Neuro-Motor Index for Upper Limb Exoskeleton Control: A Machine Learning Approach

This paper presents a hybrid machine learning model for controlling an upper limb exoskeleton to help children with cerebral palsy. The model integrates Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) networks to predict joint angles based on electromyography (EMG) signals. An important innovation is the introduction of the Neuro-Motor Index (NMI), which combines the Co-Contraction Index (CCI) and kinematic joint data to improve the accuracy of movement prediction. The model was evaluated using cross-validation and showed significant improvements in mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^2$</tex> compared to single models. These results emphasize the potential of the model for improving exoskeleton-assisted rehabilitation. Future work will focus on clinical validation and real-time optimization.