Dataset for: Multimodal Upper Limb Rehabilitation Assessment System integrating Mechanomyographic and Surface Electromyographic Signals
Description
Purpose: Objective and accurate clinical muscle strength assessment is the foun- dation for formulating personalized rehabilitation programs for stroke survivors. However, traditional scales are mostly limited by the subjectivity of clinicians. This study proposes a quantitative assessment framework integrating surface electromyography (sEMG) and mechanomyography (MMG). Methods: This study collected multimodal signals from the deltoid muscle. High-dimensional features were extracted from the time domain, frequency domain, and cepstral domain, with the innovative introduction of Mel Frequency Cepstral Coefficients (MFCC) to capture fine mechanical vibrations in MMG sig- nals. Five commonly used machine learning models were constructed based on multimodal data and individual MMG data respectively, including Support Vec- tor Machine (SVM), Random Forest (RF), Decision Tree, Logistic Regression, and Gradient Boosting Tree. The model performance was evaluated using four metrics: Accuracy, Precision, Recall, and F1 Score. Feature importance analy- sis was conducted for both data approaches to elucidate the electro-mechanical signal fusion mechanism。Results: Among the five evaluated machine learning models, the RF model achieved the best performance with an F1 score of 0.9279, representing a 27.14% improvement compared to the single-modal electromyography model. The F1 scores of the Logistic Regression, Decision Tree, SVM, and Gradient Boost- ing Tree models were 0.8139, 0.8820, 0.9035, and 0.9148 respectively. Feature importance analysis revealed that, through feature weight transformation, the relationship between sEMG and MMG is not a simple linear mapping but exhibits complex nonlinear dynamic characteristics. Conclusion: The integration of sEMG and MMG signals effectively enhances the accuracy of rehabilitation assessments. This non-invasive, high-precision technology offers a novel rehabilitation assessment method for clinical settings, presenting new feasibility for objective rehabilitation evaluation in both clinical and remote rehabilitation environments
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Institutions
- Guangzhou University of Chinese Medicine