TY - GEN
T1 - Velocity modulation assistance for stroke rehabilitation based on EMG muscular condition.
AU - Castiblanco, Jenny C.
AU - Arteaga, Maria V.
AU - Mondragon, Ivan F.
AU - Ortmann, Steffen
AU - Alvarado-Rojas, C.
AU - Colorado, Julian D.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Robotic-assisted systems have been playing a key role in improving and speeding up motor recovery during stroke rehabilitation therapies. This paper presents an approach to determine velocity patterns based on the analysis of the EMG muscular condition of the hand. To this purpose, we conducted an experimental protocol with 18 subjects participating as volunteers, with the aim of acquiring EMG signals for three levels of the muscular condition: non-fatigue, transition-to-fatigue, and fatigue. Artificial Neural Networks (ANN) were trained to identify the aforementioned muscular condition levels, while a Sugeno-Type Fuzzy Inference system was used to determine the velocity based on the output of the ANN classifiers. Results indicate the proposed approach can be used for the accurate modulation of pinch-grip therapies according to the muscular condition. These are promising results towards the development of EMG-driven robotic-assistance rehabilitation therapies for stroke patients.
AB - Robotic-assisted systems have been playing a key role in improving and speeding up motor recovery during stroke rehabilitation therapies. This paper presents an approach to determine velocity patterns based on the analysis of the EMG muscular condition of the hand. To this purpose, we conducted an experimental protocol with 18 subjects participating as volunteers, with the aim of acquiring EMG signals for three levels of the muscular condition: non-fatigue, transition-to-fatigue, and fatigue. Artificial Neural Networks (ANN) were trained to identify the aforementioned muscular condition levels, while a Sugeno-Type Fuzzy Inference system was used to determine the velocity based on the output of the ANN classifiers. Results indicate the proposed approach can be used for the accurate modulation of pinch-grip therapies according to the muscular condition. These are promising results towards the development of EMG-driven robotic-assistance rehabilitation therapies for stroke patients.
UR - https://www.scopus.com/pages/publications/85095596289
U2 - 10.1109/BioRob49111.2020.9224401
DO - 10.1109/BioRob49111.2020.9224401
M3 - Conference contribution
AN - SCOPUS:85095596289
T3 - Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
SP - 509
EP - 514
BT - 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
PB - IEEE Computer Society
T2 - 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
Y2 - 29 November 2020 through 1 December 2020
ER -