Exercise data analysis system and method for amyotrophic lateral sclerosis assessment

Abstract

The invention relates to the field of medical systems, in particular to an exercise data analysis system and method for amyotrophic lateral sclerosis assessment, and the method comprises the steps: firstly obtaining and storing the illness condition data of each position of a patient and the training data of each position of training exercise to be selected, and carrying out the sclerosis severity analysis and the corresponding position exercise matching analysis; carrying out motion safety analysis on the corresponding position, then obtaining the matching abnormity of the motion and the body of the patient through the motion matching abnormity condition and the motion safety abnormity analysis, and finally selecting the training motion corresponding to the minimum matching abnormity as the rehabilitation training motion output. Weighted weights of movement matching abnormity and movement safety abnormity are distributed according to the constitution abnormity calculated according to the body constitution parameters of the patient and the safety range standard deviation, an adaptive rehabilitation training scheme can be accurately customized, the training pertinence and effectiveness are improved, the individual difference of the patient is fully considered, and the rehabilitation training efficiency is improved. Rehabilitation training is ensured to conform to physical conditions and achieve a good effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data, and particularly relates to a motion data analysis system and method for amyotrophic lateral sclerosis assessment. BACKGROUND

[0002] Amyotrophic lateral sclerosis, also known as Lou Gehrig's disease, is a progressive and fatal neurodegenerative disease that mainly affects motor neurons in the brain and spinal cord, causing these neurons to gradually degenerate and die. As the disease progresses, patients may experience muscle weakness, atrophy, difficulty swallowing, speech disorders, and eventually death due to respiratory muscle paralysis. Currently, the cause of amyotrophic lateral sclerosis is not fully understood, and there is no effective cure. Therefore, it is particularly important to provide scientific treatment and rehabilitation intervention for patients. Rehabilitation training plays an important role in the treatment of amyotrophic lateral sclerosis. Proper exercise can help patients maintain muscle strength and joint mobility, slow down the progression of muscle atrophy, improve the quality of life of patients, and prolong the life of patients to some extent. However, existing rehabilitation training programs have many limitations.

[0003] Most current rehabilitation training programs are based on general standards and experience, without fully considering individual differences of each patient. The severity of the disease, physical fitness, muscle damage location and extent of different patients are different. The general training program cannot accurately meet the specific needs of each patient, which may lead to poor training results, or even aggravate the patient's condition due to inappropriate exercise. When developing a rehabilitation training program, the existing evaluation method often only focuses on part of the patient's physical indicators or exercise capacity, and lacks comprehensive and detailed analysis of the patient's overall condition. For example, the patient's limb movement ability may be evaluated, but the function of the respiratory muscles, swallowing muscles and other parts is ignored. In addition, the evaluation of the matching of exercise and the patient's body is not deep enough, and the safety and effectiveness of exercise are not fully considered, which may lead to the risk of injury or excessive fatigue of the patient during training.

[0004] Due to the above limitations of existing rehabilitation training programs, many amyotrophic lateral sclerosis patients cannot receive effective rehabilitation treatment. After a period of training, the patient's muscle strength and function may not have improved significantly, or even the patient's condition may worsen. In addition, inappropriate exercise may also cause physical pain and psychological pressure to the patient, affecting the patient's quality of life and treatment confidence.

[0005] Therefore, the present application provides a motion data analysis system and method for amyotrophic lateral sclerosis assessment. SUMMARY

[0006] In order to overcome the defects and deficiencies existing in the prior art, the application provides a movement data analysis system and method for amyotrophic lateral sclerosis evaluation, which analyzes the sclerosis severity and the movement matching of corresponding positions, analyzes the movement safety of corresponding positions, then obtains the movement matching abnormality of the patient's body through the movement matching abnormality and the movement safety abnormality, finally selects the training movement corresponding to the minimum matching abnormality as the output of the rehabilitation training movement, and the physical abnormality is distributed to the weighted weight of the movement matching abnormality and the movement safety abnormality according to the standard deviation of the physical fitness parameter and the safety range of the patient's body, which can accurately customize the rehabilitation training scheme, improve the training pertinence and effectiveness, fully consider the individual differences of patients, and ensure that the rehabilitation training meets the physical condition and achieves good results.

[0007] In order to achieve the above purpose, the application adopts the following technical scheme: In a first aspect, the application provides a movement data analysis method for amyotrophic lateral sclerosis evaluation, comprising the following steps: S100, obtaining the disease condition data of each position of the patient and the training data of each position of each selected training movement; S200, analyzing the sclerosis severity through the disease condition data of each position of the patient, and analyzing the movement matching of corresponding positions through the movement amount, movement transmission influence and sclerosis severity of corresponding positions in the movement process; S300, analyzing the movement safety of corresponding positions through the disease development of the patient and the training process of the movement; S400, analyzing the movement selection through the movement safety analysis results and the movement matching analysis results of each position; S500, selecting the required rehabilitation training movement according to the movement selection analysis results.

[0008] In an implementation manner of the application, the step S100 comprises the following specific contents: Step 110, obtaining the disease condition of each position of the patient through the patient disease condition questionnaire, and the corresponding disease condition includes the joint activity condition and muscle elasticity condition of each position of the patient, and the physical fitness characteristic conditions such as body temperature, heart rate and lung capacity, and the weight of safety occupied by the physical fitness characteristic is analyzed; Step 120, obtaining the training data of each position of the patient in each selected training movement through the movement image module, which includes the movement trajectory condition of each position of the patient and the movement amount data of each position, wherein the movement amount is the product of the movement frequency and the standardized single movement distance, the standardization process is to divide the corresponding value by the standard amount, and the nearest distance of the corresponding position relative to the neural center is also obtained to evaluate the importance of the corresponding position; Step 130, store the acquired data in the corresponding storage component to facilitate data retrieval.

[0009] In an implementation form of the application, the hardening severity analysis in step S200 comprises the following specific steps: Step 210, acquire the joint mobility and muscle elasticity of each position of the patient, and obtain the joint mobility anomaly of the corresponding position by dividing the joint mobility data by the joint mobility safety value of the corresponding position, wherein the joint mobility safety value of the corresponding position is the average joint mobility of the corresponding position of the personnel of the corresponding age, and the position is divided by the intermediate region between the joint and the adjacent joint; Step 220, obtain the muscle elasticity anomaly of the corresponding position by dividing the muscle elasticity of the corresponding position of the patient by the muscle elasticity safety value of the corresponding position, wherein the muscle elasticity safety value of the corresponding position is the average muscle elasticity of the corresponding position of the personnel of the corresponding age, and the calculation of the muscle elasticity anomaly can clearly present the difference between the muscle elasticity of the patient and the normal level of the same age group. Muscle elasticity is an important indicator reflecting muscle health status. This step can help doctors judge the hardening degree of the patient's muscle, and combine the joint mobility anomaly to more comprehensively evaluate the patient's physical condition. The muscle elasticity of the same age group has a certain average level, i.e. the muscle elasticity safety value. Dividing the muscle elasticity of the corresponding position of the patient by the safety value can obtain the abnormal degree of muscle elasticity; Step 230, obtain the hardening severity of the corresponding position by weighted sum of the muscle elasticity anomaly and the joint mobility anomaly of the corresponding position. By weighted sum of the muscle elasticity anomaly and the joint mobility anomaly, the influence of the two key factors of joint and muscle on the hardening severity is comprehensively considered. This comprehensive evaluation method is more comprehensive and accurate than single index evaluation.

[0010] In an implementation form of the application, the corresponding position motion matching analysis in step S200 comprises the following specific contents: Step 240, acquire the motion amount of each position in the motion process, obtain the hardening coefficient by dividing the hardening severity by the hardening severity standard value, and obtain the motion coefficient by dividing the motion amount of the corresponding position by the safety motion amount. The hardening coefficient reflects the relative relationship between the hardening degree of each position of the patient and the standard value. The motion coefficient reflects the comparison between the motion amount of each position of the patient and the safety motion amount. Through the calculation of the two coefficients, the hardening degree and the motion amount can be quantified, which provides important basic data for subsequent analysis of motion matching; Step 250, obtaining the hardening coefficient of the corresponding position and the average value of the hardening coefficient of the position intersecting with the position, obtaining the hardening standard coefficient of the corresponding position by multiplying the average value of the hardening coefficient of the intersecting position by the motion transmission coefficient and adding the hardening coefficient of the position, wherein the motion transmission coefficient is inversely proportional to the skin elasticity, and the value is obtained in the following manner: the skin elasticity standard value is divided by the skin elasticity of the corresponding position, the influence of the hardening coefficient of the adjacent position and the effect of the skin elasticity on the motion transmission are considered, and the calculated hardening standard coefficient more comprehensively reflects the actual hardening condition of each position of the body; Step 260, obtaining the motion matching coefficient of the corresponding position by dividing the hardening standard coefficient of the corresponding position by the motion coefficient, obtaining the deviation of the motion matching coefficient of the corresponding position from the motion matching standard coefficient as the motion matching abnormality analysis result of the corresponding position, so that the position with greater corresponding hardening abnormality is paid more attention to, the motion matching accuracy is improved, the motion matching coefficient can intuitively reflect the matching degree between the hardening condition and the motion amount of each position of the patient, by obtaining the deviation of the motion matching coefficient from the motion matching standard coefficient, the motion matching abnormality of each position can be accurately analyzed, so that the doctor can pay more attention to the position with greater hardening abnormality, and the accuracy of motion matching is improved.

[0011] In an implementation manner of the present application, the motion safety analysis of the corresponding position in the step S300 comprises the following specific contents: Obtaining the hardening standard coefficient of each position, and obtaining the motion change rate of the corresponding position in the motion process, wherein the motion change rate is the average value of the unit time displacement amount of the part in the motion process, the displacement amount is the volume of the union of the three-dimensional images of the corresponding position at the previous and next two time points minus the volume of the intersection of the three-dimensional images divided by the volume of the union of the three-dimensional images; obtaining the motion safety abnormality by weighted sum of the standardized displacement change rate and the standardized hardening standard coefficient, obtaining the motion safety abnormality by combining the hardening standard coefficient and the motion change rate, which can comprehensively consider the hardening condition of each position of the body and the dynamic change in the motion process, the hardening standard coefficient comprehensively reflects the actual hardening condition of each position, and the motion change rate reflects the dynamic displacement characteristics of the part in the motion process, and the combination of the two can more accurately evaluate the safety abnormality in the motion process.

[0012] In an implementation manner of the present application, the motion selection analysis in the step S400 comprises the following specific contents: The motion matching abnormality of the corresponding position and the motion safety abnormality are weighted and summed to obtain the motion abnormality of the corresponding position, the motion abnormalities of all positions are averaged to obtain the matching abnormality of the corresponding motion and the patient's body, the minimum matching abnormality is obtained, the training motion is set as the patient's rehabilitation training motion, and the output is output to the medical port.

[0013] In a second aspect, the present application further provides a motion data analysis system for amyotrophic lateral sclerosis evaluation, comprising: A data acquisition module acquires disease condition data of each position of a patient and training data of each position of each selected training motion. A sclerosis severity analysis module analyzes sclerosis severity based on the disease condition data of each position of the patient. A matching analysis module analyzes the motion matching of each position based on the motion amount, motion transmission influence and sclerosis severity of each position during the motion process. A safety analysis module analyzes the motion safety of each position based on the disease development of the patient and the training process of the motion. A motion selection analysis module analyzes the motion selection based on the motion safety analysis result and the motion matching analysis result of each position, and selects the required rehabilitation training motion based on the motion selection analysis result.

[0014] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a motion data analysis method for amyotrophic lateral sclerosis evaluation by calling the computer program stored in the memory.

[0015] In a fourth aspect, the present application provides a computer readable storage medium storing instructions, which, when executed on a computer, cause the computer to execute a motion data analysis method for amyotrophic lateral sclerosis evaluation.

[0016] Compared with the prior art, the present application has the following advantages and beneficial effects: The scheme first acquires patient position condition data and selected training movement position training data and stores them, analyzes the hardening severity, analyzes the corresponding position movement matching, analyzes the corresponding position movement safety, then obtains the movement and patient body matching exception through the movement matching exception and movement safety analysis, finally selects the training movement corresponding to the minimum matching exception as the rehabilitation training movement output, and the body constitution exception assigns the weighted weight of the movement matching exception and the movement safety exception according to the patient body constitution parameter and the safety range standard deviation calculation, can accurately customize the adaptive rehabilitation training scheme, improves the training pertinence and effectiveness, fully considers the individual differences of patients, and ensures that the rehabilitation training meets the physical condition and achieves good results. BRIEF DESCRIPTION OF DRAWINGS

[0017] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments thereof, read in conjunction with the accompanying drawings: Figure 1 The module composition structure of the system embodiment of the present application is shown in the figure; Figure 2 The overall flow structure of the method embodiment of the present application is shown in the figure; Figure 3 The flow structure of the hardening severity analysis of the method embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0018] The technical scheme of the present application will be described in detail below by means of the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, and are not limitations of the technical scheme of the present application. In the case of no conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0019] Please refer to Figure 2 and Figure 3 , Figure 2 The figure is a whole flow schematic diagram of the movement data analysis method for amyotrophic lateral sclerosis evaluation provided by the embodiment of the present application, and specifically includes the following steps: S100, acquiring the condition data of each position of the patient and the training data of each position of each selected training movement; In this embodiment, the required rehabilitation training movement is selected.

[0020] In one implementation manner of the present application, step S100 includes the following specific contents: Step 110, obtain the condition of each position of the patient through the patient condition checklist, and the corresponding condition includes the joint activity of each position of the patient and the muscle elasticity, and the physical characteristics such as body temperature, heart rate and lung capacity, the weight of safety is analyzed through the physical characteristics, the professional patient condition checklist is used to comprehensively obtain the condition information of each position of the patient, which specifically covers the joint activity of each position of the patient, the muscle elasticity, and also includes the physical characteristics such as body temperature, heart rate and lung capacity, all patient information is accurately obtained by corresponding professional sensor, in this process, it has been clearly informed that the information provider will strictly keep the information confidential, and the collected information does not contain the patient's name and other contents that can directly identify the personal identity, which fully meets the requirements of relevant laws and regulations, fully safeguards the legal rights and interests and personal privacy of the patient, and aims to help the patient realize the physical health goal in a safe and compliant manner; Step 120, obtain the training data of each position of the patient when performing each selected training movement through the motion image module, including the motion trajectory of each position of the patient and the motion amount data of each position, wherein the motion amount is the product of the number of movements and the standardized single movement distance, the standardization process is to divide the corresponding value by the standard amount, and the nearest distance of the corresponding position relative to the neural center is also obtained to evaluate the importance of the corresponding position; Step 130, store the obtained data in the corresponding storage component to facilitate data retrieval; S200, analyze the severity of hardening through the condition data of each position of the patient, analyze the motion matching of each position through the motion amount, motion transmission influence and severity of hardening of the motion process of the corresponding position; In this embodiment, the severity of hardening analysis in step S200 includes the following specific steps: Step 210, obtain the joint activity of each position of the corresponding patient and the muscle elasticity, obtain the joint activity anomaly of the corresponding position by dividing the joint activity safety value of the corresponding position by the joint activity data, wherein the joint activity safety value of the corresponding position is the average value of the joint activity of the corresponding position of the corresponding age person, wherein the position is divided by the middle area between the joints and adjacent joints, for example, the elbow position is the middle part of the upper arm and the lower arm; Step 220, obtain the muscle elasticity abnormality of the corresponding position by dividing the muscle elasticity safety value of the corresponding position by the muscle elasticity of the corresponding position of the patient, wherein the muscle elasticity safety value of the corresponding position is the average value of the muscle elasticity of the corresponding position of the personnel of the corresponding age, the calculation of the muscle elasticity abnormality can clearly present the difference between the muscle elasticity of the patient and the normal level of the same age group, the muscle elasticity is an important indicator reflecting the muscle health condition, this step can help the doctor to judge the hardening degree of the muscle of the patient, combined with the joint range of motion abnormality, more comprehensively evaluate the physical condition of the patient, the muscle elasticity of the same age group has a certain average level, that is, the muscle elasticity safety value, dividing the muscle elasticity of the corresponding position of the patient by the safety value can obtain the abnormality degree of the muscle elasticity, this comparison method conforms to the conventional method of judging the health condition by comparing with the normal standard in medicine, and provides important muscle data support for subsequent comprehensive evaluation; Step 230, obtain the hardening severity of the corresponding position by weighted sum of the muscle elasticity abnormality and the joint range of motion abnormality of the corresponding position, by weighted sum of the muscle elasticity abnormality and the joint range of motion abnormality, the influence of the two key factors of joint and muscle on the hardening severity is comprehensively considered. This comprehensive evaluation method is more comprehensive and accurate than single index evaluation, and can more truly reflect the hardening condition of each position of the patient's body, and provides a reliable basis for subsequent treatment and exercise guidance. In the physiological structure of the human body, the joint range of motion and the muscle elasticity are closely related, and both are directly related to the hardening degree of the body. The weighted sum method can give different weights according to the influence degree of the two on the hardening degree, so as to more scientifically calculate the hardening severity of each position; In the embodiment, the corresponding position movement matching analysis in step S200 specifically includes the following specific contents: Step 240, obtain the movement amount of each position in the movement process, obtain the hardening coefficient by dividing the hardening severity by the hardening severity standard value, obtain the movement coefficient by dividing the movement amount of the corresponding position by the safety movement amount, the hardening coefficient reflects the relative relationship between the hardening degree of each position of the patient and the standard value, the movement coefficient reflects the comparison situation of the movement amount of each position of the patient and the safety movement amount, through the calculation of the two coefficients, the hardening degree and the movement amount can be quantified, which provides important basic data for subsequent analysis of movement matching, comparing the hardening severity with the hardening severity standard value, and comparing the movement amount with the safety movement amount is a standardized processing method, which conforms to the common comparison analysis method in medicine and exercise science, and can objectively reflect the hardening condition and movement amount of each position of the patient in the movement process; Step 250, obtaining the hardening coefficient of the corresponding position and the average value of the hardening coefficient of the position adjacent to the position, and obtaining the hardening standard coefficient of the corresponding position by multiplying the average value of the hardening coefficient of the adjacent position by the motion transmission coefficient and adding the hardening coefficient of the position, wherein the motion transmission coefficient is inversely proportional to the skin elasticity, and the value is obtained by dividing the standard amount of skin elasticity by the skin elasticity of the corresponding position. Considering the influence of the hardening coefficient of the adjacent position and the effect of skin elasticity on motion transmission, the calculated hardening standard coefficient more comprehensively reflects the actual hardening condition of each position of the body. By introducing the motion transmission coefficient, the mutual influence between each part of the body can be more accurately considered, and the evaluation result is more in line with the actual physiological condition. In human motion, the hardening conditions of adjacent positions will affect each other, and the skin elasticity will affect the transmission of motion in each part of the body. The value of the motion transmission coefficient inversely proportional to the skin elasticity conforms to the principle of human physiological mechanics. By calculating the hardening standard coefficient in this way, the actual hardening condition of each position in motion can be more accurately reflected; Step 260, obtaining the motion matching coefficient of the corresponding position by dividing the hardening standard coefficient of the corresponding position by the motion coefficient, and obtaining the deviation between the motion matching coefficient of the corresponding position and the motion matching standard coefficient as the motion matching abnormality analysis result of the corresponding position. In this way, the position with greater corresponding hardening abnormality receives more attention in motion, and the motion matching accuracy is improved. The motion matching coefficient can directly reflect the matching degree between the hardening condition and the motion amount of each position of the patient. By obtaining the deviation between the motion matching coefficient and the motion matching standard coefficient, the motion matching abnormality of each position can be accurately analyzed, so that the doctor can pay more attention to the position with greater hardening abnormality, improve the accuracy of motion matching, and thus develop a more reasonable motion plan. In the process of motion rehabilitation, it is very important to ensure that the motion amount of the patient matches the hardening condition of the body. By comparing the motion matching coefficient and the standard coefficient, the mismatch between motion and body condition can be found, which provides a scientific basis for adjusting the motion plan and conforms to the basic principle of motion rehabilitation; S300, motion safety analysis of the corresponding position is performed according to the development of the patient's condition and the training process of the motion; In the embodiment, the motion safety analysis of the corresponding position in step S300 includes the following specific contents: The hardening standard coefficient corresponding to each position is obtained, and the motion change rate of the position corresponding to each position in the motion process is obtained, wherein the motion change rate is the average value of the displacement amount per unit time of the position in the motion process, and the displacement amount is the volume of the union of the three-dimensional images of the corresponding position at the two time points minus the volume of the intersection of the three-dimensional images divided by the volume of the union of the three-dimensional images; the motion safety anomaly is obtained by weighted summation of the standardized displacement change rate and the standardized hardening standard coefficient, the motion safety anomaly is calculated by combining the hardening standard coefficient and the motion change rate, the hardening condition and the dynamic change in the motion process of each position of the body can be comprehensively considered, the hardening standard coefficient comprehensively reflects the actual hardening condition of each position, the motion change rate reflects the dynamic displacement characteristics of the position in the motion process, and the combination of the two can more accurately evaluate the safety anomaly in the motion process. The motion change rate is measured by the average value of the displacement amount per unit time, the calculation of the displacement amount uses the union volume of the three-dimensional images of the corresponding position at the two time points minus the intersection volume divided by the union volume, which is a scientific and reasonable calculation method and can accurately reflect the actual change of the position in the motion process. S400, performing motion selection analysis by the motion safety analysis result and the motion matching analysis result of each position; In the embodiment, the motion selection analysis in step S400 includes the following specific contents: The motion matching abnormality and the motion safety abnormality corresponding to the position are weighted and summed to obtain the motion abnormality corresponding to the position, the motion abnormality of all positions is averaged to obtain the matching abnormality of the corresponding motion and the patient's body, the minimum matching abnormality is obtained, the training motion is set as the patient's rehabilitation training motion, and is output to the medical port. It should be noted that the weight of the motion matching abnormality and the motion safety abnormality is distributed according to the physical condition of the patient's body. The body with good physical fitness can allocate more weight of the motion safety abnormality. The specific allocation method is: the physical abnormality is obtained by weighted summing the standard deviation of each parameter of the patient's body and the safety range of each parameter, the weight of the motion matching abnormality is obtained by multiplying the physical abnormality by the set physical abnormality weight coefficient, the weight of the motion safety abnormality is obtained by subtracting the weight of the motion matching abnormality from the value 1, the motion abnormality is obtained by weighted summing the motion matching abnormality and the motion safety abnormality corresponding to the position, and the matching abnormality of the corresponding motion and the patient's body is obtained by averaging the motion abnormality of all positions. Finally, the training motion corresponding to the minimum matching abnormality is selected as the patient's rehabilitation training motion and is output to the medical port. The most suitable rehabilitation training scheme can be accurately customized for the patient, the pertinence and effectiveness of rehabilitation training are maximized, and at the same time, the weight of the motion matching abnormality and the motion safety abnormality is distributed according to the physical condition of the patient's body. The weight of the motion safety abnormality is allocated more for the patient with good physical fitness, the individual differences of the patient are fully considered, the most suitable training motion is obtained for patients with different physical fitness under the premise of ensuring motion safety, the physical abnormality is obtained by weighted summing the standard deviation of each parameter of the patient's body and the safety range of each parameter, and the weight of the motion matching abnormality and the motion safety abnormality is calculated based on this. This method is scientific and reasonable because the standard deviation can accurately reflect the degree of deviation of the patient's physical fitness parameters from the safety range, and then the weight is reasonably adjusted to ensure that the rehabilitation training meets the patient's physical condition and achieves good rehabilitation effect. S500, selecting the required rehabilitation training motion according to the motion selection analysis result, and the patient trains regularly according to the output rehabilitation training motion; In the embodiment, the acquisition method of the set weight and the set coefficient is: the historical data of the patient's condition of each position and the training data of each position of the selected training motion are acquired, and the judgment result of whether the historical personnel rehabilitation effect meets the rehabilitation requirement is acquired. The historical data is introduced into each step of the embodiment to select the required rehabilitation training motion, the calculation result and the judgment result are introduced into the Matlab fitting software for data fitting, and the set weight and the set coefficient corresponding to the maximum judgment accuracy are output.

[0021] The embodiment has the advantages that the rehabilitation training scheme can be accurately customized and adapted, the training pertinence and effectiveness are improved, the individual differences of patients are fully considered, and it is ensured that the rehabilitation training meets the physical condition and achieves good results.

[0022] See Figure 1 , Figure 1 is a structural schematic diagram of a motion data analysis system for amyotrophic lateral sclerosis evaluation provided by the embodiment of the application, comprising: a data acquisition module, which acquires disease condition data of each position of a patient and training data of each position of each selected training motion; a sclerosis severity analysis module, which performs sclerosis severity analysis through the disease condition data of each position of the patient; a matching analysis module, which performs corresponding position motion matching analysis through the motion amount, motion transmission influence and sclerosis severity of each position in the motion process; a safety analysis module, which performs corresponding position motion safety analysis through the disease development of the patient and the training process of the motion; a motion selection analysis module, which performs motion selection analysis through the motion safety analysis result and the motion matching analysis result of each position, and selects the required rehabilitation training motion according to the motion selection analysis result, wherein Figure 1 the arrow in the figure is a data transmission direction.

[0023] The steps of implementing the corresponding functions of each parameter and each unit module in the motion data analysis system for amyotrophic lateral sclerosis evaluation of the application can refer to the parameters and steps in the embodiments of the motion data analysis method for amyotrophic lateral sclerosis evaluation, and will not be repeated here.

[0024] The embodiment of the application further provides an electronic device, comprising a memory, a processor and a communication bus; the memory and the processor are connected through the communication bus. The memory stores the motion data analysis method for amyotrophic lateral sclerosis evaluation provided by the above-mentioned embodiment, which can be loaded and executed by the processor.

[0025] The memory can be used to store instructions, programs, codes, code sets or instruction sets. The memory can include a storage program area and a storage data area, wherein the storage program area can store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the motion data analysis method for amyotrophic lateral sclerosis evaluation provided by the above-mentioned embodiment, etc.; the storage data area can store data involved in the motion data analysis method for amyotrophic lateral sclerosis evaluation provided by the above-mentioned embodiment, etc.

[0026] The processor can include one or more processing cores. The processor invokes data stored in the memory by running or executing instructions, programs, code sets, or instruction sets stored in the memory, performs various functions of the present application, and processes data. The processor can be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It can be understood that, for different devices, the electronic device for implementing the functions of the above processor can also be other, and the embodiments of the present application are not specifically limited.

[0027] The communication bus can include a path for transmitting information between the above components. The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0028] The embodiment of the present application provides a computer readable storage medium, which stores a computer program capable of being loaded and executed by a processor to analyze motion data for amyotrophic lateral sclerosis evaluation.

[0029] In the embodiment of the present application, the computer readable storage medium can be a tangible device that keeps and stores instructions for use by an instruction execution device. The computer readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the above. Specifically, the computer readable storage medium can be a portable computer disk, a hard disk, a U disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a stand-alone random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical coding device, and any combination of the above.

[0030] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0031] The description above only concerns preferred embodiments of the application and the explanation of the principles of the technology used. The person skilled in the art will understand that the scope of the application is not limited to the technical solutions formed by the specific combinations of the technical features described above, but also covers other technical solutions formed by any combinations of the technical features described above or their equivalent features without deviating from the concept of the application. For example, the technical solutions formed by the mutual replacement of the above-mentioned features and the technical features applied in the application (but not limited to) having similar functions.

Claims

1. A motion data analysis system for assessing amyotrophic lateral sclerosis (ALS), characterized in that, Includes the following specific modules: The data acquisition module acquires data on the patient's condition at various locations and training data for each location of each candidate training exercise. The sclerosis severity analysis module analyzes the severity of sclerosis based on the patient's condition data at various locations. The matching analysis module performs motion matching analysis at corresponding positions by considering the amount of motion, the influence of motion transmission, and the severity of hardening at each position during the motion process. The safety analysis module analyzes the safety of movement at corresponding positions based on the patient's condition development and the exercise training process. The exercise selection analysis module analyzes the exercise selection based on the results of exercise safety analysis and exercise matching analysis at each location, and selects the necessary rehabilitation training exercises based on the results of the exercise selection analysis.

2. The exercise data analysis system for assessing amyotrophic lateral sclerosis (ALS) according to claim 1, characterized in that, The hardening severity analysis includes the following specific steps: The range of motion and muscle elasticity of the joints at each location of the corresponding patient are obtained. The abnormal range of motion of the joints at the corresponding location is obtained by dividing the safe range of motion of the joints at the corresponding location by the range of motion data. The safe range of motion of the joints at the corresponding location is the average range of motion of the joints at the corresponding location of the corresponding age group. The abnormality of muscle elasticity at the corresponding location is obtained by dividing the safe value of muscle elasticity at the corresponding location by the muscle elasticity at the corresponding location of the patient. The safe value of muscle elasticity at the corresponding location is the average value of muscle elasticity at the corresponding location for people of the corresponding age. The severity of stiffness at a given location is determined by weighted summation of abnormal muscle elasticity and abnormal joint mobility at that location.

3. The exercise data analysis system for assessing amyotrophic lateral sclerosis (ALS) according to claim 2, characterized in that, The corresponding positional motion matching analysis specifically includes the following: The amount of exercise at each location during the exercise process is obtained. The hardening coefficient is obtained by dividing the hardening severity by the hardening severity standard value. The exercise coefficient is obtained by dividing the amount of exercise at each location by the safe amount of exercise. Obtain the hardening coefficient at the corresponding position and the average hardening coefficient of the positions intersecting with that position. Multiply the average hardening coefficient of the intersecting positions by the motion transmission coefficient and add the hardening coefficient of that position to obtain the standard hardening coefficient of the corresponding position. The motion matching coefficient at the corresponding position is obtained by dividing the hardening standard coefficient at the corresponding position by the motion coefficient. The deviation between the motion matching coefficient at the corresponding position and the motion matching standard coefficient is used as the result of the motion matching anomaly analysis at the corresponding position.

4. The exercise data analysis system for assessing amyotrophic lateral sclerosis (ALS) according to claim 1, characterized in that, The motion safety analysis at the corresponding location includes the following specific contents: The hardening standard coefficients for each location are obtained, and the motion change rate for each location during the motion is also obtained. The motion change rate is the average displacement per unit time of the part during the motion process, and the displacement is the volume of the union of the three-dimensional images of the corresponding locations at two different times minus the volume of the intersection of the three-dimensional images divided by the volume of the union of the three-dimensional images. The motion safety anomaly is obtained by weighted summation of the standardized displacement change rate and the standardized hardening standard coefficients.

5. The exercise data analysis system for assessing amyotrophic lateral sclerosis (ALS) according to claim 1, characterized in that, The motion selection analysis includes the following specific components: The system obtains the motion matching anomalies and motion safety anomalies at the corresponding locations, performs a weighted summation, obtains the motion anomalies at the corresponding locations, averages the motion anomalies at all locations to obtain the corresponding motion matching anomalies with the patient's body, obtains the minimum matching anomaly corresponding to the training motion, sets it as the patient's rehabilitation training motion, and outputs it to the medical port. The weighting of the motion matching anomalies and motion safety anomalies is allocated according to the patient's physical condition.

6. The exercise data analysis system for assessing amyotrophic lateral sclerosis (ALS) according to claim 1, characterized in that, The patient's condition data was obtained through a patient condition examination form, and the corresponding condition data included the range of motion of the patient's joints in various locations and the elasticity of the muscles.

7. A method for analyzing exercise data for assessing amyotrophic lateral sclerosis (ALS), implemented based on the exercise data analysis system for ALS assessment according to any one of claims 1-6, characterized in that, Specifically, the following steps are included: S100: Obtain patient condition data for each location and training data for each location of each candidate training exercise; S200. Analyze the severity of hardening by analyzing the patient's condition data at each location, and analyze the movement matching of corresponding locations by analyzing the amount of movement, the influence of movement transmission, and the severity of hardening during the movement process. S300: Conduct a safety analysis of movement in the corresponding positions based on the patient's condition development and the exercise training process; S400: Motion selection analysis is performed based on the motion safety analysis results and motion matching analysis results at each location; S500: Select the necessary rehabilitation exercises based on the results of the exercise selection analysis.

8. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the motion data analysis method for assessing amyotrophic lateral sclerosis as described in claim 7 by calling the computer program stored in the memory.

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