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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
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Identification of Patients with Sarcopenia Using Gait Parameters Based on Inertial Sensors.

Jeong-Kyun Kim1,2, Myung-Nam Bae2, Kang Bok Lee2

  • 1Department of Computer Software, ICT, University of Science and Technology, Daejeon 34113, Korea.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary

Wearable inertial sensors can accurately monitor sarcopenia, a condition causing muscle loss in older adults. This study achieved 95% accuracy using gait analysis and statistical parameters, enabling daily life assessment.

Keywords:
Shapley Additive explanationsXAIgait analysisgait parameterinertial measurement unitssarcopeniasmart insole

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Kinesiology

Background:

  • Sarcopenia significantly impacts the quality of life in older adults, contributing to various age-related diseases.
  • Accurate and accessible methods for diagnosing and monitoring muscle loss are crucial for geriatric care.
  • Gait analysis using wearable inertial sensors offers a promising approach for objective assessment.

Purpose of the Study:

  • To investigate the efficacy of inertial sensor-based gait analysis for sarcopenia detection.
  • To compare the accuracy of traditional machine learning models with deep learning approaches for sarcopenia identification.
  • To identify key gait parameters that best discriminate between individuals with and without sarcopenia.

Main Methods:

  • Gait signals from 10 sarcopenia and 10 normal subjects were collected using inertial sensor-based gait devices.
  • Spatial-temporal and descriptive statistical parameters across seven gait phases were analyzed.
  • Machine learning models including Support Vector Machines (SVM), random forest, and multilayer perceptron were employed, alongside deep learning methods.
  • Shapley Additive explanations were utilized for parameter selection to enhance classification accuracy.

Main Results:

  • Descriptive statistical parameters derived from gait phases yielded higher classification accuracy compared to raw data used in deep learning.
  • Knowledge-based gait parameter detection outperformed automatic feature selection methods in identifying sarcopenia.
  • The SVM model, utilizing 20 selected descriptive statistical parameters, achieved the highest accuracy of 95% in sarcopenia identification.

Conclusions:

  • Gait analysis using wearable inertial sensors and descriptive statistical parameters is a highly accurate method for sarcopenia detection.
  • This approach enables objective monitoring of sarcopenia in daily life settings.
  • The findings support the development of wearable technology for proactive management of age-related muscle loss.