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ORIGINAL RESEARCH article

Front. Public Health
Sec. Aging and Public Health
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1443188
This article is part of the Research Topic AI-Driven Healthcare Delivery, Ageism, and Implications for Older Adults: Emerging Trends and Challenges in Public Health View all 4 articles

Sarcopenia Diagnosis Using Skeleton-based Gait Sequence and Footpressure Image Datasets

Provisionally accepted
Muhammad T. Naseem Muhammad T. Naseem Na-Hyun Kim Na-Hyun Kim Haneol Seo Haneol Seo Jaemok Lee Jaemok Lee Chul-Min Chung Chul-Min Chung Sunghoon Shin Sunghoon Shin Chan-Su Lee Chan-Su Lee *
  • Yeungnam University, Gyeongsan, Republic of Korea

The final, formatted version of the article will be published soon.

    Introduction: Sarcopenia is a common age-related disease, defined as a decrease in muscle strength and function owing to reduced skeletal muscle. One way to diagnose sarcopenia is through gait analysis and foot-pressure imaging. Motivation and Research Gap: We collected our own multimodal dataset from 100 subjects, consisting of both foot-pressure and skeleton data with real patients, which provides a unique resource for future studies aimed at more comprehensive analyses. While artificial intelligence has been employed for sarcopenia detection, previous studies have predominantly focused on skeleton-based datasets without exploring the combined potential of skeleton and foot pressure dataset. This study conducts separate experiments for foot-pressure and skeleton datasets, it demonstrates the potential of each data type in sarcopenia classification. Methods: This study had two components. First, we collected skeleton and foot-pressure datasets and classified them into sarcopenia and non-sarcopenia groups based on grip strength, gait performance, and appendicular skeletal muscle mass. Second, we performed experiments on the foot-pressure dataset using the ResNet-18 and spatiotemporal graph convolutional network (ST-GCN) models on the skeleton dataset to classify normal and abnormal gaits due to sarcopenia. For an accurate diagnosis, real-time walking of 100 participants was recorded at 30 fps as RGB + D images. The skeleton dataset was constructed by extracting 3D skeleton information comprising 25 feature points from the image, whereas the foot-pressure dataset was constructed by exerting pressure on the foot-pressure plates. Results: As a baseline evaluation, the accuracies of sarcopenia classification performance from foot-pressure image using Resnet-18 and skeleton sequences using ST-GCN were identified as 77.16% and 78.63%, respectively. Discussion: The experimental results demonstrated the potential applications of sarcopenia and non-sarcopenia classifications based on foot-pressure images and skeleton sequences.

    Keywords: Sarcopenia, deep learning, Convolutional Neural Network, spatio-temporal graph convolutional networks, Foot pressure, Skeleton

    Received: 03 Jun 2024; Accepted: 04 Nov 2024.

    Copyright: © 2024 Naseem, Kim, Seo, Lee, Chung, Shin and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Chan-Su Lee, Yeungnam University, Gyeongsan, Republic of Korea

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.