Advancement in assistive technology has led to the commercial availability of multi-dexterous robotic prostheses for the upper extremity. The relatively low performance of the currently used techniques to detect the intention of the user to control such advanced robotic prostheses, however, limits their use. This article explores the use of force myography (FMG) as a potential alternative to the well-established surface electromyography. Specifically, the use of FMG to control different grips of a commercially available robotic hand, Bebionic3, is investigated. Four male transradially amputated subjects participated in the study, and a protocol was developed to assess the prediction accuracy of 11 grips. Different combinations of grips were examined, ranging from 6 up to 11 grips. The results indicate that it is possible to classify six primary grips important in activities of daily living using FMG with an accuracy of above 70% in the residual limb. Additional strategies to increase classification accuracy, such as using the available modes on the Bebionic3, allowed results to improve up to 88.83 and 89.00% for opposed thumb and non-opposed thumb modes, respectively.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.