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Efficiently Recording the Eye-Hand Coordination to Incoordination Spectrum

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Published on: March 21, 2019

Age-related differences in finger interdependence during complex hand movements.

Lisa Klemm^1,2, Esther Kuehn^3,4,5,6,7, Avinash Kalyani³

¹Department of Neurology, University Medical Center, Magdeburg, Germany.

Journal of Applied Physiology (Bethesda, Md. : 1985)

|May 2, 2024

Summary

Older adults show less unintentional finger movement (interdependence) in daily tasks, unlike simple tapping. This reduced interdependence may signal aging, aiding age group prediction for complex movements.

Keywords:

daily life kinematics finger enslaving finger independence movement features support vector machine

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Area of Science:

Gerontology
Biomechanics
Human Movement Science

Background:

Healthy aging is associated with reduced finger dexterity, impacting quality of life.
Age-related changes in finger kinematics and interdependence are not fully understood, especially during daily activities.
Existing research on finger interdependence across the lifespan presents ambiguous findings regarding age-related differences.

Purpose of the Study:

To investigate age-related differences in finger interdependence during daily-life-inspired movements and a sequential finger tapping task.
To explore the potential of finger interdependence as a biomarker for aging in complex motor tasks.
To determine if machine learning can predict age group based on finger interdependence patterns.

Main Methods:

Utilized an exoskeleton data glove to record kinematic data from 17 younger and 17 older adults.
Assessed five daily-life-inspired finger movements and a thumb-to-finger tapping task.
Applied inferential statistics and machine learning algorithms to analyze finger interdependence and predict age group.

Main Results:

A general decrease in unintentional finger comovement (interdependence) with age was observed in daily-life movements.
Finger tapping showed a trend towards increased interdependence in older adults compared to younger adults.
Machine learning successfully predicted age group from daily-life movement interdependence, but not from finger tapping.

Conclusions:

Decreased finger interdependence in specific daily tasks may serve as a marker for human aging.
Age-related effects on finger interdependence are task-dependent, differing between complex daily movements and simple tapping.
Finger interdependence patterns offer potential for machine learning-based age prediction in older adults' movements.