Old-age sarcopenia risk prediction and grouping multi-motion intervention system and method

Abstract

The invention discloses a senile sarcopenia risk prediction and grouping multi-motion intervention system and method, and the system comprises a data layer which is used for data collection, storage and task standardization; the service layer is used for processing and analyzing the data acquired by the data layer, performing sarcopenia risk assessment on the elderly patients, and appointing an exercise training scheme according to a risk assessment result and a data analysis result; the application layer comprises personal information management, patient information management, medical care information management and system maintenance management; and the user side is used for personal information management and exercise demand assessment, and preliminarily assessing the exercise demand according to own feelings. Data are collected through a physical condition scale and a tumble risk prediction tool, the model construction module is used for analysis, the sarcopenia risk of the elderly patient can be accurately evaluated, high-risk people can be recognized in the early stage of the disease, a basis is provided for follow-up intervention, an exercise scheme is adjusted according to a data analysis result, the management efficiency is improved, and the management cost is reduced. And high-quality services can be provided for more old people.

Description

Technical Field

[0001] The present invention relates to a system and method for predicting the risk of sarcopenia in the elderly and for intervening in multiple exercises in groups. Background Art

[0002] As people age, especially after the age of 50, their muscle mass and strength continue to decrease, with leg muscle mass decreasing by 1% to 2% and strength decreasing by 1.5% to 5% each year. This decline in muscle mass and strength has a negative impact on the health of the elderly, is closely related to falls and a decline in the quality of life of the elderly, and often leads to a series of adverse consequences such as physical dysfunction, weakness, falls, fractures, re-hospitalization, and even death.

[0003] Difficulty in early diagnosis: The clinical symptoms of sarcopenia are hidden, and mild patients are difficult to detect in time. When obvious clinical symptoms appear, it often means that the loss of muscle mass and function has reached a very serious level. This makes early detection and intervention particularly difficult. Missing the opportunity for early intervention may lead to further development of the disease and increase the health burden of patients.

[0004] Existing technologies have defects: (i) Lack of risk prediction. Most of the equipment currently available on the market for detecting sarcopenia focuses on diagnosis after the disease occurs, and there are serious deficiencies in risk prediction before the disease occurs. This means that it is impossible to identify high-risk groups in advance when the disease has not yet occurred but there is a potential risk, so it is impossible to take targeted preventive measures in time to reduce the probability of disease. The lack of an effective risk prediction mechanism means that when facing sarcopenia, the elderly can only passively receive treatment after the disease occurs, but cannot take active prevention in the early stage, which greatly limits the prevention and control effect of sarcopenia. (ii) Lack of personalization and dynamic adjustment of exercise intervention. Existing exercise training programs generally have the problem of being too generalized, and do not fully take into account the huge differences between individual elderly people in terms of physical condition, exercise ability, health status, etc. The physical conditions of each elderly person are unique, and their tolerance and adaptability to exercise are different. However, the existing training programs adopt a "one-size-fits-all" approach and cannot meet the personalized needs of different elderly people. In addition, these programs are not dynamically adjusted according to the real-time physical reactions and rehabilitation progress of the elderly during exercise. As exercise progresses, the physical condition of the elderly may change, such as gradual increase in muscle strength and improvement in physical function. If the exercise plan cannot be adjusted accordingly, it will not be able to continuously and effectively promote the recovery of the elderly, and may even lead to injuries and other adverse consequences due to mismatched exercise intensity. Summary of the invention

[0005] The object of the present invention is to provide a technical solution for a multi - exercise intervention system and method for predicting and grouping the risk of sarcopenia in the elderly, aiming at the deficiencies of the existing technology. This system collects data through a physical condition scale and a fall risk prediction tool, and uses a model construction module for analysis, which can accurately evaluate the risk of sarcopenia in elderly patients. It can identify high - risk populations in the early stage of the disease, even when clinical symptoms are not obvious, providing a basis for subsequent interventions, changing the previous situation where detection can only be carried out after the occurrence of sarcopenia, enabling preventive work to be carried out in advance, adjusting the exercise plan according to the data analysis results, reducing the workload of manual recording and analysis, improving management efficiency, making more rational use of medical and nursing resources, and being able to provide high - quality services for more elderly people.

[0006] To solve the above - mentioned technical problems, the present invention adopts the following technical solutions:

[0007] A multi - exercise intervention system for predicting and grouping the risk of sarcopenia in the elderly, characterized in that it includes a data layer for data collection, storage, and standardization tasks;

[0008] A service layer for processing and analyzing the data collected by the data layer, and conducting a risk assessment of sarcopenia in elderly patients, and formulating an exercise training plan according to the risk assessment results and data analysis results;

[0009] An application layer including personal information management, patient information management, medical staff information management, and system maintenance management;

[0010] And a user terminal for personal information management and exercise demand assessment, initially assessing the exercise demand according to one's own feelings.

[0011] This system collects data through a physical condition scale and a fall risk prediction tool, and uses a model construction module for analysis, which can accurately evaluate the risk of sarcopenia in elderly patients. It can identify high - risk populations in the early stage of the disease, even when clinical symptoms are not obvious, providing a basis for subsequent interventions, changing the previous situation where detection can only be carried out after the occurrence of sarcopenia, enabling preventive work to be carried out in advance, adjusting the exercise plan according to the data analysis results, reducing the workload of manual recording and analysis, improving management efficiency, making more rational use of medical and nursing resources, and being able to provide high - quality services for more elderly people.

[0012] Furthermore, the data layer includes a data collection module, a data storage module, and a data standardization module. The data collection module is used to collect the age, gender, body mass index, physical fitness test data, and fall history data of elderly patients. The data storage module is used to store the collected data and manage it safely and orderly. The data standardization module is used to uniformly format and standardize the collected data so that the data meets the system analysis requirements.

[0013] Further, the service layer includes a data access and statistics module, a data query and analysis module, a label extraction and risk assessment module, and a solution recommendation module. The data access and statistics module is used to store and statistically analyze the data collected by the data layer. The data query and analysis module is used to mine and analyze the data to provide a data basis for risk assessment. The label extraction and risk assessment module is used to assess the risk of sarcopenia in elderly patients based on the processed data. The solution recommendation module specifies an exercise training plan according to the risk assessment results and the individual differences of elderly patients.

[0014] Further, the solution recommendation module pushes an exercise level plan, and the exercise level plan determines the pushed exercise level based on the sarcopenia risk degree, physical function status, and exercise tolerance ability of elderly patients.

[0015] Further, the classification criteria for exercise levels include the muscle strength test results, balance ability test results, and endurance test results of elderly patients.

[0016] Further, the service layer uses machine learning algorithms to analyze the collected data, including the physical indicators and lifestyle factors of elderly patients, and uses historical data containing information on sarcopenia patients and non-sarcopenia patients for training during the training process.

[0017] Further, the service layer also includes an exercise feedback correction module. The exercise feedback correction module uses a far-infrared sensor to monitor the exercise amplitude, exercise frequency, and exercise duration data during the exercise process in real time, and feeds back these data to correct the exercise process according to the preset standard data.

[0018] Further, the exercise feedback correction module includes a warning unit that issues a warning signal when the monitored data exceeds the preset value.

[0019] Further, the user side includes a patient side, a medical staff side, and a management side. The patient side is used to view the exercise plan and receive sarcopenia knowledge recommendations at the same time. The medical staff side is used for medical staff to manage patient information, track the patient's exercise progress, and give guidance on the exercise plan. The management side is used to be responsible for the maintenance and management of the system to ensure the stable operation of the system and conduct data queries.

[0020] A method of using a multi-exercise intervention system for predicting and grouping sarcopenia risks in the elderly as described above, characterized by including the following steps:

[0021] S1. The data layer uses a physical fitness status scale and a fall risk prediction tool to collect relevant data of elderly patients, input these data into a risk assessment model, and output the risk level of the patient suffering from sarcopenia by analyzing the associations between the data;

[0022] S2, the solution recommendation module of the service layer receives the risk assessment results and data analysis results, and gives a comprehensive exercise plan;

[0023] S3. According to the patient's age, physical condition, exercise ability, risk of sarcopenia and other data analysis results, the exercise plan is divided into levels 1 to 6, and the corresponding exercise level plan is pushed;

[0024] S4. During the patient's exercise, use far-infrared equipment to monitor the patient's movement data, prompt the patient to correct his posture through the display screen or voice, and feed the data back to the data layer to provide a basis for the adjustment of subsequent exercise plans.

[0025] The intervention system has simple steps and can objectively predict and score the risk of sarcopenia in elderly patients. It is a visual and continuous assessment system with high clinical application efficiency and easy to promote and use.

[0026] The present invention has the following beneficial effects due to the adoption of the above technical solution:

[0027] 1. Accurate and effective risk prediction: By collecting data through the physical condition scale and fall risk prediction tool and analyzing it using the model building module, the risk of sarcopenia in elderly patients can be accurately assessed, and high-risk groups can be identified in the early stages of the disease, even when clinical symptoms are not obvious. For example, a risk assessment of the elderly population in a community can accurately divide them into low, medium, and high risk levels based on age, body mass index, physical test data, fall history and other information, providing a basis for subsequent intervention, changing the previous situation where detection could only be carried out after sarcopenia occurred, and enabling prevention work to be carried out in advance;

[0028] 2. Significant effect of exercise intervention: The system formulates a multi-component exercise training plan based on the risk assessment results and the individual conditions of the patients, and divides the exercise levels into precise push notifications. In actual applications, personalized exercise plans are implemented for the elderly with different risks and physical conditions. For frail and high-risk elderly people, low-intensity joint movement exercises are arranged, and the exercise intensity is gradually increased as the physical function improves; low-risk elderly people with better physical conditions perform jogging combined with strength equipment training. After a period of intervention, many elderly people have increased muscle strength, improved physical function, reduced the risk of falling, and significantly improved their quality of life, proving that the multi-component exercise intervention system can effectively improve the muscle condition of the elderly;

[0029] 3. Ensured sports safety: During the exercise process, the far-infrared sensor monitors the exercise data in real time. When the exercise data is abnormal, such as incorrect exercise postures or exercise intensity exceeding the safe range, the system will issue an alarm in a timely manner and prompt for correction. This function effectively avoids the injuries caused by improper postures or excessive exercise of the elderly during the exercise process, ensures sports safety, and enables the elderly to carry out exercise intervention training with more confidence.

[0030] 4. Improved medical care management efficiency: The application layer of the system provides convenient patient information management and exercise plan viewing functions for the medical care side. Medical staff can track the exercise progress of patients in real time through the system, adjust the exercise plan based on the data analysis results, reduce the workload of manual recording and analysis, improve the management efficiency, make more rational use of medical resources, and be able to provide high-quality services to more elderly people.

[0031] 5. Enhanced health awareness: The sarcopenia knowledge recommendation function on the patient side enables the elderly and their families to have a deeper understanding of sarcopenia. During the use of the system, the elderly realize the importance of early prevention and exercise intervention, the enthusiasm for actively participating in exercise training increases, the health awareness is significantly enhanced, and a good atmosphere of self-health management is formed, which helps to maintain physical health in the long term. Brief Description of the Drawings

[0032] The present invention will be further described below in conjunction with the drawings:

[0033] Figure 1 It is a block diagram of the intervention system in a risk prediction and grouping multi-exercise intervention system and method for sarcopenia in the elderly of the present invention;

[0034] Figure 2 It is a flowchart of the present invention. Detailed Embodiments

[0035] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0036] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0037] It should be noted that the terms "first", "second", etc. in the description, claims and the above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion.

[0038] As Figure 1 shown, a multi-exercise intervention system for predicting and grouping the risk of sarcopenia in the elderly according to the present invention includes a data layer, a service layer, an application layer and a client.

[0039] The data layer is used for data collection, storage and standardization tasks; the data layer includes a data collection module, a data storage module and a data standardization module. The data collection module is used to collect the age, gender, body mass index, physical fitness test data and fall history data of elderly patients. The data storage module is used to store the collected data and manage it safely and orderly. The data standardization module is used to uniformly format and standardize the collected data so that the data meets the system analysis requirements.

[0040] The data collection module uses a physical fitness status scale and a fall risk prediction tool to collect multi-dimensional information of elderly patients. The physical fitness status scale covers scores in multiple dimensions such as activities of daily living ability, walking speed, and grip strength. The fall risk prediction tool judges the fall risk by evaluating factors such as the patient's balance ability, vision, and drug use. These information provide a comprehensive data basis for accurately evaluating the risk of sarcopenia in the follow-up. For example, by analyzing the relationship between walking speed and muscle strength, and the potential connection between fall risk and sarcopenia, the possibility of a patient suffering from sarcopenia can be evaluated more accurately.

[0041] The service layer is used to process and analyze the data collected by the data layer, and conduct a sarcopenia risk assessment for elderly patients, and specify an exercise training plan according to the risk assessment results and data analysis results.

[0042] The service layer cleans the data, removes duplicate and incorrect data, and fills in missing values. Using data analysis algorithms, such as correlation analysis, to find data features closely related to the risk of sarcopenia, providing strong data support for formulating exercise plans in the follow-up. For example, if it is found that grip strength is highly correlated with the risk of sarcopenia, when formulating an exercise plan, training items to enhance grip strength can be designed specifically.

[0043] The service layer includes a data access and statistics module, a data query and analysis module, a label extraction and risk assessment module, and a solution recommendation module. The data access and statistics module is used to store and statistically analyze the data collected by the data layer. The data query and analysis module is used to mine and analyze the data to provide a data basis for risk assessment. The label extraction and risk assessment module is used to assess the sarcopenia risk of elderly patients based on the processed data. The solution recommendation module specifies an exercise training plan according to the risk assessment results and the individual differences of elderly patients.

[0044] The solution recommendation module pushes an exercise level plan, and the exercise level plan determines the pushed exercise level based on the sarcopenia risk degree, physical function status, and exercise tolerance ability of elderly patients.

[0045] The classification criteria for exercise levels include the muscle strength test results, balance ability test results, and endurance test results of elderly patients.

[0046] The solution recommendation module formulates a multi-component exercise training plan for elderly patients at risk of sarcopenia according to the risk assessment results and the individual conditions of the patients. The plan includes various components such as strength training, aerobic exercise, and flexibility training. For high-risk patients, if there are joint diseases, high-impact exercises will be avoided, and strength training items such as sitting leg raises and resistance band training will be recommended to enhance muscle strength; aerobic exercises such as slow walking and swimming will be selected to improve cardiopulmonary function and endurance; flexibility training such as yoga stretching will be arranged to improve joint mobility.

[0047] The service layer uses machine learning algorithms to analyze the collected data, including the physical indicators and lifestyle factors of elderly patients. Machine learning algorithms include, but are not limited to, decision tree algorithms and neural network algorithms. Historical data containing information on sarcopenia patients and non-sarcopenia patients is used for training during the training process.

[0048] Lifestyle factors include diet, daily activity level, sleep quality, etc.

[0049] The service layer also includes an exercise feedback correction module. The exercise feedback correction module uses far-infrared sensors to real-time monitor the exercise amplitude, exercise frequency, and exercise duration data during the exercise process, and feeds back these data to correct the exercise process according to the preset standard data. The data monitored by the far-infrared sensors is transmitted to the system through wireless communication technology, and the wireless communication technology can include Bluetooth, WiFi, etc.

[0050] The exercise feedback correction module includes a warning unit that emits a warning signal when the monitored data exceeds the preset value.

[0051] The application layer includes personal information management, patient information management, medical staff information management, and system maintenance management.

[0052] The client is used for personal information management and exercise need assessment, and initially assesses exercise needs based on personal feelings.

[0053] The client includes a patient end, a medical staff end, and a management end. The patient end is used to view exercise plans and receive recommendations on sarcopenia knowledge. The medical staff end is used for medical staff to manage patient information, track patients' exercise progress, and provide guidance on exercise plans. The management end is responsible for the maintenance and management of the system to ensure its stable operation and conduct data queries.

[0054] This system collects data through a physical fitness status scale and a fall risk prediction tool, and uses a model construction module for analysis. It can accurately assess the sarcopenia risk of elderly patients, identify high-risk populations in the early stage of the disease, even when clinical symptoms are not obvious, and provide a basis for subsequent interventions. It changes the previous situation where detection can only be carried out after sarcopenia occurs, enables preventive work to be carried out in advance, adjusts exercise plans according to the data analysis results, reduces the workload of manual recording and analysis, improves management efficiency, enables more reasonable utilization of medical staff resources, and can provide high-quality services for more elderly people.

[0055] Such as Figure 2 shown, a method of using a sarcopenia risk prediction and grouped multi-exercise intervention system for the elderly as described above includes the following steps:

[0056] S1. The data layer uses a physical fitness status scale and a fall risk prediction tool to collect relevant data of elderly patients. The physical fitness status scale includes scores in multiple dimensions such as activities of daily living ability, walking speed, and grip strength. The fall risk prediction tool judges the fall risk by evaluating factors such as the patient's balance ability, vision, and medication use. These data are input into a risk assessment model, and by analyzing the associations between the data, the risk level of the patient suffering from sarcopenia is output, such as the relationship between walking speed and muscle strength, and the potential connection between fall risk and sarcopenia. The risk level of the patient suffering from sarcopenia is output, divided into low risk, medium risk, and high risk;

[0057] S2. The program recommendation module of the service layer receives the risk assessment results and data analysis results, and gives a comprehensive exercise plan;

[0058] For high-risk patients, the program recommendation module combines the specific physical conditions of the patients. For example, if there are joint diseases, high-impact exercises are avoided, and a comprehensive exercise plan including strength training, aerobic exercise, and flexibility training is recommended. For strength training, sitting leg raises, resistance band training, etc. can be selected to enhance muscle strength; aerobic exercises such as slow walking and swimming to improve cardiopulmonary function and endurance; flexibility training such as yoga stretching to improve joint mobility.

[0059] S3. According to the data analysis results of various aspects such as the patient's age, physical condition, exercise ability, and sarcopenia risk level, the exercise plan is divided into levels 1 to 6, and the corresponding exercise level plan is pushed.

[0060] Level 1 is low-intensity, short-duration, and simple exercise, suitable for patients with relatively weak bodies, high risk levels, and poor exercise ability, such as simple joint movement exercises, with each exercise lasting 10 - 15 minutes. Level 6 is high-intensity, long-duration, and relatively complex exercise, suitable for patients with good physical conditions, low risk levels, and strong exercise ability, such as jogging combined with strength equipment training, with each exercise lasting 45 - 60 minutes.

[0061] S4. During the patient's exercise, a far-infrared device is used to monitor the patient's exercise data, and the patient is prompted to correct their posture through a display screen or voice, while the data is fed back to the data layer to provide a basis for adjusting the subsequent exercise plan. Such as exercise posture, exercise amplitude, exercise frequency, etc. For example, the far-infrared sensor can real-time monitor whether the patient's movements are standard during strength training and whether there are compensatory movements. If it is detected that the patient's exercise posture is incorrect, the system immediately issues an alarm.

[0062] The usage method steps of this intervention system are simple, it can objectively score the risk prediction of sarcopenia in elderly patients, it is a visual and continuous assessment system, with high clinical application efficiency and is easy to promote and use.

[0063] The above are only specific embodiments of the present invention, but the technical features of the present invention are not limited thereto. Any simple changes, equivalent replacements, or modifications made based on the present invention to achieve substantially the same technical effects are all covered by the protection scope of the present invention.

Claims

1. A system for predicting the risk of sarcopenia in the elderly and for grouping multi-exercise intervention, characterized by: include The data layer is used for data collection, storage and standardization tasks; The service layer is used to process and analyze the data collected by the data layer, conduct sarcopenia risk assessment for elderly patients, and specify exercise training programs based on the risk assessment results and data analysis results; Application layer, including personal information management, patient information management, medical information management and system maintenance management; and the user side, which is used for personal information management and exercise needs assessment, and preliminary assessment of exercise needs based on one's own feelings.

2. The system for predicting the risk of sarcopenia in the elderly and for grouping multi-exercise intervention according to claim 1, characterized in that: The data layer includes a data acquisition module, a data storage module and a data standardization module. The data acquisition module is used to collect the age, gender, body mass index, physical fitness test data and fall history data of elderly patients. The data storage module is used to store the collected data and manage it safely and orderly. The data standardization module is used to unify the format and standardize the collected data so that the data meets the system analysis requirements.

3. The system for predicting the risk of sarcopenia in the elderly and for grouping multi-exercise intervention according to claim 1, characterized in that: The service layer includes a data access and data statistics module, a data query and data analysis module, a label extraction and risk assessment module, and a scheme recommendation module. The data access and data statistics module is used to store and count the data collected by the data layer, the data query and data analysis module is used to mine and analyze the data to provide a data basis for risk assessment, the label extraction and risk assessment module is used to conduct sarcopenia risk assessment for elderly patients based on processed data, and the scheme recommendation module specifies an exercise training plan based on the risk assessment results and individual differences of elderly patients.

4. The system for predicting the risk of sarcopenia in the elderly and for grouping multi-exercise intervention according to claim 3, characterized in that: The program recommendation module pushes an exercise level program, and the exercise level program determines the pushed exercise level based on the elderly patient's sarcopenia risk level, physical function status, and exercise tolerance.

5. The system for predicting the risk of sarcopenia in the elderly and for grouping multi-exercise intervention according to claim 4, characterized in that: The exercise grade classification criteria include the elderly patient's muscle strength test results, balance ability test results and endurance test results.

6. The system for predicting the risk of sarcopenia in the elderly and for grouping multi-exercise intervention according to claim 3, characterized in that: The service layer uses a machine learning algorithm to analyze the collected data, including the physical indicators and lifestyle factors of elderly patients, and uses historical data containing information on sarcopenia patients and non-sarcopenia patients for training during the training process.

7. The system for predicting the risk of sarcopenia in the elderly and for grouping multi-exercise intervention according to claim 3, characterized in that: The service layer also includes a motion feedback correction module, which monitors the motion amplitude, motion frequency and motion duration data during the motion process in real time through a far-infrared sensor, and feeds back these data to correct the motion process according to preset standard data.

8. The system for predicting the risk of sarcopenia in the elderly and for grouping multi-exercise intervention according to claim 7, characterized in that: The motion feedback correction module includes an early warning unit, which sends out an early warning signal when the monitoring data exceeds a preset value.

9. The system for predicting the risk of sarcopenia in the elderly and for grouping multi-exercise intervention according to claim 1, characterized in that: The user end includes a patient end, a medical end and a management end. The patient end is used to view exercise plans and receive sarcopenia knowledge recommendations. The medical end is used by medical staff to manage patient information, track patient exercise progress, and provide guidance on exercise plans. The management end is responsible for system maintenance and management, ensuring stable system operation, and performing data queries.

10. A method for using the elderly sarcopenia risk prediction and grouped multi-exercise intervention system according to any one of claims 1 to 9, characterized in that The steps include: S1. The data layer uses the physical condition scale and fall risk prediction tool to collect relevant data of elderly patients, inputs these data into the risk assessment model, and outputs the patient's risk level of sarcopenia by analyzing the correlation between the data; S2, the solution recommendation module of the service layer receives the risk assessment results and data analysis results, and gives a comprehensive exercise plan; S3. According to the patient's age, physical condition, exercise ability, risk of sarcopenia and other data analysis results, the exercise plan is divided into levels 1 to 6, and the corresponding exercise level plan is pushed; S4. During the patient's exercise, use far-infrared equipment to monitor the patient's movement data, prompt the patient to correct his posture through the display screen or voice, and feed the data back to the data layer to provide a basis for the adjustment of subsequent exercise plans.

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