Control within a virtual environment is correlated to functional outcomes when using a physical prosthesis

Levi Hargrove, Laura Miller, Kristi Turner, Todd Kuiken, Levi Hargrove, Laura Miller, Kristi Turner, Todd Kuiken

Abstract

Background: Advances such as targeted muscle reinnervation and pattern recognition control may provide improved control of upper limb myoelectric prostheses, but evaluating user function remains challenging. Virtual environments are cost-effective and immersive tools that are increasingly used to provide practice and evaluate prosthesis control, but the relationship between virtual and physical outcomes-i.e., whether practice in a virtual environment translates to improved physical performance-is not understood.

Methods: Nine people with transhumeral amputations who previously had targeted muscle reinnervation surgery were fitted with a myoelectric prosthesis comprising a commercially available elbow, wrist, terminal device, and pattern recognition control system. Virtual and physical outcome measures were obtained before and after a 6-week home trial of the prosthesis.

Results: After the home trial, subjects showed statistically significant improvements (p < 0.05) in offline classification error, the virtual Target Achievement Control test, and the physical Southampton Hand Assessment Procedure and Box and Blocks Test. A trend toward improvement was also observed in the physical Clothespin Relocation task and Jebsen-Taylor test; however, these changes were not statistically significant. The median completion time in the virtual test correlated strongly and significantly with the Southampton Hand Assessment Procedure (p = 0.05, R = - 0.86), Box and Blocks Test (p = 0.007, R = - 0.82), Jebsen-Taylor Test (p = 0.003, R = 0.87), and the Assessment of Capacity for Myoelectric Control (p = 0.005,R = - 0.85). The classification error performance only had a significant correlation with the Clothespin Relocation Test (p = 0.018, R = .76).

Conclusions: In-home practice with a pattern recognition-controlled prosthesis improves functional control, as measured by both virtual and physical outcome measures. However, virtual measures need to be validated and standardized to ensure reliability in a clinical or research setting.

Trial registration: This is a registered clinical trial: NCT03097978 .

Keywords: Myoelectric control; Outcomes; Pattern recognition; Prosthetics.

Conflict of interest statement

Ethics approval and consent to participate

This study was approved by the Northwestern University Institutional Review Board. All subjects provided written informed consent prior to participation in the study.

Consent for publication

Informed consent sheets with optional consent for publication of images are available.

Competing interests

Drs Kuiken and Hargrove have an interest in Coapt LLC; however no Coapt products were used in this research.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Representative data from a prosthesis-guided training sequence. Data labels are provided by prosthesis movement; the resulting EMG patterns are used to train a pattern recognition system as described by Kuiken et al. [16]
Fig. 2
Fig. 2
Outcome measures when using a virtual prosthesis (left) or a physical prosthesis (right). Measures were performed before and after a 6-week home trial. *Denotes statistical significance at p = 0.05
Fig. 3
Fig. 3
Statistically significant relationships between virtual and physical outcome measures. Each relationship was strong, with a Pearson correlation coefficient |R| > 0.75

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