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Latest Trends in Gait Analysis Using Deep Learning Techniques: A Systematic Review

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Artificial Intelligence and Speech Technology (AIST 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1546))

Abstract

Marker-less analysis of human gait has made considerable progress in recent years. However, developing a gait analysis system capable of extracting reliable and precise kinematic data in a standard and unobtrusive manner remains an open challenge. This narrative review considers the transformation of methods for extracting gait extremity information from videos or images, perceived how analysis methods have improved from arduous manual procedures to semi-objective and objective marker-based systems and then marker-less systems. The gait analysis systems widely used restrict the analysis process with the use of markers, inhibited environmental conditions, and long processing duration. Such limitations can impede the use of a gait analysis system in multiple applications. Advancement in marker-less pose estimation and Q-learning-based techniques are opening the possibility of adopting productive methods for estimating precise poses of humans and information of movement from video frames. Vision-Based gait analysis techniques are capable of providing a cost-effective, unobtrusive solution for estimation of stick images and thus the analysis of the gait. This work provides a comprehensive review of marker-less computer vision and deep neural network-based gait analysis, parameters, design specifications, and the latest trends. This survey provides a birds-eye view of the domain. This review aims to introduce the latest trends in gait analysis using computer vision methods thus provide a single platform to learn various marker-less methods for the analysis of the gait that is likely to have a future impact in bio-mechanics while considering the challenges with accuracy and robustness that are yet to be addressed.

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Authors and Affiliations

  1. Indira Gandhi Delhi Technical University, Delhi, India

    Dimple Sethi & Sourabh Bharti

  2. National Institute of Technology, Delhi, India

    Chandra Prakash

Authors
  1. Dimple Sethi
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  2. Chandra Prakash
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  3. Sourabh Bharti
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Editors and Affiliations

  1. Indira Gandhi Delhi Technical University for Women, Delhi, India

    Amita Dev

  2. Kamrah Institute of Information Technology, Gurgaon, India

    S. S. Agrawal

  3. Indira Gandhi Delhi Technical University for Women, Delhi, India

    Arun Sharma

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Sethi, D., Prakash, C., Bharti, S. (2022). Latest Trends in Gait Analysis Using Deep Learning Techniques: A Systematic Review. In: Dev, A., Agrawal, S.S., Sharma, A. (eds) Artificial Intelligence and Speech Technology. AIST 2021. Communications in Computer and Information Science, vol 1546. Springer, Cham. https://doi.org/10.1007/978-3-030-95711-7_31

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  • DOI: https://doi.org/10.1007/978-3-030-95711-7_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95710-0

  • Online ISBN: 978-3-030-95711-7

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