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UMGGOE_Vujaklija_et_al_Frontiers_Revised_Final_20161012_V1.pdf (569.83 kB)

Translating Research on Myoelectric Control into Clinics – Are the Performance Assessment Methods Adequate?

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posted on 2018-01-22, 16:03 authored by Ivan Vujaklija, Aidan D. Roche, Timothy Hasenoehrl, Agnes Sturma, Sebastian Amsuess, Dario Farina, Oskar C. Aszmann
Missing an upper limb dramatically impairs daily-life activities. Significant efforts in overcoming the issues arising from this disability have been made in both academia and industry, although their clinical outcome is still limited. Translation of prosthetic research into clinics has been challenging because of the difficulties in meeting the necessary requirements of the market. In this perspective, we focus on myocontrol algorithms for upper limb prostheses and we emphasize that one relevant factor determining the relatively small clinical impact of these methods is the limit of commonly used laboratory performance metrics. The laboratory conditions, in which the majority of the solutions are being evaluated, fail to sufficiently replicate real-life challenges. We qualitatively substantiate this argument with data from seven transradial amputees. Their ability to control a myoelectric prosthesis was tested by measuring the accuracy of offline EMG signal classification, as a typical laboratory performance metrics, as well as by clinical scores when performing standard tests of daily living. Despite all subjects reached relatively high classification accuracy offline, their clinical scores were largely different and were not strongly predicted by classification accuracy. As argued in previous reports, we reinforce the suggestion to test myocontrol systems using clinical tests on amputees, fully fitted with sockets and prostheses highly resembling the systems they would use in daily living, as evaluation benchmark. Agreement on this level of testing for systems developed in research laboratories would facilitate clinically relevant progresses in this field.

Funding

European Union’s Horizon 2020 research and innovation program 687795 (project INPUT)

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