Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effects of Sensory Constraints on Sensorimotor Functions in People With Chronic Ankle Instability: A Systematic Review and Meta-analysis.

Sports health·2026
Same author

Effects of Mobility-Fit, a tailored multicomponent physical activity program with upper-limb emphasis, on strength, mobility and fall risk among older adults in long-term care: a cluster randomised controlled trial.

Age and ageing·2025
Same author

Correction: Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study.

JMIR aging·2025
Same author

The Role of Physical Activity and Physical Function in Predicting Physical Frailty Transitions in Chinese Older Adults: Longitudinal Observational Study From CHARLS.

JMIR aging·2025
Same author

Effects of Dual-Tasking on Stepping Strategy and Inter-Joint Coordination During Walking in Older Fallers and Non-Fallers.

Innovation in aging·2025
Same author

"More Than Intensity: It Is How Pain Affects What I Do": Unveiling the Multifaceted Impact of Pain in Older People on Daily Life.

Journal of applied gerontology : the official journal of the Southern Gerontological Society·2025

Related Experiment Video

Updated: Jan 17, 2026

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.2K

Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily

Xiaoping Zheng1, Ziwei Zeng1, Kimberley S van Schooten2,3

  • 1Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong).

JMIR Aging
|September 15, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning effectively identifies frailty in long-term care using wearable sensor data. Dynamic gait analysis, including variability and asymmetry, offers sensitive frailty indicators for improved detection and management.

Keywords:
frailtygaitlong-term caremachine learningphysical activity

More Related Videos

Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty
07:27

Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty

Published on: October 6, 2016

10.6K
Frailty Assessment in an Aging Mouse Model
06:58

Frailty Assessment in an Aging Mouse Model

Published on: September 23, 2025

436

Related Experiment Videos

Last Updated: Jan 17, 2026

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.2K
Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty
07:27

Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty

Published on: October 6, 2016

10.6K
Frailty Assessment in an Aging Mouse Model
06:58

Frailty Assessment in an Aging Mouse Model

Published on: September 23, 2025

436

Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Frailty is prevalent in over 50% of long-term care (LTC) residents, necessitating early detection for potential reversibility.
  • Machine learning (ML) shows promise for frailty detection in community settings, but its application in LTC requires further investigation.
  • Dynamic gait characteristics may provide more sensitive frailty indicators than traditional measures like gait speed.

Purpose of the Study:

  • To assess the efficacy of ML models in identifying frailty among LTC residents.
  • To utilize gait and daily physical activity data from a single accelerometer for frailty detection.
  • To explore the potential of dynamic gait outcomes as sensitive frailty markers in LTC.

Main Methods:

  • A cross-sectional analysis of 51 LTC residents using baseline data from a randomized controlled trial.
  • Frailty status assessed via the FRAIL-NH scale.
  • Gait data collected during a 5-meter walk and daily physical activity monitored for approximately one week using a 3D accelerometer.
  • 34 dynamic and spatial-temporal gait outcomes, 3 physical activity variables, and 6 demographic characteristics extracted.
  • Five ML models trained using leave-one-out cross-validation; performance evaluated by accuracy and AUC.
  • Explainable AI (XAI) techniques employed for outcome interpretability.

Main Results:

  • The extreme gradient boosting model achieved the highest performance with 86.3% accuracy and an AUC of 0.92.
  • XAI analysis indicated that frail individuals exhibited more variable, complex, and asymmetric gait patterns.
  • Key gait indicators for frailty included higher stride length variability, increased sample entropy, and a higher gait symmetry score.

Conclusions:

  • Dynamic gait outcomes, particularly variability and asymmetry, are more sensitive indicators of frailty in LTC settings than traditional spatial-temporal measures.
  • ML models trained on accelerometer data show significant potential for accurate frailty detection in LTC.
  • These findings can inform enhanced strategies for frailty detection and management in long-term care.