Multiple-wearable-sensor-based gait classification and analysis in patients with neurological disorders
Open accessAbstract
The aim of this study was to conduct a comprehensive analysis of the placement of multiple wearable sensors for the purpose of analyzing and classifying the gaits of patients with neurological disorders. Seven inertial measurement unit (IMU) sensors were placed at seven locations: the lower back (L5) and both sides of the thigh, distal tibia (shank), and foot. The 20 subjects selected to participate in this study were separated into two groups: stroke patients (11) and patients with neurological disorders other than stroke (brain concussion, spinal injury, or brain hemorrhage) (9). The temporal parameters of gait were calculated using a wearable device, and various features and sensor configurations were examined to establish the ideal accuracy for classifying different groups. A comparison of the various methods and features for classifying the three groups revealed that a combination of time domain and gait temporal feature-based classification with the Multilayer Perceptron (MLP) algorithm outperformed the other methods of feature-based classification. The classification results of different sensor placements revealed that the sensor placed on the shank achieved higher accuracy than the other sensor placements (L5, foot, and thigh). The placement-based classification of the shank sensor achieved 89.13% testing accuracy with the Decision Tree (DT) classifier algorithm. The results of this study indicate that the wearable IMU device is capable of differentiating between the gait patterns of healthy patients, patients with stroke, and patients with other neurological disorders. Moreover, the most favorable results were reported for the classification that used the combination of time domain and gait temporal features as the model input and the shank location for sensor placement.
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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 [59]
The Classification section (2.6) describes a gait-classification system, but its binary classification distinguishes stroke patients from patients with other neurological disorders, rather than normal from abnormal gait. The Experimental Protocol section (2.3) says participants walked approximately 12 m, not 10 m. The Introduction mentions 10 m tests, but attributes them to other studies. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA single included study used the lower back.
“and finally a single study used the lower back [59].”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [59]
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- Authors
- Hsu, W.-C., Sugiarto, T., Lin, Y.-J., Yang, F.-C., Lin, Z.-Y., Sun, C.-T., Hsu, C.-L., & Chou, K.-N.
- Affiliations
- National Taiwan University of Science and Technology (TW)
- Tri-Service General Hospital (TW)
- Taipei City Hospital (TW)
- Industrial Technology Research Institute (TW)
- Corresponding author
- Hsu, Wei-Chun
- Chou, Kuan-Nien
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-10-11
- Volume
- 18
- Issue
- 10
- Pages
- 3397
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6210399
- OpenAlex
- W2897218246
- Cited by
- 101
- References
- 39
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 17 · 2023: 12 · 2022: 19 · 2021: 13 · 2020: 15 · 2019: 5
- FWCI
- 3.30
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Tri-Service General Hospital (TSGH-NTUST-107-04)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- gait classification, wearable sensors, inertial measurement unit, sensor placement, neurological disorders, stroke, concussion, spinal injuries, cerebral hemorrhage, time-domain features, multilayer perceptron, decision tree, gait analysis
- MeSH terms
- Wearable Electronic Devices, Aged, Algorithms, Foot, Gait, Humans, Middle Aged, Monitoring, Physiologic, Nervous System Diseases, Thigh, Reproducibility of Results, Stroke
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