Real-Time User-Independent Slope Prediction Using Deep Learning for Modulation of Robotic Knee Exoskeleton Assistance

Abstract

Ground slope incline is a critical environmental variable that influences exoskeleton control parameters since human biological joint demand is correlated to changes in slope incline. Current literature methods take a heuristic approach by numerically calculating the slope incline from on-board mechanical sensors. However, these methods often require a user-specific tuning procedure and are prone to noise and sensor drift when tested in a dynamic setting, such as overground locomotion. In this study, we propose the use of a deep learning slope prediction model capable of generalizing across users and terrain. To evaluate this approach, we collected training data (N = 10) and utilized a convolutional neural network to predict the inclination angle and actively modulate the peak assistance magnitude of a bilateral robotic knee exoskeleton in real-time. From online validation results (N = 3), our model predicted the slope incline with an average RMSE of 1.5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> during treadmill and overground walking. Furthermore, our model accurately predicted the slope incline in the extrapolated region outside of the training data with an average RMSE of 1.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> during treadmill and overground walking. Our study's findings showcase the feasibility of using deep learning models to actively modulate exoskeleton assistance, translating this technology to more realistic locomotion environments.