Exploring a Fatigue Compensation Approach for Joint Angle Regression Through sEMG and NIRS

Under conditions of muscle fatigue, the efficacy of using electromyographic signals for decoding joint angles significantly deteriorates, highlighting the importance of optimizing decoding under fatigue. In this context, we combined Near-Infrared Spectroscopy (NIRS) signals with surface electromyography (sEMG) data, utilizing an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) to enhance adaptive noise reduction. This approach enabled the extraction of Intrinsic Mode Functions reflecting muscle activity characteristics from complex physiological signals. Through correlation analysis, redundant components were eliminated. We proposed a methodology that adeptly captures intricate features and long-term dependencies in time-series data while preserving sensitivity to short-term dynamics, thereby enhancing the precision of joint angle decoding. Following fatigue induction in subjects, the inclusion of near-infrared compensation led to a variable reduction in RMSE values, all of which remained below 5. Furthermore, the correlation coefficient R was enhanced, never falling below 93%. Consequently, our findings demonstrate that integrating near-infrared spectroscopy features into the model exhibits fatigue resistance, thereby improving the precision of joint angle predictions under conditions of fatigue to a certain degree.