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<p>Frailty is a common condition in older adults, characterized, among other things, by impairments in gait and movement patterns. The proposed FRAILPOL repository addresses the critical gap in geriatric research by offering a comprehensive, open-access, five body-worn inertial sensors (ankles, wrists, and back of sacrum) signals recorded during the Time Up and Go test of 668 participants, community-dwelling older adults. The gait data, as well as the stride-based spatio-temporal parameters along with demographic and health-related information, including cognitive health data, have been grouped according to established clinical criteria into three classes (robust, pre-frailty, and frailty). The technical verification includes classification by reporting results for both binary (robust, frailty) and multi-class (robust, pre-frailty, frailty) classification using classical machine learning models with acceptable accuracy.</p>

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Database for Prevalence and Determinants of Frailty in the Elderly with Quantifying Functional Mobility

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月18日

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  • Agnieszka Szczȩsna,
  • Arslan Amjad,
  • Monika Błaszczyszyn,
  • Magdalena Sacha,
  • Piotr Feusette,
  • Robert Zieliński,
  • Piotr Wittek,
  • Wojciech Wolański,
  • Mariusz Konieczny,
  • Zbigniew Borysiuk,
  • Jerzy Sacha

摘要

Frailty is a common condition in older adults, characterized, among other things, by impairments in gait and movement patterns. The proposed FRAILPOL repository addresses the critical gap in geriatric research by offering a comprehensive, open-access, five body-worn inertial sensors (ankles, wrists, and back of sacrum) signals recorded during the Time Up and Go test of 668 participants, community-dwelling older adults. The gait data, as well as the stride-based spatio-temporal parameters along with demographic and health-related information, including cognitive health data, have been grouped according to established clinical criteria into three classes (robust, pre-frailty, and frailty). The technical verification includes classification by reporting results for both binary (robust, frailty) and multi-class (robust, pre-frailty, frailty) classification using classical machine learning models with acceptable accuracy.