average age 28)). Each subject wore a motion sensor 2 (activity meter: Active Style Pro HJA-750C, OMRON Healthcare, Japan) on their lower back for 7 consecutive days, 10 hours or more per day. The measurement information obtained is summarized in Figures 3 to 6.

[0032] Furthermore, from the detection results of the triaxial acceleration sensor of the activity monitor used in this experiment, metabolic equivalents (METs) per minute were extracted using the "activity monitor application" included with the activity monitor based on algorithms such as those described in Patent Document 1 and reference materials (K Ohkawara, Y Oshima, Y Hikihara et al. Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm. Br J Nutr 2011; 105: 1681-1691. and Y Oshima, K Kawaguchi, S Tanaka et al. Classifying household and locomotive activities using a triaxial accelerometer. Gait Posture 2010; 31: 370-374.), and these were used for PA evaluation. Step count information was also extracted at the same time.

[0033] The activity level of each person is tabulated by category. In the figure, the open triangle marks indicate the coordinate positions of the activity level and number of steps for each person in the group of healthy subjects (healthy group), and the black circle marks indicate the coordinate positions of the activity level and number of steps for each person in the patient group. Note that the data from this experiment does not include exercise performed according to the exercise menu instructions for both healthy subjects and patients. For the distributions in each figure, the r value indicates the correlation coefficient, the P value indicates the correlation value, and the straight line indicates the regression line for the healthy group.

[0034] Next, we will analyze Figures 3 to 6. Comparing the activity level Ex and step counts between the two groups, the patient group had significantly lower values ​​for MVPA (Figure 4(A)), long-distance MVPA (Figure 5), and step counts (see the bottom row of Figure 6), while their LPA (Figure 3(B)) was higher. There were no significant differences between the two groups in SED and LPA, but the patient group had slightly lower step counts (Figure 3(A)). The patient group also had lower values ​​for MPA and VPA (Figures 4(B) and (C)), which are subdivisions of MVPA.

[0035] Regarding the correlation between activity level Ex and step counts, no significant correlation was observed for SED or LPA (Figures 3(A) and (B)), whereas a strong positive correlation was observed for MVPA in both groups (r = 0.963, r = 0.814) (Figure 4(A)). Furthermore, when divided into MPA and VPA, as shown in Figures 4(B) and (C), there was a similarly strong positive correlation with MPA (r = 0.966, r = 0.816), but a weak positive correlation with VPA (r = 0.490).

[0036] Furthermore, as shown in Figure 5, with regard to long-bout MVPA, the correlation was lower in the patient group (r = 0.458) compared to the healthy group (r = 0.882). Furthermore, the patient group had low activity levels regardless of the number of steps taken, and only two patients (8.7%) met the recommended standard of 150 minutes or more per week compared to eight patients (28.6%) in the healthy group, showing a difference between the two groups.

[0037] Next, Figure 6 is a graph showing the activity levels of the healthy and patient groups in each category in this experiment, as well as the average number of steps shown in the bottom row and each correlation value p. In this experiment, activity assessment using Motion Sensor 2 revealed that the patient group had lower activity levels in the MPA, VPA, and long-term MVPA categories. By dividing the activity levels into intensity categories based on the measurements from Motion Sensor 2, we were able to quantitatively evaluate physical activity levels within each category and qualitatively evaluate the levels between categories. As a result, we were able to see distinctive differences between the two groups in the assessment of physical activity levels, including the categories SED, LPA, and MVPA, as well as the categories MPA, VPA, and long-term MVPA. Utilizing these differences is expected to be a powerful evaluation tool for supporting therapeutic intervention (rehabilitation) and evaluating the effectiveness of interventions.

[0038] In other words, among the above-mentioned categories, the criteria for a category to be applicable as a tool for supporting rehabilitation and evaluating its effectiveness are that the distributions of the healthy group and the patient group are significantly different (different) from each other, and that a correlation is observed in the distribution of the healthy group. The criteria for an applicable category (a category selected for support) are that there is a significant difference between the patient's activity level and step count in the early stages of rehabilitation and the distribution information of the healthy group, which is the target of recovery. By utilizing the existence of such a significant difference from the healthy group, the measurement result position of the patient undergoing rehabilitation on a coordinate system and the distribution of the healthy group shown in Figures 3 to 5 can be displayed, making it easy to visually recognize the positional relationship between the two (the healthy group and the patient). This positional relationship can be used to support medical professionals in gradually setting an exercise menu for the patient. The exercise menu includes the type of exercise, exercise intensity, exercise duration, etc. Furthermore, since the patient's daily activity is also included in the measurement results, it is possible to accurately and easily set the next exercise menu based on a comprehensive understanding of these.

[0039] By providing effective support in such gradual setting, it becomes possible to gradually move from the outside of the distribution of the healthy group toward said distribution side, and ultimately to effectively guide the patient into the distribution, that is, to provide appropriate exercise support so as to smoothly recover. Note that the categories used for comparison may be all categories, focusing on more or less significant differences, or may be narrowed down to at least one or more predetermined categories by utilizing more significant differences.

[0040] Returning to Fig. 1, the image display processing unit 313 displays image information on the display unit 32. That is, the image display processing unit 313 displays image information, which is measurement data for a predetermined period of time immediately preceding the patient's death, in this case 10 hours, processed by the activity amount processing unit 312, expanded on a coordinate system, as well as distribution information of the healthy group in at least one or more predetermined categories used for the comparison, as an image on the coordinate system. The categories that satisfy the conditions may be fixed, or may be switched as appropriate depending on the degree of recovery associated with rehabilitation.

[0041] The image information of the patient and healthy group may be displayed on each coordinate system image, or may be superimposed on a shared coordinate system image. The image information of the patient's activity amount may display data for all categories, or only categories corresponding to those on the healthy group side. In the mode of superimposing the data on the same coordinate system image, it is preferable to display the display mark on the patient side in a different display format from that on the healthy group side, for example, by changing the shape, size, blinking, color, etc., so that it can be distinguished. When displayed in a shared format, the position of the patient's measurement data relative to the distribution of the healthy group is displayed so that it can be easily distinguished.

[0042] The rehabilitation success case data storage unit 352 stores at least one or more past success cases in which knee joint disease was successfully recovered through rehabilitation, including the rehabilitation time points and the exercise menu set at each time point, as well as the patient's measurement data at each time. The input processing unit 314 extracts measurement data similar to that of the patient or success case data at similar rehabilitation time points from the rehabilitation success case data as needed, and displays it on the display unit 32 automatically by the image display processing unit 313 or via the operation unit 33 to support the setting of the exercise menu.

[0043] Here, an example of a patient's rehabilitation improvement history is explained using the sample diagram in Figure 7. Figure 7 illustrates an image of rehabilitation support, showing the progress of one patient's rehabilitation improvement in a certain category (e.g., MVPA). The patient's coordinate position, which was within the patient group distribution at the beginning of rehabilitation, gradually moves toward or approaches the healthy group distribution (indicated by the dashed arrows in Figure 7) as the patient completes the prescribed rehabilitation exercise menu every day over several days, and finally falls within the healthy group distribution, i.e., improvement and recovery. As shown in Figure 7, the patient's gradual measurement data history can be overlaid to confirm the progress of recovery, which can lead to the accurate setting of the next exercise menu.

[0044] 8 is a flowchart showing an example of measurement data processing executed by the processor (CPU) of the control unit 31. First, the patient's measurement data (activity intensity METs measured per unit time, number of steps) is acquired from the motion sensor 2, and the patient's identification information is input from the motion sensor 2 or the operation unit 33 (step S1). Next, the measurement data of the activity intensity METs per unit time is classified (allocated) into each category (step S3). Next, the activity amount Ex for each category is calculated by multiplying the activity intensity METs within each category by the activity time, and this is stored in the measurement data storage unit 34 together with the number of steps (step S5).

[0045] FIG. 9 is a flowchart showing an example of rehabilitation support processing executed by the processor (CPU) of the control unit 31. First, the image display processing unit 313 displays distribution information (activity amount Ex, number of steps) for a pre-selected category, which has information that can be significantly compared with the patient's data, among the activity intensities of the healthy group acquired in advance, on the coordinate system of the corresponding category (step S11). Next, the image display processing unit 313 displays marks on the shared coordinate system at the coordinate positions of the data (activity amount Ex, number of steps) obtained from the patient's side, for the same category as the selected category, so that the marks are superimposed on the marks for the healthy group in a manner that makes them distinguishable (step S13). Furthermore, the image display processing unit 313 automatically or via an instruction from an operator displays marks on the same coordinate system as the patient's past data in a manner that makes them distinguishable as rehabilitation history (step S15). Furthermore, if necessary, via an instruction from an operator, displays guidance on rehabilitation exercise menu candidates at positions corresponding to each category (step S17). Alternatively, successful rehabilitation case data may be displayed. Then, the input processing unit 314 records the rehabilitation exercise content set by the medical professional in association with the patient ID, for example, in the measurement data storage unit 34 (step S19).

[0046] In the present invention, a target group of healthy individuals is uniformly set, but healthy individuals may be divided by age group, sex, etc. to create healthy group data, and comparison data closer to the patient's attributes may be used. Furthermore, longitudinal evaluation of the postoperative course of treatment (rehabilitation) for patients with knee joint disease may also be included, and the present invention may also be applicable to the evaluation and guidance of the treatment effects of sports injuries, rehabilitation, and return to sports.

[0047] In the present invention, the measurement processing unit 22 may be provided on the information processing device 3 side, or conversely, the activity amount processing unit 312 may be provided on the motion sensor 2 side. Furthermore, the patient side data and the distribution information of the healthy control group may be output by printing out using a printer instead of the display unit 32 that displays images.

[0048] Furthermore, the rehabilitation exercise content may be set not only on a daily basis, but also for several days, and the measurement data may be collected and evaluated all at once.

[0049] Furthermore, communication between the patient's motion sensor 2 and the hospital's information processing device 3 may be short-distance communication or may be via a WAN or other mode that utilizes an internet environment typically between a home and a hospital.

[0050] Furthermore, in the present invention, data on a list of exercise menus may be stored in the storage unit 35 of the information processing device 3. The exercise menu list is preferably classified, for example, according to activity intensity categories and includes the type of exercise, as well as exercise intensity and exercise time. The exercise menu list can be displayed automatically or in response to an operation instruction from the operation unit 33 by the image display processing unit 313 at an appropriate location on the display unit 32 so that it can be referenced appropriately, for example, by corresponding category, thereby providing assistance in setting an exercise menu.

[0051] As described above, the exercise support device according to the present invention includes an information processing unit that supports the setting of a next exercise menu for improving a target body shape based on the subject's previous physical movement information detected by a movement sensor, and the information processing unit preferably includes a measurement information processing means, a goal information processing means, and an output processing means. The measurement information processing means calculates, based on the subject's physical movement information, an activity amount and a number of steps for each selected category of activity intensity. The goal information processing means outputs distribution information of the activity amount and the number of steps obtained in advance from a target group of subjects for each selected category. The output processing means outputs the activity amount and the number of steps calculated by the measurement information processing means to the output unit.

[0052] Moreover, it is preferable that the exercise support system according to the present invention includes the exercise support device and a movement sensor that detects prior body movement information of the subject and transmits the detection result to the information processing unit.

[0053] Furthermore, the exercise support method of the present invention is an exercise support method that supports setting a next exercise menu for improving to a target body shape based on prior physical movement information of a subject detected by a movement sensor, and preferably includes the steps of: calculating, from the subject's physical movement information, the amount of activity and the number of steps for each selected category of activity intensity; outputting distribution information of the amount of activity and the number of steps obtained in advance from a target group of subjects for each selected category; and outputting the amount of activity and the number of steps calculated in the calculating step to the output unit.

[0054] Furthermore, the program of the present invention is a program that uses a computer to assist in setting the next exercise menu for improving the subject's body to a target level, based on the subject's prior physical movement information detected by a movement sensor, and preferably causes the computer to execute the following steps: calculating the activity level and number of steps for each selected category from each category into which activity intensity is divided, based on the subject's physical movement information; outputting distribution information of the activity level and number of steps previously obtained from the target group for each selected category to an output unit; and outputting the activity level and number of steps calculated in the calculation step to the output unit.

[0055] According to these inventions, the activity amount and number of steps for each selected category of activity intensity are calculated from the subject's prior body movement information, while distribution information of the activity amount and number of steps previously obtained from a target group is output to an output unit for each selected category, and the calculated activity amount and number of steps for the subject are output to the output unit. Therefore, since it is possible to recognize the distribution information of the target group, it is possible to preferably provide support when setting the next exercise menu for improving the target body from the subject's prior body movement information detected by the motion sensor. In the above, the subject and target group are assumed to be a group of healthy individuals if the subject is a patient with a leg disease, such as a knee joint disease, or a sports injury, and if the subject is an athlete, the target group is typically assumed to be a group of top athletes in that field.

[0056] Preferably, the output unit is a display unit that displays an image. With this configuration, the measurement data of the subject and the distribution information of the target group are displayed as an image.

[0057] Preferably, the output processing means displays the amount of activity and the number of steps calculated by the measurement information processing means on a coordinate system for each of the selected segments. With this configuration, both pieces of information are displayed on the same coordinate system, making them easy to distinguish.

[0058] In addition, the present invention preferably displays the amount of activity and the number of steps calculated by the measurement information processing means in a distinguishable manner on a common coordinate system for each section displayed on the display unit. With this configuration, since both pieces of information are displayed in a distinguishable manner on a common coordinate system, it becomes easier and more accurate to distinguish between them.

[0059] Furthermore, the selected category is preferably a category in which there is a significant difference between the amount of activity and the number of steps of the subject at the initial stage of support and the distribution information of the target group. With this configuration, since the difference can be more easily recognized at the initial stage of support, for example, at the start of rehabilitation, effective support can be provided, for example, when setting an exercise menu for rehabilitation.

[0060] Furthermore, it is preferable that the selected category is a category with which the distribution information of the target group shows a relatively high correlation. With this configuration, it is possible to support the subject to improve the physical condition of the target person more accurately.

[0061] Furthermore, it is preferable that the detection of the body movement information is performed at a predetermined time between waking up and going to bed. With this configuration, it is possible to grasp the overall exercise state, including daily living activities, and use this information to set the next exercise menu.

[0062] Furthermore, the distribution information is preferably at least one of all values ​​of each activity amount and number of steps of the target group and a regression line for the distribution of the values ​​of each activity amount and number of steps of the target group. With this configuration, it is possible to output information of the target group in an appropriate manner, making it easy to recognize differences.

[0063] In the present invention, it is preferable that the subject is a patient with a knee joint disease and the target group is a group of healthy individuals. With this configuration, effective support can be provided for setting up a rehabilitation exercise menu for patients with knee joint disease. [Explanation of symbols]

[0064] 1 Exercise support system 2. Motion Sensor 21 3-axis acceleration sensor 22 Measurement processing section 3. Information processing device (exercise support device) 31 Control section (information processing section) 312 Activity amount processing unit (measurement information processing means) 313 Image display processing unit (target information processing means, output processing means) 32 Display unit (output unit) 33 Operation section 34 Measurement data storage unit 35 Storage section 351 Healthy Group Data Storage Unit

Claims

1. an information processing unit that supports the setting of a next exercise menu for improving the target body shape based on the subject's previous body movement information detected by the motion sensor; The information processing unit a measurement information processing means for calculating an activity amount and a number of steps for each selected category of activity intensity from the subject's body movement information; a target information processing means for outputting distribution information of activity amounts and step counts obtained in advance from the target group to an output unit for each of the selected categories; An exercise support device comprising: an output processing means for outputting the amount of activity and the number of steps calculated by the measurement information processing means to the output unit.

2. The exercise support device according to claim 1 , wherein the output unit is a display unit that displays an image.

3. The exercise support device according to claim 2 , wherein the output processing means displays the amount of activity and the number of steps calculated by the measurement information processing means on a coordinate system for each of the selected segments.

4. 4. The exercise support device according to claim 3, wherein the amount of activity and the number of steps calculated by the measurement information processing means are displayed in a identifiable superimposed manner on a coordinate system common to each section displayed on the display unit.

5. The exercise support device according to any one of claims 1 to 4, wherein the selected category is a category in which there is a significant difference between the amount of activity and number of steps of the subject at the initial stage of support and distribution information of the target group.

6. 6. The exercise support device according to claim 1, wherein the selected category is a category with which distribution information of the target group shows a relatively high correlation.

7. 7. The exercise support device according to claim 1, wherein the detection of the body movement information is performed at a preset time between waking up and going to bed.

8. The exercise support device according to any one of claims 1 to 7, wherein the distribution information is at least one of all values ​​of each activity amount and number of steps of the target group and a regression line for the distribution of the values ​​of each activity amount and number of steps of the target group.

9. The exercise support device according to any one of claims 1 to 8, wherein the subject is a patient with a knee joint disease, and the target group is a group of healthy individuals.

10. An exercise support system comprising: the exercise support device according to any one of claims 1 to 9; and a movement sensor that detects prior body movement information of the subject and transmits the detection results to the information processing unit.

11. An exercise support method that supports setting of a next exercise menu for improving a target body shape based on previous body movement information of a subject detected by a motion sensor, comprising: calculating an activity amount and a number of steps for each selected category of activity intensity from the subject's body movement information; outputting distribution information of the activity amount and the number of steps acquired in advance from the target group to an output unit for each of the selected categories; an output unit that outputs the amount of activity and the number of steps calculated in the calculating step.

12. A program that uses a computer to assist in setting a next exercise menu for improving a target body shape based on the subject's previous body movement information detected by a movement sensor, calculating an activity amount and a number of steps for each selected category of activity intensity from the subject's body movement information; outputting distribution information of the activity amount and the number of steps acquired in advance from the target group to an output unit for each of the selected categories; a step of outputting the activity amount and the number of steps calculated in the calculating step to the output unit;

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