Enhanced Joint Angle Estimation Using Support Vector Machine-Long Short-Term Memory Fusion with Electromyography Signals

Abstract

Due to its non-invasive nature, the Electromyography (EMG) signal has become crucial in predicting human motor intention and facilitating human-robot collaboration across various fields, including rehabilitation, assistive technologies, ergonomics, clinical diagnosis, and sport science. This study aims to enhance joint angle prediction using a two-step algorithm, leveraging a substantial dataset of twenty human subjects. The EMG data were acquired from four upper-limb muscles during an elbow flexion-extension and shoulder abduction-adduction task using Kinect V2 and Noraxon's TeleMyo Mini DTS system. In stage one, a Support Vector Machine (SVM) is used to predict joint angles directly, followed by refining predictions with a Long Short-Term Memory (LSTM) network. The impact of different sliding window sizes on algorithm performance is analyzed, and a statistical comparison between direct angle prediction and the two-stage approach is conducted to assess the proposed method's effectiveness. The proposed SVM-LSTM algorithm consistently yields enhanced Root Mean Square Error (RMSE) values (ranging from 8.45° to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$9.36^{\circ}$</tex>) for both elbow and shoulder angles, compared to RMSE values reported in the literature (ranging from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8.2^{\circ}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$19.04^{\circ}$</tex>). Statistical analysis through Friedman Analysis of Variance (ANOVA) underscores significant differences between SVM-direct and SVM-LSTM for both elbow and shoulder angles <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{p} &lt; 0.001)$</tex>. In the dynamic field of EMG-based joint angle prediction, these results hold profound significance for applications like prosthetics and rehabilitation robotics, where precise joint angle estimations are crucial.