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Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach.

Tad T Brunyé1,2, Kenny Yau2, Kana Okano2

  • 1Cognitive Science Team, US Army DEVCOM Soldier Center, Natick, MA, United States.

Frontiers in Physiology
|September 27, 2021
PubMed
Summary

Wearable sensors can predict human performance by modeling stress, sleep, and exertion. This computational approach translates physiological data into actionable insights for cognitive and physical tasks, enhancing prediction accuracy in high-stakes domains.

Keywords:
adaptive LASSOexercisehuman performancemachine learningmodelingsleepstress

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Area of Science:

  • Physiological monitoring
  • Computational modeling
  • Human performance science

Background:

  • Wearable technologies are increasingly used to measure physiological states like stress, sleep deprivation, and physical exertion.
  • Accurate classification of these states is improving, but predicting their impact on human performance remains challenging due to individual variability.
  • Applications are relevant to military operations and other high-stakes domains requiring reliable human performance assessment.

Purpose of the Study:

  • To develop a computational modeling approach for translating physiological states into actionable human performance insights.
  • To predict cognitive and physical performance outcomes based on data from wearable devices and other sources.
  • To address the challenge of individual variability in human behavior and its influence on performance.

Main Methods:

  • A conditional probabilistic model was constructed to predict human performance outcomes (reaction time, executive function, perceptuo-motor control) based on status predictors (stress, sleep, exertion).
  • Diverse data sources were used to estimate marginal probability density functions via parametric modeling and maximum likelihood estimation.
  • Joint distributions were optimized using an adaptive LASSO approach, incorporating effect sizes from meta-analyses of existing research.

Main Results:

  • The computational model successfully maintained the integrity of original data distributions and the directionality/robustness of conditional relationships.
  • The adaptive LASSO approach optimized the model by considering the strength and directionality of relationships between variables.
  • The developed framework provides a flexible and extensible solution for human performance prediction.

Conclusions:

  • The computational modeling approach offers a robust method for translating wearable sensor data into predictable human performance outcomes.
  • The framework is designed for flexibility, allowing expansion with new variables, data sources, and interaction complexities.
  • Future work includes real-time integration with wearable devices, individualized predictions, and validation in diverse settings.