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Video-Based Gait Analysis for Spinal Deformity

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13805))

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Abstract

In this paper, we explore the area of classifying spinal deformities unintrusively using machine learning and RGB cameras. We postulate that any changes to posture due to spinal deformity can induce specific changes in people’s gait. These changes are not limited to the spine’s bending but manifest in the movement of the entire body, including the feet. Thus, spinal deformities such as Kyphosis and Lordosis can be classified much more effectively by observing people’s gait. To test our claim, we present a bidirectional long short-term memory (BiLSTM) based neural network that operates using the key points on the body to classify the deformity. To evaluate the system, we captured a dataset containing 29 people simulating Kyphosis, Lordosis and their normal gait under the supervision of an orthopaedic surgeon using an RGB camera. Results suggest that gait is a better indicator of spinal deformity than spine angle.

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Acknowledgement

We thank the orthopaedic surgeon Dr. Hemant Patil for hel** us construct the spinal deformity dataset.

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Authors and Affiliations

  1. Department of CSIS, BITS Pilani, K K Birla, Goa Campus, Sancoale, 403726, Goa, India

    Himanshu Kumar Suman & Tanmay Tulsidas Verlekar

  2. APPCAIR, BITS Pilani, K K Birla, Goa Campus, Sancoale, 403726, Goa, India

    Tanmay Tulsidas Verlekar

Authors
  1. Himanshu Kumar Suman
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  2. Tanmay Tulsidas Verlekar
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Corresponding author

Correspondence to Tanmay Tulsidas Verlekar .

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Editors and Affiliations

  1. IBM Research AI and MIT-IBM Watson AI Lab, Haifa, Israel

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Suman, H.K., Verlekar, T.T. (2023). Video-Based Gait Analysis for Spinal Deformity. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13805. Springer, Cham. https://doi.org/10.1007/978-3-031-25072-9_18

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  • DOI: https://doi.org/10.1007/978-3-031-25072-9_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25071-2

  • Online ISBN: 978-3-031-25072-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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