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dc.contributor.authorGraf, Eveline-
dc.contributor.authorTouait, Ayoub-
dc.contributor.authorHaas, Michelle-
dc.contributor.authorSpiess, Martina-
dc.contributor.authorKlamroth-Marganska, Verena-
dc.contributor.authorBazeille, Stephane-
dc.contributor.authorOuld Abdeslam, Djaffar-
dc.date.accessioned2022-06-30T15:52:01Z-
dc.date.available2022-06-30T15:52:01Z-
dc.date.issued2022-07-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/25245-
dc.description.abstractIntroduction: 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.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc617: Chirurgiede_CH
dc.titleDetecting cooperative hand movements based on electromyographic data : a proof of concept studyde_CH
dc.typeKonferenz: Posterde_CH
dcterms.typeTextde_CH
zhaw.departementGesundheitde_CH
zhaw.organisationalunitInstitut für Ergotherapie (IER)de_CH
zhaw.organisationalunitInstitut für Physiotherapie (IPT)de_CH
zhaw.conference.detailsRehabWeek, Rotterdam, The Netherlands, 25-29 July 2022de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.webfeedG: IPT: Neue Technologiende_CH
zhaw.funding.zhawKünstliche Intelligenz für myoelektrisch kontrollierte kooperative Armprothesende_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Graf, E., Touait, A., Haas, M., Spiess, M., Klamroth-Marganska, V., Bazeille, S., & Ould Abdeslam, D. (2022, July). Detecting cooperative hand movements based on electromyographic data : a proof of concept study. RehabWeek, Rotterdam, the Netherlands, 25-29 July 2022.
Graf, E. et al. (2022) ‘Detecting cooperative hand movements based on electromyographic data : a proof of concept study’, in RehabWeek, Rotterdam, The Netherlands, 25-29 July 2022.
E. Graf et al., “Detecting cooperative hand movements based on electromyographic data : a proof of concept study,” in RehabWeek, Rotterdam, The Netherlands, 25-29 July 2022, Jul. 2022.
GRAF, Eveline, Ayoub TOUAIT, Michelle HAAS, Martina SPIESS, Verena KLAMROTH-MARGANSKA, Stephane BAZEILLE und Djaffar OULD ABDESLAM, 2022. Detecting cooperative hand movements based on electromyographic data : a proof of concept study. In: RehabWeek, Rotterdam, The Netherlands, 25-29 July 2022. Conference poster. Juli 2022
Graf, Eveline, Ayoub Touait, Michelle Haas, Martina Spiess, Verena Klamroth-Marganska, Stephane Bazeille, and Djaffar Ould Abdeslam. 2022. “Detecting Cooperative Hand Movements Based on Electromyographic Data : A Proof of Concept Study.” Conference poster. In RehabWeek, Rotterdam, the Netherlands, 25-29 July 2022.
Graf, Eveline, et al. “Detecting Cooperative Hand Movements Based on Electromyographic Data : A Proof of Concept Study.” RehabWeek, Rotterdam, the Netherlands, 25-29 July 2022, 2022.


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