Wearable Sensor-Based Rehabilitation Exercise Assessment for Knee Osteoarthritis
Open accessAbstract
Since the knee joint bears the full weight load of the human body and the highest pressure loads while providing flexible movement, it is the body part most vulnerable and susceptible to osteoarthritis. In exercise therapy, the early rehabilitation stages last for approximately six weeks, during which the patient works with the physical therapist several times each week. The patient is afterwards given instructions for continuing rehabilitation exercise by him/herself at home. This study develops a rehabilitation exercise assessment mechanism using three wearable sensors mounted on the chest, thigh and shank of the working leg in order to enable the patients with knee osteoarthritis to manage their own rehabilitation progress. In this work, time-domain, frequency-domain features and angle information of the motion sensor signals are used to classify the exercise type and identify whether their postures are proper or not. Three types of rehabilitation exercise commonly prescribed to knee osteoarthritis patients are: Short-Arc Exercise, Straight Leg Raise, and Quadriceps Strengthening Mini-squats. After ten subjects performed the three kinds of rehabilitation activities, three validation techniques including 10-fold cross-validation, within subject cross validation, and leave-one-subject cross validation are utilized to confirm the proposed mechanism. The overall recognition accuracy for exercise type classification is 97.29% and for exercise posture identification it is 88.26%. The experimental results demonstrate the feasibility of the proposed mechanism which can help patients perform rehabilitation movements and progress effectively. Moreover, the proposed mechanism is able to detect multiple errors at once, fulfilling the requirements for rehabilitation assessment.
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For wearable devices with cameras and motion sensors used in rehabilitation exercise evaluation for knee osteoarthritis, the accuracy for activity type categorisation was 97%, while the accuracy for exercise posture recognition was 88%.
“The accuracy for activity type categorisation was 97%, while the accuracy for exercise posture recognition was 88% [91].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [91]
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- Authors
- Chen, K.-H., Chen, P.-C., Liu, K.-C., & Chan, C.-T.
- Affiliations
- Taichung Veterans General Hospital (TW)
- National Yang Ming University (TW)
- Corresponding author
- Chan, Chia-Tai
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2015-02-12
- Volume
- 15
- Issue
- 2
- Pages
- 4193-4211
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC4367405
- OpenAlex
- W1993722932
- Cited by
- 115
- References
- 34
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 7 · 2023: 9 · 2022: 11 · 2021: 12 · 2020: 26 · 2019: 11 · 2018: 11 · 2017: 10 · 2016: 6 · 2015: 3
- FWCI
- 8.66
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Science and Technology (MOST 103-2221-E-010 -013 -MY2)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Keywords
- knee osteoarthritis, wearable sensors, home-based rehabilitation, straight leg raise, gait analysis, frequency-domain features
- MeSH terms
- Exercise Therapy, Humans, Knee Joint, Telemedicine, Osteoarthritis, Knee, Quadriceps Muscle, Remote Sensing Technology
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