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Myoelectric control of bionic hands via musculoskeletal modelling, admittance control and force feedback

Mateo, BSc Mikel (2019) Myoelectric control of bionic hands via musculoskeletal modelling, admittance control and force feedback.

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Abstract:Current myoelectric upper limb prostheses do not provide truly bio-mimetic control that allows for both intuitive control and reliable grasp dynamics. This is mainly because current machine learning approaches provide a control interface that is non-intuitive and extraordinarily different from human natural control. Furthermore, the lack of afferent feedback pathways forces the amputee to rely mainly on visual feedback to control grasp action, where the implementation of state-of-the-art haptic feedback techniques does not show conclusive results on the help of grasp force control. In this work, a subject-specific EMG-driven musculoskeletal model coupled to an admittance controller is used to drive a bionic hand. The admittance model virtually mimics the grasp dynamics of a real hand interacting with the environment. This way, the prosthetic hand (Michelangelo hand, Ottobock) can readjust its control commands during interaction by accounting for external forces. Theoretical stability boundaries of the control system are analysed for stable interaction and experimental tests are carried out comparing the proposed control framework to nonadmittance based EMG-driven musculoskeletal modelling during grasping tasks. Experimental outcomes show positive results when compared to non-admittance based control. This control framework sets the bases to enable safer prosthesis-environment interaction without the need of constant visual feedback.
Item Type:Essay (Master)
Faculty:ET: Engineering Technology
Subject:42 biology, 44 medicine, 50 technical science in general, 52 mechanical engineering
Programme:Biomedical Engineering MSc (66226)
Link to this item:http://purl.utwente.nl/essays/80234
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