sEMG-Based Lower Limb Intention Detection using Artificial Intelligence and its Impact on Assistive Human-Robot Interaction
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People with mobility-related disabilities can observe improvements in their quality of life by incorporating rehabilitation and assistive devices. To be compliant to users' needs, such robots should be intelligent to the users' intention to be able to adapt to their needs. A surface Electromyography (sEMG)-based lower limb intention-detection model is studied to augment human-robot interaction by detecting subjects' walking direction prior-to or during walking. Ten Classical Machine Learning-based models with Subject-Exclusive/Generalized strategies and a Deep Learning-based Convolutional Neural Network with an advanced transfer learning methodology (Subject-Adaptive), are employed to detect direction intentions and evaluate inter-subject robustness in one knee/foot-gesture and three walking-related scenarios. In each, sEMG signals are collected from eight muscles of nine subjects during five trials of at least nine distinct gestures/activities. The proper augmentation method of the model in an HRI controller is studied in a computer-simulated environment with IMU and sEMG data collected from subjects.
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Copyright © 2021 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2021
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khiabani-semgbasedlowerlimbintentiondetectionusing_r.pdf | 2023-05-05 | Public | Download |