dc.contributor.authorChakraborty, Debsubhra
dc.contributor.authorTahir, Yasir
dc.contributor.authorYang, Zixu
dc.contributor.authorMaszczyk, Tomasz
dc.contributor.authorDauwels, Justin
dc.contributor.authorThalmann, Daniel
dc.contributor.authorMagnenat Thalmann, Nadia
dc.contributor.authorTan, Bhing-Leet
dc.contributor.authorLee, Jimmy
dc.date.accessioned2017-08-07T04:40:41Z
dc.date.available2017-08-07T04:40:41Z
dc.date.issued2017
dc.identifier.citationChakraborty, D., Tahir, Y., Yang, Z., Maszczyk, T., Dauwels, J., Thalmann, D., et al. (2017). Assessment and Prediction of Negative Symptoms of Schizophrenia from RGB+D Movement Signals. 2017 IEEE 19th International Workshop on Multimedia Signal Processing.
dc.identifier.urihttp://hdl.handle.net/10220/43560
dc.description.abstractNegative symptoms of schizophrenia significantly affect the daily functioning of patients, especially movement and expressive gestures. The diagnosis of such symptoms is often difficult and require the expertise of a trained clinician. Apart from these subjective methods, there is little research on developing objective methods to quantify the symptoms. Therefore, we explore body movement signals as objective measures of negative symptoms. Specifically, we extract the signals from video recordings of patients being interviewed. We analysed the interviews of 69 paid participants (46 patients and 23 healthy controls) in this study. Correlation between movement signals (linear and angular speeds of upper limbs and head, acceleration and gesture angles) and subjective ratings (assigned during same interview) from the NSA-16 scale were calculated. As hypothesized, the movement signals correlated strongly with the movement impairment aspect of the NSA-16 questionnaire. Also, not quite surprisingly, strong correlations were obtained between the movement signals and speech items of NSA-16, indicating lack of associated gestures in patients during speech. These subjective ratings could also be reasonably predicted from the objective signals with an accuracy of 61-78 % using machine-learning algorithms with leave-one-out cross-validation technique. Furthermore, these objective measures can be reliably utilized to distinguish between the patient and healthy groups, as supervised learning methods can classify the two groups with 74-87 % accuracy.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.description.sponsorshipNMRC (Natl Medical Research Council, S’pore)en_US
dc.format.extent6 p.en_US
dc.language.isoenen_US
dc.rights© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.subjectSchizophreniaen_US
dc.subjectNegative symptomsen_US
dc.titleAssessment and Prediction of Negative Symptoms of Schizophrenia from RGB+D Movement Signalsen_US
dc.typeConference Paper
dc.contributor.conference2017 IEEE 19th International Workshop on Multimedia Signal Processingen_US
dc.contributor.departmentGraduate Studies Officeen_US
dc.contributor.researchInstitute for Media Innovationen_US
dc.contributor.researchInstitute of Mental Health
dc.contributor.researchInstitute for Media Innovation
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.description.versionAccepted versionen_US


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