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.
Original language | English (US) |
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Pages (from-to) | 266-281 |
Number of pages | 16 |
Journal | Future Generation Computer Systems |
Volume | 132 |
DOIs | |
State | Published - Jul 2022 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2022 Elsevier B.V.
Keywords
- Behavior modeling
- Data mining
- Machine learning
- Mobile and wearable sensing
- Personality prediction