Version 1
: Received: 18 January 2024 / Approved: 18 January 2024 / Online: 19 January 2024 (03:58:13 CET)
How to cite:
Castro Jimenez, U.; Martínez-García, E. EMG Model Calibration and Trajectory Tracking by Remote Rehabilitation Exoskeleton. Preprints2024, 2024011415. https://doi.org/10.20944/preprints202401.1415.v1
Castro Jimenez, U.; Martínez-García, E. EMG Model Calibration and Trajectory Tracking by Remote Rehabilitation Exoskeleton. Preprints 2024, 2024011415. https://doi.org/10.20944/preprints202401.1415.v1
Castro Jimenez, U.; Martínez-García, E. EMG Model Calibration and Trajectory Tracking by Remote Rehabilitation Exoskeleton. Preprints2024, 2024011415. https://doi.org/10.20944/preprints202401.1415.v1
APA Style
Castro Jimenez, U., & Martínez-García, E. (2024). EMG Model Calibration and Trajectory Tracking by Remote Rehabilitation Exoskeleton. Preprints. https://doi.org/10.20944/preprints202401.1415.v1
Chicago/Turabian Style
Castro Jimenez, U. and Edgar Martínez-García. 2024 "EMG Model Calibration and Trajectory Tracking by Remote Rehabilitation Exoskeleton" Preprints. https://doi.org/10.20944/preprints202401.1415.v1
Abstract
This study presents a novel kinematic tracking model, designed for a networked exoskeleton system that is asynchronously taught by a remote therapist. On the server side, the therapist’s rehabilitation exercises are quantitatively assessed using a monocular passive vision system. The resultant analytical metrics are then transmitted asynchronously over the network to patients equipped with exoskeletons. On the client side, the exoskeleton utilizes these analytical metrics as reference paths for exercises, complemented by electromyography (EMG) feedback. This work introduces a calibration approach aimed at estimating angular positions by utilizing EMG observations. The calibration model establishes real-time correlations between polynomial reference positions. This calibration mechanism is integrated into simulations of both upper and lower limb exoskeletons. We further explore redundant kinematics, incorporating an EMG observer for linear, time-variant rehabilitation tracking control. Our methodology is validated using vision-based metric data and experimental EMG measurements for various exercises, including shoulder flexion, elbow flexion, and rowing-like movements. This work also includes computer simulations for tracking the control of rehabilitation exercises, demonstrating the adaptability of the system in reliably, robustly, and effectively following desired trajectories.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
