| Literature DB >> 35342213 |
Runze Yan1, Whitney R Ringwald2, Julio Vega Hernandez3, Madeline Kehl2, Sang Won Bae4, Anind K Dey5, Carissa Low3, Aidan G C Wright2, Afsaneh Doryab1.
Abstract
Continuous passive sensing of daily behavior from mobile devices has the potential to identify behavioral patterns associated with different aspects of human characteristics. This paper presents novel analytic approaches to extract and understand these behavioral patterns and their impact on predicting adaptive and maladaptive personality traits. Our machine learning analysis extends previous research by showing that both adaptive and maladaptive traits are associated with passively sensed behavior providing initial evidence for the utility of this type of data to study personality and its pathology. The analysis also suggests directions for future confirmatory studies into the underlying behavior patterns that link adaptive and maladaptive variants consistent with contemporary models of personality pathology.Entities:
Keywords: Behavior Modeling; Data Mining; Machine Learning; Mobile and Wearable Sensing; Personality Prediction
Year: 2022 PMID： 35342213 PMCID： PMC8951872 DOI： 10.1016/j.future.2022.02.010
Source DB: PubMed Journal: Future Gener Comput Syst ISSN： 0167-739X Impact factor: 7.187