System and method for motor data analysis for amyotrophic lateral sclerosis assessment

Abstract

The present application relates to the field of medical systems, in particular to a motion data analysis system and method for amyotrophic lateral sclerosis evaluation, the present application first acquires the disease condition data of each position of the patient and the training data of each position of the selected training motion, and stores them, carries out sclerosis severity analysis and corresponding position motion matching analysis, carries out corresponding position motion safety analysis, then obtains the matching abnormality of motion and patient body through motion matching abnormality and motion safety abnormality analysis, finally selects the training motion corresponding to the smallest matching abnormality as the output of rehabilitation training motion, and the constitution abnormality distribution motion matching abnormality and motion safety abnormality weight is calculated according to the constitution parameter of the patient body and the standard deviation of the safety range, which can accurately customize the rehabilitation training scheme, improve the training pertinence and effectiveness, fully consider the individual difference of the patient, and ensure that the rehabilitation training not only meets the physical condition but also achieves good effect.

Description

Technical Field

[0001] This invention relates to the field of big data technology, and in particular to a system and method for analyzing exercise data for the assessment of amyotrophic lateral sclerosis (ALS). Background Technology

[0002] Amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig's disease, is a progressive and fatal neurodegenerative disease that primarily affects motor neurons in the brain and spinal cord, leading to their gradual degeneration and death. As the disease progresses, patients experience muscle weakness, atrophy, difficulty swallowing, and speech impairment, and may ultimately die from respiratory muscle paralysis. Currently, the cause of ALS is not fully understood, and there is a lack of effective cures. Therefore, scientific treatment and rehabilitation interventions are particularly important. Rehabilitation training plays an indispensable role in the treatment of ALS. Appropriate exercise can help patients maintain muscle strength and joint mobility, slow down the progression of muscle atrophy, improve their quality of life, and prolong their lives to some extent. However, existing rehabilitation training programs have many limitations.

[0003] Most current rehabilitation training programs are based on general standards and experience, failing to fully consider the individual differences of each patient. Different patients have varying degrees of disease severity, physical condition, and the location and extent of muscle damage. General training programs cannot accurately meet the specific needs of each patient, potentially leading to poor training results or even worsening the patient's condition due to inappropriate exercise. Furthermore, existing assessment methods often focus only on certain physical indicators or motor abilities, lacking a comprehensive and detailed analysis of the patient's condition across all parts of the body. For example, they may only assess limb motor abilities while neglecting the functional status of respiratory and swallowing muscles. In addition, the assessment of the compatibility between exercise and the patient's body is insufficient, failing to adequately consider the safety and effectiveness of exercise, easily leading to the risk of injury or over-fatigue during training.

[0004] Due to the aforementioned limitations of existing rehabilitation training programs, many patients with amyotrophic lateral sclerosis (ALS) are unable to receive effective rehabilitation treatment. After a period of training, patients may not see significant improvement in muscle strength and function, and their condition may even worsen. In addition, inappropriate exercise may cause physical pain and psychological stress, affecting their quality of life and confidence in treatment.

[0005] In view of this, the applicant proposes a system and method for analyzing motor data for the assessment of amyotrophic lateral sclerosis (ALS). Summary of the Invention

[0006] To overcome the defects and shortcomings of existing technologies, this invention provides a motion data analysis system and method for assessing amyotrophic lateral sclerosis (ALS). This application analyzes the severity of sclerosis and the corresponding location of motion matching, conducts motion safety analysis of the corresponding location, and then obtains the matching abnormalities between motion matching and the patient's body through motion matching abnormality analysis and motion safety abnormality analysis. Finally, the training exercise corresponding to the smallest matching abnormality is selected as the rehabilitation training exercise output. Furthermore, the weighted weights of motion matching abnormality and motion safety abnormality are assigned based on the patient's physical fitness parameters and the standard deviation of the safety range. This allows for precise customization of appropriate rehabilitation training programs, improving the training's pertinence and effectiveness, fully considering individual patient differences, and ensuring that rehabilitation training is both in line with the patient's physical condition and achieves good results.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a method for analyzing motor data for the assessment of amyotrophic lateral sclerosis (ALS), comprising the following steps:

[0009] S100: Obtain patient condition data for each location and training data for each location of each candidate training exercise;

[0010] 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.

[0011] S300: Conduct a safety analysis of movement in the corresponding positions based on the patient's condition development and the exercise training process;

[0012] S400: Motion selection analysis is performed based on the motion safety analysis results and motion matching analysis results at each location;

[0013] S500: Select the necessary rehabilitation exercises based on the results of the exercise selection analysis.

[0014] In one implementation of the present invention, step S100 includes the following specific contents:

[0015] Step 110: Obtain the patient's condition at each location through the patient condition examination form. The corresponding condition includes the range of motion of the joints and muscle elasticity at each location, as well as the patient's physical characteristics such as body temperature, heart rate and lung capacity. Analyze the weight of safety based on the physical characteristics.

[0016] Step 120: Obtain training data of the patient at each position during each of the candidate training exercises through the motion imaging module. This includes the patient's motion trajectory at each position and the motion amount data at each position. The motion amount is the product of the number of movements and the standardized distance of a single movement. The standardization process is to divide the corresponding value by the standard value. At the same time, obtain the closest distance of the corresponding position to the nerve center to assess the importance of the corresponding position.

[0017] Step 130: Store the acquired data in the corresponding storage component for easy data retrieval.

[0018] In one implementation of the present invention, the hardening severity analysis in step S200 includes the following specific steps:

[0019] Step 210: Obtain the joint range of motion and muscle elasticity of the corresponding patient at each position. Divide the safe value of joint range of motion at the corresponding position by the joint range of motion data to obtain the abnormal joint range of motion at the corresponding position. The safe value of joint range of motion at the corresponding position is the average value of joint range of motion at the corresponding position for people of the corresponding age. The position is defined by the middle area between the joint and the adjacent joint.

[0020] Step 220: Divide the muscle elasticity safety value at the corresponding location by the muscle elasticity at the corresponding location of the patient to obtain the abnormality of muscle elasticity at the corresponding location. The muscle elasticity safety value at the corresponding location is the average value of the muscle elasticity at the corresponding location of people of the same age. Calculating the abnormality of muscle elasticity can clearly show the difference between the patient's muscle elasticity and the normal level of the same age group. Muscle elasticity is an important indicator of muscle health. This step can help doctors judge the degree of hardening of the patient's muscles. Combined with abnormal joint mobility, it can more comprehensively assess the patient's physical condition. The muscle elasticity of people of the same age group has a certain average level, namely the muscle elasticity safety value. Dividing the muscle elasticity at the corresponding location of the patient by this safety value can obtain the degree of abnormality of muscle elasticity.

[0021] Step 230: The severity of stiffness at the corresponding location is obtained by weighted summation of abnormal muscle elasticity and abnormal joint mobility. By weighted summation of abnormal muscle elasticity and abnormal joint mobility, the influence of two key factors—joints and muscles—on the severity of stiffness is comprehensively considered. This comprehensive assessment method is more complete and accurate than assessment using a single indicator.

[0022] In one implementation of the present invention, the positional motion matching analysis in step S200 specifically includes the following:

[0023] Step 240: Obtain the amount of exercise at each location during the exercise process. Divide the severity of hardening by the standard value of hardening severity to obtain the hardening coefficient. Divide the amount of exercise at each location by the safe amount of exercise to obtain the exercise coefficient. The hardening coefficient reflects the relative relationship between the degree of hardening at each location and the standard value. The exercise coefficient reflects the comparison between the amount of exercise at each location and the safe amount of exercise. By calculating these two coefficients, the degree of hardening and the amount of exercise can be quantified, providing important basic data for subsequent analysis of exercise compatibility.

[0024] Step 250: Obtain the hardening coefficient at the corresponding location and the average hardening coefficient at the locations bordering that location. Multiply the average hardening coefficient at the border locations by the motion transmission coefficient and add the hardening coefficient at that location to obtain the standard hardening coefficient at the corresponding location. Here, the motion transmission coefficient is inversely proportional to the skin elasticity. The value is obtained by dividing the standard skin elasticity by the skin elasticity at the corresponding location. This takes into account the influence of the hardening coefficient at adjacent locations and the role of skin elasticity in motion transmission. The calculated standard hardening coefficient more comprehensively reflects the actual hardening situation at various locations of the body.

[0025] Step 260: Divide the standard coefficient of hardening at the corresponding location by the motion coefficient to obtain the motion matching coefficient for that location. The deviation between the motion matching coefficient and the standard coefficient is taken as the result of the motion matching anomaly analysis. This ensures that locations with larger hardening anomalies receive greater attention to motion matching, improving the accuracy of motion matching. The motion matching coefficient can intuitively reflect the degree of matching between the patient's hardening condition and the amount of motion at each location. By obtaining the deviation between the motion matching coefficient and the standard coefficient, the motion matching anomalies at each location can be accurately analyzed, allowing doctors to pay more attention to locations with larger hardening anomalies and improving the accuracy of motion matching.

[0026] In one implementation of the present invention, the motion safety analysis at the corresponding position in step S300 includes the following specific contents:

[0027] The hardening standard coefficients for each corresponding location are obtained, along with the motion change rate at that location during movement. The motion change rate is the average displacement per unit time of the body part during movement, and the displacement is calculated by subtracting the volume of the intersection of the three-dimensional images from the volume of the union of the three-dimensional images at two consecutive moments, and then dividing by the volume of the union of the three-dimensional images. Movement safety anomalies are obtained by weighted summation of the standardized displacement change rate and the standardized hardening standard coefficients. Calculating movement safety anomalies by combining the hardening standard coefficients and the motion change rate allows for a comprehensive assessment of the hardening status of each body location and the dynamic changes during movement. The hardening standard coefficients fully reflect the actual hardening status of each location, while the motion change rate reflects the dynamic displacement characteristics of the body part during movement. Combining the two allows for a more accurate assessment of safety anomalies during movement.

[0028] In one implementation of the present invention, the motion selection analysis in step S400 includes the following specific contents:

[0029] 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, and 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. It should be noted that the weighting of motion matching anomalies and motion safety anomalies is allocated according to the patient's physical condition. Patients with good physical condition can be assigned a larger weight for motion safety anomalies.

[0030] In a second aspect, the present invention also provides a motor data analysis system for assessing amyotrophic lateral sclerosis (ALS), comprising:

[0031] 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.

[0032] The sclerosis severity analysis module analyzes the severity of sclerosis based on the patient's condition data at various locations.

[0033] The matching analysis module performs motion matching analysis at corresponding positions by analyzing the amount of motion, the influence of motion transmission, and the severity of hardening at each position during the motion process.

[0034] The safety analysis module analyzes the safety of movement at corresponding positions based on the patient's condition development and the exercise training process.

[0035] 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.

[0036] Thirdly, the present invention 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 assessing amyotrophic lateral sclerosis by calling the computer program stored in the memory.

[0037] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method for analyzing motor data for the assessment of amyotrophic lateral sclerosis (ALS).

[0038] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0039] This approach first acquires and stores data on the patient's condition at various locations and training data for each location of the candidate exercises. It then analyzes the severity of hardening and the compatibility of exercises at corresponding locations, as well as the safety of exercises at those locations. Next, it analyzes abnormalities in exercise compatibility and safety to identify any mismatch between the exercise and the patient's body. Finally, it selects the exercise with the smallest mismatch as the rehabilitation training output. Furthermore, it assigns weighted weights to exercise compatibility and safety issues based on the patient's physical parameters and the standard deviation of the safety range. This allows for precise customization of appropriate rehabilitation training plans, improving the training's relevance and effectiveness, fully considering individual patient differences, and ensuring that rehabilitation training is both suitable for the patient's physical condition and achieves good results. Attached Figure Description

[0040] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0041] Figure 1 This is a schematic diagram of the module composition structure of an embodiment of the system of the present invention;

[0042] Figure 2 This is a schematic diagram of the overall process structure of an embodiment of the method of the present invention;

[0043] Figure 3 This is a schematic diagram of the process structure for analyzing the severity of hardening in an embodiment of the method of the present invention. Detailed Implementation

[0044] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0045] Please see Figure 2 as well as Figure 3 , Figure 2 This is a schematic diagram of the overall process of the exercise data analysis method for assessing amyotrophic lateral sclerosis (ALS) provided in this embodiment of the invention, which specifically includes the following steps:

[0046] S100: Obtain patient condition data for each location and training data for each location of each candidate training exercise;

[0047] In this embodiment, the desired rehabilitation training exercise is selected.

[0048] In one implementation of the present invention, step S100 includes the following specific contents:

[0049] Step 110: Obtain the patient's condition information for each location using a patient condition checklist. This information includes the patient's joint mobility and muscle elasticity, as well as physical characteristics such as body temperature, heart rate, and lung capacity. The weight of safety is determined by analyzing these physical characteristics. A professional patient condition checklist is used to comprehensively obtain information about the patient's condition for each location. This information specifically covers the patient's joint mobility and muscle elasticity, as well as physical characteristics such as body temperature, heart rate, and lung capacity. All patient information is accurately obtained using corresponding professional sensors. During this process, the information provider has been clearly informed that this information will be strictly kept confidential. Furthermore, the collected information does not contain patient names or other directly identifiable information, fully complying with relevant laws and regulations and fully protecting the patient's legal rights and privacy. The aim is to help patients achieve their health goals in a safe and compliant manner.

[0050] Step 120: Obtain training data of the patient at each position during each of the candidate training exercises through the motion imaging module. This includes the patient's motion trajectory at each position and the motion amount data at each position. The motion amount is the product of the number of movements and the standardized distance of a single movement. The standardization process is to divide the corresponding value by the standard value. At the same time, obtain the closest distance of the corresponding position to the nerve center to assess the importance of the corresponding position.

[0051] Step 130: Store the acquired data in the corresponding storage component for easy data retrieval;

[0052] 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.

[0053] In this embodiment, the hardening severity analysis in step S200 includes the following specific steps:

[0054] Step 210: Obtain the joint range of motion and muscle elasticity of the corresponding patient at each position. Divide the safe value of joint range of motion at the corresponding position by the joint range of motion data to obtain the abnormal joint range of motion at the corresponding position. The safe value of joint range of motion at the corresponding position is the average value of joint range of motion at the corresponding position for people of the corresponding age. The position is defined by the middle area between the joint and the adjacent joint, such as the elbow position, which is the part between the upper arm and the forearm.

[0055] Step 220: Divide the safe value of muscle elasticity at the corresponding location by the muscle elasticity at the corresponding location of the patient to obtain the abnormality of muscle elasticity at the corresponding location. 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 same age. Calculating the abnormality of muscle elasticity can clearly show the difference between the patient's muscle elasticity and the normal level for the same age group. Muscle elasticity is an important indicator of muscle health. This step can help doctors determine the degree of muscle stiffness in patients and, in combination with abnormal joint mobility, more comprehensively assess the patient's physical condition. Muscle elasticity in people of the same age group has a certain average level, namely the safe value of muscle elasticity. Dividing the muscle elasticity at the corresponding location of the patient by this safe value can give the degree of abnormality of muscle elasticity. This comparison method is in line with the conventional method in medicine to judge health status by comparing with normal standards, and provides important muscle-related data support for subsequent comprehensive assessment.

[0056] Step 230: The severity of stiffness at the corresponding location is obtained by weighted summation of abnormal muscle elasticity and abnormal joint mobility. By weighted summation of abnormal muscle elasticity and abnormal joint mobility, the influence of two key factors—joints and muscles—on the severity of stiffness is comprehensively considered. This comprehensive assessment method is more comprehensive and accurate than single-indicator assessment, and can more realistically reflect the stiffness status of various parts of the patient's body, providing a reliable basis for subsequent treatment and exercise guidance. In the physiological structure of the human body, joint mobility and muscle elasticity are closely related, and both are directly related to the degree of stiffness in the body. The weighted summation method can assign different weights according to the degree of influence of each on the degree of stiffness, thereby more scientifically calculating the severity of stiffness at each location.

[0057] In this embodiment, the motion matching analysis at the corresponding position in step S200 specifically includes the following:

[0058] Step 240: Obtain the exercise volume at each location during exercise. Divide the severity of hardening by the standard value of hardening severity to obtain the hardening coefficient. Divide the exercise volume at each location by the safe exercise volume to obtain the exercise coefficient. The hardening coefficient reflects the relative relationship between the patient's hardening degree at each location and the standard value. The exercise coefficient reflects the comparison between the patient's exercise volume at each location and the safe exercise volume. By calculating these two coefficients, the hardening degree and exercise volume can be quantified, providing important basic data for subsequent analysis of exercise compatibility. Comparing the hardening severity with the standard value of hardening severity and comparing the exercise volume with the safe exercise volume is a standardized processing method. This method conforms to the comparative analysis methods commonly used in medicine and sports science, and can objectively reflect the hardening status and exercise volume at each location during exercise.

[0059] Step 250: Obtain the hardening coefficient at the corresponding location and the average hardening coefficient at the locations bordering that location. Multiply the average hardening coefficient at the border locations by the motion transmission coefficient and add it to the hardening coefficient at the current location to obtain the standard hardening coefficient for that location. Here, the motion transmission coefficient is inversely proportional to skin elasticity, and its value is calculated as the standard skin elasticity divided by the skin elasticity at the corresponding location. This considers the influence of the hardening coefficients at adjacent locations and the role of skin elasticity in motion transmission. The calculated standard hardening coefficient more comprehensively reflects the actual hardening status of various body locations. By introducing the motion transmission coefficient, the mutual influence between different body parts can be more accurately considered, making the assessment results more consistent with actual physiological conditions. In human movement, the hardening status of adjacent locations will affect each other, and skin elasticity will affect the transmission of motion in different body parts. The inverse proportionality between the motion transmission coefficient and skin elasticity conforms to the principles of human biomechanics. Calculating the standard hardening coefficient in this way can more accurately reflect the actual hardening status of each location during movement.

[0060] Step 260: Divide the standard coefficient of hardening at the corresponding location by the motion coefficient to obtain the motion matching coefficient for that location. The deviation between the motion matching coefficient and the standard coefficient is used as the result of the motion matching anomaly analysis. This ensures that locations with larger hardening anomalies receive greater attention, improving the accuracy of motion matching. The motion matching coefficient directly reflects the degree of matching between the patient's hardening condition and the amount of exercise. By obtaining the deviation between the motion matching coefficient and the standard coefficient, the abnormality of motion matching at each location can be accurately analyzed, allowing doctors to pay more attention to locations with larger hardening anomalies, improving the accuracy of motion matching, and thus developing more reasonable exercise plans. In the process of exercise rehabilitation, ensuring that the patient's exercise volume matches the body's hardening condition is crucial. By comparing the motion matching coefficient and the standard coefficient, mismatches between exercise and physical condition can be identified, providing a scientific basis for adjusting the exercise plan, which aligns with the basic principles of exercise rehabilitation.

[0061] S300: Conduct a safety analysis of movement in the corresponding positions based on the patient's condition development and the exercise training process;

[0062] In this embodiment, the motion safety analysis at the corresponding position in step S300 includes the following specific contents:

[0063] The hardening standard coefficients for each location are obtained, along with the rate of change of motion at the corresponding location during movement. The rate of change of motion is the average displacement per unit time during the movement of the body part. The displacement is calculated by subtracting the volume of the intersection of the three-dimensional images at two consecutive moments from the volume of the union of the three-dimensional images at the corresponding locations, and then dividing by the volume of the union of the three-dimensional images. Movement safety anomalies are obtained by weighted summation of the standardized displacement rate of change and the standardized hardening standard coefficients. Calculating movement safety anomalies by combining the hardening standard coefficients and the rate of change of motion comprehensively considers the hardening status of each body location and the dynamic changes during movement. The hardening standard coefficients fully reflect the actual hardening status of each location, while the rate of change of motion reflects the dynamic displacement characteristics of the body part during movement. Combining the two allows for a more accurate assessment of safety anomalies during movement. The rate of change of motion is measured by the average displacement per unit time, and the displacement is calculated by subtracting the intersection volume from the volume of the union of the three-dimensional images at two consecutive moments and then dividing by the volume of the union. This calculation method is scientific and reasonable, accurately reflecting the actual changes of the body part during movement.

[0064] S400: Motion selection analysis is performed based on the motion safety analysis results and motion matching analysis results at each location;

[0065] In this embodiment, the motion selection analysis in step S400 includes the following specific contents:

[0066] The process involves weighted summation of motion matching anomalies and motion safety anomalies at corresponding locations, averaging all motion anomalies to obtain the corresponding motion-to-patient body matching anomaly, and selecting the smallest matching anomaly as the training exercise for the patient's rehabilitation training, which is then output to the medical port. It should be noted that the weighting of motion matching anomalies and motion safety anomalies is allocated based on the patient's physical condition; a better physical condition allows for a higher weighting for motion safety anomalies. Specifically, the allocation method is as follows: a weighted summation is performed based on the standard deviation of each parameter of the patient's physical condition within its safety range to obtain the physical condition anomaly; the physical condition anomaly is multiplied by a set weighting coefficient to obtain the weight of the motion matching anomaly; the weight of the motion safety anomaly is obtained by subtracting the weight of the motion matching anomaly from the value of 1; and the motion anomaly is obtained by weighted summation of the motion matching anomalies and motion safety anomalies at corresponding locations. Finally, the motion anomalies at all locations are considered... The average value of the exercise and the patient's body is calculated to determine the mismatch between the exercise and the patient's body. The exercise with the smallest mismatch is then selected as the patient's rehabilitation exercise and output to the medical system. This allows for precise customization of the most suitable rehabilitation training plan for each patient, maximizing the relevance and effectiveness of the rehabilitation training. Furthermore, weighted averages are assigned to exercise mismatch and exercise safety abnormalities based on the patient's physical condition. Patients with good physical condition are given more weight for exercise safety abnormalities, fully considering individual differences. This ensures that patients with different physical conditions can receive the most suitable training exercise while guaranteeing exercise safety. Physical abnormalities are calculated by weighted summation of the standard deviations of each patient's physical parameters from their safe range. The weights for exercise mismatch and exercise safety abnormalities are then calculated based on this. This method is scientific and reasonable because standard deviation accurately reflects the degree to which the patient's physical parameters deviate from the safe range, allowing for reasonable adjustment of weights to ensure that rehabilitation training is both suitable for the patient's physical condition and achieves good rehabilitation results.

[0067] S500: Select the necessary rehabilitation training exercises based on the results of the exercise selection analysis, and the patient will conduct regular training according to the output rehabilitation training exercises.

[0068] In this embodiment, the method for obtaining the set weights and set coefficients is as follows: obtain the patient's condition data at each location in the past and the training data at each location of the selected training exercise, and at the same time obtain the judgment results of whether the rehabilitation effect of the historical personnel meets the rehabilitation requirements. Import the historical data into each step of this embodiment to select the required rehabilitation training exercise, import the calculation results and judgment results into Matlab fitting software to fit the data, and output the set weights and set coefficients that meet the maximum judgment accuracy.

[0069] The advantages of the above embodiments are: they can accurately customize and adapt rehabilitation training programs, improve the targeting and effectiveness of training, fully consider individual differences of patients, and ensure that rehabilitation training is both in line with physical condition and achieves good results.

[0070] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of a motor data analysis system for assessing amyotrophic lateral sclerosis (ALS) provided in an embodiment of the present invention, including:

[0071] 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.

[0072] The sclerosis severity analysis module analyzes the severity of sclerosis based on the patient's condition data at various locations.

[0073] The matching analysis module performs motion matching analysis at corresponding positions by analyzing the amount of motion, the influence of motion transmission, and the severity of hardening at each position during the motion process.

[0074] The safety analysis module analyzes the safety of movement at corresponding positions based on the patient's condition development and the exercise training process.

[0075] The exercise selection analysis module analyzes the exercise safety and compatibility results at each location to select appropriate rehabilitation exercises. Figure 1 The arrows in the diagram indicate the direction of data transmission.

[0076] The parameters and steps for implementing the corresponding functions of each unit module in the exercise data analysis system for amyotrophic lateral sclerosis (ALS) assessment of the present invention described above can be referred to the parameters and steps in the embodiments of the exercise data analysis method for ALS assessment described above, and will not be repeated here.

[0077] Embodiments of the present invention also provide an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus. The memory stores a motion data analysis method for assessing amyotrophic lateral sclerosis (ALS) that can be loaded by the processor and executed as provided in the above embodiments.

[0078] The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the exercise data analysis method for amyotrophic lateral sclerosis (ALS) assessment provided in the above embodiments. The data storage area may store data involved in the exercise data analysis method for ALS assessment provided in the above embodiments.

[0079] A processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data according to the present invention. The processor may be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic devices used to implement the above-described processor functions may also be other types, and the embodiments of the present invention do not specifically limit this.

[0080] A communication bus can include a pathway for transmitting information between the aforementioned components. The communication bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Communication buses can be categorized into address buses, data buses, control buses, etc.

[0081] This invention provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments for a method of motion data analysis for assessing amyotrophic lateral sclerosis.

[0082] In this embodiment of the invention, the computer-readable storage medium can be a tangible device that holds and stores instructions used 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 thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), spoofing random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0083] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0084] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to the technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this invention.

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 on corresponding positions based on the amount of motion, motion transfer coefficient, and hardening severity during the motion process. Among them, the amount of motion is the product of the number of motions and the standardized distance of a single motion. The standardization process is to divide the corresponding value by the standard value. The motion matching analysis at the corresponding position 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 location and the average hardening coefficient at the location bordering that location. Multiply the average hardening coefficient at the border location by the motion transmission coefficient and add the hardening coefficient at that location to obtain the standard hardening coefficient at the corresponding location. The motion transmission coefficient is inversely proportional to the skin elasticity and is determined by dividing the standard skin elasticity by the skin elasticity at the corresponding location. 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 taken as the motion matching anomaly analysis result at the corresponding position. The safety analysis module analyzes the safety of exercise at different locations by using the hardening standard coefficients of various parts of the patient's body 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 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. The displacement is calculated by first subtracting the volume of the intersection of the three-dimensional images from the volume of the union of the three-dimensional images at two different times, and then dividing by the volume of the union of the three-dimensional images. The motion safety anomaly is obtained by weighted summation of the standardized motion change rate and the standardized hardening standard coefficients.

4. The exercise data analysis system for assessing amyotrophic lateral sclerosis (ALS) according to claim 3, characterized in that, The motion selection analysis includes the following specific components: The motion matching anomaly analysis results and motion safety anomalies at the corresponding locations are weighted and summed to obtain the motion anomalies at the corresponding locations. The motion anomalies at all locations are averaged to obtain the matching anomalies between the corresponding motion and the patient's body. The training exercise corresponding to the smallest matching anomaly is selected as the patient's rehabilitation training exercise and output to the medical port. The weighting of the motion matching anomaly analysis results and motion safety anomalies is allocated according to the patient's physical condition.

5. 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.

6. 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 1-5, 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 the corresponding location by analyzing the amount of movement, movement transfer coefficient and severity of hardening during the movement process. S300: Analyze the safety of movement at corresponding locations by using the standard coefficient of hardening at various parts of the patient's body and the training process of the movement. 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.

7. 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 6 by calling the computer program stored in the memory.

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