On the Sensing and Decoding of Phantom Motions for Control of the Cybernetics of the Upper-Limb Prosthesis

The cybernetic interface within an upper-limb prosthesis facilitates a Human–Machine interaction and ultimately control of the prosthesis limb. A coherent flow between the phantom motion and subsequent actuation of the prosthesis limb to produce the desired gesture hinges heavily upon the physiological sensing source and its ability to acquire quality signals, alongside appropriate decoding of these intent signals with the aid of appropriate signal processing algorithms. In this paper, we discuss the sensing and signal processing aspects of the overall prosthesis control cybernetics, with emphasis on transradial, transhumeral, and shoulder disarticulate amputations, which represent considerable upper-limb amputees typically encountered within the population.

Classification Performance and Feature Space Characteristics in Individuals with Upper Limb Loss Using Sonomyography

Objective: Sonomyography, or ultrasound-based sensing of muscle deformation, is an emerging modality for upper limb prosthesis control. Although prior studies have shown that individuals with upper limb loss can achieve successful motion classification with sonomyography, it is important to better understand the time-course over which proficiency develops. In this study, we characterized user performance during their initial and subsequent exposures to sonomyography. Method: Ultrasound images corresponding to a series of hand gestures were collected from individuals with transradial limb loss under three scenarios: during their initial exposure to sonomyography (Experiment 1), during a subsequent exposure to sonomyography where they were provided biofeedback as part of a training protocol (Experiment 2), and during testing sessions held on different days (Experiment 3). User performance was characterized by offline classification accuracy, as well as metrics describing the consistency and separability of the sonomyography signal patterns in feature space. Results: Classification accuracy was high during initial exposure to sonomyography (96.2 ± 5.9%) and did not systematically change with the provision of biofeedback or on different days. Despite this stable classification performance, some of the feature space metrics changed. Conclusions: User performance was strong upon their initial exposure to sonomyography and did not improve with subsequent exposure. Clinical Impact: Prosthetists may be able to quickly assess if a patient will be successful with sonomyography without submitting them to an extensive training protocol, leading to earlier socket fabrication and delivery.