Exercise data collection method, exercise data analysis system, exercise diagnosis support system, and analysis program

Abstract

The present invention collects movement data from motion during simple movements and assists movement diagnosis of Parkinson's disease, dementia, non-motor symptoms, and the like.　This movement data collection method for collecting data during a crawling movement or rollover movement is characterized by comprising: a step of measuring the motion of at least one part among the heel, waist, and hand during crawling movement and at least one part among the head, knees, and shoulders during rollover movement; and a step of quantifying the motion measured by the step of measuring the motion.

Description

[Technical field]

[0001] The present invention relates to a motion data collection method, a motion data analysis system, a motion diagnosis support system, and an analysis program, and more particularly to motion data during crawling or rolling over. [Background technology]

[0002] As we enter an aging society, an increasing number of elderly people are being diagnosed with Parkinson's disease, dementia including Alzheimer's disease, cancer, cardiovascular disease associated with high blood pressure, diabetes, etc. Furthermore, in addition to an aging society, a stressful society is also creating an increasing number of elderly and young people being diagnosed with non-motor symptoms such as disinhibition (easily becoming angry), apathy (loss of motivation), social disorders, attention deficit disorder, etc., which are caused by dementia.

[0003] Although recent advances in diagnostic technology and medicine have made it easier for specialists to make such diagnoses, it is extremely difficult to assess the intermediate state between a healthy state and a state requiring nursing care, or the progression of the disease, from a standing or bipedal walking posture, particularly in the case of Parkinson's disease, which can lead to a serious state requiring nursing care, with the neck bent, the back bent, and walking difficulties. For this reason, there has been a problem in that the progression of the disease is only noticed after the burden of care has become great, which leads to an even greater burden of care.

[0004] In addition, even for specialists, it has been difficult to diagnose conditions such as disinhibition and attention deficit disorder caused by non-motor symptoms. In particular, non-motor symptoms are often unconscious, and it is also difficult to evaluate them from the standing or bipedal walking posture. This has led to problems such as the inability to prevent or diagnose these conditions, which increases the burden on caregivers. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2012-240935 A [Patent Document 2] Special Publication No. 2010-526820 Summary of the Invention [Problem to be solved by the invention]

[0006] The present invention has been made in consideration of the above-mentioned circumstances, and has an objective to provide a motion data collection method for measuring and quantifying the rolling over and crawling movements that infants unconsciously acquire from the elderly, young people, children, etc., a motion data analysis system for analyzing the collected motion data, a motion diagnosis support system for displaying the collected motion data in a manner suitable for diagnosis and providing motion diagnosis support, and an analysis program for the motion data analysis system. [Means for solving the problem]

[0007] In order to solve the above problems, the motion data collection method of the present invention is a method for collecting motion data during crawling or rolling over, and is characterized by including a step of measuring the movement of one or more of the heels, lower back, and hands during crawling, and one or more of the head, knees, and shoulders during rolling over, and a step of quantifying the movement measured by the step of measuring the movement.

[0008] The crawling motion may include forward and backward motion.

[0009] The crawling exercise may further include a heel press.

[0010] The turning motion may further include a knee restraint.

[0011] It is desirable to further provide a specific rhythm signal from the outside during the crawling movement or the rolling over movement.

[0012] The step of measuring the movement is desirably carried out by measuring with marks on the parts to be measured, by recording with a component attached to the body that generates a sound in response to body swaying, by photographing, by videotaping, or by a combination of two or more of these.

[0013] The exercise data analysis system of the present invention is characterized by including an exercise data input unit that inputs quantified exercise data collected by an exercise data collection method, an exercise condition input unit that inputs the exercise conditions at the time of collecting the exercise data that were input to the exercise data input unit, an exercise data analysis unit that compares the input exercise data with predetermined thresholds set corresponding to each of the input exercise conditions and analyzes whether the exercise data is within the predetermined thresholds or is equal to or greater than the predetermined thresholds, and an analysis result notification unit that notifies when the analyzed exercise data is within the predetermined thresholds or is equal to or greater than the predetermined thresholds.

[0014] The exercise data analysis system may further include an exercise instruction unit that instructs exercise content, and an exercise data collection unit that collects exercise data by the exercise data collection method.

[0015] The exercise data analysis system may further include a server, and data, instructions and results may be transmitted and received via the server as necessary, and may be remotely operable.

[0016] The exercise diagnosis support system of the present invention includes an exercise data input unit that inputs quantified exercise data collected by an exercise data collection method, an exercise condition input unit that inputs the exercise conditions at the time of data collection input to the exercise data input unit, and an exercise condition and exercise data display unit that displays the input exercise data and the input exercise conditions in a manner suitable for diagnosis, and is characterized by providing exercise diagnosis support.

[0017] The exercise diagnosis support system may further include an AI analysis unit that classifies the type of disease or symptom based on the exercise conditions and the exercise data, and may provide exercise diagnosis support.

[0018] The exercise diagnosis support system may further include a server, and data and results may be transmitted and received via the server as necessary, and may be remotely operable.

[0019] The analysis program of the present invention is an analysis program for a motion data analysis system, and is characterized in that it includes causing a computer to receive motion data that quantifies movements during crawling or rolling over, collected by the motion data collection method, receive the motion conditions at the time of collecting the motion data, and analyze the motion data by comparing it with predetermined thresholds set corresponding to each of the motion conditions to see if the motion data is within the predetermined thresholds or is equal to or greater than the predetermined thresholds, and alerting when the analyzed motion data is within the predetermined thresholds or is equal to or greater than the predetermined thresholds. Effect of the Invention

[0020] According to the motion data collection method of the present invention, the motion during crawling or rolling over can be easily measured and quantified. Both crawling and rolling over can be performed on a bed or a mat, and can be safely performed by the elderly, people undergoing rehabilitation after a stroke, as well as healthy elderly people, young people, and children. In addition, the measurement and quantification of crawling and rolling over movements can be easily performed without using special sensors or expensive measuring equipment, using familiar items such as smartphones, video cameras, cameras, IC recorders, tape measures, rulers, etc. Furthermore, the measurement of movements can be easily performed without requiring precise units such as millimeters or centimeters.

[0021] The motion data collection method, motion data analysis system, motion diagnosis support system, and control program of the present invention can assist in the diagnosis of intermediate states from a healthy state to a state requiring nursing care and the progression of the condition in various diseases, particularly Parkinson's disease and Alzheimer's disease, and further can assist in the diagnosis of various symptoms such as non-motor disinhibition, apathy, executive dysfunction, and attention deficit disorder.

[0022] Furthermore, the rolling over and crawling movements are movements that infants acquire unconsciously, and exercisers of all ages can enjoy the exercises. [Brief description of the drawings]

[0023] [Figure 1] An example of crawling (normal) is shown. [Diagram 2] Another example of crawling (normal) is shown below. [Diagram 3] An example of crawling on all fours (dorsus of the foot not grounded) is shown. [Figure 4] Another example of crawling (dorsus of the foot not grounded) is shown. [Diagram 5] An example of crawling exercise (side-to-side pelvic movement) is shown. [Figure 6] Another example of crawling exercise (side-to-side pelvic movement) is shown below. [Figure 7] An example of normal rolling over is shown below. [Figure 8] An example of rolling over motion (head first) is shown. [Figure 9] An example of rolling over (right knee leading) is shown below. [Figure 10] An example of rolling over (right knee flexion compensation) is shown. [Figure 11] 1 shows an example of an exercise data analysis system. [Figure 12] 1 shows an example of an exercise diagnosis support system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0024] The movement data collection method of the present invention includes the steps of measuring the movement of body parts during crawling or rolling over, and converting the measured movement into a numerical value.

[0025] (How to collect crawling data) Crawling on all fours, commonly known as "crawling," is one of the movements that infants acquire unconsciously. In a healthy crawling state, the baby moves forward or backward in a straight line while alternately touching both hands and the backs of both feet (below the knees, with the toes lying flat) to the floor. However, exercisers with non-motor symptoms such as Parkinson's disease, dementia, and disinhibition attempt to compensate for trunk abnormalities, and therefore show characteristic movements in the dorsum of the foot, lower back, and hands, especially when crawling on all fours. Therefore, the movements of one or more parts of the heel, lower back, and hands are measured, and the measured movements are quantified and collected as movement data.

[0026] Figures 1 to 6 show examples of crawling on all fours. Figures 1 and 2 are examples of normal crawling, with the dorsum of the foot touching the ground and no pelvic instability. Figures 3 and 4 are examples of insufficient grounding of the dorsum of the foot, and Figures 5 and 6 are examples of the pelvis instability to the left and right from the center line of the body. In addition, although it is not visible in the photograph, it is sometimes observed that the control of the crawling movement itself is not as good as in normal crawling, and backward movement after forward movement is not possible, or the backward movement becomes slow. Furthermore, there are also cases where the pelvis sways up and down during backward movement.

[0027] The degree of contact of the dorsum of the foot with the ground (float of the dorsum of the foot) can be obtained as exercise data by measuring the movement of the heel of the athlete and quantifying the height of the heel from the floor. The degree of pelvic instability ("fake" for left-right pelvic instability, and "it" for up-down pelvic instability) can be converted into exercise data by measuring the movement of the exerciser's lower back and quantifying the left-right and up-down distances from the body's central axis, which runs from the head to the spine, to the center of the lower back. The degree of control (accuracy) of crawling can be obtained by measuring the movement of the exerciser's hands and quantifying the positions on the floor where the left and right hands come into contact with the floor, or the time from the start of the movement to each contact, to obtain movement data. It is possible to determine whether the crawling movement is successful by measuring the positions on the floor where the left and right hands come into contact with the floor. By measuring the time from the start of the movement to each contact, it is possible to determine the movement speed, changes in the movement speed, difficulty in starting the movement, etc. The above-mentioned methods for collecting exercise data are merely examples, and the data is not limited to these methods as long as the data can be quantified.

[0028] The step of measuring the movement may be performed by attaching a mark to one or more parts to be measured, such as the heel, the lower back, or the hand, and measuring with a tape measure, a ruler, or the like, and recording the data. Alternatively, the speed of movement may be measured by attaching a member such as a bell that generates a sound in response to the movement of the body to the exerciser, and recording the sound with an IC recorder, or the like. Furthermore, photographs and videos may be taken using a familiar smartphone, video camera, or other camera. Alternatively, two or more of these may be combined.

[0029] The crawling exercise may be only forward movement, but it is preferable to alternate between forward and backward movement (stepping backwards) because this makes it possible to obtain motion data of the movement when switching between forward and backward movement.

[0030] The crawling exercise may be performed while holding the heels down toward the floor from above with a third person or a tool. If the heels are raised off the floor to compensate for the trunk abnormality, the degree of pelvic instability increases, or it becomes difficult to start moving backwards, and motion data of the waist or hands can also be obtained.

[0031] During the crawling motion, a specific rhythm signal can be further applied from the outside, which makes it easier to maintain a constant rhythm of the motion and also makes it easier to determine deviations from the rhythm signal of the crawling motion. The specific rhythm signal may be a signal that indicates only the tempo, such as a metronome, or a signal like a music CD. It may also be combined with a call. Among them, a 4-beat rhythm that matches the tempo at which the left and right hands touch the floor is preferable. Among the 4-beat rhythms, a rhythm that is easy for anyone to keep in tune is good, and 337 beats are more preferable. A 337 beat is a rhythm that is expressed as / 3 steps forward (1 beat rest) / 3 steps backward (1 beat rest) / 3 steps forward (1 beat stop) / 3 steps backward (1 beat rest) / , where the period between / and / is one measure. Since 337 beats are familiar from childhood, it is easy to get into the groove, and it is thought to make crawling on all fours more enjoyable. Furthermore, it is because it is possible to obtain movement data on movements such as not being able to stop or being late in starting to move backward when the 1 beat stops after the 3 steps forward in the 3rd measure. Furthermore, the specific rhythm signal may be a voice emitted by another person performing a similar crawling exercise, or handclaps, voice, percussion sounds, etc., in accordance with the rhythm of the other person's exercise. The rhythm signal of one or more other people performing a similar crawling exercise encourages greater synchronization with the crawling exercise, making it possible to perform the crawling exercise comfortably and enjoyably. The crawling exercise using the rhythm signal of one or more other people is easy to perform in rehabilitation facilities, day care centers, etc., and is thought to increase the opportunities to perform the crawling exercise of the present invention, and also improve the effect of the exercise.

[0032] (How to collect data on turning over movements) Rolling over is one of the movements that infants acquire unconsciously. In a healthy baby, the upper limbs lead the way, and the shoulders rotate before the head, without the knees lifting or bending. However, in people with non-motor symptoms such as Parkinson's disease, executive dysfunction, and disinhibition, the head rotates before the shoulders when turning over in bed, and the knees lift or flex. Therefore, the movement of one or more parts of the head, knees, and shoulders is measured, and the measured movement is quantified and collected as movement data.

[0033] 7 to 10 show examples of rolling over motion. In the present invention, the rolling over motion is exemplified by lying on one's back with arms folded, lifting the right shoulder off the floor, returning the right shoulder to the floor, lifting the left shoulder off the floor, and returning the left shoulder to the floor, which are repeated. When the right shoulder is lifted off the floor, the body assumes a position in which the body is about to rotate to the left, but does not actually rotate and returns to the original position. Similarly, when the left shoulder is lifted off the floor, the body assumes a position in which the body is about to rotate to the right, but does not actually rotate and returns to the original position. FIG. 7 shows an example of rolling over motion in a normal state, in which the head does not rotate before both shoulders, and the knees are not raised or bent. FIG. 8 shows an example in which the head rotates before both shoulders. The degree to which the head rotates beyond the shoulders (the "neck" of the head) can be obtained by measuring the movement of the athlete's head and quantifying the distance from the center of the body to the center of the head when viewed from above, and by measuring the movement of the athlete's shoulders and quantifying the distance from the floor to the shoulders, or the distance from the floor to the shoulders at each point in time from the start of the exercise, to obtain the exercise data. By quantifying the distance from the floor to the shoulders at each point in time from the start of the exercise, the speed of the exercise, changes in the speed of the exercise, the difficulty of starting the exercise, etc. can be grasped. Figure 9 shows an example in which the lower limbs, particularly the right knee, are lifted off the floor and rotate first, while Figure 10 shows an example in which the right knee is bent and rotated. The degree to which the knees rotate beyond the shoulders can be obtained as exercise data by measuring the movement of the exerciser's knees and quantifying the height from the floor to the knees. The above-mentioned methods for collecting exercise data are merely examples, and the data is not limited to these methods as long as the data can be quantified.

[0034] The step of measuring the movement may be performed by attaching marks to the parts to be measured, namely the head, both knees, and both shoulders, and measuring and recording the data using a tape measure, ruler, or the like. Alternatively, the speed of movement may be measured by attaching a member such as a bell that generates a sound in response to the movement of the body to the exerciser, and recording the sound using an IC recorder, or the like. Furthermore, photographs and videos may be taken using a familiar smartphone, video camera, camera, or the like. Alternatively, two or more of these methods may be combined.

[0035] The turning over movement may be performed while holding the knees down toward the floor from above with a third person or a tool. When the knees are raised from the floor to compensate for the trunk abnormality during the turning over movement, the head rotates more before the shoulders, and other movement data can also be obtained. Furthermore, compared to when the knees are held down, when they are not held down, the knees may bend first, and the feet may be pressed against the floor to turn over with the lower limbs first, and this can be quantified as the movement data by quantifying the distance from the floor to the knees at each point from the start of the movement. Figures 9 and 10 show an example of turning over with the lower limbs compensating, where the lower limbs (knees) are raised or bent before the shoulders.

[0036] During the turning motion, a specific rhythm signal can be further applied from the outside, which makes it easier to maintain a constant rhythm of the motion and also makes it easier to determine deviations from the rhythm signal of the turning motion. The specific rhythm signal may be a signal that indicates only the tempo, such as a metronome, or a signal like a music CD. It may also be combined with a call. Among them, a 4-beat rhythm that matches the tempo at which the left and right hands touch the floor is preferable. Among the 4-beat rhythms, a rhythm that is easy for anyone to keep in tune is good, and 337 beats are more preferable. A 337 beat is a rhythm that is expressed as follows, assuming that the period between / and / is one measure: / Lift the right shoulder from the floor on 2 beats → Return the right shoulder to the floor on 1 beat (1 beat rest) / Lift the left shoulder from the floor on 2 beats → Return the left shoulder to the floor on 1 beat (1 beat rest) / Lift the right shoulder from the floor on 2 beats → Return the right shoulder to the floor on 1 beat (1 beat stop) / Lift the left shoulder from the floor on 2 beats → Return the left shoulder to the floor on 1 beat (1 beat rest) / This is a rhythm that is familiar to children from childhood, so it is easy to get into and is thought to make rolling over exercise more enjoyable. Furthermore, the specific rhythm signal may be a voice from another person performing a similar turning motion, or handclaps, voice, percussion sounds, etc., that are timed to the rhythm of the other person's movements. The rhythm signal from one or more other people performing a similar turning motion encourages better synchronization with the turning motion, making it possible to perform the turning motion in a comfortable and enjoyable manner. Turning motions using the rhythm signal from one or more other people are easy to perform in rehabilitation facilities, day care centers, etc., and are likely to increase opportunities to perform the turning motion of the present invention, and also improve the effect of the exercise.

[0037] (Movement Data Analysis System) The exercise data analysis system includes an exercise data input unit, an exercise condition input unit, an exercise data analysis unit, and an analysis result notifying unit.

[0038] The motion data input unit 13 receives the digitized motion data collected by the above-mentioned motion data collection method. Note that the motion data measured and digitized by the motion data collection method differs depending on whether the heel is held down, whether the backward movement is performed, etc., even for the same waist movement. For this reason, the motion condition input unit 14 receives the motion conditions at the time of motion data collection.

[0039] A predetermined threshold is set for the motion data corresponding to each input motion condition. The motion data analysis unit 15 compares the input motion data with the threshold and analyzes whether the motion data is within the predetermined threshold or is equal to or greater than the predetermined threshold. Here, the predetermined threshold value can be set to, for example, the left-right distance from the central axis of the body passing through the head and spine to the center of the waist, such as 10 cm. Note that multiple threshold values ​​may be set in stages for the same exercise conditions.

[0040] The analysis result notification unit 16 notifies when the analyzed motion data is within a predetermined threshold or is equal to or greater than a predetermined threshold. Notification methods include, for example, changing the color when the motion data is equal to or greater than the threshold, highlighting a numerical value or image, or generating a chime or warning sound. When the motion data is within the threshold, the color may be changed or a sound may be generated, and further, different colors or sounds may be used when the motion data is equal to or greater than the threshold and when the motion data is within the threshold.

[0041] The exercise data analysis system 1 may further include an exercise instruction unit 11 and an exercise data collection unit 12.

[0042] The exercise instruction unit 11 instructs the exercise content, and examples thereof include instructing the start of exercise, exercise content, and exercise conditions by voice or screen. It may also send a specific rhythm signal that matches the exercise. The specific rhythm signal may be a signal that indicates only the tempo, such as a metronome, or a signal such as a music CD. It may be combined with a call, or may be a rhythm such as 337 beats.

[0043] The exercise data collection unit 12 collects the exercise data by the exercise data collection method described above. Specifically, examples of the method include taking photographs, taking videos, and recording audio.

[0044] The exercise data analysis system 1 may further include a server 17. If necessary, data, instructions, and results can be transmitted and received via the server 17. This allows remote operation. The exercise instruction unit 11, exercise data collection unit 12, exercise data input unit 13, exercise condition input unit 14, exercise data analysis unit 15, and analysis result notification unit 16 may be tangible entities or software modules. FIG. 11 shows an example of an exercise data analysis system.

[0045] (Exercise diagnosis support system) The exercise diagnosis support system 2 includes an exercise data input unit 21, an exercise condition input unit 22, and an exercise condition and exercise data display unit 23, and performs exercise diagnosis support.

[0046] The motion data input unit 21 receives the digitized motion data collected by the above-mentioned motion data collection method. Note that the motion data measured and digitized by the motion data collection method differs depending on whether the heel is held down, whether the backward movement is performed, etc., even for the same waist movement. For this reason, the motion conditions at the time of motion data collection are input to the motion condition input unit.

[0047] The exercise condition and exercise data display unit 23 displays the input exercise data and the input exercise conditions in a manner suitable for diagnosis. Examples of the display suitable for diagnosis include, but are not limited to, making a list of the exercise data and exercise conditions, arranging the images so that the centers are aligned, and graphing the difference between a specific rhythm and the rhythm of the exercise.

[0048] The exercise diagnosis support system 2 may further include an AI analysis unit 24. The AI ​​analysis unit 24 classifies the type of disease or symptom under supervised conditions based on the exercise conditions and the exercise data. The digitized exercise data collected by the above-mentioned exercise data collection method includes exercise data on the movements of one or more parts of the body. Therefore, classifying the type of disease or symptom from multiple perspectives leads to improved accuracy of diagnosis, rather than judging the disease or symptom based on the threshold value of one piece of exercise data. Therefore, by further providing the AI ​​analysis unit 24, it is possible to provide more advanced support to experts in diagnosing diseases and symptoms.

[0049] The exercise diagnosis support system may further include a server 25. Data and display contents can be transmitted and received via the server as necessary, which enables remote operation. The exercise data input unit 21, the exercise condition input unit 22, the exercise condition and exercise data display unit 23, and the AI ​​analysis unit 24 may be tangible objects or software modules. FIG. 12 shows an example of an exercise diagnosis support system.

[0050] (Analysis program) The analysis program of the motion data analysis system of the present invention includes having a computer receive motion data that quantifies movements during crawling or rolling over, collected by the motion data collection method described above, receive the motion conditions at the time of collecting the motion data, and analyze the motion data by comparing it with predetermined thresholds set corresponding to each of the motion conditions to see if it is within the predetermined thresholds or is above the predetermined thresholds, and alerting when the analyzed motion data is within the predetermined thresholds or is above the predetermined thresholds. This analysis program is a program corresponding to the exercise data analysis system 1 described above. EXAMPLES

[0051] Examples of the present invention will be described in detail below, but the present invention is not limited to these examples.

[0052] Example 1 In the crawling exercise, a third party held down the heels of the subjects while they moved backwards to collect data. In the rolling over exercise, the subjects were asked to lie on their backs with their arms folded, and data was collected. In patients with Parkinson's disease who show noticeable freezing, measuring the movement of the hands revealed that the initiation of backward movement was delayed and the backward movement was slow. In addition, measuring the movement of the lower back revealed that there were cases in which the pelvis was observed to move side to side and cases in which it was not. Turning over in bed was also difficult, and it was found that the shoulders were slow to rise from the floor. Measuring the movement of the knees also revealed that the knees rose off the floor before the shoulders began to rotate, and that the force of the lower limbs was used to compensate for turning over in bed. Furthermore, when the patient turned over in bed, a third person held down the knees to collect movement data. By measuring the movement of both shoulders, it was found that the amount of rotation of both shoulders was further insufficient. Apathy and decreased motivation were also prominent in such patients.

[0053] Example 2 During the crawling motion, movement data was collected by giving a rhythmic signal of 337 beats, which consisted of 3 steps forward, 3 steps backward, 3 steps forward (pause for 1 beat), and 3 steps backward. In patients with Parkinson's disease dementia (dementia with Lewy bodies), who exhibit not only the motor symptoms of Parkinson's disease but also disinhibition and impulsive inattention, measuring the movements of the hand showed that there was a delay in starting reverse movement and that reverse movement was slower. In addition, it was found that forward movement was quicker than reverse, and that the patient was unable to stop when changing from forward to reverse. By measuring the movement of the lower back while crawling, it was found that some patients had lateral pelvic sway and others did not. In immature crawling cases with lateral pelvic sway, irritability and emotional instability were prominent.

[0054] Example 3 In the rolling over exercise, the subject was asked to lie on his back with his arms crossed, and movement data was collected by giving a rhythmic signal of 337 beats to the subject as he lifted his right shoulder off the floor in 2 beats → returned the right shoulder to the floor in 1 beat (1 beat rest) → lifted his left shoulder off the floor in 2 beats → returned the left shoulder to the floor in 1 beat (1 beat rest) → lifted his right shoulder off the floor in 2 beats → returned the right shoulder to the floor in 1 beat (1 beat stop) → lifted his left shoulder off the floor in 2 beats → returned the left shoulder to the floor in 1 beat (1 beat rest). In patients with Parkinson's disease and dementia with Lewy bodies who have impaired executive function, when their necks bend and their backs flex, causing problems walking, measuring the movements of the head and both shoulders shows that the movements of the shoulders are slower than the rhythmic signals, and that the head rotates before the shoulders. Furthermore, among these patients who have difficulty allocating attention and processing emotions, there are also cases where they present with visual hallucinations.

[0055] Example 4 During the crawling exercise, forward and backward movement data were collected without giving any specific rhythmic signal. In patients with Parkinson's disease who show prominent anxiety accompanied by depression and physical symptoms of the autonomic nervous system, measuring the movement of the heel area showed that the dorsum of the foot leaves the floor when walking backwards. Furthermore, measuring the movement of the heel, lower back, and hands showed that left-right coordination was inaccurate. Furthermore, in patients with Alzheimer's dementia who show prominent memory impairment, forgetfulness, and irritability and irritability (aggressive behavior), measuring the movement of the lower back showed that they walked forward quickly and showed left-right swaying of the pelvis.

[0056] Example 5 During the crawling exercise, the subject alternated between forward and backward movement (stepping back), and motion data was collected for the movement when switching between forward and backward movement.In addition, during the crawling exercise, a third person held down the heels and made the subject step backward, and motion data was collected. Even when the heels were pressed, no side-to-side sway of the pelvis was observed, but rather up-and-down sway. Among the patients with Parkinson's disease who had difficulty switching between moving forward and backward, there were patients who showed social behavioral disorders in the Iowa Gambling Task.

[0057] Regardless of Parkinson's motor symptoms or forgetfulness, the motor data during crawling or rolling over in bed are evaluated in a composite manner depending on the type of cognitive and emotional disorder. For example, it was found that apathetic / freezing exercisers are classified as slow in both crawling and rolling over, and that their heels rise from the floor when they start to move forward when crawling on all fours, and that their waists sway from side to side when they move backward. It was found that disinhibited exercisers (easily angered) are quick in both crawling and rolling over, and cannot stop when changing from forward to backward or backward to forward when crawling on all fours. It was found that exercisers with executive dysfunction rotate their heads before both shoulders when rolling over, and that when starting to move backward when crawling on all fours, the waist sways up and down when holding down the heels. It has been found that when this executive dysfunction is combined with symptoms such as the heels lifting off the floor when crawling backwards on all fours, which indicate impulsivity (irritability) and instability in emotional processing, or the hips swaying from side to side when moving forward, or immature crawling, visual hallucinations and abnormal posture can occur. [Industrial Applicability]

[0058] As mentioned above, it was found that the movement data during crawling or rolling over is closely and intricately related to diseases common to the elderly, such as Parkinson's disease, Alzheimer's dementia, and Lewy body dementia, non-motor symptoms such as apathy (loss of motivation), disinhibition, impulsivity (irritability), and executive dysfunction (attention disorder), emotional and cognitive disorders, as well as symptoms such as freezing, abnormal posture, and pain that cause falls, as well as hallucinations, neurosis, emotional instability, depression, physical symptoms of the autonomic nervous system, memory disorders, forgetfulness, irritability (aggressive behavior), and social behavior disorders. At the same time, although the crawling and rolling over movements that are acquired unconsciously during infancy are simple, it is easy to see the difference between movements in a healthy state and movements in a state with a disease or symptom. The present invention can assist in the complex diagnosis of various symptoms, including various diseases and non-motor symptoms, by collecting movement data during crawling or rolling over, and by providing a system for analyzing the collected movement data, a system for assisting in movement diagnosis, and an analysis program. [Explanation of symbols]

[0059] 1. Exercise data analysis system 11 Exercise instruction section 12 Exercise Data Collection Section 13 Exercise data input section 14 Exercise condition input section 15. Exercise Data Analysis Department 16 Analysis result notification section 17 Network Server 2 Exercise diagnosis support system 21 Exercise data input section 22 Exercise condition input section 23 Exercise conditions and exercise data display section 24 AI analysis department 25 Network Server

Claims

1. A method for collecting motion data during crawling or rolling over, comprising: A step of measuring the movement of one or more parts of the heels, lower back, and hands during crawling, and the movement of one or more parts of the head, both knees, and both shoulders during rolling over; A step of quantifying the movement measured by the step of measuring the movement; Including, The turning over exercise includes repeating the exercise of lying on one's back with arms folded, lifting the right shoulder off the floor and then returning it to its original position, and then lifting the left shoulder off the floor and then returning it to its original position, in the exercise data collection method.

2. In the step of quantifying the crawling motion, When measuring the movement of the heel, the height from the floor to the heel is converted into a numerical value. When measuring the movement of the lower back, the distance from the central axis of the body that passes through the head and spine to the center of the lower back is quantified. When measuring hand movements, the positions of the floor where the left and right hands come into contact with the floor, or the time from the start of the movement to each contact, are quantified. In the step of quantifying the turning over motion, Head movement is quantified as the distance from the center of the body to the center of the head when viewed from above. The movement of both knees is quantified by measuring the height from the floor to the knees.

2. The exercise data collection method according to claim 1, wherein the movement of both shoulders is quantified as a distance from a floor surface to the shoulders, or a distance from the floor surface to the shoulders at each time point from the start of exercise.

3. The motion data collection method according to claim 1 or 2, wherein the crawling motion includes forward motion and backward motion.

4. The motion data collection method according to any one of claims 1 to 3, wherein the crawling exercise further includes a heel press, and the movement of the waist or hands is measured.

5. The motion data collection method according to claim 1 or 2, wherein the turning motion further includes knee pressing, and the motion of the head or shoulders is measured.

6. 6. The exercise data collection method according to claim 1, further comprising the step of externally applying a specific rhythm signal during the crawling exercise or the rolling over exercise.

7. The exercise data collection method according to claim 6, wherein the specific rhythm signal is a voice emitted by one or more other people performing the crawling exercise or rolling over exercise in a similar manner, or handclaps, voices, or percussion sounds synchronized with the rhythm of the other people's exercise.

8. The exercise data collection method according to any one of claims 1 to 7, wherein the step of measuring the movement is carried out by measuring with marks on the parts to be measured, by recording with a component attached to the body that generates a sound in response to body swaying, by photographing, by videotaping, or by a combination of two or more of these.

9. An exercise data input unit that inputs the digitized exercise data collected by the exercise data collection method according to any one of claims 1 to 8; an exercise condition input unit that inputs the exercise conditions at the time of collecting the exercise data input to the exercise data input unit; An exercise data analysis unit that compares the input exercise data with a predetermined threshold value set corresponding to each of the input exercise conditions, and analyzes whether the exercise data is within the predetermined threshold value or is equal to or greater than the predetermined threshold value; an analysis result notifying unit that notifies when the analyzed motion data is included in the predetermined threshold value or is equal to or greater than the predetermined threshold value; 23. A motion data analysis system comprising:

10. An exercise instruction unit that instructs the exercise content; An exercise data collection unit that collects exercise data in the exercise data collection method; The athletic data analysis system of claim 9 , further comprising:

11. 11. The exercise data analysis system according to claim 9 or 10, further comprising a server, wherein data, instructions and results are transmitted and received via said server as necessary, and said system is remotely operable.

12. An exercise data input unit that inputs the digitized exercise data collected by the exercise data collection method according to any one of claims 1 to 8; an exercise condition input unit that inputs the exercise conditions at the time of data collection input to the exercise data input unit; an exercise condition and exercise data display unit that displays the input exercise data and the input exercise conditions in a manner suitable for diagnosis; and performing exercise diagnosis support.

13. The exercise diagnosis support system according to claim 12 , further comprising an AI analysis unit that classifies a type of disease or symptom based on the exercise conditions and the exercise data, and performs exercise diagnosis support.

14. 14. The exercise diagnosis support system according to claim 12 or 13, further comprising a server, wherein data and results are transmitted and received via the server as necessary, and the system is remotely operable.

15. An analysis program for a motion data analysis system, On the computer, receiving motion data that is a numerical representation of a crawling motion or a rolling motion, the motion data being collected by the motion data collection method according to any one of claims 1 to 8; receiving exercise conditions at the time of collecting the exercise data; using predetermined thresholds set corresponding to the respective exercise conditions and the exercise data, and analyzing whether the exercise data is within the predetermined thresholds or is equal to or greater than the predetermined thresholds; An analysis program that includes notifying when the analyzed motion data is within the predetermined threshold value or is equal to or greater than the predetermined threshold value.

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