Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study.

Journal: JMIR aging
PMID: 40053734

Abstract

BACKGROUND: Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early detection is vital for effective treatment and intervention. While many studies use wearable devices to classify depression based on physical activity, these often rely on intrusive methods. Additionally, most depression classification studies involve large participant groups and use single-stage classifiers without explainability.

Authors

  • Shayan Nejadshamsi
    Mila-Quebec Artificial Intelligence Institute, Montreal, QC, Canada.
  • Vania Karami
    Department of Pharmaceutical Sciences and Health Products, University of Camerino, Camerino, Italy.
  • Negar Ghourchian
    Aerial Technologies, Montreal, QC, Canada.
  • Narges Armanfard
  • Howard Bergman
    Family Medicine Department, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.
  • Roland Grad
  • Machelle Wilchesky
  • Vladimir Khanassov
    Family Medicine Department, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.
  • Isabelle Vedel
  • Samira Abbasgholizadeh Rahimi
    Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.

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