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Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion.

Rocío Aznar-Gimeno1, Jose Luis Perez-Lasierra2,3, Pablo Pérez-Lázaro1

  • 1Department of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón (ITA), María de Luna 7-8, 50018 Zaragoza, Spain.

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Artificial intelligence using gait analysis can detect sarcopenia and cognitive decline (CD) in older adults. This AI approach combines sensor and computer vision data for early disease screening and intervention.

Keywords:
artificial intelligencehuman pose estimationinertial measurement unitmachine learningmusculoskeletal disordersolder adultswearable sensor

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Sarcopenia and cognitive decline (CD) significantly impact aging populations' quality of life.
  • Early detection is hindered by the limitations of traditional in-person screening methods.
  • Developing accessible, regular screening tools is crucial for timely intervention.

Purpose of the Study:

  • To develop AI algorithms for detecting sarcopenia and CD using gait analysis.
  • To integrate sensor and computer vision (CV) data for enhanced predictive accuracy.
  • To explore the potential of multimodal gait analysis for early disease detection.

Main Methods:

  • A cross-sectional case-control study involving 42 older adults (≥60 years).
  • Gait patterns assessed using foot/lumbar sensors and CV data during usual walking.
  • Machine learning models developed using extracted gait variables to predict sarcopenia and CD.

Main Results:

  • AI models demonstrated high predictive accuracy for both CD and sarcopenia.
  • The best CD model achieved an F1-score of 0.914 (95% sensitivity, 92% specificity).
  • Combined sensor and CV model for sarcopenia yielded an F1-score of 0.748 (100% sensitivity, 83% specificity).

Conclusions:

  • Gait analysis via sensor and CV fusion effectively screens for sarcopenia and CD.
  • A multimodal approach significantly improves model accuracy for early detection.
  • This technology shows promise for home-based screening and intervention in aging populations.