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Automatic behavior descriptors for psychological disorder analysis.
| Content Provider | CiteSeerX |
|---|---|
| Author | Scherer, Stefan Stratou, Giota Mahmoud, Marwa Boberg, Jill Gratch, Jonathan Rizzo, Albert Skip |
| Abstract | Abstract — We investigate the capabilities of automatic nonverbal behavior descriptors to identify indicators of psychological disorders such as depression, anxiety, and post-traumatic stress disorder. We seek to confirm and enrich present state of the art, predominantly based on qualitative manual annotations, with automatic quantitative behavior descriptors. In this paper, we propose four nonverbal behavior descriptors that can be automatically estimated from visual signals. We introduce a new dataset called the Distress Assessment Interview Corpus (DAIC) which includes 167 dyadic interactions between a confederate interviewer and a paid participant. Our evaluation on this dataset shows correlation of our automatic behavior descriptors with specific psychological disorders as well as a generic distress measure. Our analysis also includes a deeper study of selfadaptor and fidgeting behaviors based on detailed annotations of where these behaviors occur. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Automatic Behavior Descriptor Psychological Disorder Analysis Automatic Quantitative Behavior Descriptor Psychological Disorder New Dataset Distress Assessment Interview Corpus Automatic Nonverbal Behavior Descriptor Present State Dyadic Interaction Visual Signal Detailed Annotation Post-traumatic Stress Disorder Qualitative Manual Annotation Confederate Interviewer Specific Psychological Disorder Nonverbal Behavior Descriptor Generic Distress Measure |
| Content Type | Text |
