Abstract
One of the prominent clinical manifestations of schizophrenia is flat or altered facial activity, and flattening of emotional expressiveness (Flat Affect). In this study we used a structured-light depth camera and dedicated software to automatically measure the facial activity of schizophrenia patients and healthy individuals during a short structured interview. Based on K-means clustering analysis, facial activity was characterized in terms of Typicality, Richness and Distribution of 7 facial-clusters. Thus we found patients' facial activity to be poorer, more typical, and characterized mainly by neutral (flat) expressions. The facial features defined in our study achieved up to 85% correct diagnosis classification rate in a SVM based two-step algorithm, and were in significant correlation with Flat Affect severity. Our results demonstrate how the use of assistive technology and data-driven computational tools allow for a comprehensive description of patients' facial behavior in clinical settings, and may contribute to the reliability and accuracy of psychiatric diagnosis.
| Original language | English |
|---|---|
| Title of host publication | 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 220-223 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781509024551 |
| DOIs | |
| State | Published - 18 Apr 2016 |
| Event | 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 - Las Vegas, United States Duration: 24 Feb 2016 → 27 Feb 2016 |
Publication series
| Name | 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 |
|---|
Conference
| Conference | 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 |
|---|---|
| Country/Territory | United States |
| City | Las Vegas |
| Period | 24/02/16 → 27/02/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.