On the Utility of Audiovisual Dialog Technologies and Signal Analytics for Real-time Remote Monitoring of Depression Biomarkers

Michael Neumann, Oliver Roessler, David Suendermann-Oeft, Vikram Ramanarayanan


Abstract
We investigate the utility of audiovisual dialog systems combined with speech and video analytics for real-time remote monitoring of depression at scale in uncontrolled environment settings. We collected audiovisual conversational data from participants who interacted with a cloud-based multimodal dialog system, and automatically extracted a large set of speech and vision metrics based on the rich existing literature of laboratory studies. We report on the efficacy of various audio and video metrics in differentiating people with mild, moderate and severe depression, and discuss the implications of these results for the deployment of such technologies in real-world neurological diagnosis and monitoring applications.
Anthology ID:
2020.nlpmc-1.7
Volume:
Proceedings of the First Workshop on Natural Language Processing for Medical Conversations
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | NLPMC | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
47–52
Language:
URL:
https://www.aclweb.org/anthology/2020.nlpmc-1.7
DOI:
10.18653/v1/2020.nlpmc-1.7
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/2020.nlpmc-1.7.pdf
Video:
 http://slideslive.com/38929892