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Cited by 175Gait Recognition and AnalysisHuman Pose and Action RecognitionBalance, Gait, and Falls Prevention

Deep Learning for Monitoring of Human Gait: A Review

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Alharthi, Abdullah S., Yunas, Syed U., Ozanyan, Krikor B.·2019·IEEE Sensors Journal
DOI 10.1109/jsen.2019.2928777
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Abstract

The essential human gait parameters are briefly reviewed, followed by a detailed review of the state of the art in deep learning for the human gait analysis. The modalities for capturing the gait data are grouped according to the sensing technology: video sequences, wearable sensors, and floor sensors, as well as the publicly available datasets. The established artificial neural network architectures for deep learning are reviewed for each group, and their performance are compared with particular emphasis on the spatiotemporal character of gait data and the motivation for multi-sensor, multi-modality fusion. It is shown that by most of the essential metrics, deep learning convolutional neural networks typically outperform shallow learning models. In the light of the discussed character of gait data, this is attributed to the possibility to extract the gait features automatically in deep learning as opposed to the shallow learning from the handcrafted gait features.

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  • Existing reviews in this context mostly comprise CNN and LSTM, leaving behind relevant and novel architectures such as Autoencoders, CapsNet, Deep Belief Networks, and GANs.

    “Moreover, even though there are reviews in this context [2], most of the surveyed works comprise Convolutional Neural Networks (CNN) [49] and Long short-term memory (LSTM) [32, 55], leaving behind some relevant and novel architectures, such as Autoencoders [108], Capsule Networks (CapsNet) [82], Deep Belief Networks [31], and Generative Adversarial Networks (GANs) [23].”

    From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [2]

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Cited by

Sorted by citations

Authors
Alharthi, A. S., Yunas, S. U., & Ozanyan, K. B.
Affiliations
University of Manchester (GB)
ORCID
Venue
IEEE Sensors Journal
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019-11-01
Volume
19
Issue
21
Pages
9575-9591
ISSN
  • 1530-437X (ISSN-L)
  • 1558-1748
  • 2379-9153
DOI
10.1109/jsen.2019.2928777
OpenAlex
W2962459300
Cited by
175
References
7
Citations by year
2026: 13 · 2025: 27 · 2024: 29 · 2023: 32 · 2022: 25 · 2021: 35 · 2020: 11 · 2019: 3
FWCI
6.52
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Sensors Journal (publishedVersion, cc-by)
  • Research Explorer (The University of Manchester) (publishedVersion, other-oa)
Funders
  • Government of Saudi Arabia
  • Engineering and Physical Sciences Research Council (EPN509565/1)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
  • Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
Keywords
gait analysis, gait monitoring, deep learning, convolutional neural networks, wearable sensors, floor sensors, video-based gait analysis, multi-sensor fusion, gait dataset, spatio-temporal data, gait feature extraction

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