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Updated: Apr 25, 2026
Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
Published on: November 7, 2014
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.
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.
