Toward Intuitive Prosthetic Control: Solving Common Issues Using Force Myography, Surface Electromyography, and Pattern Recognition in a Pilot Case Study

Despite the appearance of advanced multi-degrees of freedom (DoF) robotic hands during the past decade, prosthetic control lacks a powerful interface to facilitate all its functionalities in a manner that is acceptable for a majority of users [1]. In this article, we explore the feasibility of using a sensing technique called force myography (FMG) as an alternative or synergist to the traditional surface electromyography (sEMG) technique as a human-machine interface (HMI) for the control of a multi-DoF prosthetic hand, bebionic 3 by Ottobock, Austin, Texas. In this article, we present a prosthetic prototype developed for the Cybathlon 2016, a championship for racing pilots with disabilities using assistive robotic devices. The design of the prototype is discussed and the effect of two factors on its control is analyzed. These factors are 1) the impact of a multisensory approach and 2) the placement of FMG sensor strips within the prosthetic inner socket. Analysis is performed by comparing resulting pattern recognition accuracies. Results show that the use of both sensing modalities (FMG and EMG) together produced the highest pattern recognition accuracy (81.1%) for ten classes of motion (four wrist movements and six grip patterns). We demonstrated that FMG has the potential to be an HMI for control of upper-limb-powered prostheses. FMG also illustrates the potential for intuitive control through the use of pattern recognition. A multisensory approach could assist in increasing robustness of the HMI for prosthetic control.