| Autor: |
Engdahl S; Department of BioengineeringGeorge Mason University Fairfax VA 20030 USA., Dhawan A; Department of BioengineeringGeorge Mason University Fairfax VA 20030 USA., Bashatah A; Department of BioengineeringGeorge Mason University Fairfax VA 20030 USA., Diao G; Department of Biostatistics and BioinformaticsThe George Washington University Washington DC 20052 USA., Mukherjee B; Department of BioengineeringGeorge Mason University Fairfax VA 20030 USA., Monroe B; Hanger Clinic Laurel MD 20707 USA., Holley R; MedStar National Rehabilitation Hospital Washington DC 20010 USA., Sikdar S; Department of BioengineeringGeorge Mason University Fairfax VA 20030 USA. |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE journal of translational engineering in health and medicine [IEEE J Transl Eng Health Med] 2022 Jan 06; Vol. 10, pp. 2100311. Date of Electronic Publication: 2022 Jan 06 (Print Publication: 2022). |
| DOI: |
10.1109/JTEHM.2022.3140973 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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