TY - GEN
T1 - EMG Driven Robotic-Aided Arm Rehabilitation
AU - Bonilla, Daniel
AU - Colorado, Julian D.
AU - Laribi, Med Amine
AU - Sandoval, Juan
AU - Alvarado-Rojas, Catalina
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Robotic-assisted systems have been gaining significant traction in supporting rehabilitation tasks, enabling patients to manipulate objects by using a robotic arm controlled by the means of biological signals. In this regard, electromyography (EMG) signals are key for detecting the patient’s intention of motion, that can be replicated by the robotic arm with higher accuracy and precision. In this paper, we present an integrated EMG-driven robotic system capable of performing manipulation tasks by understanding 3 hand gestures associated with certain pick and place commands. Comprehensive experimental tests were conducted to demonstrate that the proposed system can decode EMG-based commands with an accuracy 93 %, undergoing precise robotic-assisted object manipulation.
AB - Robotic-assisted systems have been gaining significant traction in supporting rehabilitation tasks, enabling patients to manipulate objects by using a robotic arm controlled by the means of biological signals. In this regard, electromyography (EMG) signals are key for detecting the patient’s intention of motion, that can be replicated by the robotic arm with higher accuracy and precision. In this paper, we present an integrated EMG-driven robotic system capable of performing manipulation tasks by understanding 3 hand gestures associated with certain pick and place commands. Comprehensive experimental tests were conducted to demonstrate that the proposed system can decode EMG-based commands with an accuracy 93 %, undergoing precise robotic-assisted object manipulation.
KW - EMG signals
KW - Machine learning
KW - Robot-assisted systems
UR - http://www.scopus.com/inward/record.url?scp=85129287263&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-04870-8_40
DO - 10.1007/978-3-031-04870-8_40
M3 - Conference contribution
AN - SCOPUS:85129287263
SN - 9783031048692
T3 - Mechanisms and Machine Science
SP - 343
EP - 350
BT - Advances in Service and Industrial Robotics - RAAD 2022
A2 - Müller, Andreas
A2 - Brandstötter, Mathias
PB - Springer Science and Business Media B.V.
T2 - 31st International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2022
Y2 - 8 June 2022 through 10 June 2022
ER -