Frontiers in neurology · 2024
Design of upper limb muscle strength assessment system based on surface electromyography signals and joint motion.
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Wang Siqi, Lai Wei, Zhang Yipeng, Yao Junyu, Gou Xingyue, Ye Hui, Yi Jun, Cao Dong
Аннотация
PURPOSE: This study aims to develop a assessment system for evaluating shoulder joint muscle strength in patients with varying degrees of upper limb injuries post-stroke, using surface electromyographic (sEMG) signals and joint motion data.
METHODS: The assessment system includes modules for acquiring muscle electromyography (EMG) signals and joint motion data. The EMG signals from the anterior, middle, and posterior deltoid muscles were collected, filtered, and denoised to extract time-domain features. Concurrently, shoulder joint motion data were captured using the MPU6050 sensor and processed for feature extraction. The extracted features from the sEMG and joint motion data were analyzed using three algorithms: Random Forest (RF), Backpropagation Neural Network (BPNN), and Support Vector Machines (SVM), to predict muscle strength through regression models. Model performance was evaluated using Root Mean Squared Error (RMSE), R-Square (R 2), Mean Absolute Error (MAE), and Mean Bias Error (MBE), to identify the most accurate regression prediction algorithm.
RESULTS: The system effectively collected and analyzed the sEMG from the deltoid muscles and shoulder joint motion data. Among the models tested, the Support Vector Regression (SVR) model achieved the highest accuracy with an R 2 of 0.8059, RMSE of 0.2873, MAE of 0.2155, and MBE of 0.0071. The Random Forest model achieved an R 2 of 0.7997, RMSE of 0.3039, MAE of 0.2405, and MBE of 0.0090. The BPNN model achieved an R 2 of 0.7542, RMSE of 0.3173, MAE of 0.2306, and MBE of 0.0783.
CONCLUSION: The SVR model demonstrated superior accuracy in predicting muscle strength. The RF model, with its feature importance capabilities, provides valuable insights that can assist therapists in the muscle strength assessment process.
METHODS: The assessment system includes modules for acquiring muscle electromyography (EMG) signals and joint motion data. The EMG signals from the anterior, middle, and posterior deltoid muscles were collected, filtered, and denoised to extract time-domain features. Concurrently, shoulder joint motion data were captured using the MPU6050 sensor and processed for feature extraction. The extracted features from the sEMG and joint motion data were analyzed using three algorithms: Random Forest (RF), Backpropagation Neural Network (BPNN), and Support Vector Machines (SVM), to predict muscle strength through regression models. Model performance was evaluated using Root Mean Squared Error (RMSE), R-Square (R 2), Mean Absolute Error (MAE), and Mean Bias Error (MBE), to identify the most accurate regression prediction algorithm.
RESULTS: The system effectively collected and analyzed the sEMG from the deltoid muscles and shoulder joint motion data. Among the models tested, the Support Vector Regression (SVR) model achieved the highest accuracy with an R 2 of 0.8059, RMSE of 0.2873, MAE of 0.2155, and MBE of 0.0071. The Random Forest model achieved an R 2 of 0.7997, RMSE of 0.3039, MAE of 0.2405, and MBE of 0.0090. The BPNN model achieved an R 2 of 0.7542, RMSE of 0.3173, MAE of 0.2306, and MBE of 0.0783.
CONCLUSION: The SVR model demonstrated superior accuracy in predicting muscle strength. The RF model, with its feature importance capabilities, provides valuable insights that can assist therapists in the muscle strength assessment process.