A Bayesian Assessment of Real-World Behavior During Multitasking
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12559_2017_Article_9500.pdf
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1.07 MB
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Author(s)
Fei, Joan
Green, David A
Hussain, Amir
Howard, Newton
Bergmann, Jeroen H. M.
Date Issued
August 2017
Journal
Cognitive Computation
Publisher
Springer-Verlag
Citation
Bergmann, Jeroen H.M. et al. “A Bayesian Assessment of Real-World Behavior During Multitasking.” Cognitive Computation 9, 4 (August 2017): 1-9 © 2017 The Author(s)
Version
Final published version
Abstract
Multitasking is common in everyday life, but its effect on activities of daily living is not well understood. Critical appraisal of performance for both healthy individuals and patients is required. Motor activities during meal preparation were monitored in healthy individuals with a wearable sensor network during single and multitask conditions. Motor performance was quantified by the median frequencies (fm) of hand trajectories and wrist accelerations. The probability that multitasking occurred based on the obtained motor information was estimated using a Naïve Bayes Model, with a specific focus on the single and triple loading conditions. The Bayesian probability estimator showed task distinction for the wrist accelerometer data at the high and low value ranges. The likelihood of encountering a certain motor performance during well-established everyday activities, such as preparing a simple meal, changed when additional (cognitive) tasks were performed. Within a healthy population, the probability of lower acceleration frequency patterns increases when people are asked to multitask. Cognitive decline due to aging or disease might yield even greater differences.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s12559-017-9500-6
