Analysis of physiological responses from multiple subjects for emotion recognition

Psychological disorders, including emotion and behavioral disorders, are common in the modern society. For example, previous studies by the New Zealand Mental Health Survey and the US National Co-morbidity Surveys (NCS), have found that the incidence of depression from ages 18 to 32 to be around 18%...

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Bibliographic Details
Main Authors: Gu, Yuan, Wong, Kai-Juan, Tan, Su-Lim
Other Authors: School of Computer Engineering
Format: Conference or Workshop Item
Language:English
Published: 2013
Subjects:
Online Access:https://hdl.handle.net/10356/96504
http://hdl.handle.net/10220/11940
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Institution: Nanyang Technological University
Language: English
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Summary:Psychological disorders, including emotion and behavioral disorders, are common in the modern society. For example, previous studies by the New Zealand Mental Health Survey and the US National Co-morbidity Surveys (NCS), have found that the incidence of depression from ages 18 to 32 to be around 18%. Thus, there is a need to effectively monitor the emotional or affective states of these patients with psychological illness. Whilst real-time and continuous psychological monitoring systems are still not prevalent, the monitoring of physiological signals are made easier with mobile sensors that can be attached to the human body. These physiological signals can then be processed using an embedded processor to provide an alternative means for automatic emotion recognition. However, for such a system to be developed, the relationship between physiological and psychological signals has to be understood. This paper aims to address this by investigating the relationship between the emotional experiences from multiple subjects and their physiological responses, including the skin conductance, heart rate, respiration and movements of the facial muscles. In summary, preliminary evaluations described in this paper demonstrated that the heart rate, respiration, blood volume pulse and electromyography signals have an impact on the recognition rate achievable by the proposed multi-user physiological response-based emotion detection system.