Cost-Effective Prediction of Knee Joint Angle from Surface Electromyography Signals During Free Motion using Transformer Regression

This paper outlines a proof-of-concept for the precise forecasting, using a Time-Series Transformer (TST) regression framework, of the angle of the knee joint based on Surface Electromyography (sEMG) data from just three sensors on the upper thigh. To facilitate real-time prediction, the Transformer was given a hybrid input of autoregressive and exogenous data from two Inertial Measurement Units (IMUs) and the three sEMG sensors. Furthermore, the flexibility of a Transformer Model meant that the data could be continuously recorded during spontaneous, unplanned motion of the lower leg. A sliding-window approach was used to allow the model to continuously predict from a stream of live data. The model achieved high cross-validation accuracy (70-15-15 split) predicting the knee joint angle (in degrees) on a relatively small dataset of 35,613 samples--RMSE=3.97, nRMSE=0.0208, MAE=2.61, MBE=-0.45, Adjusted R ² = 0.9928 on unseen data--and outperformed benchmarks set by state-of-the-art methods, such as CNN and LSTM, while making use of fewer sensors and demonstrating sub-millisecond prediction time per sample. Additionally, the limited number of sensors and equipment used in this method allows for greater accessibility to patients in need.