Abstract
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.
Highlights
Despite the enormous investment of resources in the development of new multi-articulated upper limb prosthetics, a large proportion of individuals with upper limb loss discontinue use of their prosthesis [1]–[3]
The SMG feature space metrics were moderately correlated with cross-validation accuracy (0.47 ≤ |r| ≤ 0.76; Supplementary Fig. S1), none of the correlations were significant except for Within-class Distance (WD) (p = 0.03)
Grasp-specific metrics did not follow a consistent trend, but wrist pronation and/or supination often had the lowest Mean Semi-Principal Axis (MSA) and highest IDNN, IDAN, and Most Separable Dimension (MSD) among all grasps collected from individual participants
Summary
Despite the enormous investment of resources in the development of new multi-articulated upper limb prosthetics, a large proportion of individuals with upper limb loss discontinue use of their prosthesis [1]–[3]. Training has been correlated with increased prosthesis use [8] and satisfaction [9], patient access to rehabilitation and prosthetic services in the United States is frequently limited [10]. There is a scarcity of clinicians who specialize in treating upper limb loss [11] and possess the specialized knowledge required to train patients on effective prosthesis use. The difficulty of learning to use a prosthesis may be apparent given certain limitations in the predominant method for sensing and decoding user intent, surface electromyography (EMG).
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