Abstract
Robotic prosthetic hands with five digits have become commercially available however their use is limited to a few grip patterns due to the unnatural and unreliable human-machine interface (HMI). The research community has addressed this problem extensively by investigating Pattern Recognition (PR) based surface-electromyography (sEMG) control. This control strategy has been recently commercialized however has yet to show clinical adoption. One of the reasons identified in the literature is due to the sEMG signals that are affected by sweating, electrode shift, ambient noise, fatigue, cross-talk between adjacent muscles, signal drifting, and force level variation. Hence recently the scientific community has started proposing multi-modal sensing techniques as a solution.
This study aims to investigate the use of multi-modal sensor approach to control a robotic prosthetic hand by investigating the sparsely studied sensing mechanism called Force Myography (FMG) as a synergist to the conventional technique of sEMG. FMG uses pressure sensors on the surface of a limb to detect the volumetric changes in the underlying musculotendinous complex. This paper presents a custom prosthetic prototype instrumented with sEMG and FMG sensors and tested by a participant with a transradial amputation. Results demonstrate that this multi-sensor approach has the potential to be a valid HMI for prosthesis control.
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Ghataurah, J., Ferigo, D., Merhi, LK., Pousett, B., Menon, C. (2017). A Multi-sensor Approach for Biomimetic Control of a Robotic Prosthetic Hand. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham. https://doi.org/10.1007/978-3-319-56148-6_6
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