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Related Experiment Video

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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
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Sarcopenia diagnosis using skeleton-based gait sequence and foot-pressure image datasets.

Muhammad Tahir Naseem1, Na-Hyun Kim1, Haneol Seo1

  • 1Laboratory of Computer Vision and Human Visual Perception, Department of Electronic Engineering, Yeungnam University, Gyeongsan, Republic of Korea.

Frontiers in Public Health
|December 12, 2024
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Summary

This study shows that artificial intelligence can detect sarcopenia using foot pressure and skeleton data. Both methods show potential for classifying sarcopenia, aiding in early diagnosis of this age-related muscle condition.

Keywords:
convolutional neural networkdeep learningfoot pressuresarcopeniaskeletonspatio-temporal graph convolutional networks

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Area of Science:

  • Biomedical Engineering
  • Gerontology
  • Artificial Intelligence

Background:

  • Sarcopenia is a common age-related condition characterized by decreased muscle strength and function.
  • Current diagnostic methods for sarcopenia include gait analysis and foot-pressure imaging.
  • Previous AI-driven sarcopenia detection studies primarily used skeleton data, neglecting the combined potential of multimodal datasets.

Purpose of the Study:

  • To develop and evaluate AI models for sarcopenia classification using both foot-pressure and skeleton data.
  • To assess the individual and combined potential of foot-pressure and skeleton data for sarcopenia detection.
  • To establish a multimodal dataset for future sarcopenia research.

Main Methods:

  • A multimodal dataset of foot-pressure and skeleton data was collected from 100 participants.
  • ResNet-18 model was applied to the foot-pressure dataset for classification.
  • Spatiotemporal Graph Convolutional Network (ST-GCN) was applied to the skeleton dataset for classification.
  • Participants' gait was recorded using RGB+D cameras at 30 fps, extracting 3D skeleton data with 25 feature points.

Main Results:

  • The ResNet-18 model achieved 77.16% accuracy for sarcopenia classification using foot-pressure data.
  • The ST-GCN model achieved 78.63% accuracy for sarcopenia classification using skeleton data.
  • Both foot-pressure and skeleton data demonstrated significant potential in classifying sarcopenia.

Conclusions:

  • Foot-pressure imaging and skeleton data, when analyzed with AI, show promise for sarcopenia classification.
  • The study highlights the potential of multimodal data in improving sarcopenia detection.
  • These findings support the development of AI-powered tools for early sarcopenia diagnosis.