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Related Experiment Video

Updated: Aug 14, 2025

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Dual-Task Upper Extremity Motor Performance Measured by Video Processing as Cognitive-Motor Markers for Older Adults.

Changhong Wang1,2, Mohsen Zahiri3, Ashkan Vaziri4

  • 1School of Biomedical Engineering, Sun Yat-sen University Shenzhen Campus, Shenzhen, China, wangchh55@mail.sysu.edu.cn.

Gerontology
|January 15, 2023
PubMed
Summary

This study introduces a video-based method to remotely assess cognitive-motor performance in older adults using an elbow movement test. The video approach shows good agreement with sensor-based methods and correlates with cognitive function measures.

Keywords:
Cognitive-motor performanceDementiaDual-task conditionRemote patient monitoringTelehealth

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Cognitive Neuroscience

Background:

  • Dual-task gait is used to assess cognitive-motor performance in older adults but is limited in remote settings.
  • There is a need for accessible and safe remote assessment tools for cognitive-motor function.

Purpose of the Study:

  • To propose and validate a video processing-based approach for remote quantification of cognitive-motor performance.
  • To assess the concurrent validity and clinical associations of a novel video-based motoric-cognitive meter (MCM).

Main Methods:

  • Eighteen older adults with mild cognitive impairment or dementia performed a 20-s repetitive elbow flexion-extension test with a concurrent memory task (backward counting).
  • Forearm movements were recorded via video camera and analyzed using a proposed video-based MCM.
  • Upper extremity kinematics were also quantified using a validated wrist-worn sensor for comparison.

Main Results:

  • The video-based MCM demonstrated good agreement with a sensor-based MCM for dual-task upper extremity motor performance (r ≥ 0.530, ICC2,1 ≥ 0.681).
  • Measures from the video-based MCM showed moderate correlations with the Mini-Mental State Examination (MMSE) (r ≥ 0.496).
  • Dual-task motor performance metrics correlated with dual-task gait speed (r ≥ 0.557), and flexion time predicted MMSE scores (adjusted R2 = 0.306, p = 0.025).

Conclusions:

  • A video processing-based approach effectively quantifies dual-task upper extremity motor performance.
  • The proposed video-based MCM shows concurrent validity and associations with established cognitive markers, supporting its use in remote assessments.
  • This technology holds promise for telehealth applications in monitoring cognitive-motor health in older adults.