A Bayesian Assessment of Real-World Behavior During Multitasking.

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 encoun

SUBMITTER: Bergmann JHM 

PROVIDER: S-EPMC5722954 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature