EMG Driven Robotic-Aided Arm Rehabilitation

Daniel Bonilla; Julian D. Colorado; Med Amine Laribi; Juan Sandoval; Catalina Alvarado-Rojas

doi:10.1007/978-3-031-04870-8_40

EMG Driven Robotic-Aided Arm Rehabilitation

Daniel Bonilla, Julian D. Colorado, Med Amine Laribi, Juan Sandoval, Catalina Alvarado-Rojas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Advances in Service and Industrial Robotics - RAAD 2022
Editors	Andreas Müller, Mathias Brandstötter
Publisher	Springer Science and Business Media B.V.
Pages	343-350
Number of pages	8
ISBN (Print)	9783031048692
DOIs	https://doi.org/10.1007/978-3-031-04870-8_40
State	Published - 2022
Event	31st International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2022 - Klagenfurt, Austria Duration: 08 Jun 2022 → 10 Jun 2022

Publication series

Name	Mechanisms and Machine Science
Volume	120 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	31st International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2022
Country/Territory	Austria
City	Klagenfurt
Period	08/06/22 → 10/06/22

Keywords

EMG signals
Machine learning
Robot-assisted systems

Access to Document

10.1007/978-3-031-04870-8_40

Cite this

Bonilla, D., Colorado, J. D., Laribi, M. A., Sandoval, J., & Alvarado-Rojas, C. (2022). EMG Driven Robotic-Aided Arm Rehabilitation. In A. Müller, & M. Brandstötter (Eds.), Advances in Service and Industrial Robotics - RAAD 2022 (pp. 343-350). (Mechanisms and Machine Science; Vol. 120 MMS). Springer Science and Business Media B.V.. https://doi.org/10.1007/978-3-031-04870-8_40

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title = "EMG Driven Robotic-Aided Arm Rehabilitation",

abstract = "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{\textquoteright}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.",

keywords = "EMG signals, Machine learning, Robot-assisted systems",

author = "Daniel Bonilla and Colorado, {Julian D.} and Laribi, {Med Amine} and Juan Sandoval and Catalina Alvarado-Rojas",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 31st International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2022 ; Conference date: 08-06-2022 Through 10-06-2022",

year = "2022",

doi = "10.1007/978-3-031-04870-8_40",

language = "English",

isbn = "9783031048692",

series = "Mechanisms and Machine Science",

publisher = "Springer Science and Business Media B.V.",

pages = "343--350",

editor = "Andreas M{\"u}ller and Mathias Brandst{\"o}tter",

booktitle = "Advances in Service and Industrial Robotics - RAAD 2022",

address = "Germany",

}

Bonilla, D, Colorado, JD, Laribi, MA, Sandoval, J & Alvarado-Rojas, C 2022, EMG Driven Robotic-Aided Arm Rehabilitation. in A Müller & M Brandstötter (eds), Advances in Service and Industrial Robotics - RAAD 2022. Mechanisms and Machine Science, vol. 120 MMS, Springer Science and Business Media B.V., pp. 343-350, 31st International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2022, Klagenfurt, Austria, 08/06/22. https://doi.org/10.1007/978-3-031-04870-8_40

EMG Driven Robotic-Aided Arm Rehabilitation. / Bonilla, Daniel; Colorado, Julian D.; Laribi, Med Amine et al.
Advances in Service and Industrial Robotics - RAAD 2022. ed. / Andreas Müller; Mathias Brandstötter. Springer Science and Business Media B.V., 2022. p. 343-350 (Mechanisms and Machine Science; Vol. 120 MMS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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

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BT - Advances in Service and Industrial Robotics - RAAD 2022

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Y2 - 8 June 2022 through 10 June 2022

ER -

EMG Driven Robotic-Aided Arm Rehabilitation

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