Reliable, valid, and efficient assessment of depression is critical to identify individuals in need of treatment and to gauge treatment response. Current methods of assessment are limited to subjective measures of patient self-report and clinical interview. They fail to take into account observable measures of behavior that could better inform detection of the occurrence and severity of depression. Recent advances in computer vision, signal processing, and machine learning have potential to meet the need for improved depression screening, diagnosis, and ascertainment of severity (i.e., assessment). This chapter reviews these advances. We describe multimodal measures of behavior and physiology, how these measures can be processed to extract features sensitive to depression, and how classification or prediction may be used to provide automatic assessment of depression occurrence and severity.
|Title of host publication||The Handbook of Multimodal-Multisensor Interfaces|
|Subtitle of host publication||Signal Processing, Architectures, and Detection of Emotion and Cognition|
|Editors||Sharon Oviatt, Bjoern Schuller, Philip R. Cohen, Daniel Sonntag, Gerasimos Potamianos, Antonio Krueger|
|Place of Publication||United States|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||43|
|Publication status||Published - Oct 2018|