Continuous knee joint torque/angle prediction from surface electromyography during stair ambulation: validation and application for assistive devices

Abstract

This study focused on enhancing lower limb exoskeleton functionality for elderly individuals, particularly in navigating the challenges of stair ambulation. The increasing elderly population highlights the need for assistive technologies to support reduced mobility. The research explored control strategies for lower limb exoskeletons, integrating surface electromyography (sEMG) and kinematic data to address limitations in existing control methods. It proposed sEMG-based strategies for assisting elderly stair ambulation. The research validated an sEMG-based method for predicting knee flexion angles 100ms ahead during stair ambulation by analyzing sEMG signals from leg muscles and testing the accuracy of deep learning models, including 1-D CNN and Vanilla RNN, in predicting future knee angles. It also examined reducing sEMG sensors from nine to five using Vanilla RNN and LSTM models, aiming to simplify the sensor array and enhance user comfort. Practical applications of these models were tested under various simulated load conditions, reflecting different exoskeleton assistance levels. Bi-LSTM models were used to predict knee flexion angles and joint torques under these conditions. The study's scenarios mimicked varying exoskeleton support levels, assessing model robustness and practicality in real-world exoskeleton usage. Results showed that the Bi-LSTM models’ performance was reasonable in predicting knee flexion angles and less in predicting knee joint torques. However, enhanced predictive performance was observed when applying additional datasets for fine-tuning. Enhanced performance indicated the potential of integrating these models into exoskeleton systems for elderly assistance. In conclusion, the study showed the significance of these predictive models for developing assistive exoskeletons. Although promising, future research should include a broader participant base, use actual measured values for validation, and implement models in real-time scenarios to improve the practicality and effectiveness of exoskeletons in aiding elderly mobility, especially in complex tasks like stair ambulation.