WO2021100815A1 - Movement data collection method, movement data analysis system, movement diagnosis assistance system, and analysis program - Google Patents

Movement data collection method, movement data analysis system, movement diagnosis assistance system, and analysis program Download PDF

Info

Publication number
WO2021100815A1
WO2021100815A1 PCT/JP2020/043225 JP2020043225W WO2021100815A1 WO 2021100815 A1 WO2021100815 A1 WO 2021100815A1 JP 2020043225 W JP2020043225 W JP 2020043225W WO 2021100815 A1 WO2021100815 A1 WO 2021100815A1
Authority
WO
WIPO (PCT)
Prior art keywords
exercise
movement
exercise data
data
data collection
Prior art date
Application number
PCT/JP2020/043225
Other languages
French (fr)
Japanese (ja)
Inventor
内山　剛
知宏 仲山
Original Assignee
社会福祉法人聖隷福祉事業団
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 社会福祉法人聖隷福祉事業団 filed Critical 社会福祉法人聖隷福祉事業団
Priority to JP2021558447A priority Critical patent/JPWO2021100815A1/ja
Publication of WO2021100815A1 publication Critical patent/WO2021100815A1/en

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb

Definitions

  • the present invention relates to an exercise data collection method, an exercise data analysis system, an exercise diagnosis support system, and an analysis program. In particular, it relates to exercise data during crawl exercise or rollover exercise.
  • Parkinson's disease in particular becomes a serious condition requiring nursing care, such as a broken neck, a bent waist, and difficulty in walking. Nevertheless, it is very difficult to evaluate the progress of the intermediate state and the medical condition from the healthy state to the state requiring nursing care from the standing position and the bipedal walking posture. For this reason, there is a problem that the burden of long-term care increases, and the progress of the medical condition is noticed after the burden of long-term care increases.
  • the present invention has collected exercise data collection methods for measuring and quantifying the movements of turning over and crawl movements unconsciously acquired by infants from elderly people, young people, children, and the like.
  • an exercise diagnosis support system that displays collected exercise data in a method suitable for diagnosis to support exercise diagnosis, and an analysis program of the exercise data analysis system. Make it an issue.
  • the exercise data collection method of the present invention is an exercise data collection method during four-crawling exercise or turning over exercise, and is one of the heel, waist, and hand during four-crawling exercise.
  • the step of measuring the movement of one or more parts, one or more parts of the head, both knees, and both shoulders during the rolling exercise, and the movement measured by the step of measuring the movement are quantified. It is characterized by including steps to be performed.
  • the four-crawling movement may include forward movement and backward movement.
  • the four-crawling exercise may further include heel holding.
  • the rolling exercise may further include knee holding.
  • the steps for measuring the movement include measurement by marking the part to be measured, measurement by recording with a member that generates sound in response to the shaking of the body attached to the body, measurement by photography, and measurement by moving image. , Or a combination of two or more of them.
  • the exercise data analysis system of the present invention inputs an exercise data input unit that inputs digitized exercise data collected by an exercise data collection method, and an exercise condition at the time of collecting exercise data input to the exercise data input unit.
  • the exercise condition input unit to be input, a predetermined threshold set corresponding to each of the input exercise conditions, and the input exercise data are compared, and whether the exercise data is included in the predetermined threshold.
  • the motion data analysis unit that analyzes whether or not it is equal to or higher than the predetermined threshold, and when the analyzed motion data is included in the predetermined threshold, or when it is equal to or higher than the predetermined threshold. It is characterized by including an analysis result notification unit for notifying the data.
  • the exercise data analysis system may further include an exercise instruction unit that instructs the exercise content and an exercise data collection unit that collects exercise data by the exercise data collection method.
  • the motion data analysis system may further include a server, and if necessary, data, instructions and results may be transmitted and received via the server, and remote control may be possible.
  • the exercise diagnosis support system of the present invention inputs an exercise data input unit that inputs digitized exercise data collected by the exercise data collection method, and an exercise condition at the time of data collection input to the exercise data input unit.
  • exercise diagnosis support including an exercise condition input unit and an exercise condition and exercise data display unit that displays the input exercise data and the input exercise condition in a method suitable for diagnosis. It is characterized by.
  • the exercise diagnosis support system may further include an AI analysis unit that classifies the types of diseases and symptoms based on the exercise conditions and the exercise data, and may provide exercise diagnosis support.
  • the exercise diagnosis support system may further include a server, and if necessary, data and results may be transmitted and received via the server, and remote control may be possible.
  • the analysis program of the present invention is an analysis program of an exercise data analysis system, and causes a computer to receive exercise data obtained by quantifying the movements during crawl exercise or turning over exercises collected by the exercise data collection method. , The exercise condition at the time of collecting the exercise data is received, and the exercise data is included in the predetermined threshold by using the predetermined threshold set corresponding to each of the exercise conditions and the exercise data.
  • the analysis is performed by comparing whether or not the data is equal to or higher than the predetermined threshold value, and the notification is given when the analyzed exercise data is included in the predetermined threshold value or when the data is equal to or higher than the predetermined threshold value. It is characterized by including.
  • the exercise data collection method of the present invention it is possible to easily measure and quantify the movement during the four-crawling exercise or the rolling exercise.
  • Crawling exercises and rolling exercises can be performed on the bed or on the mat, and can be safely performed by elderly people, people undergoing rehabilitation after stroke, and healthy elderly people, young people, children, and the like.
  • the movements of crawl and rollover movements can be measured and quantified using familiar smartphones, video cameras, cameras, IC recorders, tape measures, rulers, etc., without using special sensors or expensive measuring equipment. It can be done easily.
  • the motion can be easily measured in that the accuracy of the motion measurement does not require a fine unit such as millimeter or 1 cm.
  • the exercise data analysis system the exercise diagnosis support system, and the control program of the present invention, it is intermediate from a healthy state to a state requiring nursing care in various diseases such as Parkinson's disease and Alzheimer's dementia. It is possible to support the diagnosis of the progression of a condition or a medical condition, and further, it is possible to support the diagnosis of various symptoms such as desuppression of non-motor symptoms, apathy, executive dysfunction, and attention defect disorder.
  • the roll-over exercise and the four-crawling exercise are exercises that infants unconsciously acquire, and exercisers themselves can enjoy exercising in any generation.
  • An example of four-crawling exercise (normal) is shown. Another example of four-crawling exercise (normal) is shown. An example of four-crawling exercise (foot-back contact failure) is shown. Another example of four-crawling exercise (foot-back contact failure) is shown. An example of four-crawling exercise (pelvic left-right sway) is shown. Another example of crawl movement (pelvic left-right sway) is shown. An example of turning over exercise (normal) is shown. An example of turning over exercise (head precedent) is shown. An example of turning over exercise (right knee precedent) is shown. An example of turning over exercise (compensation for right knee flexion) is shown. An example of the motion data analysis system is shown. An example of the exercise diagnosis support system is shown.
  • the exercise data collection method of the present invention includes a step of measuring the movement of a body part during a four-crawling exercise or a rolling exercise, and a step of quantifying the measured movement.
  • the four-crawling exercise is also generally called high-high, and is one of the exercises that infants unconsciously acquire.
  • both hands and the backs of both feet are alternately brought into contact with the floor surface to move forward or backward (backward).
  • exercisers with non-motor symptoms such as Parkinson's disease, dementia and disinhibition try to compensate for trunk abnormalities, so the movements of the back, hips and hands are characteristic, especially in the four-crawling exercise. .. Therefore, the movement of one or more parts of the heel, the lumbar region, and the hand is measured, the measured movement is quantified, and the movement data is collected.
  • FIGS. 1 and 6 show an example of a four-crawling exercise.
  • FIGS. 1 and 2 are examples of four crawls in a normal state, in which the back of the foot touches the ground and no pelvic sway is observed.
  • 3 and 4 are examples of foot dorsal ground contact failure
  • FIGS. 5 and 6 are examples of the pelvis swaying from side to side from the center line of the body.
  • the control of the four-crawling movement itself is not good, the backward movement cannot be performed after the forward movement, and the backward movement becomes slow. There are times.
  • the pelvis sways up and down during backward movement there are cases where the pelvis sways up and down during backward movement.
  • the degree of foot contact (“uki” on the back of the foot) can be obtained as exercise data by measuring the movement of the heel of the exerciser and quantifying the height from the floor to the heel.
  • the degree of pelvic sway (“pretend” of pelvic left-right sway, "I” of vertical sway) measures the movement of the exerciser's lumbar region, and is left and right from the central axis of the body through the spine from the head to the center of the lumbar region.
  • Motion data can be obtained by quantifying the distance and the vertical distance.
  • the degree of control (accuracy) of the four-crawling movement measures the movement of the exerciser's hand, and the position of the floor surface where the left and right hands and the floor surface sequentially contact, or the time from the start of the exercise to each contact. Can be obtained as exercise data by quantifying. From each position of the floor surface where the left and right hands and the floor surface come into contact with each other, it is possible to grasp whether or not the crawl movement is performed. By measuring the time from the start of exercise to each contact, it is possible to grasp the exercise speed, the change in exercise speed, the difficulty of starting exercise, and the like. It should be noted that the above-mentioned method for collecting exercise data is merely an example, and is not limited to these methods as long as it can be quantified.
  • a mark may be attached to any one or more of the heel, the waist, and the hand, and the measurement may be performed with a measure or a ruler and recorded as data.
  • a member such as a bell that generates a sound in response to the shaking of the body may be attached to the exerciser and recorded by an IC recorder or the like to measure the exercise speed.
  • a photograph or a moving image may be taken using a familiar smartphone, video camera, camera or the like. Alternatively, it may be performed by a combination of two or more of them.
  • the four-crawling movement may be only the forward movement, but it is preferable to alternately perform the forward movement and the backward movement (backward movement). This is because the motion data of the motion when switching between the forward motion and the reverse motion can also be acquired.
  • the four-crawling exercise may be performed after pressing the heel part from above toward the floor surface with a third party or an instrument. If the heel is raised from the floor to compensate for the trunk abnormality, the movement data of the lumbar or hand movement such as the degree of pelvic sway becomes larger or it becomes difficult to start backwards is also included. Because it can be obtained.
  • a specific rhythm signal can be further given from the outside.
  • Such a rhythm signal makes it easy to maintain a constant rhythm of movement, and it is also easy to determine a deviation from the rhythm signal of crawl movement.
  • the specific rhythm signal may be a signal indicating only the tempo such as a metronome, or a signal such as a music CD. It may also be combined with a shout.
  • a four-beat beat that matches the tempo at which the left and right hands come into contact with the floor is preferable. Among the four beats, the rhythm that anyone can easily take is good, and the 337 beat is more preferable.
  • 337 beats are defined as 1 bar between / and / / 3 forward steps (1 beat break) / 3 backward steps (1 beat break) / 3 forward steps (1 beat stop) / 3 backward steps (1 beat) It is a rhythm expressed by (holiday) /, and since it has been familiar since childhood, it is easy to get rid of it, and it is thought that the crawl exercise will be more fun. Further, it is possible to acquire motion data of movements such as not stopping or delaying the reverse start when one beat is stopped three steps forward in the third measure.
  • the specific rhythm signal may be a voice emitted from another person performing the same four-crawling movement, a clapping, a voice, a percussion instrument sound, or the like in accordance with the rhythm of the movement of the other person.
  • the rhythm signal of one or more others who perform the same crawl exercise promotes more synchronization with the quadruple exercise, and the quadruple exercise can be performed comfortably and hurt. It is considered that the four-crawling exercise by the rhythm signal of one or more other people is easy to be carried out at a rehabilitation facility, a day service, etc. Be done.
  • the roll-over exercise is also one of the exercises that infants unconsciously acquire. In a healthy state, the rolling motion is performed with the upper limbs leading and both shoulders rotating before the head, and the knees do not float or bend. However, exercisers with non-motor symptoms such as Parkinson's disease, executive dysfunction, and disinhibition try to compensate for trunk abnormalities. You can see the movement of floating and bending. Therefore, the movement of one or more parts of the head, both knees, and both shoulders is measured, the measured movement is quantified, and the movement data is collected.
  • the rolling motion is exemplified by repeating the process of folding the arms on the back, separating the right shoulder from the floor surface, returning the right shoulder to the floor surface, separating the left shoulder from the floor surface, and returning the left shoulder to the floor surface. .. If the right shoulder is separated from the floor, the posture will try to rotate to the left, but in reality, it will return to the original posture without rotating. Similarly, when the left shoulder is separated from the floor surface, the posture is to try to rotate to the right, but it actually returns to the original posture without rotating. FIG.
  • FIG. 7 shows an example of a rolling motion in a normal state, in which the head does not rotate ahead of both shoulders, and the knees do not float or bend.
  • FIG. 8 shows an example in which the head rotates before both shoulders.
  • the degree to which the head rotates before both shoulders measures the movement of the exerciser's head and measures the distance from the center point of the body to the center point of the head in top view.
  • the movement of both shoulders of the exerciser is measured, and the distance from the floor to the shoulder or the distance from the floor to the shoulder at each point in time from the start of the exercise is quantified to obtain exercise data. Can be done.
  • FIG. 9 shows an example in which the lower limbs, particularly the right knee, floats from the floor surface and rotates in advance
  • FIG. 10 shows an example in which the right knee bends and rotates.
  • the degree to which the knees rotate before both shoulders can be obtained as exercise data by measuring the movements of both knees of the exerciser and quantifying the height from the floor to the knees. It should be noted that the above-mentioned method for collecting exercise data is merely an example, and is not limited to these methods as long as it can be quantified.
  • the parts to be measured on the head, both knees, and both shoulders may be marked, measured with a measure, a ruler, etc., and recorded as data.
  • a member such as a bell that generates a sound in response to the shaking of the body may be attached to the exerciser and recorded by an IC recorder or the like to measure the exercise speed.
  • a photograph or a moving image may be taken using a familiar smartphone, video camera, camera or the like. Alternatively, it may be performed by a combination of two or more of them.
  • the rolling exercise may be performed after pressing the knee from above toward the floor with a third party or equipment. If the trunk abnormality is compensated by raising the knee from the floor during the rolling exercise, exercise data such as the degree of rotation of the head before both shoulders can be obtained at the same time. Is. Furthermore, compared to the case where the knee is pressed, when the knee is not pressed, the knee flexion precedes and the foot is pressed against the floor surface, and the lower limb precedes and turns over. Exercise data can be obtained by quantifying the distance from the floor to the knee at the time point. 9 and 10 are examples of turning over at the cost of the lower limbs, and the lower limbs (knees) are floating or flexed before both shoulders.
  • a specific rhythm signal can be further given from the outside during the rolling exercise.
  • Such a rhythm signal makes it easy to maintain a constant exercise rhythm, and also makes it easy to determine a deviation from the rhythm signal of the rolling motion.
  • the specific rhythm signal may be a signal indicating only the tempo such as a metronome, or a signal such as a music CD. It may be combined with a shout.
  • a four-beat beat that matches the tempo at which the left and right hands come into contact with the floor is preferable.
  • the rhythm that anyone can easily take is good, and the 337 beat is more preferable.
  • the specific rhythm signal may be a voice emitted from another person performing the same rolling motion, a clapping, a voice, a percussion instrument sound, or the like in accordance with the rhythm of the other person's movement.
  • the rhythm signal of one or more other people who perform the same rolling exercise promotes more synchronization with the rolling exercise, and the rolling exercise can be performed comfortably and honest. It is considered that the rolling exercise by the rhythm signal of one or more other people is easy to be carried out at a rehabilitation facility, a day service, etc., it is easy to increase the chances of carrying out the rolling exercise of the present invention, and the effect of the exercise is also improved.
  • 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 notification unit.
  • the exercise data input unit 13 inputs the quantified exercise data collected by the exercise data collection method described above.
  • the exercise data measured and quantified by the exercise data collection method differs depending on whether the heel is pressed or the reverse exercise is performed even with the same lumbar movement. Therefore, the exercise condition input unit 14 inputs the exercise condition at the time of collecting the exercise data.
  • a predetermined threshold value is set in the exercise data corresponding to each of the input exercise conditions.
  • the exercise data analysis unit 15 compares the threshold value with the input exercise data, and analyzes whether the exercise data is included in the predetermined threshold value or is equal to or higher than the predetermined threshold value.
  • the predetermined threshold value for example, the left-right distance from the central axis of the body passing through the head to the spine to the center of the lumbar region can be set to 10 cm or the like.
  • a plurality of threshold values under the same exercise condition may be set stepwise.
  • the analysis result notification unit 16 notifies when the analyzed exercise data is included in a predetermined threshold value or when it is equal to or higher than a predetermined threshold value.
  • Examples of the notification method include changing the color when the threshold value is exceeded, highlighting a numerical value or an image, and generating a chime sound or a warning sound.
  • the color may be changed when the value is within the threshold value, a sound may be generated, and the color or sound may be displayed differently when the value is above the threshold value and when the sound is within the threshold value.
  • the exercise data analysis system 1 may further include an exercise instruction unit 11 and an exercise data collection unit 12.
  • the exercise instruction unit 11 instructs the exercise content, and examples thereof include those instructing the start of exercise, the exercise content, and the exercise conditions by voice or screen.
  • a specific rhythm signal may be sent in accordance with the exercise.
  • the specific rhythm signal may be a signal indicating only the tempo such as a metronome, or a signal such as a music CD. It may be combined with a shout or a rhythm such as 337 beats.
  • the exercise data collection unit 12 collects exercise data by the exercise data collection method described above. Specifically, photography, moving image shooting, recording, and the like are exemplified.
  • the motion data analysis system 1 may further include a server 17. Data, instructions and results can be sent and received via the server 17 as needed. This enables remote control.
  • FIG. 11 shows an example of the motion data analysis system.
  • the exercise diagnosis support system 2 includes an exercise data input unit 21, an exercise condition input unit 22, an exercise condition and an exercise data display unit 23, and provides exercise diagnosis support.
  • the exercise data input unit 21 inputs the quantified exercise data collected by the exercise data collection method described above.
  • the exercise data measured and quantified by the exercise data collection method differs depending on whether the heel is pressed or the reverse exercise is performed even with the same lumbar movement. Therefore, the exercise condition input unit inputs the exercise condition at the time of collecting the exercise data.
  • the exercise condition and exercise data display unit 23 displays the input exercise data and the input exercise condition by a method suitable for diagnosis.
  • Displaying a method suitable for diagnosis includes listing exercise data and exercise conditions, aligning the centers of images, and graphing the difference between a specific rhythm and exercise rhythm. Not limited to these.
  • the exercise diagnosis support system 2 may further include an AI analysis unit 24.
  • the AI analysis unit 24 classifies the types of diseases and symptoms under supervised conditions based on exercise conditions and exercise data.
  • the quantified exercise data collected by the exercise data collection method described above includes the exercise data of the movement of one or more parts. Therefore, it is possible to improve the accuracy of diagnosis by classifying the types of diseases and symptoms from multiple viewpoints, rather than judging the diseases and symptoms by the threshold value of one point of exercise data. Therefore, by further providing the AI analysis unit 24, it is possible to support the diagnosis of diseases and symptoms of specialists to a higher degree.
  • the exercise diagnosis support system may further include a server 25. Data and display contents can be sent and received via the server as needed. This enables remote control.
  • 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 modules on software.
  • FIG. 12 shows an example of the exercise diagnosis support system.
  • the analysis program of the exercise data analysis system of the present invention causes a computer to receive the exercise data obtained by quantifying the movement during the four-crawling exercise or the rolling exercise, which is collected by the above-mentioned exercise data collection method, and collects the exercise data.
  • the exercise data at the time is received, and the exercise data is included in the predetermined threshold value or is equal to or higher than the predetermined threshold value by using the predetermined threshold value set corresponding to each of the exercise conditions and the exercise data.
  • Such an analysis program is a program corresponding to the above-mentioned motion data analysis system 1.
  • Example 1 In the four-crawling exercise, a third party pressed the heel to move backward and collected exercise data. In addition, in the rolling exercise, the arms were folded on the back and exercise data was collected. In patients with Parkinson's disease who had prominent freezing, it was found that the backward start was delayed and the backward movement was delayed by measuring the movement of the hand part. In addition, by measuring the movement of the lumbar region, it was found that there are cases where left and right pelvic sway is observed and cases where it is not observed. It was found that turning over was also difficult and the shoulders were slowly lifted from the floor.
  • Example 2 In the four-crawling exercise, exercise data was collected by giving a rhythm signal of 337 beats in the order of 3 forward steps ⁇ 3 backward steps ⁇ 3 forward steps (1 beat stop) ⁇ 3 backward steps.
  • a rhythm signal of 337 beats in the order of 3 forward steps ⁇ 3 backward steps ⁇ 3 forward steps (1 beat stop) ⁇ 3 backward steps.
  • measuring the movement of the hand region delays backward initiation.
  • the forward movement was quicker than the reverse movement and could not be stopped when changing from forward movement to reverse movement.
  • Example 3 In the rolling exercise, fold your arms on your back and separate your right shoulder from the floor with 2 beats ⁇ return your right shoulder to the floor with 1 beat (1 beat break) ⁇ separate your left shoulder from the floor with 2 beats ⁇ left shoulder with 1 beat Return to the floor (1 beat rest) ⁇ 2 beats to separate the right shoulder from the floor ⁇ 1 beat to return the right shoulder to the floor (1 beat stop) ⁇ 2 beats to separate the left shoulder from the floor ⁇ 1 beat The movement data was collected by giving a rhythm signal of 337 beats to return the left shoulder to the floor surface (1 beat break).
  • Example 4 In the four-crawling movement, forward and backward movement data were collected without giving a specific rhythm signal. In patients with Parkinson's disease who were prominently anxious with depression and physical symptoms of the autonomic nervous system, measuring the movement of the heel site showed that the back of the foot was separated from the floor when moving backward. Furthermore, by measuring the movements of the heel, lumbar, and hand parts, it was found that the left-right coordination was inaccurate. In addition, in patients with Alzheimer's disease who have conspicuous irritability and irritation (aggressive behavior) in addition to memory impairment and forgetfulness, by measuring the movement of the lumbar region, the movement of the lumbar region is measured so that the patient can move forward quickly and in the pelvis. It turned out that left and right sway was seen.
  • Example 5 In the four-crawling movement, forward movement and backward movement (backward movement) were alternately performed, and motion data of the movement when switching between forward movement and reverse movement was collected. Furthermore, in the four-crawling exercise, a third party pressed the heel to move backward and collected exercise data. Left-right pelvic sway is not observed even when the heel is pressed, and in Parkinson's disease that sways up and down, in some cases where it is difficult to switch between forward and backward, social behavioral disorders are caused by the Iowa gambling task. There were patients who admitted.
  • Parkinson's motor data are evaluated in a complex manner during crawl exercise or rollover exercise, depending on the type of cognitive and emotional disorders, regardless of motor symptoms or forgetfulness. For example, in apathy / freezing exercisers, both crawl and rollover exercises are slow, and the heels rise from the floor at the start of the four-crawling exercise, and the lumbar region swings from side to side when moving backward. It turned out to be classified by. It was found that disinhibited (prone to get angry) exercisers were quick during both crawl and rollover exercises, and that quadruple exercises could not stop when changing from forward to backward and backward to forward.
  • diseases that are common in the elderly such as Parkinson's disease, Alzheimer's disease, and Levy body dementia, apathy (decreased motivation), depression, impact (irritability), and executive dysfunction (attention disorder).
  • apathy decreased motivation
  • depression depression
  • impact irritability
  • executive dysfunction executive dysfunction
  • other non-motor symptoms emotional and cognitive disorders, freezing, posture abnormalities, pain, etc. that cause falls, as well as vision, neuropathy, emotional instability, depression, physical symptoms of the autonomic nervous system, memorization
  • symptoms such as power disorder, forgetfulness, irritation (aggressive behavior), and social behavior disorder are closely and complicatedly related to exercise data during crawl exercise or rollover exercise.
  • the present invention provides various types of exercise data by collecting exercise data during crawl exercise or rolling exercise, and by providing a system for analyzing the collected exercise data, a system for supporting exercise diagnosis, and an analysis program. It can support complex diagnosis of various symptoms including illness and non-motor symptoms.
  • Exercise data analysis system 11 Exercise instruction unit 12
  • Exercise data collection unit 13 Exercise data input unit 14
  • Exercise condition input unit 15 Exercise data analysis unit 16
  • Analysis result notification unit 17 Network server 2
  • Exercise diagnosis support system 21 Exercise data input unit 22
  • Exercise conditions Input unit 23 Exercise condition and exercise data display unit 24
  • AI analysis unit 25 Network server

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Medical Informatics (AREA)
  • Physics & Mathematics (AREA)
  • Dentistry (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Physiology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

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

運動データ収集方法、運動データ解析システム、運動診断支援システム、及び、解析プログラムExercise data collection method, exercise data analysis system, exercise diagnosis support system, and analysis program
　本発明は、運動データ収集方法、運動データ解析システム、運動診断支援システム、及び、解析プログラムに関する。特に、四つ這い運動時又は寝返り運動時の運動データに関する。 The present invention relates to an exercise data collection method, an exercise data analysis system, an exercise diagnosis support system, and an analysis program. In particular, it relates to exercise data during crawl exercise or rollover exercise.
　高齢社会を迎え、パーキンソン病、アルツハイマー型を含む認知症、がん、高血圧に伴う循環器系疾患、糖尿病等と診断される高齢者が増えてきている。さらに、高齢社会に加えストレス社会も相まって、非運動症状である、認知症に起因する脱抑制（怒りやすさ）、アパシー（やる気の低下）、社会的障害、注意欠陥障害等の診断を受ける高齢者や若者が増えてきている。 With the aging society, the number of elderly people diagnosed with Parkinson's disease, dementia including Alzheimer's disease, cancer, cardiovascular disease associated with hypertension, diabetes, etc. is increasing. Furthermore, in combination with an aging society and a stressed society, elderly people who are diagnosed with non-motor symptoms such as dementia-induced dementia (easiness to get angry), apathy (decreased motivation), social disorders, and attention deficit disorders. The number of people and young people is increasing.
　近年の診断技術や医学の進歩により、専門家による上記診断は容易になってきているものの、特にパーキンソン病は、首が折れ下がり腰が曲がり歩行に支障が出るといった深刻な要介護状態になるにも関わらず、健常状態から要介護状態になるまでの中間状態や病状の進行を、立位や二足歩行姿勢から評価することは非常に難しい。このため、介護負担が大きくなってから病状の進行に気づき、ますます介護負担が増すという問題があった。 Although the above diagnosis by specialists has become easier due to recent advances in diagnostic technology and medicine, Parkinson's disease in particular becomes a serious condition requiring nursing care, such as a broken neck, a bent waist, and difficulty in walking. Nevertheless, it is very difficult to evaluate the progress of the intermediate state and the medical condition from the healthy state to the state requiring nursing care from the standing position and the bipedal walking posture. For this reason, there is a problem that the burden of long-term care increases, and the progress of the medical condition is noticed after the burden of long-term care increases.
　また、非運動症状の脱抑制や注意欠陥障害等の病状の診断は、専門家においても難しい面があった。特に、非運動症状は無意識であることが多く、同様に立位や二足歩行姿勢から評価することも難しい。このため、予防や診断ができず介護負担が増すという問題があった。 In addition, it was difficult for specialists to diagnose medical conditions such as disinhibition of non-motor symptoms and attention deficit disorder. In particular, non-motor symptoms are often unconscious, and it is also difficult to evaluate from a standing position or a bipedal walking posture. For this reason, there is a problem that prevention and diagnosis cannot be performed and the burden of long-term care increases.
特開２０１２－２４０９３５号公報Japanese Unexamined Patent Publication No. 2012-240935 特表２０１０－５２６８２０号公報Special Table 2010-526820 Gazette
　本発明は、上記事情を鑑みたものであり、乳幼児が無意識で獲得する寝返り運動及び四つ這い運動の動きを、高齢者、若者、小児等から測定し数値化する運動データ収集方法、収集した運動データを解析する運動データ解析システム、収集された運動データを診断に適した方法で表示して運動診断支援を行う運動診断支援システム、及び、該運動データ解析システムの解析プログラムの提供することを課題とする。 In view of the above circumstances, the present invention has collected exercise data collection methods for measuring and quantifying the movements of turning over and crawl movements unconsciously acquired by infants from elderly people, young people, children, and the like. To provide an exercise data analysis system that analyzes exercise data, an exercise diagnosis support system that displays collected exercise data in a method suitable for diagnosis to support exercise diagnosis, and an analysis program of the exercise data analysis system. Make it an issue.
　上記課題を解決するために、本発明の運動データ収集方法は、四つ這い運動時又は寝返り運動時の運動データ収集方法であって、四つ這い運動時は踵部、腰部、手のいずれか１か所以上の部位、寝返り運動時は頭部、両膝、両肩のいずれか１か所以上の部位の動きを測定するステップと、前記動きを測定するステップによって測定した動きを、数値化するステップと、を含むことを特徴とする。 In order to solve the above problems, the exercise data collection method of the present invention is an exercise data collection method during four-crawling exercise or turning over exercise, and is one of the heel, waist, and hand during four-crawling exercise. The step of measuring the movement of one or more parts, one or more parts of the head, both knees, and both shoulders during the rolling exercise, and the movement measured by the step of measuring the movement are quantified. It is characterized by including steps to be performed.
　前記四つ這い運動は、前進運動及び後進運動を含んでもよい。 The four-crawling movement may include forward movement and backward movement.
　前記四つ這い運動は踵押さえをさらに含んでもよい。 The four-crawling exercise may further include heel holding.
　前記寝返り運動は膝押さえをさらに含んでもよい。 The rolling exercise may further include knee holding.
　前記四つ這い運動時又は寝返り運動時に、外部から特定のリズム信号をさらに与えることが望ましい。 It is desirable to further give a specific rhythm signal from the outside during the above-mentioned four-crawling exercise or turning over exercise.
　前記動きを測定するステップは、測定する部位にマークを付した計測による測定、体の揺れに反応して音が発生する部材を体に付した録音による測定、写真撮影による測定、動画撮影による測定、又は、それら２以上の組み合わせにより行われることが望ましい。 The steps for measuring the movement include measurement by marking the part to be measured, measurement by recording with a member that generates sound in response to the shaking of the body attached to the body, measurement by photography, and measurement by moving image. , Or a combination of two or more of them.
　本発明の運動データ解析システムは、運動データ収集方法で収集した、数値化された運動データを入力する、運動データ入力部と、前記運動データ入力部に入力された運動データ収集時の運動条件を入力する、運動条件入力部と、前記入力された運動条件それぞれに対応して設定された所定の閾値と、前記入力された運動データを比較し、運動データが前記所定の閾値内に含まれるか、あるいは、前記所定の閾値以上であるかを解析する、運動データ解析部と、前記解析された運動データが、前記所定の閾値に含まれている場合に、あるいは、前記所定の閾値以上の場合に、報知する、解析結果報知部と、を含むことを特徴とする。 The exercise data analysis system of the present invention inputs an exercise data input unit that inputs digitized exercise data collected by an exercise data collection method, and an exercise condition at the time of collecting exercise data input to the exercise data input unit. The exercise condition input unit to be input, a predetermined threshold set corresponding to each of the input exercise conditions, and the input exercise data are compared, and whether the exercise data is included in the predetermined threshold. Or, when the motion data analysis unit that analyzes whether or not it is equal to or higher than the predetermined threshold, and when the analyzed motion data is included in the predetermined threshold, or when it is equal to or higher than the predetermined threshold. It is characterized by including an analysis result notification unit for notifying the data.
　運動データ解析システムは、運動内容を指示する、運動指示部と、前記運動データ収集方法で、運動データを収集する、運動データ収集部と、をさらに含んでもよい。 The exercise data analysis system may further include an exercise instruction unit that instructs the exercise content and an exercise data collection unit that collects exercise data by the exercise data collection method.
　運動データ解析システムは、サーバをさらに含み、必要に応じ前記サーバを介してデータ、指示や結果が送受信され、遠隔操作可能であってもよい。 The motion data analysis system may further include a server, and if necessary, data, instructions and results may be transmitted and received via the server, and remote control may be possible.
　本発明の運動診断支援システムは、運動データ収集方法で収集した、数値化された運動データを入力する、運動データ入力部と、前記運動データ入力部に入力されたデータ収集時の運動条件を入力する、運動条件入力部と、前記入力された運動データ及び前記入力された運動条件を、診断に適した方法で表示する、運動条件及び運動データ表示部と、を含み、運動診断支援を行うことを特徴とする。 The exercise diagnosis support system of the present invention inputs an exercise data input unit that inputs digitized exercise data collected by the exercise data collection method, and an exercise condition at the time of data collection input to the exercise data input unit. To provide exercise diagnosis support, including an exercise condition input unit and an exercise condition and exercise data display unit that displays the input exercise data and the input exercise condition in a method suitable for diagnosis. It is characterized by.
　運動診断支援システムは、前記運動条件及び前記運動データに基づき疾病や症状の種類を分類する、ＡＩ解析部をさらに含み、運動診断支援を行ってもよい。 The exercise diagnosis support system may further include an AI analysis unit that classifies the types of diseases and symptoms based on the exercise conditions and the exercise data, and may provide exercise diagnosis support.
　運動診断支援システムは、サーバをさらに含み、必要に応じ前記サーバを介してデータや結果が送受信され、遠隔操作可能であってもよい。 The exercise diagnosis support system may further include a server, and if necessary, data and results may be transmitted and received via the server, and remote control may be possible.
　本発明の解析プログラムは、運動データ解析システムの解析プログラムであって、コンピュータに、前記運動データ収集方法で収集した、四つ這い運動時又は寝返り運動時の動きを数値化した運動データを受信させ、前記運動データ収集時の運動条件を受信させ、前記運動条件それぞれに対応して設定された所定の閾値と、前記運動データを用い、運動データが前記所定の閾値内に含まれるか、あるいは、前記所定の閾値以上であるかを比較して解析させ、前記解析された運動データが、前記所定の閾値に含まれている場合に、又は、前記所定の閾値以上の場合に、報知させることを含むことを特徴とする。 The analysis program of the present invention is an analysis program of an exercise data analysis system, and causes a computer to receive exercise data obtained by quantifying the movements during crawl exercise or turning over exercises collected by the exercise data collection method. , The exercise condition at the time of collecting the exercise data is received, and the exercise data is included in the predetermined threshold by using the predetermined threshold set corresponding to each of the exercise conditions and the exercise data. The analysis is performed by comparing whether or not the data is equal to or higher than the predetermined threshold value, and the notification is given when the analyzed exercise data is included in the predetermined threshold value or when the data is equal to or higher than the predetermined threshold value. It is characterized by including.
　本発明の運動データ収集方法によれば、四つ這い運動時又は寝返り運動時の動きを簡便に測定でき、数値化できる。四つ這い運動も寝返り運動もベッド上やマット上で可能であり、高齢者や脳卒中後のリハビリ中の人でも、また、健常な高齢者、若者、小児等でも安全に実施することができる。
　また、四つ這い運動や寝返り運動の動きの測定や数値化は、特殊なセンサや高価な測定機器を使うことなく、身近なスマートフォンやビデオカメラ、カメラ、ＩＣレコーダー、メジャーや定規等を用いて簡便に行うことができる。さらに、動きの測定の精度もミリメートルや１ｃｍといった細かい単位を要求されない点においても、簡便に動きの測定が可能である。 According to the exercise data collection method of the present invention, it is possible to easily measure and quantify the movement during the four-crawling exercise or the rolling exercise. Crawling exercises and rolling exercises can be performed on the bed or on the mat, and can be safely performed by elderly people, people undergoing rehabilitation after stroke, and healthy elderly people, young people, children, and the like.
In addition, the movements of crawl and rollover movements can be measured and quantified using familiar smartphones, video cameras, cameras, IC recorders, tape measures, rulers, etc., without using special sensors or expensive measuring equipment. It can be done easily. Further, the motion can be easily measured in that the accuracy of the motion measurement does not require a fine unit such as millimeter or 1 cm.
　本発明の運動データ収集方法、運動データ解析システム、運動診断支援システム、及び、制御プログラムによれば、各種疾病、とりわけパーキンソン病、アルツハイマー型認知症等において健常状態から要介護状態になるまでの中間状態や病状の進行の診断を支援することができ、さらには、非運動症状の脱抑制、アパシー、遂行機能障害、注意欠陥障害等の各種症状の診断を支援することができる。 According to the exercise data collection method, the exercise data analysis system, the exercise diagnosis support system, and the control program of the present invention, it is intermediate from a healthy state to a state requiring nursing care in various diseases such as Parkinson's disease and Alzheimer's dementia. It is possible to support the diagnosis of the progression of a condition or a medical condition, and further, it is possible to support the diagnosis of various symptoms such as desuppression of non-motor symptoms, apathy, executive dysfunction, and attention defect disorder.
　さらには、寝返り運動及び四つ這い運動の動きは乳幼児が無意識で獲得する運動であり、いずれの世代においても運動者自身が楽しく運動をすることができる。 Furthermore, the roll-over exercise and the four-crawling exercise are exercises that infants unconsciously acquire, and exercisers themselves can enjoy exercising in any generation.
四つ這い運動（正常）の一例を示す。An example of four-crawling exercise (normal) is shown. 四つ這い運動（正常）の他の一例を示す。Another example of four-crawling exercise (normal) is shown. 四つ這い運動（足背接地不全）の一例を示す。An example of four-crawling exercise (foot-back contact failure) is shown. 四つ這い運動（足背接地不全）の他の一例を示す。Another example of four-crawling exercise (foot-back contact failure) is shown. 四つ這い運動（骨盤左右動揺）の一例を示す。An example of four-crawling exercise (pelvic left-right sway) is shown. 四つ這い運動（骨盤左右動揺）の他の一例を示す。Another example of crawl movement (pelvic left-right sway) is shown. 寝返り運動（正常）の一例を示す。An example of turning over exercise (normal) is shown. 寝返り運動（頭部先行）の一例を示す。An example of turning over exercise (head precedent) is shown. 寝返り運動（右膝先行）の一例を示す。An example of turning over exercise (right knee precedent) is shown. 寝返り運動（右膝屈曲代償）の一例を示す。An example of turning over exercise (compensation for right knee flexion) is shown. 運動データ解析システムの一例を示す。An example of the motion data analysis system is shown. 運動診断支援システムの一例を示す。An example of the exercise diagnosis support system is shown.
　本発明の運動データ収集方法は、四つ這い運動時又は寝返り運動時の体の部位の動きを測定するステップと、測定した動きを数値化するステップと、を含むものである。 The exercise data collection method of the present invention includes a step of measuring the movement of a body part during a four-crawling exercise or a rolling exercise, and a step of quantifying the measured movement.
（四つ這い運動データ収集方法）
　四つ這い運動は一般的にはハイハイとも呼ばれ、乳幼児が無意識に獲得する運動の一つである。健常状態の四つ這い運動は、両手と両足背（膝から下。爪先は寝かせた状態）を交互に床面に接触させながらまっすぐに前進又は後進（後ずさり）する。
　しかしながら、パーキンソン病、認知症や脱抑制等の非運動症状の運動者は、体幹異常を代償しようとするため、特に四つ這い運動においては足背、腰、手の動きに特徴が見られる。そこで、踵部、腰部、手のいずれか１か所以上の部位の動きを測定し、測定した動きを数値化し、運動データとして収集する。 (Crawling exercise data collection method)
The four-crawling exercise is also generally called high-high, and is one of the exercises that infants unconsciously acquire. In a healthy four-crawling exercise, both hands and the backs of both feet (below the knees, with the toes lying down) are alternately brought into contact with the floor surface to move forward or backward (backward).
However, exercisers with non-motor symptoms such as Parkinson's disease, dementia and disinhibition try to compensate for trunk abnormalities, so the movements of the back, hips and hands are characteristic, especially in the four-crawling exercise. .. Therefore, the movement of one or more parts of the heel, the lumbar region, and the hand is measured, the measured movement is quantified, and the movement data is collected.
　図１～図６に、四つ這い運動の例を示す。図１及び図２は正常状態の四つ這いの例であり、足背が接地し、骨盤動揺も見られない。図３及び図４は足背接地不全の例であり、図５及び図６は骨盤が体の中心線から左右に動揺する例である。
　また、写真には表れないが、正常状態の四つ這い運動に比べ、四つ這い運動自体の制御がうまくいかず、前進運動後に後進運動ができない、後進運動が緩慢になるといった特徴が観測されることがある。さらには、後進運動時に骨盤が上下に動揺する例もある。 1 to 6 show an example of a four-crawling exercise. FIGS. 1 and 2 are examples of four crawls in a normal state, in which the back of the foot touches the ground and no pelvic sway is observed. 3 and 4 are examples of foot dorsal ground contact failure, and FIGS. 5 and 6 are examples of the pelvis swaying from side to side from the center line of the body.
In addition, although it does not appear in the photograph, compared to the normal four-crawling movement, the control of the four-crawling movement itself is not good, the backward movement cannot be performed after the forward movement, and the backward movement becomes slow. There are times. Furthermore, there are cases where the pelvis sways up and down during backward movement.
　足背接地の度合い（足背の「ウキ」）は、運動者の踵部の動きを測定し、床面から踵部までの高さを数値化することにより運動データとすることができる。
　骨盤動揺の度合い（骨盤の左右動揺の「フリ」、上下動揺の「オレ」）は、運動者の腰部の動きを測定し、頭から背骨を通る体の中心軸から腰部の中心までの左右の距離及び上下の距離を数値化することにより運動データとすることができる。
　四つ這い運動の制御の度合い（正確性）は、運動者の手の動きを測定し、左右の手と床面が順次接触する床面の各位置、又は、運動開始から各接触までの時間を数値化することにより運動データとすることができる。左右の手と床面が順次接触する床面の各位置により、四つ這い運動ができているかを把握することができる。運動開始から各接触までの時間を測定することにより、運動速度、運動速度の変化、運動開始の困難性等を把握することができる。
　なお、上記運動データの収集方法は例示にすぎず、数値化可能であれば、これらの方法に限定されない。 The degree of foot contact (“uki” on the back of the foot) can be obtained as exercise data by measuring the movement of the heel of the exerciser and quantifying the height from the floor to the heel.
The degree of pelvic sway ("pretend" of pelvic left-right sway, "I" of vertical sway) measures the movement of the exerciser's lumbar region, and is left and right from the central axis of the body through the spine from the head to the center of the lumbar region. Motion data can be obtained by quantifying the distance and the vertical distance.
The degree of control (accuracy) of the four-crawling movement measures the movement of the exerciser's hand, and the position of the floor surface where the left and right hands and the floor surface sequentially contact, or the time from the start of the exercise to each contact. Can be obtained as exercise data by quantifying. From each position of the floor surface where the left and right hands and the floor surface come into contact with each other, it is possible to grasp whether or not the crawl movement is performed. By measuring the time from the start of exercise to each contact, it is possible to grasp the exercise speed, the change in exercise speed, the difficulty of starting exercise, and the like.
It should be noted that the above-mentioned method for collecting exercise data is merely an example, and is not limited to these methods as long as it can be quantified.
　動きを測定するステップは、踵部、腰部、手のいずれか１か所以上の測定する部位にマークを付し、メジャーや定規等で計測してデータとして記録してもよい。また、体の揺れに反応して音が発生する鈴等の部材を運動者に付し、ＩＣレコーダー等で録音して運動速度を測定してもよい。さらには、身近なスマートフォンやビデオカメラ、カメラ等を用いて写真撮影、動画撮影を行ってもよい。又は、それら２以上の組み合わせにより行ってもよい。 In the step of measuring the movement, a mark may be attached to any one or more of the heel, the waist, and the hand, and the measurement may be performed with a measure or a ruler and recorded as data. Further, a member such as a bell that generates a sound in response to the shaking of the body may be attached to the exerciser and recorded by an IC recorder or the like to measure the exercise speed. Further, a photograph or a moving image may be taken using a familiar smartphone, video camera, camera or the like. Alternatively, it may be performed by a combination of two or more of them.
　四つ這い運動は、前進運動のみでもよいが、前進運動及び後進運動（後ずさり）を交互に行うことが好ましい。前進運動と後進運動の切り替え時の動きの運動データも併せて取得することができるからである。 The four-crawling movement may be only the forward movement, but it is preferable to alternately perform the forward movement and the backward movement (backward movement). This is because the motion data of the motion when switching between the forward motion and the reverse motion can also be acquired.
　四つ這い運動は、第三者や器具等で踵部を上から床面に向かって押さえた上で行ってもよい。踵部を床面から上げることで体幹異常を代償していた場合、骨盤動揺の度合いがより大きくなる、あるいは、後進始動が困難となる等の、腰部又は手の動きの運動データも併せて取得することができるからである。 The four-crawling exercise may be performed after pressing the heel part from above toward the floor surface with a third party or an instrument. If the heel is raised from the floor to compensate for the trunk abnormality, the movement data of the lumbar or hand movement such as the degree of pelvic sway becomes larger or it becomes difficult to start backwards is also included. Because it can be obtained.
　四つ這い運動時に、外部から特定のリズム信号をさらに与えることができる。かかるリズム信号は、運動のリズムを一定に維持しやすくするとともに、四つ這い運動のリズム信号からのずれも判断しやすい。
　特定のリズム信号は、メトロノームのようなテンポのみを示す信号でも、音楽ＣＤのような信号でもよい。また、掛け声との組み合わせでもよい。なかでも、左右の手が床面に接触するテンポに合わせた４拍子が好ましい。４拍子の中でも誰でも調子が取りやすいリズムがよく、３３７拍子がより好ましい。３３７拍子とは、／と／の間を１小節とすると、　／前進３歩（１拍休み）／後進３歩（１拍休み）／前進３歩（１拍停止）／後進３歩（１拍休み）／　で表されるリズムであり、幼少の頃より慣れ親しんでいるためノリやすく、四つ這い運動がより楽しくなると考えられる。さらに、３小節目の前進３歩後の１拍停止時に、止まれない、後進始動が遅れるといった動きの運動データも併せて取得することができるからである。
　さらに、特定のリズム信号は、同様な四つ這い運動を行う他人から発せられる声や、該他人の運動のリズムに合わせた手拍子、声、打楽器音等であってもよい。同様な四つ這い運動を行う１人又は複数の他人のリズム信号は、四つ這い運動に対しより同調を促すものであり、心地よく楽しく四つ這い運動を行うことができる。１人又は複数の他人のリズム信号による四つ這い運動は、リハビリテーション施設やデイサービス等で実施しやすく、本発明の四つ這い運動の実施機会を増やしやすく、また、運動の効果も向上すると考えられる。 During crawl exercise, a specific rhythm signal can be further given from the outside. Such a rhythm signal makes it easy to maintain a constant rhythm of movement, and it is also easy to determine a deviation from the rhythm signal of crawl movement.
The specific rhythm signal may be a signal indicating only the tempo such as a metronome, or a signal such as a music CD. It may also be combined with a shout. Of these, a four-beat beat that matches the tempo at which the left and right hands come into contact with the floor is preferable. Among the four beats, the rhythm that anyone can easily take is good, and the 337 beat is more preferable. 337 beats are defined as 1 bar between / and / / 3 forward steps (1 beat break) / 3 backward steps (1 beat break) / 3 forward steps (1 beat stop) / 3 backward steps (1 beat) It is a rhythm expressed by (holiday) /, and since it has been familiar since childhood, it is easy to get rid of it, and it is thought that the crawl exercise will be more fun. Further, it is possible to acquire motion data of movements such as not stopping or delaying the reverse start when one beat is stopped three steps forward in the third measure.
Further, the specific rhythm signal may be a voice emitted from another person performing the same four-crawling movement, a clapping, a voice, a percussion instrument sound, or the like in accordance with the rhythm of the movement of the other person. The rhythm signal of one or more others who perform the same crawl exercise promotes more synchronization with the quadruple exercise, and the quadruple exercise can be performed comfortably and happily. It is considered that the four-crawling exercise by the rhythm signal of one or more other people is easy to be carried out at a rehabilitation facility, a day service, etc. Be done.
（寝返り運動データ収集方法）
　寝返り運動も、乳幼児が無意識に獲得する運動の一つである。健常状態の寝返り運動は、上肢が先行し、両肩のほうが頭部より先に回旋して行われ、膝が浮いたり屈曲したりすることはない。
　しかしながら、パーキンソン病や遂行機能障害、脱抑制等の非運動症状の運動者は、体幹異常を代償しようとするため、寝返り運動で頭部のほうが両肩より先に回旋する動きや、膝が浮いたり屈曲したりする動きが見られる。そこで、頭部、両膝、両肩のいずれか１か所以上の部位の動きを測定し、測定した動きを数値化し、運動データとして収集する。 (How to collect rolling exercise data)
The roll-over exercise is also one of the exercises that infants unconsciously acquire. In a healthy state, the rolling motion is performed with the upper limbs leading and both shoulders rotating before the head, and the knees do not float or bend.
However, exercisers with non-motor symptoms such as Parkinson's disease, executive dysfunction, and disinhibition try to compensate for trunk abnormalities. You can see the movement of floating and bending. Therefore, the movement of one or more parts of the head, both knees, and both shoulders is measured, the measured movement is quantified, and the movement data is collected.
　図７～図１０に、寝返り運動の例を示す。本発明において寝返り運動は、仰向けで腕組みを行い、右肩を床面から離し→右肩を床面に戻し→左肩を床面から離し→左肩を床面に戻す、を繰り返すものが例示される。右肩を床面から離した場合は左回旋しようとする体勢となるが、実際には回旋しないで元の体勢に戻る。同様に、左肩を床面から離した場合は右回旋しようとする体勢となるが、実際には回旋しないで元の体勢に戻る。図７は正常状態の寝返り運動の例であり、頭部が両肩より先に回旋せず、膝の浮きや屈曲もない。図８は頭部が両肩より先に回旋する例である。
　頭部が両肩より先に回旋する度合い（頭部の「クビ」）は、運動者の頭部の動きを測定し、上面視において体の中心点から頭部の中心点までの距離を数値化し、かつ、運動者の両肩の動きを測定し、床面から肩までの距離、又は、運動開始から各時点での床面から肩までの距離を数値化することにより運動データとすることができる。運動開始から各時点での床面から肩までの距離を数値化することにより、運動速度、運動速度の変化、運動開始の困難性等を把握することができる。
　図９は下肢、特に右膝が床面から浮いて先行して回旋する例であり、図１０は右膝が屈曲して回旋する例である。
　膝が両肩より先に回旋する度合いは、運動者の両膝の動きを測定し、床面から膝までの高さを数値化することにより運動データとすることができる。
　なお、上記運動データの収集方法は例示にすぎず、数値化可能であれば、これらの方法に限定されない。 7 to 10 show an example of a roll-over exercise. In the present invention, the rolling motion is exemplified by repeating the process of folding the arms on the back, separating the right shoulder from the floor surface, returning the right shoulder to the floor surface, separating the left shoulder from the floor surface, and returning the left shoulder to the floor surface. .. If the right shoulder is separated from the floor, the posture will try to rotate to the left, but in reality, it will return to the original posture without rotating. Similarly, when the left shoulder is separated from the floor surface, the posture is to try to rotate to the right, but it actually returns to the original posture without rotating. FIG. 7 shows an example of a rolling motion in a normal state, in which the head does not rotate ahead of both shoulders, and the knees do not float or bend. FIG. 8 shows an example in which the head rotates before both shoulders.
The degree to which the head rotates before both shoulders (the "kubi" of the head) measures the movement of the exerciser's head and measures the distance from the center point of the body to the center point of the head in top view. The movement of both shoulders of the exerciser is measured, and the distance from the floor to the shoulder or the distance from the floor to the shoulder at each point in time from the start of the exercise is quantified to obtain exercise data. Can be done. By quantifying the distance from the floor surface to the shoulder at each time point from the start of exercise, it is possible to grasp the exercise speed, the change in exercise speed, the difficulty of starting exercise, and the like.
FIG. 9 shows an example in which the lower limbs, particularly the right knee, floats from the floor surface and rotates in advance, and FIG. 10 shows an example in which the right knee bends and rotates.
The degree to which the knees rotate before both shoulders can be obtained as exercise data by measuring the movements of both knees of the exerciser and quantifying the height from the floor to the knees.
It should be noted that the above-mentioned method for collecting exercise data is merely an example, and is not limited to these methods as long as it can be quantified.
　動きを測定するステップは、頭部、両膝、両肩の測定する部位にマークを付し、メジャーや定規等で計測してデータとして記録してもよい。また、体の揺れに反応して音が発生する鈴等の部材を運動者に付し、ＩＣレコーダー等で録音して運動速度を測定してもよい。さらには、身近なスマートフォンやビデオカメラ、カメラ等を用いて写真撮影、動画撮影を行ってもよい。又は、それら２以上の組み合わせにより行ってもよい。 In the step of measuring the movement, the parts to be measured on the head, both knees, and both shoulders may be marked, measured with a measure, a ruler, etc., and recorded as data. Further, a member such as a bell that generates a sound in response to the shaking of the body may be attached to the exerciser and recorded by an IC recorder or the like to measure the exercise speed. Further, a photograph or a moving image may be taken using a familiar smartphone, video camera, camera or the like. Alternatively, it may be performed by a combination of two or more of them.
　寝返り運動は、第三者や器具等で膝を上から床面に向かって押さえた上で行ってもよい。寝返り運動時に膝を床面から上げることで体幹異常を代償していた場合、頭部が両肩より先に回旋する度合いがより大きくなる等の、運動データも併せて取得することができるからである。さらに膝を押さえた場合に比し、押さえない場合には、膝の屈曲が先行し足を床面に押しつけて下肢先行で寝返りする代償の動きが見られることがあり、これを運動開始から各時点での床面から膝までの距離を数値化することにより運動データとすることができる。図９及び図１０は下肢の代償で寝返る例であり、両肩より先に下肢（膝）が浮き、あるいは屈曲している。 The rolling exercise may be performed after pressing the knee from above toward the floor with a third party or equipment. If the trunk abnormality is compensated by raising the knee from the floor during the rolling exercise, exercise data such as the degree of rotation of the head before both shoulders can be obtained at the same time. Is. Furthermore, compared to the case where the knee is pressed, when the knee is not pressed, the knee flexion precedes and the foot is pressed against the floor surface, and the lower limb precedes and turns over. Exercise data can be obtained by quantifying the distance from the floor to the knee at the time point. 9 and 10 are examples of turning over at the cost of the lower limbs, and the lower limbs (knees) are floating or flexed before both shoulders.
　寝返り運動時に、外部から特定のリズム信号をさらに与えることができる。かかるリズム信号は、運動のリズムを一定に維持しやすくするとともに、寝返り運動のリズム信号からのずれも判断しやすい。
　特定のリズム信号は、メトロノームのようなテンポのみを示す信号でも、音楽ＣＤのような信号でもよい。掛け声との組み合わせでもよい。なかでも、左右の手が床面に接触するテンポに合わせた４拍子が好ましい。４拍子の中でも誰でも調子が取りやすいリズムがよく、３３７拍子がより好ましい。３３７拍子とは、／と／の間を１小節とすると、　／２拍で右肩を床面から離す→１拍で右肩を床面に戻す（１拍休み）／２拍で左肩を床面から離す→１拍で左肩を床面に戻す（１拍休み）／２拍で右肩を床面から離す→１拍で右肩を床面に戻す（１拍停止）／２拍で左肩を床面から離す→１拍で左肩を床面に戻す（１拍休み）／　で表されるリズムであり、幼少の頃より慣れ親しんでいるためノリやすく、寝返り運動がより楽しくなると考えられる。
　さらに、特定のリズム信号は、同様な寝返り運動を行う他人から発せられる声や、該他人の運動のリズムに合わせた手拍子、声、打楽器音等であってもよい。同様な寝返り運動を行う１人又は複数の他人のリズム信号は、寝返り運動に対しより同調を促すものであり、心地よく楽しく寝返り運動を行うことができる。１人又は複数の他人のリズム信号による寝返り運動は、リハビリテーション施設やデイサービス等で実施しやすく、本発明の寝返り運動の実施機会を増やしやすく、また、運動の効果も向上すると考えられる。 A specific rhythm signal can be further given from the outside during the rolling exercise. Such a rhythm signal makes it easy to maintain a constant exercise rhythm, and also makes it easy to determine a deviation from the rhythm signal of the rolling motion.
The specific rhythm signal may be a signal indicating only the tempo such as a metronome, or a signal such as a music CD. It may be combined with a shout. Of these, a four-beat beat that matches the tempo at which the left and right hands come into contact with the floor is preferable. Among the four beats, the rhythm that anyone can easily take is good, and the 337 beat is more preferable. With 337 beats, if there is one bar between / and /, the right shoulder is separated from the floor at / 2 beats → the right shoulder is returned to the floor at 1 beat (1 beat rest) / the left shoulder is floored at 2 beats. Move away from the surface → Return the left shoulder to the floor with 1 beat (1 beat rest) / Move the right shoulder off the floor with 2 beats → Return the right shoulder to the floor with 1 beat (Stop 1 beat) / Left shoulder with 2 beats It is a rhythm expressed by moving the left shoulder back to the floor with one beat (1 beat rest) /, and since it has been familiar since childhood, it is easy to get rid of and it is thought that the rolling exercise becomes more fun.
Further, the specific rhythm signal may be a voice emitted from another person performing the same rolling motion, a clapping, a voice, a percussion instrument sound, or the like in accordance with the rhythm of the other person's movement. The rhythm signal of one or more other people who perform the same rolling exercise promotes more synchronization with the rolling exercise, and the rolling exercise can be performed comfortably and happily. It is considered that the rolling exercise by the rhythm signal of one or more other people is easy to be carried out at a rehabilitation facility, a day service, etc., it is easy to increase the chances of carrying out the rolling exercise of the present invention, and the effect of the exercise is also improved.
（運動データ解析システム）
　運動データ解析システムは、運動データ入力部、運動条件入力部、運動データ解析部、及び、解析結果報知部を含む。 (Exercise 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 notification unit.
　運動データ入力部１３は、上述した運動データ収集方法で収集した、数値化された運動データが入力される。なお、運動データ収集方法で測定され数値化された運動データは、同じ腰部の動きでも踵部の押さえがあるか、後進運動時か等により異なる。このため、運動条件入力部１４は、運動データ収集時の運動条件が入力される。 The exercise data input unit 13 inputs the quantified exercise data collected by the exercise data collection method described above. The exercise data measured and quantified by the exercise data collection method differs depending on whether the heel is pressed or the reverse exercise is performed even with the same lumbar movement. Therefore, the exercise condition input unit 14 inputs the exercise condition at the time of collecting the exercise data.
　運動データには、入力された運動条件それぞれに対応して所定の閾値が設定される。運動データ解析部１５は、かかる閾値と入力された運動データを比較し、運動データが所定の閾値内に含まれるか、あるいは、所定の閾値以上であるかを解析する。
　ここで、所定の閾値とは、たとえば頭から背骨を通る体の中心軸から腰部の中心までの左右の距離を１０ｃｍ等に設定することができる。なお、同じ運動条件での閾値を段階的に複数設定してもよい。 A predetermined threshold value is set in the exercise data corresponding to each of the input exercise conditions. The exercise data analysis unit 15 compares the threshold value with the input exercise data, and analyzes whether the exercise data is included in the predetermined threshold value or is equal to or higher than the predetermined threshold value.
Here, as the predetermined threshold value, for example, the left-right distance from the central axis of the body passing through the head to the spine to the center of the lumbar region can be set to 10 cm or the like. In addition, a plurality of threshold values under the same exercise condition may be set stepwise.
　解析結果報知部１６は、解析された運動データが、所定の閾値に含まれている場合に、あるいは、所定の閾値以上の場合に、報知する。報知の方法は、たとえば閾値以上の場合に色を変えて表示させたり、数値や画像をハイライトさせたり、また、チャイム音や警告音を発生させたりする等が挙げられる。閾値以内であるときに色を変えても、音を発生させてもよく、さらには、閾値以上の場合と閾値以内である場合に別の色や音で表示させてもよい。 The analysis result notification unit 16 notifies when the analyzed exercise data is included in a predetermined threshold value or when it is equal to or higher than a predetermined threshold value. Examples of the notification method include changing the color when the threshold value is exceeded, highlighting a numerical value or an image, and generating a chime sound or a warning sound. The color may be changed when the value is within the threshold value, a sound may be generated, and the color or sound may be displayed differently when the value is above the threshold value and when the sound is within the threshold value.
　運動データ解析システム１には、さらに運動指示部１１、運動データ収集部１２を備えていてもよい。 The exercise data analysis system 1 may further include an exercise instruction unit 11 and an exercise data collection unit 12.
　運動指示部１１は、運動内容を指示するものであり、運動の開始や運動内容、運動条件を音声や画面で指示するものが例示される。また、運動に合わせる特定のリズム信号を送ってもよい。特定のリズム信号は、メトロノームのようなテンポのみを示す信号でも、音楽ＣＤのような信号でもよい。掛け声との組み合わせでもよく、３３７拍子のようなリズムでもよい。 The exercise instruction unit 11 instructs the exercise content, and examples thereof include those instructing the start of exercise, the exercise content, and the exercise conditions by voice or screen. In addition, a specific rhythm signal may be sent in accordance with the exercise. The specific rhythm signal may be a signal indicating only the tempo such as a metronome, or a signal such as a music CD. It may be combined with a shout or a rhythm such as 337 beats.
　運動データ収集部１２は、上述した運動データ収集方法で運動データが収集される。具体的には、写真撮影、動画撮影、録音等が例示される。 The exercise data collection unit 12 collects exercise data by the exercise data collection method described above. Specifically, photography, moving image shooting, recording, and the like are exemplified.
　運動データ解析システム１は、さらにサーバ１７を含んでもよい。必要に応じサーバ１７を介してデータ、指示や結果を送受信できる。これにより、遠隔操作が可能となる。
　なお、運動指示部１１、運動データ収集部１２、運動データ入力部１３、運動条件入力部１４、運動データ解析部１５、解析結果報知部は１６、有体物であっても、ソフトウェア上のモジュールであってもよい。
　図１１に、運動データ解析システムの一例を示す。 The motion data analysis system 1 may further include a server 17. Data, instructions and results can be sent and received via the server 17 as needed. This enables remote control.
The exercise instruction unit 11, the exercise data collection unit 12, the exercise data input unit 13, the exercise condition input unit 14, the exercise data analysis unit 15, the analysis result notification unit 16, and even if they are tangible objects, they are modules on the software. You may.
FIG. 11 shows an example of the motion data analysis system.
（運動診断支援システム）
　運動診断支援システム２は、運動データ入力部２１、運動条件入力部２２、運動条件及び運動データ表示部２３を含み、運動診断支援を行う。 (Exercise diagnosis support system)
The exercise diagnosis support system 2 includes an exercise data input unit 21, an exercise condition input unit 22, an exercise condition and an exercise data display unit 23, and provides exercise diagnosis support.
　運動データ入力部２１は、上述した運動データ収集方法で収集した、数値化された運動データが入力される。なお、運動データ収集方法で測定され数値化された運動データは、同じ腰部の動きでも踵部の押さえがあるか、後進運動時か等により異なる。このため、運動条件入力部は、運動データ収集時の運動条件が入力される。 The exercise data input unit 21 inputs the quantified exercise data collected by the exercise data collection method described above. The exercise data measured and quantified by the exercise data collection method differs depending on whether the heel is pressed or the reverse exercise is performed even with the same lumbar movement. Therefore, the exercise condition input unit inputs the exercise condition at the time of collecting the exercise data.
　運動条件及び運動データ表示部２３は、入力された運動データ及び前記入力された運動条件が、診断に適した方法で表示される。診断に適した方法の表示とは、運動データと運動条件を一覧表にしたり、画像の中心を揃えて並べたり、特定のリズムと運動のリズムの差をグラフ化したり等が例示されるが、これらに限られない。 The exercise condition and exercise data display unit 23 displays the input exercise data and the input exercise condition by a method suitable for diagnosis. Displaying a method suitable for diagnosis includes listing exercise data and exercise conditions, aligning the centers of images, and graphing the difference between a specific rhythm and exercise rhythm. Not limited to these.
　運動診断支援システム２は、ＡＩ解析部２４をさらに備えてもよい。
　ＡＩ解析部２４は、運動条件及び運動データに基づき、教師有の条件で疾病や症状の種類を分類する。上述した運動データ収集方法で収集した、数値化された運動データは、１か所以上の部位の動きの運動データを含む。このため、運動データ１点の閾値で疾病や症状を判断するよりも、多観点から疾病や症状の種類を分類することが、診断の精度を向上することに繋がる。
　しがたって、ＡＩ解析部２４をさらに備えることにより、専門家の疾病や症状の診断を、より高度に支援することができる。 The exercise diagnosis support system 2 may further include an AI analysis unit 24.
The AI analysis unit 24 classifies the types of diseases and symptoms under supervised conditions based on exercise conditions and exercise data. The quantified exercise data collected by the exercise data collection method described above includes the exercise data of the movement of one or more parts. Therefore, it is possible to improve the accuracy of diagnosis by classifying the types of diseases and symptoms from multiple viewpoints, rather than judging the diseases and symptoms by the threshold value of one point of exercise data.
Therefore, by further providing the AI analysis unit 24, it is possible to support the diagnosis of diseases and symptoms of specialists to a higher degree.
　運動診断支援システムは、さらにサーバ２５を含んでもよい。必要に応じサーバを介してデータや表示内容を送受信できる。これにより、遠隔操作が可能となる。
　なお、運動データ入力部２１、運動条件入力部２２、運動条件及び運動データ表示部２３、ＡＩ解析部２４は、有体物であっても、ソフトウェア上のモジュールであってもよい。
　図１２に、運動診断支援システムの一例を示す。 The exercise diagnosis support system may further include a server 25. Data and display contents can be sent and received via the server as needed. This enables remote control.
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 modules on software.
FIG. 12 shows an example of the exercise diagnosis support system.
（解析プログラム）
　本発明の運動データ解析システムの解析プログラムは、コンピュータに、上述した運動データ収集方法で収集した、四つ這い運動時又は寝返り運動時の動きを数値化した運動データを受信させ、前記運動データ収集時の運動条件を受信させ、前記運動条件それぞれに対応して設定された所定の閾値と、前記運動データを用い、運動データが所定の閾値内に含まれるか、あるいは、所定の閾値以上であるかを比較して解析させ、前記解析された運動データが、所定の閾値に含まれている場合に、又は、前記所定の閾値以上の場合に、報知させることを含む。
　かかる解析プログラムは、上述した運動データ解析システム１に対応するプログラムである。 (Analysis program)
The analysis program of the exercise data analysis system of the present invention causes a computer to receive the exercise data obtained by quantifying the movement during the four-crawling exercise or the rolling exercise, which is collected by the above-mentioned exercise data collection method, and collects the exercise data. The exercise data at the time is received, and the exercise data is included in the predetermined threshold value or is equal to or higher than the predetermined threshold value by using the predetermined threshold value set corresponding to each of the exercise conditions and the exercise data. This includes comparing and analyzing the data, and notifying the analyzed exercise data when it is included in a predetermined threshold value or when it is equal to or higher than the predetermined threshold value.
Such an analysis program is a program corresponding to the above-mentioned motion data analysis system 1.
　以下に本発明の実施例を具体的に説明するが、本発明はこれらの実施例に何ら限定されるものではない。 Examples of the present invention will be specifically described below, but the present invention is not limited to these examples.
（実施例１）
　四つ這い運動において、第三者により踵部を押さえて後進運動させ、運動データを収集した。また、寝返り運動において、仰向けに腕組みさせ、運動データを収集した。
　パーキンソン病ですくみの目立つ患者では、手の部位の動きを測定することにより、後進始動が遅れること、及び、後進動作が遅くなることが分かった。加えて、腰部の動きも測定することにより、骨盤の左右動揺が観察される例とされない例があることが分かった。寝返りも同様に困難であり、肩の床からの挙上が緩慢であることが分かった。膝の動きも測定することにより、肩の回旋始動に先行して膝が床より浮き、下肢の力を借りて寝返りを補っていることが分かった。
　さらに、寝返り運動において、第三者により膝部を押さえて、運動データを収集した。両肩の部位の動きを測定することにより、両肩の回旋量がさらに不足することが分かった。この様な患者では、アパシー・意欲低下も目立った。 (Example 1)
In the four-crawling exercise, a third party pressed the heel to move backward and collected exercise data. In addition, in the rolling exercise, the arms were folded on the back and exercise data was collected.
In patients with Parkinson's disease who had prominent freezing, it was found that the backward start was delayed and the backward movement was delayed by measuring the movement of the hand part. In addition, by measuring the movement of the lumbar region, it was found that there are cases where left and right pelvic sway is observed and cases where it is not observed. It was found that turning over was also difficult and the shoulders were slowly lifted from the floor. By measuring the movement of the knee, it was found that the knee was lifted from the floor prior to the start of rotation of the shoulder, and the force of the lower limbs was used to supplement the turning over.
Furthermore, in the rolling exercise, exercise data was collected by pressing the knee by a third party. By measuring the movement of both shoulders, it was found that the amount of rotation of both shoulders was further insufficient. In such patients, apathy and decreased motivation were also noticeable.
（実施例２）
　四つ這い運動において、前進３歩→後進３歩→前進３歩（１拍停止）→後進３歩を、３３７拍子のリズム信号を与えることにより運動データを収集した。
　パーキンソン病の運動症状に加え、脱抑制および衝動的で不注意を呈するパーキンソン病認知症（レビー小体型認知症）を含めた患者では、手の部位の動きを測定することにより、後進始動が遅れること、及び、後進動作が遅くなることが分かったことに加え、前進は後進に比し素早く、前進から後進変更時に停止できないことが分かった。
　これらの患者では、ハイハイの腰部の動きも測定することにより、骨盤の左右動揺が観察される例とされない例があることが分かった。骨盤の左右が動揺する幼若なハイハイを呈する場合、易怒性や情動の不安定さが目立った。 (Example 2)
In the four-crawling exercise, exercise data was collected by giving a rhythm signal of 337 beats in the order of 3 forward steps → 3 backward steps → 3 forward steps (1 beat stop) → 3 backward steps.
In patients with Parkinson's disease motor symptoms, as well as disinhibition and impulsive and inattentive Parkinson's disease dementia (Lewy body dementias), measuring the movement of the hand region delays backward initiation. In addition to the fact that the reverse movement was found to be slower, it was found that the forward movement was quicker than the reverse movement and could not be stopped when changing from forward movement to reverse movement.
In these patients, by measuring the movement of the lumbar region of high and high, it was found that there were cases where left and right pelvic sway was observed and cases where it was not observed. Irritability and emotional instability were conspicuous when presenting a childish high-high swaying left and right pelvis.
（実施例３）
　寝返り運動において、仰向けに腕組みさせ、２拍で右肩を床面から離す→１拍で右肩を床面に戻す（１拍休み）→２拍で左肩を床面から離す→１拍で左肩を床面に戻す（１拍休み）→２拍で右肩を床面から離す→１拍で右肩を床面に戻す（１拍停止）→２拍で左肩を床面から離す→１拍で左肩を床面に戻す（１拍休み）を、３３７拍子のリズム信号を与えることにより運動データを収集した。
　遂行機能障害を有するパーキンソン病およびレビー小体型認知症の患者で、首が折れ下がり腰が曲がり歩行に支障が出る場合、頭部及び両肩の動きを測定することにより、両肩の動きがリズム信号より緩慢となるのに加え、頭部が両肩よりも先に回旋することが分かった。さらに、これらの注意の配分・情動処理が苦手な中に、幻視を呈する症例も含まれた。 (Example 3)
In the rolling exercise, fold your arms on your back and separate your right shoulder from the floor with 2 beats → return your right shoulder to the floor with 1 beat (1 beat break) → separate your left shoulder from the floor with 2 beats → left shoulder with 1 beat Return to the floor (1 beat rest) → 2 beats to separate the right shoulder from the floor → 1 beat to return the right shoulder to the floor (1 beat stop) → 2 beats to separate the left shoulder from the floor → 1 beat The movement data was collected by giving a rhythm signal of 337 beats to return the left shoulder to the floor surface (1 beat break).
In patients with Parkinson's disease and Lewy body dementias with executive dysfunction, when the neck is bent and the waist is bent and walking is impaired, the movement of both shoulders is rhythmic by measuring the movement of the head and both shoulders. In addition to being slower than the signal, it was found that the head turned before both shoulders. In addition, there were cases in which hallucinations were exhibited while these attention distribution and emotional processing were not good.
（実施例４）
　四つ這い運動において、特定のリズム信号を与えることなく前進及び後進の運動データを収集した。
　パーキンソン病で抑うつ及び自律神経系の身体症状を伴う不安の目立つ患者では、踵部の部位の動きを測定することにより、後進時に足背が床面から離れることが分かった。さらに、踵部、腰部、手の部位の動きを測定することにより、左右協調性が不正確であることが分かった。また、記銘力障害・物忘れに加え、易怒性・苛立ち（攻撃的行動）が目立つアルツハイマー型認知症の患者では、腰部の部位の動きを測定することにより、前進は素早く、かつ、骨盤の左右の動揺が見られることが分かった。 (Example 4)
In the four-crawling movement, forward and backward movement data were collected without giving a specific rhythm signal.
In patients with Parkinson's disease who were prominently anxious with depression and physical symptoms of the autonomic nervous system, measuring the movement of the heel site showed that the back of the foot was separated from the floor when moving backward. Furthermore, by measuring the movements of the heel, lumbar, and hand parts, it was found that the left-right coordination was inaccurate. In addition, in patients with Alzheimer's disease who have conspicuous irritability and irritation (aggressive behavior) in addition to memory impairment and forgetfulness, by measuring the movement of the lumbar region, the movement of the lumbar region is measured so that the patient can move forward quickly and in the pelvis. It turned out that left and right sway was seen.
（実施例５）
　四つ這い運動において、前進運動および後進運動（後ずさり）を交互に行い、前進運動と後進運動の切り替え時の動きの運動データを収集した。さらに、四つ這い運動において、第三者により踵部を押さえて後進運動させ、運動データを収集した。
　踵部を押さえても骨盤の左右動揺は観察されず、上下に動揺するパーキンソン病の中で、前進と後進の切り替えが困難な症例の中には、アイオワ・ギャンブリング課題で社会的行動障害を認める患者が存在した。 (Example 5)
In the four-crawling movement, forward movement and backward movement (backward movement) were alternately performed, and motion data of the movement when switching between forward movement and reverse movement was collected. Furthermore, in the four-crawling exercise, a third party pressed the heel to move backward and collected exercise data.
Left-right pelvic sway is not observed even when the heel is pressed, and in Parkinson's disease that sways up and down, in some cases where it is difficult to switch between forward and backward, social behavioral disorders are caused by the Iowa gambling task. There were patients who admitted.
　パーキンソン運動症状や物忘れに関わらず、認知および情動障害の種類により、四つ這い運動時又は寝返り運動時の運動データは複合的に評価される。たとえば、アパシー・すくみの運動者は、四つ這い運動も寝返り運動も緩慢となり、かつ、四つ這い運動の前進開始時は踵部が床面から浮きあがり、後進時は腰部が左右に振れることで分類されることが分かった。脱抑制（怒りやすい）の運動者は、四つ這い運動時も寝返り運動時も素早く、四つ這い運動では前進から後進および後進から前進の変更時に停止できないことが分かった。遂行機能障害の運動者は、寝返り運動時には両肩より頭部の回旋が先行し、四つ這い運動時には踵押さえにより後進始動時に腰部の上下動揺が見られることが分かった。この遂行機能障害に、衝動性（易怒性）や情動処理の不安定さを示す四つ這い運動の後進中に踵部が床面から浮き上がったり、前進時に腰部が左右に振れたり幼若なハイハイが加わると、幻視や姿勢異常が見られることが分かった。 Parkinson's motor data are evaluated in a complex manner during crawl exercise or rollover exercise, depending on the type of cognitive and emotional disorders, regardless of motor symptoms or forgetfulness. For example, in apathy / freezing exercisers, both crawl and rollover exercises are slow, and the heels rise from the floor at the start of the four-crawling exercise, and the lumbar region swings from side to side when moving backward. It turned out to be classified by. It was found that disinhibited (prone to get angry) exercisers were quick during both crawl and rollover exercises, and that quadruple exercises could not stop when changing from forward to backward and backward to forward. It was found that in exercisers with executive dysfunction, the rotation of the head precedes both shoulders during the rollover exercise, and the lumbar region sways up and down at the time of reverse start due to the heel press during the four-crawling exercise. This executive dysfunction causes the heel to rise from the floor during backward movement, which indicates impulsivity (irritability) and instability of emotional processing, and the lumbar region to swing from side to side when moving forward. It was found that when high-high was added, hallucinations and abnormal posture were observed.
　上述したように、パーキンソン病、アルツハイマー型認知症、レビー小体型認知症等の高齢者に多い疾病、アパシー（意欲の低下）、脱抑制、衝撃性（易怒性）、遂行機能障害（注意障害）等の非運動症状や情動・認知障害、また、転倒の原因となるすくみ、姿勢異常、痛み等、さらには、幻視、神経症、情緒不安定、抑うつ、自律神経系の身体症状、記銘力障害、物忘れ、苛立ち（攻撃的行動）、社会的行動障害等の症状と、四つ這い運動時又は寝返り運動時の運動データは密接にかつ複雑に関連していることが分かった。同時に、乳幼児期に無意識に獲得する四つ這い運動や寝返り運動はシンプルでありながら、健常状態での動きと疾病や症状を有する状態での動きの差が分かりやすい。
　本発明は、四つ這い運動時又は寝返り運動時の運動データを収集することで、また、収集した運動データを解析するシステムや運動の診断を支援するシステム、解析プログラムを提供することで、各種疾病や非運動症状を含む各種症状の複雑な診断を支援することができる。 As mentioned above, diseases that are common in the elderly such as Parkinson's disease, Alzheimer's disease, and Levy body dementia, apathy (decreased motivation), depression, impact (irritability), and executive dysfunction (attention disorder). ) And other non-motor symptoms, emotional and cognitive disorders, freezing, posture abnormalities, pain, etc. that cause falls, as well as vision, neuropathy, emotional instability, depression, physical symptoms of the autonomic nervous system, memorization It was found that symptoms such as power disorder, forgetfulness, irritation (aggressive behavior), and social behavior disorder are closely and complicatedly related to exercise data during crawl exercise or rollover exercise. At the same time, the four-crawling exercise and the rolling exercise that are unconsciously acquired in infancy are simple, but the difference between the movement in a healthy state and the movement in a state with illness or symptom is easy to understand.
The present invention provides various types of exercise data by collecting exercise data during crawl exercise or rolling exercise, and by providing a system for analyzing the collected exercise data, a system for supporting exercise diagnosis, and an analysis program. It can support complex diagnosis of various symptoms including illness and non-motor symptoms.
１　　運動データ解析システム
１１　運動指示部
１２　運動データ収集部
１３　運動データ入力部
１４　運動条件入力部
１５　運動データ解析部
１６　解析結果報知部
１７　ネットワークサーバ
２　　運動診断支援システム
２１　運動データ入力部
２２　運動条件入力部
２３　運動条件及び運動データ表示部
２４　ＡＩ解析部
２５　ネットワークサーバ　 1 Exercise data analysis system 11 Exercise instruction unit 12 Exercise data collection unit 13 Exercise data input unit 14 Exercise condition input unit 15 Exercise data analysis unit 16 Analysis result notification unit 17 Network server 2 Exercise diagnosis support system 21 Exercise data input unit 22 Exercise conditions Input unit 23 Exercise condition and exercise data display unit 24 AI analysis unit 25 Network server

Claims (15)

  1. 　四つ這い運動時又は寝返り運動時の運動データ収集方法であって、
    　四つ這い運動時は踵部、腰部、手のいずれか１か所以上の部位、寝返り運動時は頭部、両膝、両肩のいずれか一か所以上の部位の動きを測定するステップと、
    　前記動きを測定するステップによって測定した動きを、数値化するステップと、
    を含む、運動データ収集方法。     It is a method of collecting exercise data during crawl exercise or turning over exercise.
    A step to measure the movement of one or more parts of the heel, lumbar region, and hands during crawl exercise, and one or more parts of the head, knees, and shoulders during rollover exercise. ,
    A step of quantifying the movement measured by the step of measuring the movement, and a step of quantifying the movement.
    Exercise data collection methods, including.
  2. 　四つ這い運動時の前記数値化するステップにおいて、
    　　踵部の動きを測定する場合は、床面から踵部までの高さを数値化し、
    　　腰部の動きを測定する場合は、頭から背骨を通る体の中心軸から腰部の中心までの距離を数値化し、
    　　手の動きを測定する場合は、左右の手と床面が順次接触する床面の各位置、又は、運動開始から各接触までの時間を数値化し、
    　寝返り運動時の前記数値化するステップにおいて、
    　　頭部の動きは、上面視において体の中心点から頭部の中心点までの距離を数値化し、
    　　両膝の動きは、床面から膝までの高さを数値化し、
    　　両肩の動きは、床面から肩までの距離、又は、運動開始から各時点での前記床面から肩までの距離を数値化する、請求項１に記載の運動データ収集方法。     In the step of quantifying during the four-crawling exercise
    When measuring the movement of the heel, quantify the height from the floor to the heel.
    When measuring lumbar movement, quantify the distance from the central axis of the body through the head to the spine to the center of the lumbar region.
    When measuring hand movement, quantify the position of the floor surface where the left and right hands and the floor surface contact in sequence, or the time from the start of exercise to each contact.
    In the step of quantifying during the rolling exercise,
    The movement of the head quantifies the distance from the center point of the body to the center point of the head in top view.
    The movement of both knees quantifies the height from the floor to the knees,
    The exercise data collection method according to claim 1, wherein the movement of both shoulders quantifies the distance from the floor surface to the shoulders or the distance from the floor surface to the shoulders at each time point from the start of the exercise.
  3. 　前記四つ這い運動は、前進運動及び後進運動を含む、請求項１又は２に記載の運動データ収集方法。 The exercise data collection method according to claim 1 or 2, wherein the four-crawling exercise includes a forward movement and a reverse movement.
  4. 　前記四つ這い運動は踵押さえをさらに含み、腰部又は手の部位の動きを測定する、請求項１～３いずれか一項に記載の運動データ収集方法。 The exercise data collection method according to any one of claims 1 to 3, wherein the four-crawling exercise further includes heel pressing and measures the movement of the lumbar region or the hand portion.
  5. 　前記寝返り運動は膝押さえをさらに含み、頭部又は両肩の部位の動きを測定する、請求項１又は２に記載の運動データ収集方法。 The exercise data collection method according to claim 1 or 2, wherein the rolling exercise further includes knee pressing and measures the movement of a portion of the head or both shoulders.
  6. 　前記四つ這い運動時又は寝返り運動時に、外部から特定のリズム信号をさらに与える、請求項１～５いずれか一項に記載の運動データ収集方法。 The exercise data collection method according to any one of claims 1 to 5, further giving a specific rhythm signal from the outside during the four-crawling exercise or the rolling exercise.
  7. 　前記特定のリズム信号は、前記四つ這い運動又は寝返り運動を同様に行う１人又は複数の他人から発せられる声や、該他人の運動のリズムに合わせた手拍子、声、打楽器音である、請求項６に記載の運動データ収集方法。 The specific rhythm signal is a voice emitted from one or more other people who perform the four-crawling movement or the rolling movement in the same manner, or a clapping, voice, or percussion instrument sound in accordance with the rhythm of the movement of the other person. Item 6. The exercise data collection method according to item 6.
  8. 　前記動きを測定するステップは、測定する部位にマークを付した計測による測定、体の揺れに反応して音が発生する部材を体に付した録音による測定、写真撮影による測定、動画撮影による測定、又は、それら２以上の組み合わせにより行われる、請求項１～７いずれか一項に記載の運動データ収集方法。 The steps for measuring the movement include measurement by marking the part to be measured, measurement by recording with a member that generates sound in response to the shaking of the body attached to the body, measurement by photography, and measurement by moving image. , Or the exercise data collection method according to any one of claims 1 to 7, which is performed by a combination of two or more of them.
  9. 　請求項１～８いずれか一項に記載の運動データ収集方法で収集した、数値化された運動データを入力する、運動データ入力部と、
    　前記運動データ入力部に入力された運動データ収集時の運動条件を入力する、運動条件入力部と、
    　前記入力された運動条件それぞれに対応して設定された所定の閾値と、前記入力された運動データを比較し、運動データが前記所定の閾値内に含まれるか、あるいは、前記所定の閾値以上であるかを解析する、運動データ解析部と、
    　前記解析された運動データが、前記所定の閾値に含まれている場合に、あるいは、前記所定の閾値以上の場合に、報知する、解析結果報知部と、
    を含む、運動データ解析システム。     An exercise data input unit for inputting quantified exercise data collected by the exercise data collection method according to any one of claims 1 to 8.
    An exercise condition input unit for inputting exercise conditions at the time of collecting exercise data input to the exercise data input unit, and an exercise condition input unit.
    The predetermined threshold value set corresponding to each of the input exercise conditions is compared with the input exercise data, and the exercise data is included in the predetermined threshold value or is equal to or higher than the predetermined threshold value. An exercise data analysis unit that analyzes the existence,
    An analysis result notification unit that notifies when the analyzed motion data is included in the predetermined threshold value or is equal to or higher than the predetermined threshold value.
    Exercise data analysis system, including.
  10. 　運動内容を指示する、運動指示部と、
    　前記運動データ収集方法で、運動データを収集する、運動データ収集部と、
    を、さらに含む、請求項９に記載の運動データ解析システム。     An exercise instruction unit that instructs the exercise content,
    An exercise data collection unit that collects exercise data by the exercise data collection method,
    The motion data analysis system according to claim 9, further comprising.
  11. 　サーバをさらに含み、必要に応じ前記サーバを介してデータや指示や結果が送受信される、遠隔操作可能な、請求項９又は１０に記載の運動データ解析システム。 The motion data analysis system according to claim 9 or 10, further including a server, to which data, instructions and results are transmitted and received via the server as needed, which can be remotely controlled.
  12. 　請求項１～８いずれか一項に記載の運動データ収集方法で収集した、数値化された運動データを入力する、運動データ入力部と、
    　前記運動データ入力部に入力されたデータ収集時の運動条件を入力する、運動条件入力部と、
    　前記入力された運動データ及び前記入力された運動条件を、診断に適した方法で表示する、運動条件及び運動データ表示部と、
    を含み、運動診断支援を行う、運動診断支援システム。     An exercise data input unit for inputting quantified exercise data collected by the exercise data collection method according to any one of claims 1 to 8.
    An exercise condition input unit for inputting 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 condition in a method suitable for diagnosis.
    An exercise diagnosis support system that provides exercise diagnosis support, including.
  13. 　前記運動条件及び前記運動データに基づき疾病や症状の種類を分類する、ＡＩ解析部をさらに含み、運動診断支援を行う、請求項１２に記載の運動診断支援システム。 The exercise diagnosis support system according to claim 12, further including an AI analysis unit that classifies the types of diseases and symptoms based on the exercise conditions and the exercise data, and provides exercise diagnosis support.
  14. 　サーバをさらに含み、必要に応じ前記サーバを介してデータや結果が送受信される、遠隔操作可能な、請求項１２又は１３に記載の運動診断支援システム。 The exercise diagnosis support system according to claim 12 or 13, which further includes a server and can be remotely controlled to send and receive data and results via the server as needed.
  15. 　運動データ解析システムの解析プログラムであって、
    　コンピュータに、
    　請求項１～８いずれか一項に記載の運動データ収集方法で収集した、四つ這い運動時又は寝返り運動時の動きを数値化した運動データを受信させ、
    　前記運動データ収集時の運動条件を受信させ、
    　前記運動条件それぞれに対応して設定された所定の閾値と、前記運動データを用い、運動データが前記所定の閾値内に含まれるか、あるいは、前記所定の閾値以上であるかを比較して解析させ、
    　前記解析された運動データが、前記所定の閾値に含まれている場合に、又は、前記所定の閾値以上の場合に、報知させることを含む、解析プログラム。     It is an analysis program of the motion data analysis system.
    On the computer
    Receive the exercise data that quantifies the movement during the four-crawling exercise or the rolling exercise, which is collected by the exercise data collection method according to any one of claims 1 to 8.
    Receive the exercise conditions at the time of collecting the exercise data,
    Using the exercise data and the predetermined threshold value set corresponding to each of the exercise conditions, the analysis is performed by comparing whether the exercise data is included in the predetermined threshold value or is equal to or more than the predetermined threshold value. Let me
    An analysis program including notifying the analyzed motion data when it is included in the predetermined threshold value or when it is equal to or higher than the predetermined threshold value.
PCT/JP2020/043225 2019-11-22 2020-11-19 Movement data collection method, movement data analysis system, movement diagnosis assistance system, and analysis program WO2021100815A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2021558447A JPWO2021100815A1 (en) 2019-11-22 2020-11-19

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2019211064 2019-11-22
JP2019-211064 2019-11-22

Publications (1)

Publication Number Publication Date
WO2021100815A1 true WO2021100815A1 (en) 2021-05-27

Family

ID=75980134

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2020/043225 WO2021100815A1 (en) 2019-11-22 2020-11-19 Movement data collection method, movement data analysis system, movement diagnosis assistance system, and analysis program

Country Status (2)

Country Link
JP (1) JPWO2021100815A1 (en)
WO (1) WO2021100815A1 (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7223103B1 (en) 2021-11-09 2023-02-15 株式会社電通国際情報サービス Extrapyramidal Symptom Diagnosis Device and Extrapyramidal Symptom Diagnosis Program
TWI846436B (en) 2023-04-20 2024-06-21 國立中山大學 System and method for assessing movement disorders in parkinson's disease using brain activity

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007330500A (en) * 2006-06-15 2007-12-27 Matsushita Electric Works Ltd Sleeping evaluation system
JP2012005745A (en) * 2010-06-28 2012-01-12 Tateyama System Laboratory Ltd Bed nursing system
JP2012071003A (en) * 2010-09-29 2012-04-12 Omron Healthcare Co Ltd Safe nursing system and method for controlling the safe nursing system
JP6429301B1 (en) * 2018-01-11 2018-11-28 株式会社元気広場 Motion acquisition support device, motion acquisition support system, and control method of motion acquisition support device

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007330500A (en) * 2006-06-15 2007-12-27 Matsushita Electric Works Ltd Sleeping evaluation system
JP2012005745A (en) * 2010-06-28 2012-01-12 Tateyama System Laboratory Ltd Bed nursing system
JP2012071003A (en) * 2010-09-29 2012-04-12 Omron Healthcare Co Ltd Safe nursing system and method for controlling the safe nursing system
JP6429301B1 (en) * 2018-01-11 2018-11-28 株式会社元気広場 Motion acquisition support device, motion acquisition support system, and control method of motion acquisition support device

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ARAI, SUMIYO ET AL.: "Examination of Factors Related to Trunk Movement Patterns in Rolling Over in Sleep Focusing", RIGAKURYOHO KAGAKU, vol. 26, no. 6, 16 June 2011 (2011-06-16), pages 769 - 772, XP055825770 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7223103B1 (en) 2021-11-09 2023-02-15 株式会社電通国際情報サービス Extrapyramidal Symptom Diagnosis Device and Extrapyramidal Symptom Diagnosis Program
JP2023070542A (en) * 2021-11-09 2023-05-19 株式会社電通国際情報サービス Diagnostic equipment of extrapyramidal symptom and diagnostic program of extrapyramidal symptom
TWI846436B (en) 2023-04-20 2024-06-21 國立中山大學 System and method for assessing movement disorders in parkinson's disease using brain activity

Also Published As

Publication number Publication date
JPWO2021100815A1 (en) 2021-05-27

Similar Documents

Publication Publication Date Title
Mendes Jr et al. Sensor fusion and smart sensor in sports and biomedical applications
Schwenk et al. Sensor-based balance training with motion feedback in people with mild cognitive impairment
Steiner et al. Effects of therapeutic horse riding on gait cycle parameters and some aspects of behavior of children with autism
JP2022088612A (en) Balance testing and training system and method
US9149222B1 (en) Enhanced system and method for assessment of disequilibrium, balance and motion disorders
US9526946B1 (en) Enhanced system and method for vibrotactile guided therapy
US8652071B2 (en) Systems, devices, and methods for interpreting movement
Grewal et al. Balance rehabilitation: promoting the role of virtual reality in patients with diabetic peripheral neuropathy
TW201121615A (en) Gymnastic system
JP2019154489A (en) Athletic ability evaluation system
US11914776B2 (en) System and method for evaluation, detection, conditioning, and treatment of neurological functioning and conditions
US20230062345A1 (en) System and method for targeted neurological therapy using brainwave entrainment
JP2018121930A (en) Gait evaluation method
WO2021100815A1 (en) Movement data collection method, movement data analysis system, movement diagnosis assistance system, and analysis program
US11355029B2 (en) Method and system for estimating a demand or an attentional cost associated with the execution of a task or attention sharing strategies developed by an individual
JP2006192258A (en) Muscle output/motion following ability evaluating and training system
WO2017187671A1 (en) Assistance method, assistance system, and program
JP2019024579A (en) Rehabilitation support system, rehabilitation support method, and program
KR101823567B1 (en) System And Method For Smart Recumbent Rehabilitation Exercise
Ofori Dance-based exergaming in older adults: examining effect on movement kinematics and physical function
Steiner et al. Effect of therapeutic riding on gait cycle parameters and behavioural skills of autistic children
JP2021003540A (en) Rehabilitation support system, rehabilitation support method, and rehabilitation support program
Giannoni Rehabilitation technologies for sensory-motor-cognitive impairments
Islam Wearable technologies to support lower limb rehabilitation and clinical practice: user requirements, design, and evaluation
Peeters Design guidelines for vibrotactile motion steering

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20890882

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2021558447

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20890882

Country of ref document: EP

Kind code of ref document: A1