Research on Predicting Joint Rotation Angles Through Mechanomyography Signals and the Broad Learning System

To address the limitation of current upper limb rehabilitation exoskeletons—where pattern recognition-based assistance disrupts patients’ continuous motion—this study proposes a mechanomyography-based model for predicting shoulder and elbow joint angles. Small contact microphones were employed to collect mechanomyography signals, leveraging their ability to capture vibration signals above 8 Hz, making them ideal for mechanomyography acquisition. After extracting raw mechanomyography data, a bandpass filter (10–50 Hz) was applied to eliminate low- and high-frequency noise. To reduce computational overhead during model training, a Broad Learning System was adopted, which iteratively refines predictions by incrementally expanding nodes in the feature and enhancement layers rather than adding hidden layers. The Slime Mold Algorithm was further used to optimize hyperparameters of the Broad Learning System, enhancing prediction accuracy. Experimental results demonstrate that mechanomyography signals exhibit a typical central frequency range of 10–50 Hz, and the Slime Mold Algorithm-optimized Broad Learning System model achieved a minimum coefficient of determination (R2) of 0.978, effectively predicting arm joint angles. This approach shows promise for exoskeletons, combining high control accuracy, real-time joint angle prediction, and computational efficiency.