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Affective computing for assessing treatments in psychiatry

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Abstract: In order to evaluate therapies in psychiatry, it is crucial to able to assess the negative symptoms of mentally ill people. In an ongoing study, we are designing data-driven methods, inspired by affective computing, to assess the negative symptoms of mentally ill people.

The underlying idea is that the severity of negative symptoms and impairments in neurocognition and social cognition in mentally ill people can be evaluated objectively and conveniently by speech and motor characteristics. Impoverished speech patterns (e.g., taking prolonged time to respond to a question, decreased percentage of speaking in a conversation, etc.) and motor patterns are often associated with higher severity of negative symptoms, and with poorer cognitive and functioning performance.

Specifically, we conducted an observational study involving 56 patients with schizophrenia and 26 healthy participants without any mental illness. All participants underwent a battery comprising of clinical, neuro- and social cognitive and functioning assessments in a single visit. Audio and video recording were conducted during a social responsivity task. We designed algorithms to extract the prosodic and conversational speech features and motor features from the audio and video clips, respectively, and applied pattern recognition methods to identify objective sociological metrics for assessing the severity of negative symptoms, cognitive performance and functioning levels of patients.

In the future, after additional optimization and testing, this technology might be used as a convenient, efficient and objective evaluation tool in routine clinical practice, potentially integrated in mobile apps or social robots.

In this talk, we will present our results so far, and will elaborate on the challenges and plans for future research.

Bio: Dr. Justin Dauwels is an Associate Professor of the School of Electrical and Electronic Engineering at the Nanyang Technological University (NTU) in Singapore. He also serves as Deputy Director of the ST Engineering – NTU corporate lab, which comprises 100+ PhD students, research staff and engineers, developing novel autonomous systems for airport operations and transportation.

His research interests are in data analytics with applications to intelligent transportation systems, autonomous systems, and analysis of human behaviour and physiology. He obtained his PhD degree in electrical engineering at the Swiss Polytechnical Institute of Technology (ETH) in Zurich in December 2005. Moreover, he was a postdoctoral fellow at the RIKEN Brain Science Institute (2006-2007) and a research scientist at the Massachusetts Institute of Technology (2008-2010). He has been a JSPS postdoctoral fellow (2007), a BAEF fellow (2008), a Henri-Benedictus Fellow of the King Baudouin Foundation (2008), and a JSPS invited fellow (2010, 2011).

His research on intelligent transportation systems has been featured by the BBC , Straits Times, Lianhe Zaobao, Channel 5, and numerous technology websites. His research team has won several best paper awards at international conferences. Besides his academic efforts, the team of Dr. Justin Dauwels also collaborates intensely with local start-ups, SMEs, and agencies, in addition to MNCs, in the field of data-driven transportation, logistics, and human behavior analysis.

This talk is part of the Bio-Inspired Robotics Lab (BIRL) Seminar Series series.

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