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Can human movement analysis contribute to usability understanding?

Juan-Manuel Belda-Lois1, Helios de-Rosario, Romà Pons

  • 1Instituto de Biomecánica de Valencia, Camino de Vera s/n, Valencia, Spain. juanma.belda@ibv.upv.es

Human Movement Science
|June 29, 2010
PubMed
Summary

This study reveals that human-machine interface learning involves concurrent processes linked to hand movement coordination. Age influences learning progression, but older adults learn technological systems effectively.

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Science
  • Motor Learning

Background:

  • Current human-machine interface (HMI) evaluation methods often overlook human movement and the learning process.
  • Traditional cognitive models view learning as a single continuous process, neglecting potential concurrent subprocesses.
  • Emerging evidence suggests learning may involve concurrent processes operating on different timescales.

Purpose of the Study:

  • To experimentally investigate if learning implies concurrent processes.
  • To determine if these processes relate to hand movement coordination.
  • To explore age-related variations (young adult vs. elderly) in HMI learning.

Main Methods:

  • Experimental analysis of user interaction with a washing machine and a domotic system.

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  • Inclusion of 23 and 20 participants, respectively, categorized by age (adults 40-50, elderly over 55).
  • Focus on analyzing human movements and learning progression during interface use.

    • Main Results:

    • Experimental data support the existence of concurrent learning processes, consistent with findings in motor tasks.
    • The learning process is demonstrably associated with observable changes in movement patterns.
    • Learning progression varies with age, yet elderly individuals show equal capability in mastering technological systems compared to younger adults.

    Conclusions:

    • Human-machine interface learning is not a singular process but involves concurrent subprocesses.
    • Coordinative aspects of hand movement are integral to the HMI learning trajectory.
    • Age is a factor in learning speed and pattern, but does not impede the ultimate ability of elderly users to learn new technologies.