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  • Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification

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    Author(s)
    Sikandar, Tasriva
    Rabbi, Mohammad F
    Ghazali, Kamarul H
    Altwijri, Omar
    Alqahtani, Mahdi
    Almijalli, Mohammed
    Altayyar, Saleh
    Ahamed, Nizam U
    Griffith University Author(s)
    Rabbi, Fazle
    Year published
    2021
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    Abstract
    Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ...
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    Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.
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    Journal Title
    Sensors
    Volume
    21
    Issue
    8
    DOI
    https://doi.org/10.3390/s21082836
    Copyright Statement
    © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
    Subject
    Biomechanics
    Electrical engineering
    Exercise physiology
    Sports science and exercise not elsewhere classified
    Analytical chemistry
    Ecology
    Distributed computing and systems software
    Publication URI
    http://hdl.handle.net/10072/403989
    Collection
    • Journal articles

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