Medical auxiliary system for sarcopenia and cognitive impairment based on artificial intelligence

Abstract

The invention discloses a sarcopenia and cognitive disorder medical auxiliary system based on artificial intelligence in the field of medical auxiliary systems, and the system comprises a real-time monitoring module, a data processing module and an intelligent auxiliary module which are in signal connection. The real-time monitoring module is used for acquiring and displaying data of muscle or other physiological signals in real time, collecting and analyzing the data in time and providing related feedback; the data processing module is used for receiving data fed back by the real-time monitoring module and storing and managing the data; and the intelligent auxiliary module is used for creating an evaluation model and quoting the data in the data processing module to the evaluation model to obtain a node. By collecting a large amount of medical data and training a machine learning model, the illness state, the disease course and the treatment effect of the patient are analyzed and predicted, and the workload of medical staff can be effectively reduced.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of medical auxiliary systems, and in particular to a sarcopenia and cognitive impairment medical auxiliary system based on artificial intelligence. BACKGROUND

[0002] The term sarcopenia was first proposed by American scholar Irwin H. Rosenberg, derived from Greek ("sarx" means "muscle" and "Penia" means "loss"), which refers to age-related skeletal muscle mass loss, muscle strength decline and / or reduced physical activity capacity. Sarcopenia can be divided into primary and secondary according to the cause. Sarcopenia in dialysis patients is a common complication in patients with chronic kidney disease (CKD), especially in patients with progressive hemodialysis (MHD). This secondary sarcopenia poses a serious threat to the quality of life and health of patients, and needs to be paid special attention and managed.

[0003] Sarcopenia is a quite common problem in dialysis patients. The main causes include: during dialysis, a large amount of protein is lost with urine, resulting in a large loss of protein and nitrogenous substances in a short period of time for patients; dialysis patients may face problems such as loss of appetite, nausea, vomiting, etc., which will affect their dietary intake; the kidney's function of synthesizing hormones to maintain bone health is impaired, such as reduced activation of vitamin D and impaired calcium absorption; chronic kidney disease can lead to osteoporosis, increasing the risk of fractures. The harm of sarcopenia to patients mainly manifests in the following aspects: sarcopenia leads to a decrease in muscle mass, patients may feel weak and lack of energy, affecting their ability to carry out daily life activities; insufficient calcium load and reduced activation of vitamin D can lead to bone health problems such as fractures and bone pain; protein loss and malnutrition can lead to decreased immune system function, making patients more susceptible to infection; sarcopenia is associated with an increased incidence of cardiovascular disease, which is particularly dangerous for dialysis patients.

[0004] Cognitive impairment is a cognitive function impairment caused by various reasons, usually involving one or more cognitive domains such as language ability, memory, executive function, attention and orientation. It can be divided into mild cognitive impairment and dementia according to the severity of cognitive impairment. Cognitive impairment refers to patients who have no difficulty in activities of daily living (ADL), but have a higher likelihood of developing dementia. Cognitive impairment is between normal aging and dementia, and can progress to the most severe dementia. The most common subtype of dementia is Alzheimer's disease (AD), followed by vascular dementia, Lewy body dementia and frontotemporal dementia. Cognitive impairment is associated with physical function impairment and decreased quality of life, and is associated with early mortality. Lower education, cardiovascular risk factors, lifestyle factors, depressive symptoms, sleep disorders, traumatic brain injury, etc. are all related factors of cognitive impairment.

[0005] The incidence of cognitive impairment in dialysis patients is relatively high, and studies have shown that about 30-70% of dialysis patients experience varying degrees of cognitive decline. Cognitive impairment in dialysis patients is often chronic and can last for years, severely affecting quality of life. Cognitive impairment is very harmful to dialysis patients, including but not limited to the following aspects: cognitive impairment can cause patients to have poor memory and decision-making ability, reducing their quality of life. Cognitive impairment can cause patients to forget to take medication or ignore medical advice, affecting the effectiveness of dialysis treatment. Cognitive decline can cause patients to have accidents in daily life, such as falling or mixing up medications. Cognitive impairment can trigger mental health problems such as anxiety and depression. Managing cognitive impairment requires additional medical resources and costs, including medication and rehabilitation services.

[0006] There are some associated factors between sarcopenia and cognitive impairment, especially in dialysis patients. Sarcopenia and cognitive impairment are both associated with metabolic disorders, which can lead to protein metabolism disorders in CKD patients, affecting muscle and brain cell function. CKD and sarcopenia are both associated with chronic inflammatory states, which can cause inflammatory reactions in the body and brain, affecting cognitive function. Sarcopenia is often associated with malnutrition, which can also have a negative impact on brain function and increase the risk of cognitive impairment. Sarcopenia and cognitive impairment are both associated with cardiovascular health problems. Cardiovascular disease can affect blood supply to the brain, which in turn affects cognitive function. Certain drugs used to treat CKD or dialysis can have an impact on cognitive function, especially when used long-term.

[0007] However, in reality, today's sarcopenia and cognitive impairment medical assistance systems require a large amount of data to train accurate models. However, data collection for rare diseases such as sarcopenia and cognitive impairment is relatively difficult, so the size of the data may be small, which affects the performance of the algorithm and the accuracy of the model. Therefore, the present invention proposes a sarcopenia and cognitive impairment medical assistance system based on artificial intelligence to solve the above problems. SUMMARY

[0008] The present invention proposes a sarcopenia and cognitive impairment medical assistance system based on artificial intelligence, which collects a large amount of medical data to train machine learning models to analyze and predict patient conditions, disease progression, and treatment effectiveness, and can also effectively reduce the workload of medical personnel.

[0009] To achieve the above purpose, the technical scheme of the present invention is as follows: a sarcopenia and cognitive impairment medical assistance system based on artificial intelligence, comprising a real-time monitoring module, a data processing module and an intelligent assistance module, all modules are signal connected.

[0010] The real-time monitoring module is used to obtain and display muscle or other physiological signal data in real time, collect and analyze data in a timely manner, and provide relevant feedback.

[0011] The data processing module is used to receive data feedback from the real-time monitoring module and store and manage the data.

[0012] The intelligent assistance module is used to create an evaluation model and reference data in the data processing module to the evaluation model to draw conclusions.

[0013] After adopting the above scheme, the following principles and beneficial effects are realized: The system monitors the patient's muscle or other physiological signals in real time, transmits the data to the data processing module for storage and management. Then, the intelligent assistance module creates an evaluation model using these data, and evaluates the patient's condition through analysis and processing of the data. The system can provide immediate feedback and effective assistance to help medical staff better understand the patient's condition.

[0014] The system can monitor the patient's physiological signals in real time, provide timely feedback and diagnosis results, help medical staff more accurately assess the condition of patients with sarcopenia and cognitive impairment, monitor changes in the disease, and provide personalized treatment and rehabilitation programs based on the evaluation results. In addition, it can effectively reduce the workload of medical staff.

[0015] Further, the real-time monitoring module includes a monitoring unit for monitoring and collecting physiological signals for leg muscles; a signal processing unit responsible for amplifying, filtering and processing physiological signals obtained from the monitoring unit; a data transmission unit for transmitting processed physiological signal data to the data processing module through wireless or wired means.

[0016] Principles and benefits: The monitoring unit is used to monitor and collect physiological signals for leg muscles, such as using electromyography sensors and other devices. The monitoring unit transmits the collected physiological signals to the signal processing unit.

[0017] The signal processing unit is responsible for amplifying, filtering and processing physiological signals obtained from the monitoring unit. Amplification can increase the strength of the signal to facilitate subsequent analysis. Filtering can remove noise and interference to extract the required physiological signals. The processing function can digitize, encode and calibrate the physiological signals to provide accurate input for subsequent data analysis.

[0018] The data transmission unit is responsible for transmitting processed physiological signal data to the data processing module. The transmission method can be wireless or wired, and the appropriate communication technology is selected according to the specific situation, such as Bluetooth or Wi-Fi. The data transmission unit ensures that the data is transmitted safely and stably to the data processing module for further processing and analysis.

[0019] The real-time monitoring module designed in this way can obtain physiological signals of leg muscles in real time, and transmit data to the data processing module through signal processing and transmission; it can realize monitoring and analysis of leg muscle activity, providing helpful assistance for subsequent diagnosis and evaluation therapy.

[0020] Further, the data processing module also includes the following steps: converting the analog signal into digital form; applying a filter to the data to remove noise and interference; extracting useful features or parameters from the raw data to describe and represent the signal; using statistical methods, signal processing algorithms, and machine learning techniques to analyze the data to gain deeper insights and extract relevant information.

[0021] Principle and beneficial effects: The analog signal obtained from the monitoring unit is converted into digital form, and an analog-to-digital converter (ADC) is used to discretize the continuous analog signal into a digital signal, which facilitates subsequent processing and analysis; a filter is applied to process the data to remove noise and interference, and the filter can be designed in different ways, such as low-pass filter, high-pass filter or band-pass filter, according to actual needs to select the appropriate filtering method to improve data quality and accuracy; useful features or parameters are extracted from the raw data to describe and represent the signal, which can reflect important information such as signal frequency, amplitude, and trend; statistical methods, signal processing algorithms, and machine learning techniques are used to analyze the data, statistical methods can be used for descriptive statistics and inferential statistics to understand the distribution, correlation, and regularity of the data, signal processing algorithms can be applied to filtering, spectral analysis, time-frequency analysis, etc. to reveal the characteristics and regularities of the data, and machine learning techniques can be applied to data pattern recognition, classification, prediction, etc. through model training for data analysis and decision support.

[0022] The data processing module can convert raw data into digital form and remove noise and interference through filtering to improve data quality. Feature extraction simplifies data and extracts key information to provide a basis for subsequent analysis. The application of statistical methods, signal processing algorithms, and machine learning techniques can help analyze data in depth, extract useful information hidden in data, and obtain more comprehensive and accurate insights.

[0023] Further, when creating the evaluation model, the intelligent assistance module needs to reference the data processed by the data processing module to the evaluation model; after the data is referenced, the intelligent assistance module can use these data to train the evaluation model; after training is completed, the intelligent assistance module can use the evaluation model to evaluate new data; according to the output of the evaluation model, the intelligent assistance module can generate corresponding conclusions or suggestions.

[0024] Principle and beneficial effects: The intelligent assistance module needs to incorporate the data processed by the data processing module into the evaluation model. This means that the processed data is passed as input to the evaluation model for subsequent training and evaluation.

[0025] Once the data is incorporated into the evaluation model, the intelligent auxiliary module can use this data to train the evaluation model. By using labeled or known data, the model can learn the patterns, correlations, and regularities in the data, and further optimize its own parameters to improve its ability to predict or evaluate unknown data.

[0026] After the evaluation model has completed training, the intelligent assistance module can use the model to evaluate new data. By inputting new data samples, the model can perform operations such as prediction, classification, and scoring based on the rules and algorithms derived from previous training to obtain evaluation results for the data.

[0027] Based on the output of the evaluation model, the intelligent assistance module can generate corresponding conclusions or suggestions. This can be determined according to the specific application scenario. For example, in the medical field, the evaluation model can be used to assess the condition and generate diagnostic results and treatment suggestions to assist doctors in making decisions.

[0028] The intelligent assistance module uses processed data to power the evaluation model, optimizing model parameters with training data to improve the accuracy of predictions on new data. By evaluating new data using the evaluation model, the evaluation process can be automated and standardized, improving efficiency and consistency. The final conclusions or recommendations can provide reference and guidance for decision-makers, helping them make more informed decisions. Furthermore, the intelligent assistance module can reduce human workload, accelerate the decision-making process, and improve resource utilization efficiency.

[0029] Furthermore, the monitoring unit in the real-time monitoring module includes a detection component and a base plate. The top of the base plate is provided with a first slide groove and a second slide groove. A sliding plate is slidably connected to both the first slide groove and the second slide groove. A spring is fixedly connected to the side of the first slide groove and the second slide groove near the edge of the base plate. The end of the spring away from the first slide groove and the second slide groove is fixedly connected to the sliding plate. A fixing device is provided on the top wall of the sliding plate. A pressure sensor is fixedly connected to the sliding plate. The detection component includes an electromyography (EMG) sensor. The EMG sensor signal is connected to a controller. The controller signal is connected to the pressure sensor signal.

[0030] Principle and Benefits: By using a fixation device to secure the patient's feet to the sliding plate, the risk of injury due to slippage during testing can be avoided, while ensuring the reliability of the test results. The process utilizes electromyography (EMG) sensors and pressure sensors to monitor data in real time. The patient places both feet on the sliding plate and then pushes it forcefully. The EMG sensors, attached to the patient's leg muscles, detect and record the electrical signals generated during muscle contraction. By analyzing these signals, information such as muscle activation level, coordination, and fatigue level can be assessed, providing medical personnel with objective muscle status data. The pressure sensor at the top of the sliding plate also monitors in real time, transmitting all data to the controller for recording and analysis. This allows medical personnel to promptly obtain information about the patient's muscle status and make informed decisions and interventions. This process ensures patient safety and the reliability of the test.

[0031] Furthermore, support rods are fixedly connected to both sides of the top of the base plate, and auxiliary rings are fixedly connected to the support rods. Strain gauges are embedded in the auxiliary rings, and the strain gauges are connected to the controller signals.

[0032] Principle and beneficial effects: When the patient is undergoing the test, he holds the auxiliary ring on the support rod with both hands. When the patient holds the auxiliary ring, he applies a certain gripping force to the auxiliary ring. The strain gauge embedded in the auxiliary ring can respond to the deformation or strain of the ring caused by the gripping force and transmit the sensed strain of the auxiliary ring to the controller in real time.

[0033] By monitoring with strain gauges, the controller can acquire the force and deformation applied by the patient to the auxiliary ring in real time, providing accurate monitoring data. Based on the monitoring data, the controller can assess the patient's grip strength and the stability of the ring, provide timely feedback, help the patient adjust the appropriate grip strength, and provide better support. The patient's grip on the auxiliary ring can effectively prevent slipping and falling, provide additional support, and increase safety.

[0034] Furthermore, cylinders are embedded in the side walls of both the first and second slides, and clamps are fixedly connected to the output ends of the cylinders. Anti-slip layers are fixedly connected to the side of the clamps away from the cylinders, and the cylinders are signal-connected to the controller.

[0035] Principle and beneficial effects: The controller sends commands to the cylinder via received signals and controls its movement. When the cylinder is activated by the controller's signal, it generates thrust or pull force, which is transmitted to the clamping plate through its connection with the clamping plate. The movement of the clamping plate depends on the working state of the cylinder (e.g., extension, retraction, etc.). The anti-slip layer increases the friction between the clamping plate and the sliding plate, ensuring that the sliding plate is firmly clamped without slipping under applied force.

[0036] By using cylinders and controllers, automated clamping plate operation can be achieved, saving manual labor and improving work efficiency. The thrust or pull force provided by the cylinders ensures that the clamps firmly hold the sliding plate, increasing clamping stability. The presence of an anti-slip layer increases the friction between the clamps and the sliding plate, reducing the possibility of slippage and improving operational safety. By controlling the cylinders through the controller, the movement mode and force of the clamps can be adjusted to adapt to different work requirements and the characteristics of the sliding plate.

[0037] In summary, the first and second slides have cylinders embedded in their side walls and are connected to clamping plates and anti-slip layers, which can realize automated clamping plate operation and provide stable clamping force and anti-slip safety, thereby improving work efficiency and operational reliability.

[0038] Furthermore, the fixing device includes a buckle, which is fixedly connected to one side of the top of the sliding plate, and a strap that matches the buckle is fixedly connected to the other side of the top of the sliding plate.

[0039] Principle and beneficial effects: The combination of buckle and strap, that is, by passing the strap through the buckle and tightening it, can fix the patient's foot and achieve foot fixation.

[0040] The main purpose of the fixation device is to provide stable support, ensuring that the patient's feet maintain the correct position and posture during the examination, which helps prevent instability when the patient moves or performs activities and reduces the risk of injury; the combination of buckles and straps can limit the range of motion of the feet and avoid unnecessary movement interference. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating an embodiment of the AI-based medical assistance system for sarcopenia and cognitive impairment according to the present invention.

[0042] Figure 2 This is a top view of the monitoring unit in an embodiment of the AI-based medical assistance system for sarcopenia and cognitive impairment of the present invention.

[0043] Figure 3 This is a front view of the monitoring unit in an embodiment of the AI-based medical assistance system for sarcopenia and cognitive impairment of the present invention. Detailed Implementation

[0044] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0045] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "vertical", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0046] In the description of this invention, unless otherwise specified and limited, it should be noted that the terms "installation", "connection" and "linking" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two components. They can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0047] The following detailed description illustrates the specific implementation method:

[0048] The reference numerals in the accompanying drawings include: base plate 1, first slide groove 2, second slide groove 3, sliding plate 4, spring 5, support rod 6, auxiliary ring 7, buckle 8, and strap 9.

[0049] Example 1, basically as shown in the attached document. Figure 1 As shown: A medical assistance system for sarcopenia and cognitive impairment based on artificial intelligence includes a real-time monitoring module, a data processing module, and an intelligent assistance module, all of which are interconnected by signals;

[0050] The real-time monitoring module is used to acquire and display data of muscle or other physiological signals in real time, collect and analyze data in a timely manner, and provide relevant feedback. The real-time monitoring module includes a monitoring unit for monitoring and acquiring physiological signals for leg muscles; a signal processing unit for amplifying, filtering, and processing the physiological signals acquired from the monitoring unit; and a data transmission unit for transmitting the processed physiological signal data to the data processing module wirelessly or via wired means.

[0051] The data processing module receives data from the real-time monitoring module, stores and manages the data. The data processing module also processes the data by: converting analog signals into digital form; applying filters to the data to remove noise and interference; extracting useful features or parameters from the raw data to describe and represent the signal; and using statistical methods, signal processing algorithms, and machine learning techniques to analyze the data to gain deeper insights and extract relevant information.

[0052] The intelligent assistance module is used to create an evaluation model and reference the data from the data processing module to draw conclusions. When creating the evaluation model, the intelligent assistance module needs to reference the data processed by the data processing module. After referencing the data, the intelligent assistance module can use this data to train the evaluation model. After training, the intelligent assistance module can use the evaluation model to evaluate new data. Based on the output of the evaluation model, the intelligent assistance module can generate corresponding conclusions or suggestions.

[0053] The specific implementation process is as follows: The real-time monitoring module is responsible for acquiring and displaying data on muscle or other physiological signals, and collecting, processing, and transmitting this data through the monitoring unit, signal processing unit, and data transmission unit. The data processing module receives feedback data from the real-time monitoring module, converts it into digital form, and applies processing methods such as filters to extract useful features and parameters. It then uses statistical methods, signal processing algorithms, and machine learning techniques to analyze the data and further mine information.

[0054] The intelligent assistance module is used to create evaluation models and draw conclusions from data processed by the data processing module. When creating the evaluation model, the intelligent assistance module needs to incorporate the data processed by the data processing module into the evaluation model and use this data to train the model. After training, the intelligent assistance module can use the evaluation model to evaluate new data and generate corresponding conclusions or suggestions based on the model's output. This process helps people understand and evaluate the state of muscles or other physiological signals in real time and make corresponding adjustments and decisions.

[0055] Model establishment process: 259 hospitalized patients were selected and divided into four groups: no sarcopenia and no cognitive impairment (n=92); sarcopenia only (n=33); cognitive impairment only (n=80); and sarcopenia with cognitive impairment (n=54). All patients completed Inbody S10 bioelectric impedance analysis, 6m gait test, grip strength measurement, and cognitive function assessment. Analysis of variance and unordered logistic regression models were used to explore the influencing factors related to sarcopenia with cognitive impairment in hospitalized patients. Multiple linear regression analysis was used to explore the relationship between sarcopenia and its defining components (muscle mass, gait speed, grip strength) and different cognitive domains such as attention, memory, verbal fluency, language, and visuospatial ability, and three models adjusted for confounding factors were constructed.

[0056] Example 2 differs from the above examples in that, as shown in the appendix... Figure 2As shown: The monitoring unit in the real-time monitoring module includes a detection component and a base plate 1. The top of the base plate 1 is provided with a first slide groove 2 and a second slide groove 3. Sliding plates 4 are slidably connected to both the first slide groove 2 and the second slide groove 3. Springs 5 ​​are fixedly connected to the side of the first slide groove 2 and the second slide groove 3 near the edge of the base plate 1 by bolts. The end of the spring 5 away from the first slide groove 2 and the second slide groove 3 is fixedly connected to the sliding plate 4 by bolts. A fixing device is provided on the top wall of the sliding plate 4. In this embodiment, the fixing device is an elastic band. A pressure sensor is fixedly connected to the sliding plate 4 by bolts. In this embodiment, the pressure sensor model is SF45-65 pressure-sensitive sensor. The detection component includes an electromyography (EMG) sensor. The EMG sensor signal is connected to a controller. In this embodiment, the controller model is a programmable controller NX7-48ADR. The controller is connected to the pressure sensor signal.

[0057] The specific implementation process is as follows: When using the device, the patient places both feet on the sliding plates 4 of the first sliding groove 2 and the second sliding groove 3 respectively, and fixes the feet in place using a fixing device. By fixing the feet on the sliding plates 4 using a fixing device, the risk of injury due to slipping during the test can be avoided, ensuring the safety of the patient and the reliability of the test. Then, the electromyography sensor is attached to the patient's leg muscles.

[0058] The patient pushes the sliding plate 4 in opposite directions with force, forward and backward, with each foot. Simultaneously, the electromyography (EMG) sensor monitors the muscles in the patient's legs, recording the electrical signals generated during muscle contraction. Analyzing these signals allows for the assessment of muscle activation levels, coordination, and fatigue levels. Simultaneously, a pressure sensor on top of the sliding plate 4 also monitors in real time. Both the pressure sensor and the EMG sensor transmit data to the controller for recording and analysis, enabling medical staff to promptly obtain information about the patient's muscle status and make rapid decisions and interventions.

[0059] Overall, using a sliding plate, electromyography (EMG) sensor, and pressure sensor for muscle activity assessment offers advantages such as safety, accuracy, and real-time monitoring. This assessment method can help healthcare professionals better understand a patient's muscle condition and develop appropriate treatment and training plans.

[0060] Example 3 differs from the above examples in that, as shown in the appendix... Figure 3 As shown: Support rods 6 are fixedly connected to both sides of the top of the base plate 1 by bolts. Auxiliary rings 7 are fixedly connected to the support rods 6 by bolts. Strain gauges are embedded in the auxiliary rings 7. The strain gauges are connected to the controller signal. Cylinders are embedded in the side walls of the first slide groove 2 and the second slide groove 3. In this embodiment, the cylinder model is Jinggong CJ1B. The output end of the cylinder is fixedly connected to a clamping plate. The side of the clamping plate away from the cylinder is fixedly connected to an anti-slip layer. The cylinder is connected to the controller signal.

[0061] The specific implementation process is as follows: When the patient is undergoing testing, they can hold the auxiliary ring 7 on the support rod 6 with both hands for support and to prevent slipping. While the patient holds the auxiliary ring 7, the strain gauges on the auxiliary ring 7 monitor the force exerted by the patient on the auxiliary ring 7 in real time and transmit the data to the controller. In summary, by fixing the auxiliary ring 7 to the support rod 6 and connecting the strain gauges to the controller, the force exerted by the patient on the auxiliary ring 7 can be monitored in real time and accurate feedback can be provided to improve patient stability and safety.

[0062] The controller is pre-set with a range of values ​​for normal force conditions. When the data received by the controller exceeds the normal range, the controller will activate the cylinder. The cylinder pushes the clamping plate, which clamps the two sides of the sliding plate 4. The anti-slip layer on the clamping plate increases the friction between the clamping plate and the sliding plate 4, which can effectively prevent the sliding plate 4 from sliding and keep it in place.

[0063] Example 4 differs from the above examples in that the fixing device includes a buckle 8, which is fixedly connected to one side of the top of the sliding plate 4 by bolts, and a strap 9 matching the buckle 8 is fixedly connected to the other side of the top of the sliding plate 4 by bolts.

[0064] The specific implementation process is as follows: When the patient's foot is placed on the sliding plate 4, the strap 9 is passed through the buckle 8 to fix the foot. Fixing the foot provides stable support, ensuring that the patient's posture on the sliding plate 4 is correct, balanced, and stable; by fixing the foot, the strap 9 can reduce unnecessary movement of the foot on the sliding plate 4, reduce interference factors, and improve the accuracy and efficiency of the test.

[0065] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific structures and / or characteristics in the solutions are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the structure of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A medical assistance system for sarcopenia and cognitive impairment based on artificial intelligence, characterized in that: It includes a real-time monitoring module, a data processing module, and an intelligent assistance module, all of which are interconnected by signals; The real-time monitoring module is used to instantly acquire and display data on muscle or other physiological signals, collect and analyze data in a timely manner, and provide relevant feedback. The data processing module is used to receive data from the real-time monitoring module, and to store and manage the data; The intelligent assistance module is used to create an evaluation model and reference the data from the data processing module into the evaluation model to draw conclusions.

2. The artificial intelligence-based medical assistance system for sarcopenia and cognitive impairment according to claim 1, characterized in that: The real-time monitoring module includes a monitoring unit for collecting physiological signals for monitoring leg muscles; and a signal processing unit for amplifying, filtering, and processing the physiological signals acquired from the monitoring unit. A data transmission unit used to transmit processed physiological signal data to the data processing module wirelessly or via wired means.

3. The artificial intelligence-based medical assistance system for sarcopenia and cognitive impairment according to claim 2, characterized in that: The data processing module also processes data in the following ways: converting analog signals into digital form; applying filters to the data to remove noise and interference; extracting useful features or parameters from the raw data to describe and represent the signal; and using statistical methods, signal processing algorithms, and machine learning techniques to analyze the data to gain deeper insights and extract relevant information.

4. The artificial intelligence-based medical assistance system for sarcopenia and cognitive impairment according to claim 3, characterized in that: When creating an evaluation model, the intelligent assistance module needs to incorporate the data processed by the data processing module into the evaluation model. After the data is incorporated, the intelligent assistance module can use this data to train the evaluation model. After training, the intelligent assistance module can use the evaluation model to evaluate new data. Based on the output of the evaluation model, the intelligent assistance module can generate corresponding conclusions or suggestions.

5. The artificial intelligence-based medical assistance system for sarcopenia and cognitive impairment according to claim 4, characterized in that: The monitoring unit in the real-time monitoring module includes a detection component and a base plate. The top of the base plate is provided with a first slide groove and a second slide groove. A sliding plate is slidably connected to both the first slide groove and the second slide groove. A spring is fixedly connected to the side of the first slide groove and the second slide groove near the edge of the base plate. The end of the spring away from the first slide groove and the second slide groove is fixedly connected to the sliding plate. A fixing device is provided on the top wall of the sliding plate. A pressure sensor is fixedly connected to the sliding plate. The detection component includes an electromyography (EMG) sensor. The EMG sensor signal is connected to a controller. The controller signal is connected to the pressure sensor signal.

6. The artificial intelligence-based medical assistance system for sarcopenia and cognitive impairment according to claim 5, characterized in that: Support rods are fixedly connected to both sides of the top of the base plate. Auxiliary rings are fixedly connected to the support rods. Strain gauges are embedded in the auxiliary rings and are connected to the controller signals.

7. The artificial intelligence-based medical assistance system for sarcopenia and cognitive impairment according to claim 6, characterized in that: Cylinders are embedded in the side walls of both the first and second slides. A clamp is fixedly connected to the output end of the cylinder. An anti-slip layer is fixedly connected to the side of the clamp away from the cylinder. The cylinder is connected to the controller signal.

8. The artificial intelligence-based medical assistance system for sarcopenia and cognitive impairment according to claim 7, characterized in that: The fixing device includes a buckle, which is fixedly connected to one side of the top of the sliding plate, and a strap that matches the buckle is fixedly connected to the other side of the top of the sliding plate.

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