Shoulder joint stability evaluation system based on surface electromyogram signals

Abstract

The invention belongs to the technical field of shoulder joint detection, and discloses a shoulder joint stability evaluation system based on surface electromyogram signals, which comprises a data acquisition module, a data processing module and an evaluation analysis module. According to the invention, by synchronously acquiring the angle signal and the surface electromyogram signal, the muscle activity dynamic change of the shoulder joint in the whole movement process can be accurately captured in real time, the evaluation accuracy is remarkably improved, and a foundation is built for accurate diagnosis; in a signal processing link, a plurality of filtering methods are comprehensively applied to carry out deep purification on original surface electromyogram signals, various interference factors such as 50Hz power frequency interference, low-frequency motion artifacts and high-frequency noise are effectively eliminated, the purity and reliability of the signals are guaranteed, and high-quality data are provided for subsequent analysis; and meanwhile, a characterization index of an amplitude absolute value mean value is introduced, so that the muscle activity intensity is reflected more visually and accurately, and the internal correlation between the shoulder joint stability and the muscle activity is deeply excavated.

Description

Technical Field

[0001] The present invention belongs to the technical field of shoulder joint detection, and in particular relates to a shoulder joint stability assessment system based on surface electromyography signals. Background Art

[0002] As the most flexible ball-and-socket joint in the body, the shoulder joint can perform complex movements such as flexion, extension, adduction, abduction, rotation, and circumduction. However, its structural characteristics such as the large difference in area between the head of the joint and the socket of the joint, and the thin and loose joint capsule also make it a joint that is extremely vulnerable to injury. The stabilizing structures of the shoulder joint are divided into active and passive types. The active stabilizing structures include the deltoid muscle, biceps brachii, and rotator cuff muscles, while the passive stabilizing structures include the geometric shape of the glenoid fossa, the labrum, the joint capsule, and the glenohumeral ligament. Shoulder instability refers to the deviation of the humeral head from the center of the glenoid fossa. It can be divided into dislocation, subluxation, and simple pain according to the degree; it can be divided into anterior, posterior, and multi-directional instability according to the direction, with anterior instability being the most common; and it can be divided into mechanical and functional instability according to whether there is anatomical disorder on MRI examination. Currently, there are universal treatment standards for mechanical anterior shoulder instability internationally. Surgical plans are primarily formulated based on the presence and extent of glenoid defects, as well as the redundancy of the anterior joint capsule. However, an in-depth analysis of 1,283 inpatient cases of anterior shoulder instability admitted to our hospital in recent years revealed significant limitations in simply adhering to these conventional standards. Following treatment according to this plan, approximately 17.3% of patients experienced shoulder instability or limited range of motion. This result serves as a warning to clinicians that when formulating treatment plans for patients, they should not rely solely on traditional standards, but rather comprehensively and comprehensively consider more individualized factors to optimize surgical plans and effectively improve treatment outcomes. Summary of the Invention

[0003] The purpose of the present invention is to provide a shoulder joint stability assessment system based on surface electromyography signals to solve the problems raised in the above background technology.

[0004] In order to achieve the above object, the present invention provides the following technical solution: a shoulder joint stability assessment system based on surface electromyography signals, the system comprising: a data acquisition module, a data processing module, and an assessment and analysis module;

[0005] The data acquisition module is used to accurately collect angle data of the test-side shoulder joint and surface electromyographic signals of multiple parts in the shoulder joint stability assessment, and ensure that the two types of signals are synchronously and accurately acquired for subsequent analysis;

[0006] The data processing module is mainly used to systematically process the collected shoulder joint surface electromyographic signals and angle signals: first, purify the signal quality and filter out various noise interferences; then establish a precise correspondence between the angle and the electromyographic signal; finally, quantify the muscle activity intensity by calculating the absolute value mean of the amplitude, providing a reliable analysis basis for subsequent shoulder joint stability assessment;

[0007] The evaluation and analysis module compares the surface electromyographic signal data of the patient and the healthy subject, determines the angle value corresponding to the maximum mean absolute value of the amplitude of each channel, and evaluates the degree of instability of the patient's shoulder joint by analyzing the difference in the angle values.

[0008] Preferably, the data acquisition module includes an angle data acquisition core unit, a surface electromyography signal acquisition core unit, and a synchronous acquisition control core unit;

[0009] The angle data acquisition core unit relies on the gyroscope angle sensor to accurately collect the test side shoulder joint angle data in real time. Its supporting PC host software is responsible for receiving and displaying the data and performing preliminary format processing and data verification.

[0010] The core unit for surface electromyography (SEMG) signal acquisition uses a six-channel SEMG sensor, with a reference electrode located at the acromion and electrodes placed at multiple locations on the upper pectoralis major muscle. This allows for precise acquisition of SEMG signals from the shoulder joint on the test side. The accompanying PC-side host software receives the signals, displays waveforms on the interface, and performs preliminary filtering and amplification preprocessing.

[0011] The synchronous acquisition control core unit uses GUI code written in Python as the data acquisition "command center", coordinating the start and stop of the two supporting software to ensure strict synchronization of the angle and electromyographic signal, providing accurate escort for subsequent analysis.

[0012] Preferably, the data processing module includes a pre-processing unit, an angle-electromyographic signal corresponding unit, and an amplitude calculation unit;

[0013] The pre-processing unit uses the FilterDesigner toolbox of MATLAB to carefully design a filtering scheme for the collected original surface electromyography signal; through a 50Hz notch filter, a 20-240Hz bandpass filter and a comb notch filter, it effectively filters out power frequency, artifacts, drift and high-frequency noise, thereby improving signal purity;

[0014] The angle-electromyographic signal corresponding unit relies on the synchronously collected data, divides the time period of each 1° change based on the angle signal, accurately matches the corresponding time period, and obtains the surface electromyographic signal amplitude under the angle change;

[0015] The amplitude calculation unit measures the strength of the surface electromyographic signal by the average of the absolute value of the amplitude. The absolute value of the signal is first taken, and then the average is calculated, which intuitively reflects the muscle activity level of the corresponding time period.

[0016] Preferably, the specific steps of the evaluation method are as follows:

[0017] S1: Preliminary information collection and test preparation: Guide the subjects to register basic and specific information and sign a consent form, standardize the exposed test shoulder to maintain a standard posture, accurately install the sensor, and ensure compliance with the assessment and a solid foundation for signal collection;

[0018] S2: Start synchronous signal acquisition: Start the angle and electromyography signal acquisition software and confirm that they are running smoothly. Run the Python GUI code for precise control, so that the two software can start and stop synchronously and strictly control the time alignment to ensure accurate analysis.

[0019] S3: Surface EMG signal acquisition: The subjects were clearly instructed to slowly and steadily externally rotate their shoulder joints to the maximum angle, and the original EMG signals of the entire 90° external rotation were collected for subsequent in-depth analysis.

[0020] S4: Surface EMG signal preprocessing: Use MATLAB Filter Designer to plan the filtering process, sequentially using a 50Hz notch filter, a 20-240Hz bandpass filter, and a comb notch filter to improve the purity and accuracy of the surface EMG signal;

[0021] S5: Angle-EMG signal correspondence and amplitude calculation: Based on the collected angle signal, the mathematical method is used to determine the corresponding time period for each 1° angle change. Due to signal synchronization, the EMG signal is accurately matched, and its amplitude is then calculated segment by segment.

[0022] S6: Evaluation, Analysis, and Report Generation: Compare the MAV data of each channel of the two to draw a curve to find the key angle, evaluate the stability of the shoulder joint based on this, and then output a graphic report with rich content to provide a reference for clinical diagnosis and treatment.

[0023] Preferably, the specific steps of the preliminary information collection and test preparation in S1 are as follows:

[0024] Step 1: Information registration: Guide patients or healthy subjects to fill in detailed information, including basic and specific information, and sign an informed consent form to ensure that the assessment process is legal and compliant and the information is complete;

[0025] Step 2: Posture standardization: The subject is required to expose the shoulder joint on the test side and position it in a standard posture, that is, keep the affected shoulder abducted 90°, the elbow flexed 90°, and the forearm parallel to the ground;

[0026] Step 3: Hardware Deployment: Accurately install the test equipment, wear the gyroscope angle sensor on the wrist of the test limb, and connect the six-channel surface muscle electrical signal sensor to the test shoulder.

[0027] Preferably, the specific steps of starting the signal synchronous acquisition in S2 are as follows:

[0028] Step 1: Software startup: Open the angle signal acquisition software and the surface electromyography signal acquisition software respectively, make sure the software runs normally and there are no errors, and prepare the software for synchronous acquisition;

[0029] Step 2: Synchronous control: Run the GUI code written in Python and precisely control the two software signal acquisition interfaces through code logic, so that they start and end synchronously, achieving strict alignment of the angle signal and the surface electromyography signal in the time dimension, and avoiding the influence of time difference on subsequent analysis.

[0030] Preferably, the specific steps of collecting surface electromyography signals in S3 are as follows:

[0031] Step 1: Movement Instruction: Clearly inform the patient or healthy subject of the test movement requirements and instruct them to externally rotate the shoulder joint to the maximum angle as smoothly and slowly as possible, emphasizing the smoothness of the movement and reducing additional signal interference caused by excessive movement or jitter;

[0032] Step 2: Signal acquisition: While the subject is performing external rotation, the original surface electromyographic signals are continuously collected during the external rotation activity when the shoulder joint is abducted 90°, fully recording muscle activity information and providing rich data for subsequent analysis.

[0033] Preferably, the specific steps of surface electromyography signal preprocessing in S4 are as follows:

[0034] Step 1: Filter planning: Use the FilterDesigner toolbox in MATLAB to plan the filtering process. Select a 50Hz notch filter, a 20-240Hz bandpass filter, and a comb notch filter in turn to deal with interference in different frequency bands.

[0035] Step 2: Filtering operation: Filter the original surface electromyographic signal according to the planned order. First, use a 50Hz notch filter to remove the interference of the power frequency and its multiple harmonics. Then use a 20-240Hz bandpass filter to remove low-frequency motion artifacts and baseline drift. Finally, use a comb notch filter to eliminate high-frequency noise to improve signal purity and accuracy.

[0036] Preferably, the specific steps of angle-electromyographic signal correspondence and amplitude calculation in S5 are as follows:

[0037] Step 1: Time-angle correspondence: Based on the collected angle signal, mathematical methods are used to determine the time period corresponding to each 1° change in angle. Since the angle and surface electromyography signals are collected synchronously, the surface electromyography signals within each angle change time period are accurately matched;

[0038] Step 2: Amplitude calculation: For each segment of surface electromyographic signal corresponding to each 1° change in angle, the mean absolute amplitude (MAV) formula is used to calculate its average intensity.

[0039] MAV is to first take the absolute value of the amplitude of the electromyographic signal and then calculate the average. The formula is as follows:

[0040]

[0041] Where: N is the signal length; x i is the signal amplitude of the i-th sample point.

[0042] Preferably, the specific steps of evaluation analysis and report generation in S6 are as follows:

[0043] Step 1: Data comparison: Compare the MAV data of the surface electromyography signals of each channel of the patients and healthy subjects, draw the MAV-angle curve, determine the angle value corresponding to the maximum MAV of each channel, and intuitively present the difference between the two;

[0044] Step 2: Stability Assessment: Analyze the differences in key angles between patients and healthy subjects, and assess the degree of shoulder instability in patients based on clinical knowledge. If the angle value corresponding to the maximum MAV of a certain channel in the patient differs significantly from that of the healthy subjects, it indicates abnormal muscle activity in the corresponding channel, reflecting shoulder joint stability issues.

[0045] Step 3: Report Output: The evaluation and analysis results are output in the form of a combination of charts and text to generate a shoulder joint stability assessment report. The report covers the patient's basic information, collected data, MAV angle change curves of each channel, comparison of key angle values between the patient and healthy subjects, shoulder joint stability assessment conclusions and recommendations, providing a comprehensive and accurate reference for clinical diagnosis and treatment.

[0046] The beneficial effects of the present invention are as follows:

[0047] By synchronously collecting angle signals and surface electromyography signals, the present invention is able to capture the dynamic changes of muscle activity throughout the entire shoulder joint movement in real time and accurately, significantly improving the accuracy of assessment and laying a solid foundation for accurate diagnosis; in the signal processing link, a variety of filtering methods are comprehensively used to deeply purify the original surface electromyography signals, effectively eliminating various interference factors such as 50Hz power frequency interference, low-frequency motion artifacts and high-frequency noise, ensuring signal purity and reliability, and providing high-quality data for subsequent analysis; at the same time, the mean absolute value of amplitude (MAV) is introduced as a characterization indicator to more intuitively and accurately reflect the intensity of muscle activity, and deeply explore the intrinsic relationship between shoulder joint stability and muscle activity. After comparative analysis of patient and healthy subject data, the degree of shoulder joint instability in patients can be objectively and quantitatively assessed, providing a scientific basis for clinical diagnosis and treatment, assisting in the formulation of personalized plans, effectively improving treatment effects, and accelerating patient recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of the evaluation method of the present invention;

[0049] Figure 2 This is a flow chart of the data collection phase of the present invention;

[0050] Figure 3 This is a flow chart of the data processing stage of the present invention;

[0051] Figure 4 This is a diagram of the shoulder joint posture before testing of the present invention;

[0052] Figure 5 This is a schematic diagram of the electrode sheet application position of the present invention;

[0053] Figure 6 This is a schematic diagram of the second electrode application position of the present invention;

[0054] Figure 7 Schematic diagram of the electrode application position of the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] like Figures 1 to 7 As shown, an embodiment of the present invention provides a shoulder joint stability assessment system based on surface electromyography signals, which includes: a data acquisition module, a data processing module, and an assessment and analysis module.

[0057] The data acquisition module is used to accurately collect angle data of the test-side shoulder joint and surface electromyographic signals of multiple parts in the shoulder joint stability assessment, and ensure that the two types of signals are synchronously and accurately acquired for subsequent analysis;

[0058] The data processing module is mainly used to systematically process the collected shoulder joint surface electromyographic signals and angle signals: first, purify the signal quality and filter out various noise interferences; then establish a precise correspondence between the angle and the electromyographic signal; finally, quantify the muscle activity intensity by calculating the mean of the absolute value of the amplitude, providing a reliable analysis basis for subsequent shoulder joint stability assessment.

[0059] The evaluation and analysis module compares the surface electromyographic signal data of the patient and the healthy subject, determines the angle value corresponding to the maximum mean absolute value of the amplitude of each channel, and evaluates the degree of instability of the patient's shoulder joint by analyzing the difference in the angle values.

[0060] The data acquisition module includes an angle data acquisition core unit, a surface electromyography signal acquisition core unit, and a synchronous acquisition control core unit;

[0061] The angle data acquisition core unit relies on the gyroscope angle sensor to accurately collect the test side shoulder joint angle data in real time. Its supporting PC host software is responsible for receiving and displaying the data and performing preliminary format processing and data verification.

[0062] The core unit for surface electromyography (SEMG) signal acquisition uses a six-channel SEMG sensor, with a reference electrode placed at the acromion and electrodes placed at multiple locations, including the upper bundle of the pectoralis major, to accurately collect SEMG signals from the shoulder joint on the test side. The accompanying PC-side host software receives the signal, displays the waveform on the interface, and performs preliminary filtering and amplification preprocessing.

[0063] The synchronous acquisition control core unit uses GUI code written in Python as the data acquisition "command center", coordinating the start and stop of the two supporting software to ensure strict synchronization of the angle and electromyographic signal, providing accurate escort for subsequent analysis.

[0064] The data processing module includes a pre-processing unit, an angle-electromyographic signal corresponding unit, and an amplitude calculation unit;

[0065] The pre-processing unit uses the FilterDesigner toolbox of MATLAB to carefully design a filtering scheme for the collected original surface electromyography signal; through a 50Hz notch filter, a 20-240Hz bandpass filter and a comb notch filter, it effectively filters out power frequency, artifacts, drift and high-frequency noise, thereby improving signal purity;

[0066] The angle-electromyographic signal corresponding unit relies on the synchronously collected data, divides the time period of each 1° change based on the angle signal, accurately matches the corresponding time period, and obtains the surface electromyographic signal amplitude under the angle change;

[0067] The amplitude calculation unit measures the strength of the surface electromyographic signal by the mean absolute amplitude value (MAV). The absolute value of the signal is first taken, and then the mean value is calculated to intuitively reflect the muscle activity level of the corresponding time period.

[0068] The specific steps of the evaluation method are as follows:

[0069] S1: Preliminary information collection and test preparation: Guide the subjects to register basic and specific information and sign a consent form, standardize the exposed test shoulder to maintain a standard posture, accurately install the sensor, and ensure compliance with the assessment and a solid foundation for signal collection;

[0070] S2: Start synchronous signal acquisition: Start the angle and electromyography signal acquisition software and confirm that they are running smoothly. Run the Python GUI code for precise control, so that the two software can start and stop synchronously and strictly control the time alignment to ensure accurate analysis.

[0071] S3: Surface EMG signal acquisition: The subject was clearly instructed to slowly and steadily externally rotate the shoulder joint to the maximum angle (normal 90°). The original EMG signal of the subject was collected throughout the entire 90° external rotation for subsequent in-depth analysis.

[0072] S4: Surface EMG signal preprocessing: Use MATLAB Filter Designer to plan the filtering process, sequentially using 50Hz notch, 20-240Hz bandpass, and comb notch filter to improve the purity and accuracy of the surface EMG signal;

[0073] S5: Angle-EMG signal correspondence and amplitude calculation: Based on the collected angle signal, the mathematical method is used to determine the corresponding time period for each 1° angle change. Due to signal synchronization, the EMG signal is accurately matched, and its amplitude is then calculated segment by segment.

[0074] S6: Evaluation, Analysis, and Report Generation: Compare the MAV data of each channel of the two to draw a curve to find the key angle, evaluate the stability of the shoulder joint based on this, and then output a graphic report with rich content to provide a reference for clinical diagnosis and treatment.

[0075] The specific steps for the preliminary information collection and test preparation in S1 are as follows:

[0076] Step 1: Information registration: Guide patients or healthy subjects to fill in detailed information, including basic information (name, age, gender) and specific information (patients need to add the main diagnosis, surgery date and name), and sign the informed consent form to ensure the legality and compliance of the evaluation process and the completeness of the information;

[0077] Step 2: Posture Standardization: The subject is required to expose the shoulder joint on the test side and adopt a standard posture, that is, keep the affected shoulder abducted 90°, the elbow flexed 90°, and the forearm parallel to the ground. This posture can stabilize the test environment and reduce signal deviation caused by posture differences;

[0078] Step 3: Hardware Deployment: Accurately install the test equipment, attach the gyroscope angle sensor to the wrist of the test-side limb, and connect the six-channel surface muscle electrical signal sensor to the test-side shoulder. Ensure that the electrode sheet fits tightly against the muscle and is positioned accurately, providing the hardware foundation for signal acquisition.

[0079] Example 1: Assessment of shoulder joint stability after surgery in a patient

[0080] Preliminary information collection and test preparation

[0081] Guide the patient to fill in basic information such as name, age, gender, etc., add the main diagnosis (such as postoperative rotator cuff tear), operation date and name, and sign the informed consent form.

[0082] The patient was asked to expose the shoulder joint on the test side and to position it in a standard position (the affected shoulder was abducted 90°, the elbow was flexed 90°, and the forearm was parallel to the ground).

[0083] Accurately install the equipment, wear the gyroscope angle sensor on the wrist of the test side, and connect the six-channel surface electromyography sensor to the shoulder of the test side, ensuring that the electrode sheet fits tightly to the muscle and is positioned accurately.

[0084] Signal synchronous acquisition starts

[0085] Open the angle signal acquisition software and surface electromyography signal acquisition software respectively and confirm that they are running normally.

[0086] Run the PythonGUI code to start and stop the two software synchronously, achieving strict alignment of the angle and surface electromyography signals in the time dimension.

[0087] Surface electromyography signal acquisition

[0088] Clearly tell the patient to smoothly and slowly externally rotate the shoulder joint to the maximum angle (about 90°), emphasizing the smoothness of the movement.

[0089] When the patient performs external rotation, the original surface electromyographic signals are continuously collected during the external rotation activity when the shoulder joint is abducted 90°.

[0090] Surface EMG signal preprocessing

[0091] The MATLAB Filter Designer toolbox was used to plan the filtering process, and a 50 Hz notch filter, a 20-240 Hz bandpass filter, and a comb notch filter were selected in turn.

[0092] The original signal is filtered in the planned order. First, the power frequency and its multiple harmonic interference are filtered out through a 50Hz notch filter. Then a 20-240Hz bandpass filter is used to remove low-frequency motion artifacts and baseline drift. Finally, a comb notch filter is used to eliminate high-frequency noise.

[0093] Angle-EMG signal correspondence and amplitude calculation

[0094] Based on the collected angle signals, mathematical methods are used to determine the time period corresponding to each 1° change in angle, and accurately match the surface electromyographic signals within each angle change time period.

[0095] The mean absolute value (MAV) formula was used to calculate the average strength of each signal segment.

[0096] Evaluation analysis and report generation

[0097] The MAV data of the surface electromyographic signals of each channel of patients and healthy subjects were compared, and the MAV-angle curve was drawn to determine the angle value corresponding to the maximum MAV of each channel.

[0098] In-depth analysis of the differences in key angle values between patients and healthy subjects was conducted, and the degree of shoulder instability in patients was assessed in combination with clinical knowledge.

[0099] The evaluation and analysis results are output in the form of a combination of charts and text to generate a shoulder joint stability assessment report, which covers the patient's basic information, data collection status, MAV angle change curves of each channel, comparison of key angle values between patients and healthy subjects, shoulder joint stability assessment conclusions and suggestions, etc., providing a reference for subsequent rehabilitation treatment.

[0100] The specific steps for starting the synchronous acquisition of signals in S2 are as follows:

[0101] Step 1: Software startup: Open the angle signal acquisition software and the surface electromyography signal acquisition software respectively, make sure the software runs normally and there are no errors, and prepare the software for synchronous acquisition;

[0102] Step 2: Synchronous control: Run the GUI code written in Python and precisely control the two software signal acquisition interfaces through code logic, so that they start and end synchronously, achieving strict alignment of the angle signal and the surface electromyography signal in the time dimension, and avoiding the influence of time difference on subsequent analysis.

[0103] The specific steps of collecting surface electromyography signals in S3 are as follows:

[0104] Step 1: Movement Instruction: Clearly inform the patient or healthy subject of the test movement requirements and instruct them to externally rotate the shoulder joint to the maximum angle (normally 90°) as smoothly and slowly as possible. Emphasize the smoothness of the movement to reduce additional signal interference caused by excessive movement or jitter.

[0105] Step 2: Signal acquisition: While the subject is performing external rotation, the original surface electromyographic signals are continuously collected during the external rotation activity when the shoulder joint is abducted 90°, fully recording muscle activity information and providing rich data for subsequent analysis.

[0106] Example 2: Shoulder Joint Stability Assessment of Healthy Subjects (as Control Group)

[0107] Preliminary information collection and test preparation

[0108] Guide healthy subjects to fill in basic information such as name, age, and gender, without the need to add special information, and sign the informed consent form.

[0109] The subjects were asked to expose the shoulder joint on the test side and to position themselves in a standard posture (affected shoulder abducted 90°, elbow flexed 90°, forearm parallel to the ground).

[0110] Accurately install the equipment, wear the gyroscope angle sensor on the wrist of the test side, and connect the six-channel surface electromyography sensor to the shoulder of the test side, ensuring that the electrode sheet fits tightly to the muscle and is positioned accurately.

[0111] Signal synchronous acquisition starts

[0112] Open the angle signal acquisition software and surface electromyography signal acquisition software respectively and confirm that they are running normally.

[0113] Run the PythonGUI code to start and stop the two software synchronously, achieving strict alignment of the angle and surface electromyography signals in the time dimension.

[0114] Surface electromyography signal acquisition

[0115] Healthy subjects were clearly instructed to slowly and steadily externally rotate the shoulder joint to the maximum angle (approximately 90°), emphasizing the smoothness of the movement.

[0116] When the subjects performed external rotation movements, the original surface electromyographic signals were continuously collected during the external rotation activity when the shoulder joint was abducted 90°.

[0117] Surface EMG signal preprocessing

[0118] The MATLAB Filter Designer toolbox was used to plan the filtering process, and a 50 Hz notch filter, a 20-240 Hz bandpass filter, and a comb notch filter were selected in turn.

[0119] The original signal is filtered in the planned order. First, the power frequency and its multiple harmonic interference are filtered out through a 50Hz notch filter. Then a 20-240Hz bandpass filter is used to remove low-frequency motion artifacts and baseline drift. Finally, a comb notch filter is used to eliminate high-frequency noise.

[0120] Angle-EMG signal correspondence and amplitude calculation

[0121] Based on the collected angle signals, mathematical methods are used to determine the time period corresponding to each 1° change in angle, and accurately match the surface electromyographic signals within each angle change time period.

[0122] The mean absolute value (MAV) formula was used to calculate the average strength of each signal segment.

[0123] Evaluation analysis and report generation

[0124] Compare the MAV data of the surface electromyographic signals of each channel of the healthy subjects, draw the MAV-angle curve, and determine the angle value corresponding to the maximum MAV of each channel.

[0125] The evaluation and analysis results are output in the form of a combination of charts and text to generate a shoulder joint stability assessment report, which covers basic information of the subjects, data collection, MAV angle change curves of each channel, key angle values, etc., which serves as the control group data for subsequent patient evaluations.

[0126] The specific steps of surface electromyography signal preprocessing in S4 are as follows:

[0127] Step 1: Filter planning: Use the FilterDesigner toolbox in MATLAB to plan the filtering process. Select a 50Hz notch filter, a 20-240Hz bandpass filter, and a comb notch filter in turn to deal with interference in different frequency bands.

[0128] Step 2: Filtering operation: Filter the original surface electromyographic signal according to the planned order. First, use a 50Hz notch filter to remove the interference of the power frequency and its multiple harmonics. Then use a 20-240Hz bandpass filter to remove low-frequency motion artifacts and baseline drift. Finally, use a comb notch filter to eliminate high-frequency noise to improve signal purity and accuracy.

[0129] The specific steps of angle-electromyographic signal correspondence and amplitude calculation in S5 are as follows:

[0130] Step 1: Time-angle correspondence: Based on the collected angle signal, mathematical methods are used to determine the time period corresponding to each 1° change in angle. Since the angle and surface electromyography signals are collected synchronously, the surface electromyography signals within each angle change time period can be accurately matched;

[0131] Step 2: Amplitude calculation: For each segment of surface electromyographic signal corresponding to each 1° change in angle, the mean absolute amplitude (MAV) formula is used to calculate its average intensity.

[0132] MAV is to first take the absolute value of the amplitude of the electromyographic signal and then calculate the average. The formula is as follows:

[0133]

[0134] Where: N is the signal length; x i is the signal amplitude of the i-th sample point.

[0135] The specific steps of the evaluation analysis and report generation in S6 are as follows:

[0136] Step 1: Data comparison: Compare the MAV data of the surface electromyography signals of each channel of the patients and healthy subjects, draw the MAV-angle curve, determine the angle value corresponding to the maximum MAV of each channel, and intuitively present the difference between the two.

[0137] Step 2: Stability Assessment: Analyze the differences in key angles between patients and healthy subjects, and assess the degree of shoulder instability in patients based on clinical knowledge. If the angle value corresponding to the maximum MAV of a certain channel in the patient deviates significantly from that of the healthy subjects, it indicates abnormal muscle activity in the corresponding channel, reflecting shoulder joint stability issues.

[0138] Step 3: Report output: The evaluation and analysis results are output in the form of a combination of charts and text to generate a shoulder joint stability assessment report. The report covers the patient's basic information, collected data, MAV angle change curves of each channel, comparison of key angle values between patients and healthy subjects, shoulder joint stability assessment conclusions and suggestions, etc., providing a comprehensive and accurate reference basis for clinical diagnosis and treatment.

[0139] Example 3: Regular Assessment of Athletes' Shoulder Joint Stability (Monitoring Sports Injury Risk)

[0140] Preliminary information collection and test preparation

[0141] Guide athletes to fill in basic information such as name, age, gender, etc., supplement special information (such as sports, years of training, previous shoulder injury history, etc.), and sign an informed consent form.

[0142] The athlete is asked to expose the shoulder joint on the testing side and position it in the standard posture (affected shoulder 90° abduction, elbow 90° flexion, forearm parallel to the ground).

[0143] Accurately install the equipment, wear the gyroscope angle sensor on the wrist of the test side, and connect the six-channel surface electromyography sensor to the shoulder of the test side, ensuring that the electrode sheet fits tightly to the muscle and is positioned accurately.

[0144] Signal synchronous acquisition starts

[0145] Open the angle signal acquisition software and surface electromyography signal acquisition software respectively and confirm that they are running normally.

[0146] Run the PythonGUI code to start and stop the two software synchronously, achieving strict alignment of the angle and surface electromyography signals in the time dimension.

[0147] Surface electromyography signal acquisition

[0148] Clearly instruct athletes to smoothly and slowly externally rotate their shoulders to their maximum angle (approximately 90°), emphasizing smooth movement.

[0149] When the athlete performs external rotation, the original surface electromyographic signals are continuously collected during the external rotation activity when the shoulder joint is abducted 90°.

[0150] Surface EMG signal preprocessing

[0151] The MATLAB Filter Designer toolbox was used to plan the filtering process, and a 50 Hz notch filter, a 20-240 Hz bandpass filter, and a comb notch filter were selected in turn.

[0152] The original signal is filtered in the planned order. First, the power frequency and its multiple harmonic interference are filtered out through a 50Hz notch filter. Then a 20-240Hz bandpass filter is used to remove low-frequency motion artifacts and baseline drift. Finally, a comb notch filter is used to eliminate high-frequency noise.

[0153] Angle-EMG signal correspondence and amplitude calculation

[0154] Based on the collected angle signals, mathematical methods are used to determine the time period corresponding to each 1° change in angle, and accurately match the surface electromyographic signals within each angle change time period.

[0155] The mean absolute value (MAV) formula was used to calculate the average strength of each signal segment.

[0156] Evaluation analysis and report generation

[0157] Compare the MAV data of the athlete's surface electromyography signals in each channel in this evaluation with previous evaluations, draw a curve of MAV change with angle, determine the angle value corresponding to the maximum MAV of each channel, and analyze the change trend.

[0158] In-depth analysis of the differences in key angle values when athletes are evaluated at different time points, and assessment of changes in shoulder joint stability based on the characteristics of the sport, if it is found that the corresponding angle value when the MAV of a certain channel is maximum deviates significantly from the previous one, it indicates abnormal muscle activity corresponding to this channel, which may indicate shoulder joint stability problems or sports injury risks.

[0159] The evaluation and analysis results are output in the form of a combination of charts and text to generate a shoulder joint stability assessment report, which covers the athlete's basic information, data collection status, MAV angle change curves of each channel, comparison of key angle values, shoulder joint stability assessment conclusions and suggestions, etc., providing a reference for athletes to adjust their training plans and prevent sports injuries.

[0160] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0161] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A shoulder joint stability assessment system based on surface electromyography signals, characterized by: The system includes: data acquisition module, data processing module, evaluation and analysis module; The data acquisition module is used to accurately collect angle data of the test-side shoulder joint and surface electromyographic signals of multiple parts in the shoulder joint stability assessment, and ensure that the two types of signals are synchronously and accurately acquired for subsequent analysis; The data processing module is mainly used to systematically process the collected shoulder joint surface electromyographic signals and angle signals: first, purify the signal quality and filter out various noise interferences; then establish a precise correspondence between the angle and the electromyographic signal; finally, quantify the muscle activity intensity by calculating the absolute value mean of the amplitude, providing a reliable analysis basis for subsequent shoulder joint stability assessment; The evaluation and analysis module compares the surface electromyographic signal data of the patient and the healthy subject, determines the angle value corresponding to the maximum mean absolute value of the amplitude of each channel, and evaluates the degree of instability of the patient's shoulder joint by analyzing the difference in the angle values.

2. A shoulder joint stability assessment system based on surface electromyography signals according to claim 1, characterized in that: The data acquisition module includes an angle data acquisition core unit, a surface electromyography signal acquisition core unit, and a synchronous acquisition control core unit; The angle data acquisition core unit relies on the gyroscope angle sensor to accurately collect the test side shoulder joint angle data in real time. Its supporting PC host software is responsible for receiving and displaying the data and performing preliminary format processing and data verification. The core unit for surface electromyography (SEMG) signal acquisition uses a six-channel SEMG sensor, with a reference electrode placed at the acromion and electrodes placed at multiple locations on the upper pectoralis major muscle to accurately collect SEMG signals from the shoulder joint on the test side. The supporting PC host software receives the signal, displays the waveform on the interface, and performs preliminary filtering and amplification preprocessing; The synchronous acquisition control core unit uses GUI code written in Python as the data acquisition "command center", coordinating the start and stop of the two supporting software to ensure strict synchronization of angles and electromyographic signals, providing accurate escort for subsequent analysis.

3. The shoulder joint stability assessment system based on surface electromyography signals according to claim 1, characterized in that: The data processing module includes a pre-processing unit, an angle-electromyographic signal corresponding unit, and an amplitude calculation unit; The pre-processing unit uses the FilterDesigner toolbox of MATLAB to carefully design a filtering scheme for the collected original surface electromyography signal; through a 50Hz notch filter, a 20-240Hz bandpass filter and a comb notch filter, it effectively filters out power frequency, artifacts, drift and high-frequency noise, thereby improving signal purity; The angle-electromyographic signal corresponding unit relies on the synchronously collected data, divides the time period of each 1° change based on the angle signal, accurately matches the corresponding time period, and obtains the surface electromyographic signal amplitude under the angle change; The amplitude calculation unit measures the strength of the surface electromyographic signal by the average of the absolute value of the amplitude. The absolute value of the signal is first taken, and then the average is calculated, which intuitively reflects the muscle activity level of the corresponding time period.

4. A shoulder joint stability assessment method based on surface electromyography signals, characterized by: The specific steps of this evaluation method are as follows: S1: Preliminary information collection and test preparation: Guide the subjects to register basic and specific information and sign a consent form, standardize the exposed test shoulder to maintain a standard posture, accurately install the sensor, and ensure compliance with the assessment and a solid foundation for signal collection; S2: Start synchronous signal acquisition: Start the angle and electromyography signal acquisition software and confirm that they are running smoothly. Run the Python GUI code for precise control, so that the two software can start and stop synchronously and strictly control the time alignment to ensure accurate analysis. S3: Surface EMG signal acquisition: The subjects were clearly instructed to slowly and steadily externally rotate their shoulder joints to the maximum angle, and the original EMG signals of the entire 90° external rotation were collected for subsequent in-depth analysis. S4: Surface EMG signal preprocessing: Use MATLAB Filter Designer to plan the filtering process, sequentially using 50Hz notch, 20-240Hz bandpass, and comb notch filter to improve the purity and accuracy of the surface EMG signal; S5: Angle-EMG signal correspondence and amplitude calculation: Based on the collected angle signal, the mathematical method is used to determine the corresponding time period for each 1° angle change. Due to signal synchronization, the EMG signal is accurately matched, and its amplitude is then calculated segment by segment. S6: Evaluation, Analysis, and Report Generation: Compare the MAV data of each channel of the two to draw a curve to find the key angle, evaluate the stability of the shoulder joint based on this, and then output a graphic report with rich content to provide a reference for clinical diagnosis and treatment.

5. The method for evaluating shoulder joint stability based on surface electromyography signals according to claim 4, wherein: The specific steps for the preliminary information collection and test preparation in S1 are as follows: Step 1: Information registration: Guide patients or healthy subjects to fill in detailed information, including basic and specific information, and sign an informed consent form to ensure that the assessment process is legal and compliant and the information is complete; Step 2: Posture standardization: The subject is required to expose the shoulder joint on the test side and position it in a standard posture, that is, keep the affected shoulder abducted 90°, the elbow flexed 90°, and the forearm parallel to the ground; Step 3: Hardware Deployment: Accurately install the test equipment, wear the gyroscope angle sensor on the wrist of the test limb, and connect the six-channel surface muscle electrical signal sensor to the test shoulder.

6. The method for evaluating shoulder joint stability based on surface electromyography signals according to claim 4, wherein: The specific steps for starting the signal synchronous acquisition in S2 are as follows: Step 1: Software startup: Open the angle signal acquisition software and the surface electromyography signal acquisition software respectively, make sure the software runs normally and there are no errors, and prepare the software for synchronous acquisition; Step 2: Synchronous control: Run the GUI code written in Python and precisely control the two software signal acquisition interfaces through code logic, so that they start and end synchronously, achieving strict alignment of the angle signal and the surface electromyography signal in the time dimension, and avoiding the influence of time difference on subsequent analysis.

7. The method for evaluating shoulder joint stability based on surface electromyography signals according to claim 4, wherein: The specific steps of collecting surface electromyography signals in S3 are as follows: Step 1: Movement Instruction: Clearly inform the patient or healthy subject of the test movement requirements and instruct them to externally rotate the shoulder joint to the maximum angle as smoothly and slowly as possible, emphasizing the smoothness of the movement and reducing additional signal interference caused by excessive movement or jitter; Step 2: Signal acquisition: While the subject is performing external rotation, the original surface electromyographic signals are continuously collected during the external rotation activity when the shoulder joint is abducted 90°, fully recording muscle activity information and providing rich data for subsequent analysis.

8. The method for assessing shoulder joint stability based on surface electromyography signals according to claim 4, wherein: The specific steps of surface electromyography signal preprocessing in S4 are as follows: Step 1: Filter Planning: Use the FilterDesigner toolbox in MATLAB to plan the filtering process. Select a 50Hz notch filter, a 20-240Hz bandpass filter, and a comb notch filter in sequence to address interference in different frequency bands. Step 2: Filtering operation: Filter the original surface electromyographic signal according to the planned order. First, use a 50Hz notch filter to remove the interference of the power frequency and its multiple harmonics. Then use a 20-240Hz bandpass filter to remove low-frequency motion artifacts and baseline drift. Finally, use a comb notch filter to eliminate high-frequency noise to improve signal purity and accuracy.

9. The method for evaluating shoulder joint stability based on surface electromyography signals according to claim 4, wherein: The specific steps of angle-electromyographic signal correspondence and amplitude calculation in S5 are as follows: Step 1: Time-angle correspondence: Based on the collected angle signal, mathematical methods are used to determine the time period corresponding to each 1° change in angle. Since the angle and surface electromyography signals are collected synchronously, the surface electromyography signals within each angle change time period are accurately matched; Step 2: Amplitude calculation: For each segment of surface electromyographic signal corresponding to each 1° change in angle, the mean absolute amplitude (MAV) formula is used to calculate its average intensity. MAV is to first take the absolute value of the amplitude of the electromyographic signal and then calculate the average. The formula is as follows: Where: N is the signal length; x i is the signal amplitude of the i-th sample point.

10. The method for evaluating shoulder joint stability based on surface electromyography signals according to claim 4, wherein: The specific steps of evaluation analysis and report generation in S6 are as follows: Step 1: Data comparison: Compare the MAV data of the surface electromyography signals of each channel of the patients and healthy subjects, draw the MAV-angle curve, determine the angle value corresponding to the maximum MAV of each channel, and intuitively present the difference between the two; Step 2: Stability Assessment: Analyze the differences in key angles between patients and healthy subjects, and assess the degree of shoulder instability in patients based on clinical knowledge. If the angle value corresponding to the maximum MAV of a certain channel in the patient differs significantly from that of the healthy subjects, it indicates abnormal muscle activity in the corresponding channel, reflecting shoulder joint stability issues. Step 3: Report Output: The evaluation and analysis results are output in the form of a combination of charts and text to generate a shoulder joint stability assessment report. The report covers the patient's basic information, collected data, MAV angle change curves of each channel, comparison of key angle values between the patient and healthy subjects, shoulder joint stability assessment conclusions and recommendations, providing a comprehensive and accurate reference for clinical diagnosis and treatment.

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