Publication type: Conference poster
Type of review: Peer review (abstract)
Title: Detecting cooperative hand movements based on electromyographic data : a proof of concept study
Authors: Graf, Eveline
Touait, Ayoub
Haas, Michelle
Spiess, Martina
Klamroth-Marganska, Verena
Bazeille, Stephane
Ould Abdeslam, Djaffar
et. al: No
Conference details: RehabWeek, Rotterdam, The Netherlands, 25-29 July 2022
Issue Date: Jul-2022
Language: English
Subject (DDC): 006: Special computer methods
617: Surgery
Abstract: Introduction: Individuals missing a part of their upper extremity often use prostheses to increase their ability to conduct activities of daily living. However, the function of the upper limb is challenging to replace, as it includes gross and fine motor activities. The most challenging tasks to execute with a prosthesis are cooperative hand movements. Prostheses can be controlled by recorded EMG signals of the remaining muscles, potentially also by utilising EMG signals of the contralateral side. However, it is not trivial to adequately record and decode these signals in order to turn them into appropriate movement commands for the prosthesis. Consequently, the goal of this proof-of-concept study was to utilize a machine learning algorithm to identify tasks based on bilateral EMG-recordings in able-bodied individuals. Methods: Bilateral surface electromyography of two arm muscles (forearm extensors and flexors) was measured in two participants while performing two tasks of daily living (cut bread and cutting/eating with fork and knife). Each task was repeated 15 times on both sides. The raw signal of both muscles was processed (removal of DC offset, rectification, 3rd order low pass filter) before dividing the data into 80% used for training a sequential model with 4 layers. 20% of the data were used to test the model. Results and Discussion: The machine learning algorithm resulted in a correct rate of task classification of 83.33%. This result is slightly lower than previous studies which showed an accuracy of 92.60% (1) or 98.78% respectively (2) using machine learning to classify different hand movements through surface electromyography. Conclusion: The machine learning algorithm used in this pilot study showed promising results of being able to identify tasks of daily living based on surface electromyography. Future research will extend the algorithm to additional tasks and aim at improving the algorithms by incorporating additional muscle groups.
URI: https://digitalcollection.zhaw.ch/handle/11475/25245
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Health Sciences
Organisational Unit: Institute of Occupational Therapy (IER)
Institute of Physiotherapy (IPT)
Published as part of the ZHAW project: Künstliche Intelligenz für myoelektrisch kontrollierte kooperative Armprothesen
Appears in collections:Publikationen Gesundheit

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