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Lower Limb Wearable Capacitive Sensing and Its Applications to Recognizing Human Gaits
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In this paper, we present an approach to sense human body capacitance and apply it to recognize lower limb locomotion modes. The proposed wearable sensing system includes sensing bands, a signal processing circuit and a gait event detection module. Experiments on long-term working stability, adaptability to disturbance and locomotion mode recognition are carried out to validate the effectiveness of the proposed approach. Twelve able-bodied subjects are recruited, and eleven normal gait modes are investigated. With an event-dependent linear discriminant analysis classifier and feature selection procedure, four time-domain features are used for pattern recognition and satisfactory recognition accuracies (97:3% ± 0:5%, 97:0% ± 0:4%, 95:6% ± 0:9% and 97:0% ± 0:4% for four phases of one gait cycle respectively) are obtained. The accuracies are comparable with that from electromyography-based systems and inertial-based systems. The results validate the effectiveness of the proposed lower limb capacitive sensing approach in recognizing human normal gaits.
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LDA performs linear classification with a homogeneous covariance assumption, has strong real-time performance and low computational cost, and is used for limb motion intent recognition from EEG signals.
“| LDA | Linear classification, homogeneous covariance | Strong real-time performance, low | Limb motion intent recognition from EEG |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [233]
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Metadata
- Authors
- Zheng, E., Chen, B., Wei, K., & Wang, Q.
- Affiliations
- Peking University (CN)
- Corresponding author
- Wang, Qining
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2013-10-01
- Volume
- 13
- Issue
- 10
- Pages
- 13334-13355
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC3859067
- OpenAlex
- W1997244162
- Cited by
- 26
- References
- 36
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 1 · 2022: 3 · 2020: 4 · 2019: 2 · 2018: 1 · 2017: 4 · 2016: 2 · 2015: 1 · 2014: 5
- FWCI
- 3.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61005082, 61020106005)
- Ministry of Education of the People's Republic of China (20100001120005)
- Peking University (3J0865600)
- 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 › Non-Invasive Vital Sign Monitoring
- Keywords
- capacitive sensing, wearable sensors, lower extremity, gait recognition, locomotion mode recognition, gait event detection, linear discriminant analysis, time-domain features, feature selection, gait phases
- MeSH terms
- Equipment Design, Gait, Humans, Leg, Sensitivity and Specificity, Transducers, Pressure, Reproducibility of Results, Monitoring, Ambulatory, Equipment Failure Analysis, Electric Capacitance, Actigraphy
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