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Classification of Transient Myoelectric Signals for the Control of Multi-Grasp Hand Prostheses
Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology.
2018 (English)In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 26, no 9, p. 1756-1764Article in journal (Refereed) Published
Abstract [en]

Understanding the neurophysiological signals underlying voluntary motor control and decoding them for controlling limb prostheses is one of the major challenges in applied neuroscience and rehabilitation engineering. While pattern recognition of continuous myoelectric (EMG) signals is arguably the most investigated approach for hand prosthesis control, its underlying assumption is poorly supported, i.e., that repeated muscular contractions produce consistent patterns of steady-state EMGs. In fact, it still remains to be shown that pattern recognition-based controllers allow natural control over multiple grasps in hand prosthesis outside well-controlled laboratory settings. Here, we propose an approach that relies on decoding the intended grasp from forearm EMG recordings associated with the onset of muscle contraction as opposed to the steady-state signals. Eight unimpaired individuals and two hand amputees performed four grasping movements with a variety of arm postures while EMG recordings subsequently processed to mimic signals picked up by conventional myoelectric sensors were obtained from their forearms and residual limbs, respectively. Off-line data analyses demonstrated the feasibility of the approach also with respect to the limb position effect. The sampling frequency and length of the classified EMG window that off-line resulted in optimal performance were applied to a controller of a research prosthesis worn by one hand amputee and proved functional in real-time when operated under realistic working conditions.

Place, publisher, year, edition, pages
IEEE, 2018. Vol. 26, no 9, p. 1756-1764
Keywords [en]
Myoelectric control, pattern recognition, onset of muscle contraction, prosthetic hand, transient control
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:umu:diva-152263DOI: 10.1109/TNSRE.2018.2861465ISI: 000444618100012PubMedID: 30072331OAI: oai:DiVA.org:umu-152263DiVA, id: diva2:1252588
Funder
Swedish Research Council, VR 2016-01635EU, European Research Council, 679820Available from: 2018-10-02 Created: 2018-10-02 Last updated: 2018-10-02Bibliographically approved

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Edin, Benoni B.

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