Please use this identifier to cite or link to this item:
Title: Assessment and Prediction of Negative Symptoms of Schizophrenia from RGB+D Movement Signals
Authors: Chakraborty, Debsubhra
Tahir, Yasir
Yang, Zixu
Maszczyk, Tomasz
Dauwels, Justin
Thalmann, Daniel
Magnenat Thalmann, Nadia
Tan, Bhing-Leet
Lee, Jimmy
Keywords: Negative symptoms
Issue Date: 2017
Source: Chakraborty, 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.
Abstract: Negative 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.
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers
IGS Conference Papers
NTC Conference Papers

Files in This Item:
File Description SizeFormat 
MMSP_2017_Deb.pdfMain article314.98 kBAdobe PDFThumbnail

Page view(s) 10

Updated on Jan 27, 2023

Download(s) 20

Updated on Jan 27, 2023

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.