Exploration of human activity recognition using a single sensor for stroke survivors and able-bodied people
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Commonly used sensors like accelerometers, gyroscopes, surface electromyography sensors, etc., which provide a convenient and practical solution for human activity recognition (HAR), have gained extensive attention. However, which kind of sensor can provide adequate information in achieving a satisfactory performance, or whether the position of a single sensor would play a significant effect on the performance in HAR are sparsely studied. In this paper, a comparative study to fully investigate the performance of the aforementioned sensors for classifying four activities (walking, tooth brushing, face washing, drinking) is explored. Sensors are spatially distributed over the human body, and subjects are categorized into three groups (able-bodied people, stroke survivors, and the union of both). Performances of using accelerometer, gyroscope, sEMG, and their combination in each group are evaluated by adopting the Support Vector Machine classifier with the Leave-One-Subject-Out Cross-Validation technique, and the optimal sensor position for each kind of sensor is presented based on the accuracy. Experimental results show that using the accelerometer could obtain the best performance in each group. The highest accuracy of HAR involving stroke survivors was 95.84 ± 1.75% (mean ± standard error), achieved by the accelerometer attached to the extensor carpi ulnaris. Furthermore, taking the practical application of HAR into consideration, a novel approach to distinguish various activities of stroke survivors based on a pre-trained HAR model built on healthy subjects is proposed, the highest accuracy of which is 77.89 ± 4.81% (mean ± standard error) with the accelerometer attached to the extensor carpi ulnaris.
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k-Nearest Neighbour was used in three included works.
“k-Nearest Neighbour (kNN) is another algorithm that was used in three included works [64,51,46].”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [51]
Many studies have shown that effective systems can be designed with a very small number of sensors, sometimes a single sensor.
“many studies proved the possibility to design effective systems with a very small number of sensors (sometimes a single sensor is sufficient) as is the case in [43,51].”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [51]
In study [51], the SVM-rbf model achieved 82.47% accuracy.
“SVM-rbf 82.47%.”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [51]
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Qin, H., Meng, L., Chen, C., Zhu, G., Wu, X., Zhang, A., & Chen, W. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871724 · Cited by 1 · see citing works
Sensor Integration for Gait Analysis
Çelik, Y., Vitório, R. R., Powell, D., Moore, J., Young, F., Coulby, G., Tung, J. Y., Nouredanesh, M., Ellis, R., Izmailova, E. S., et al. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822548-6.00139-4 · Cited by 1 · see citing works
Nandakishora, Y., Christydass, S. P. J., Bhargav, K., & Kumar, S. S. (2025) · doi:10.1002/9781394302895.ch8 · Cited by 1 · see citing works
Deep Neural Network Based Hybrid Feature Selection
Zhou, Y., Zhang, X., & Kwong, S. (2025) · SpringerBriefs in computer science · doi:10.1007/978-981-96-2687-8_5
Passarelli, R. P., Acar, M., Demirel, M., Öztürk, Ç., & Ekser, B. (2025) · Health Sciences Review · doi:10.1016/j.hsr.2025.100246
Motion Intention Decoding: The Role of Data Parameters in Motor Unit-Based Decoders
Meng, L., & Hu, X. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11343284
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Digital biomarkers derived from wearable sensors in neurorehabilitation: a scoping review
Shalabi, K. M., Mahmoud, H., Mohamed, S. H. P., & Shaik, S. A. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1908796
- Authors
- Meng, L., Zhang, A., Chen, C., Wang, X., Jiang, X., Tao, L., Fan, J., Wu, X., Dai, C., Zhang, Y., Vanrumste, B., Tamura, T., & Chen, W.
- Affiliations
- Fudan University (CN)
- Department of Neurological Rehabilitation Medicine, The First Rehabilitation Hospital of Shanghai, Kongjiang Branch, Shanghai 200093, China
- Center of Rehabilitation Therapy, The First Rehabilitation Hospital of Shanghai, Shanghai 200090, China
- KU Leuven (BE)
- Waseda University (JP)
- Corresponding author
- Zhang, Anjing
- Chen, Chen
- Chen, Wei
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-01-26
- Volume
- 21
- Issue
- 3
- Pages
- 799
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7865661
- OpenAlex
- W3124139391
- Cited by
- 45
- References
- 62
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 7 · 2023: 6 · 2022: 10 · 2021: 3
- FWCI
- 2.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lirias (KU Leuven) (acceptedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key R&D Program of China (2017YFE0112000)
- Shanghai Pujiang Program (19PJ1401100)
- Shanghai Municipal Science and Technology Major Project (2017SHZDZX01)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- human activity recognition, stroke survivors, single sensor, accelerometer, gyroscope, surface electromyography, sensor position, extensor carpi ulnaris, activity classification, pre-trained model transfer, able-bodied people
- MeSH terms
- Wearable Electronic Devices, Humans, Human Activities, Walking, Survivors, Stroke
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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