MuscleRehab: Improving Unsupervised Physical Rehabilitation by Monitoring and Visualizing Muscle Engagement
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Unsupervised physical rehabilitation traditionally has used motion tracking to determine correct exercise execution. However, motion tracking is not representative of the assessment of physical therapists, which focus on muscle engagement. In this paper, we investigate if monitoring and visualizing muscle engagement during unsupervised physical rehabilitation improves the execution accuracy of therapeutic exercises by showing users whether they target the right muscle groups. To accomplish this, we use wearable electrical impedance tomography (EIT) to monitor muscle engagement and visualize the current state on a virtual muscle-skeleton avatar. We use additional optical motion tracking to also monitor the user’s movement. We conducted a user study with 10 participants that compares exercise execution while seeing muscle + motion data vs. motion data only, and also presented the recorded data to a group of physical therapists for post-rehabilitation analysis. The results indicate that monitoring and visualizing muscle engagement can improve both the therapeutic exercise accuracy during rehabilitation, and post-rehabilitation evaluation for physical therapists.
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The design requirements align with several other research studies that have extracted design requirements for recovery, such as post-operative cancer care, physical injury, cognitive rehabilitation, and recovery in critical care units.
“Our design requirements align with several other research studies that have extracted design requirements for recovery, such as post-operative cancer care [4], physical injury [73, 82], cognitive rehabilitation [36], and recovery in critical care units[66].”
From A Data Visualization Tool for Patients and Healthcare Providers to Communicate during Inpatient Stroke Rehabilitation (2024) · cites this paper as [82]
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Hengjang, O., Mongkolphan, N., Tehasan, K., & Ouypornkochagorn, T. (2026) · International Electrical Engineering Congress (iEECON) · doi:10.1109/ieecon68065.2026.11707149
- Authors
- Zhu, J., Lei, Y., Shah, A., Schein, G., Ghaednia, H., Schwab, J., Harteveld, C., & Mueller, S.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Massachusetts Institute of Technology (US)
- Massachusetts General Hospital (US)
- Northeastern University (US)
- ORCID
- Venue
- Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology
- Publisher
- ACM
- Published
- 2022-10-28
- Pages
- 1-14
- OpenAlex
- W4307475983
- Cited by
- 40
- References
- 55
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 9 · 2023: 3 · 2022: 2
- FWCI
- 26.74
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Symposium on User Interface Software and Technology (UIST) (publishedVersion, cc-by)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Topics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical and Bioimpedance Tomography
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- electrical impedance tomography, wearable sensors, optical motion tracking, physiotherapy assessment
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