Muscle Fatigue Analysis and Stress Detection from Surface EMG and ECG Using Deep Learning for Upper-Limb Trauma Rehabilitation

Abstract

Abstract Background The repetitive nature of physical rehabilitation exercises may result in an excess of muscular fatigue, which can adversely impact an individual's motor function, leading to discomfort or even physical injury. Moreover, individuals who have undergone traumatic experiences tend to encounter difficulties with concentration, which can significantly impede their physical capabilities. Regrettably, existing therapy approaches do not appear to consider the potential mental exhaustion of their patients. Developing Bidirectional Long Short-Term Memory (Bi-LSTM) model for the assessment of Muscle fatigue stage and mental stress condition during physical rehabilitation of trauma injured patient was the aim of this study.Methods 188 EMG signal data and 223 ECG signal data were collected from Jimma University physiotherapy clinic and prepared for signal processing. Since 4th order Butterworth filter perform better than other, it was chosen to denoise the data. The data then split in to a ratio of 60:20:20 train, validate and test data. Finally, the developed Bi-LSTM model was deployed.Result The Bi-LSTM model achieved an accuracy of 95% for multiclass muscle fatigue classification and 97% accuracy was achieved during the binary classification of mental stress. The GUI provides a setting appropriate for routine model usage.Conclusion The obtained result indicates that monitoring the muscle condition and mental status of trauma injured patient can be performed in clinical setup for an effective physical rehabilitation.