Real-Time User-Independent Slope Prediction Using Deep Learning for Modulation of Robotic Knee Exoskeleton Assistance
| Autor: | Dawit Lee, Aaron J. Young, Inseung Kang, Dean D. Molinaro, Alexander Yu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
030506 rehabilitation
Control and Optimization Mean squared error Computer science 0206 medical engineering Biomedical Engineering Terrain 02 engineering and technology Convolutional neural network Data modeling 03 medical and health sciences Artificial Intelligence Torque Simulation business.industry Mechanical Engineering Deep learning 020601 biomedical engineering Computer Science Applications Exoskeleton Human-Computer Interaction Noise Control and Systems Engineering Computer Vision and Pattern Recognition Artificial intelligence 0305 other medical science business |
| Zdroj: | IEEE Robotics and Automation Letters. 6:3995-4000 |
| ISSN: | 2377-3774 |
| Popis: | 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 $^{\circ }$ 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 $^{\circ }$ 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. |
| Databáze: | OpenAIRE |
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