Monitoring Muscle Engagement via Electrical Impedance Tomography for Unsupervised Physical Rehabilitation
Authors: 
Junyi Zhu, 
Yuxuan Lei, Aashini Shah, Gila Schein, Hamid Ghaednia, Joseph Schwab, Casper Harteveld, 
Stefanie MuellerAuthors Info & Claims
Junyi Zhu, Yuxuan Lei, Aashini Shah, Gila Schein, Hamid Ghaednia, Joseph Schwab, Casper Harteveld, Stefanie MuellerAuthors Info & ClaimsArticle No.: 110, Pages 1 - 3
Abstract
In this demo, we present MuscleRehab, a virtual reality rehabilitation system that tracks user’s motion via optical motion tracking and user’s muscle engagement via electrical impedance tomography (EIT) and visualizes the data on a virtual muscle-skeleton avatar. By deploying MuscleRehab, 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. The results indicate that monitoring and visualizing muscle engagement can improve both the therapeutic exercise accuracy for users during rehabilitation, and post-rehabilitation evaluation for physical therapists. We introduce each element of MuscleRehab system, and demonstrate our custom wearable EIT devices with phantom and on-body setups.
References
[1]
Adegboyega Akinsiku, Ignacio Avellino, Yasmin Graham, and Helena M. Mentis. 2021. It’s Not Just the Movement: Experiential Information Needed for Stroke Telerehabilitation(CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 661, 12 pages. https://doi.org/10.1145/3411764.3445663
[2]
HTC Corporation. [n. d.]. HTC VIVE Pro 2 Headset. https://www.vive.com/us/product/vive-pro2/overview/
[3]
NaturalPoint Inc.[n. d.]. OptiTrack Motion Capture Systems. https://optitrack.com
[4]
Tiago Jesus, Michel Landry, and Helen Hoenig. 2019. Global Need for Physical Rehabilitation: Systematic Analysis from the Global Burden of Disease Study 2017. (01 2019). https://doi.org/10.20944/preprints201901.0060.v1
[5]
Aisling Kelliher, Setor Zilevu, Thanassis Rikakis, Tamim Ahmed, Yen Truong, and Steven L. Wolf. 2020. Towards Standardized Processes for Physical Therapists to Quantify Patient Rehabilitation. Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376706
[6]
Predrag Klasnja, Sunny Consolvo, and Wanda Pratt. 2011. How to Evaluate Technologies for Health Behavior Change in HCI Research. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Vancouver, BC, Canada) (CHI ’11). Association for Computing Machinery, New York, NY, USA, 3063–3072. https://doi.org/10.1145/1978942.1979396
[7]
World Health Organization. 2017. The need to scale up rehabilitation. Technical documents. 9 p. pages.
[8]
M. Spalevic, Lidija Dimitrijević, Mirjana Kocic, Ivona Stankovic, and V. Zivkovic. 2014. AB1124 The Importance of the Early Rehabilitation after Total Knee Replacement in Osteoarthritis and Rheumatoid Arthritis Patients. Annals of the Rheumatic Diseases 73 (06 2014), 1173–1174. https://doi.org/10.1136/annrheumdis-2014-eular.4638
[9]
Junyi Zhu, Yuxuan Lei, Aashini Shah, Gila Schein, Hamid Ghaednia, Joseph H Schwab, Casper Harteveld, and Stefanie Mueller. 2022. MuscleRehab: Improving Unsupervised Physical Rehabilitation by Monitoring and Visualizing Muscle Engagement. In The 35th Annual ACM Symposium on User Interface Software and Technology (Bend, Oregon, USA) (UIST ’22). Association for Computing Machinery, New York, NY, USA.
[10]
Junyi Zhu, Jackson Snowden, Joshua Verdejo, Emily Chen, Paul Zhang, Hamid Ghaednia, Joseph H. Schwab, and Stefanie Mueller. 2021. EIT-kit: An Electrical Impedance Tomography Toolkit for Health and Motion Sensing. In Proceedings of the 34th Annual ACM Symposium on User Interface Software and Technology. https://doi.org/10.1145/3472749.3474758
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- Monitoring Muscle Engagement via Electrical Impedance Tomography for Unsupervised Physical Rehabilitation
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Published In
October 2022
413 pages
ISBN:9781450393218
DOI:10.1145/3526114
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Published: 20 December 2022
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UIST '22: The 35th Annual ACM Symposium on User Interface Software and Technology
October 29 - November 2, 2022
OR, Bend, USA
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