Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2022

Abstract

Background: The increasing demands concerning stroke rehabilitation and in-home exercise promotion grew the need for affordable and accessible assistive systems to promote patients' compliance in therapy. These assistive systems require quantitative methods to assess patients' quality of movement and provide feedback on their performance. However, state-of-the-art quantitative assessment approaches require expensive motion-capture devices, which might be a barrier to the development of low-cost systems.Methods: In this work, we develop a low-cost virtual coach (VC) that requires only a laptop with a webcam to monitor three upper extremity rehabilitation exercises and provide real-time visual and audio feedback on compensatory motion patterns exclusively from image 2D positional data analysis. To assess compensation patterns quantitatively, we propose a Rule-based (RB) and a Neural Network (NN) based approaches. Using the dataset of 15 post-stroke patients, we evaluated these methods with Leave-One-Subject-Out (LOSO) and Leave-One-Exercise-Out (LOEO) cross-validation and the F-1 score that measures the accuracy (geometric mean of precision and recall) of a model to assess compensation motions. In addition, we conducted a pilot study with seven volunteers to evaluate system performance and usability.Results: For exercise 1, the RB approach assessed four compensation patterns with a F-1 score of 76.69%. For exercises 2 and 3, the NN-based approach achieved a F-1 score of 72.56% and 79.87%, respectively. Concerning the user study, they found that the system is enjoyable (hedonic value of 4.54/5) and relevant (utilitarian value of 4.86/5) for rehabilitation administration. Additionally, volunteers' enjoyment and interest (Hedonic value perception) were correlated with their perceived VC performance (rho = 0.53).Conclusions: The VC performs analysis on 2D videos from a built-in webcam of a laptop and accurately identifies compensatory movement patterns to provide corrective feedback. In addition, we discuss some findings concerning system performance and usability.

Keywords

Upper extremity stroke rehabilitation therapy, Virtual coach, Compensation assessment, 2D video analysis

Discipline

Graphics and Human Computer Interfaces | Rehabilitation and Therapy

Research Areas

Intelligent Systems and Optimization

Publication

Journal of NeuroEngineering and Rehabilitation

Volume

19

Issue

1

First Page

1

Last Page

16

ISSN

1743-0003

Identifier

10.1186/s12984-022-01053-z

Publisher

BioMed Central

Copyright Owner and License

Authors-CC-BY

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Additional URL

https://doi.org/10.1186/s12984-022-01053-z

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