Detection and classification of stroke gaits by deep neural networks employing inertial measurement units
Open accessAbstract
This paper develops Deep Neural Network (DNN) models that can recognize stroke gaits. Stroke patients usually suffer from partial disability and develop abnormal gaits that can vary widely and need targeted treatments. Evaluation of gait patterns is crucial for clinical experts to make decisions about the medication and rehabilitation strategies for the stroke patients. However, the evaluation is often subjective, and different clinicians might have different diagnoses of stroke gait patterns. In addition, some patients may present with mixed neurological gaits. Therefore, we apply artificial intelligence techniques to detect stroke gaits and to classify abnormal gait patterns. First, we collect clinical gait data from eight stroke patients and seven healthy subjects. We then apply these data to develop DNN models that can detect stroke gaits. Finally, we classify four common gait abnormalities seen in stroke patients. The developed models achieve an average accuracy of 99.35% in detecting the stroke gaits and an average accuracy of 97.31% in classifying the gait abnormality. Based on the results, the developed DNN models could help therapists or physicians to diagnose different abnormal gaits and to apply suitable rehabilitation strategies for stroke patients.
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Some movement classification systems distinguish between normal and abnormal gaits for lower-limb rehabilitation, with participants executing 10 m walks.
“Some researchers implemented systems to distinguish between normal and abnormal gaits for lower-limb rehabilitation [56,59,60], in which participants executed 10 m walks.”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [60]
The Introduction and §2 (Collection and Processing; page unavailable) describe a rehabilitation-oriented system that distinguishes stroke gaits from normal gaits. Section 2 says patients had to be able to walk ten meters indoors to qualify, but describes the actual walking tests only as being performed at a comfortable pace; it does not establish that participants executed 10 m walks during testing. (LLM, conf=0.88)
Experimental — accuracy not yet measuredData bias causing assessment algorithms not to generalise to new patients with different degrees of severity was very common among the reviewed papers, with researchers complaining about poor generalisation.
“In the same context, data bias which is another issue can cause the assessment algorithm to evolve in an unpredictable manner and not generalise to new patients that have different degrees of severity.This was very common among the reviewed papers, where researchers complained about their algorithms not generalising well.[41,49,52,58,60].”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [60]
The Discussion (page not available) says the small number of enrolled patients limits the study and calls for a larger study to validate the model, which supports concern about its applicability to other patients. It does not report data bias, unpredictable algorithm behaviour, poor generalisation to patients with different severity levels, or a complaint that its algorithm failed to generalise. (LLM, conf=0.78)
Experimental — accuracy not yet measuredIn study [60], the MLP model achieved 99.35% accuracy.
“99.35%”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [60]
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- Authors
- Wang, F.-C., Chen, S.-F., Lin, C.-H., Shih, C.-J., Lin, A.-C., Yuan, W., Li, Y.-C., & Kuo, T.-Y.
- Affiliations
- National Taiwan University (TW)
- Cheng Hsin General Hospital (TW)
- National Taiwan University Hospital (TW)
- Corresponding author
- Wang, Fu-Cheng
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-03-07
- Volume
- 21
- Issue
- 5
- Pages
- 1864
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7962128
- OpenAlex
- W3133782269
- Cited by
- 50
- References
- 44
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 10 · 2023: 10 · 2022: 8 · 2021: 4
- FWCI
- 2.43
- Type
- journal article
- Language
- en
- Open access
- gold
- 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, Taiwan (109-2634-F-002-027-, 108-2634-F-002-016-, 107-2634-F-002-018)
- National Taiwan University (105-MSL01, 104-MSL01)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- inertial measurement unit, deep neural network, gait analysis, stroke rehabilitation, gait recognition, wearable sensors
- MeSH terms
- Adult, Artificial Intelligence, Female, Gait, Humans, Male, Middle Aged, Neural Networks, Computer, Stroke
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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