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Towards valence detection from EMG for Virtual Reality applications.

Mavridou, I., Seiss, E., Hamedi, M., Balaguer-Ballester, E. and Nduka, C., 2018. Towards valence detection from EMG for Virtual Reality applications. In: 12th International Conference on Disability, Virtual Reality and Associated Technologies (ICDVRAT 2018), 4 - 6 September 2018, Nottingham, UK.

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cameraready_ICDVRAT2018_paper_46.pdf - Accepted Version
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The current practical restraints for facial expression recognition in Virtual Reality (VR) led to the development of a novel wearable interface called Faceteq. Our team designed a pilot feasibility study to explore the effect of spontaneous facial expressions on eight EMG sensors, incorporated on the Faceteq interface. Thirty-four participants took part in the study where they watched a sequence of video stimuli while self-rating their emotional state. After a specifically designed signal pre-processing, we aimed to classify the responses into three classes (negative, neutral, positive). A C-SVM classifier was cross-validated for each participant, reaching an out-of-sample average accuracy of 82.5%. These preliminary results have encouraged us to enlarge our dataset and incorporate data from different physiological signals to achieve automatic detection of combined arousal and valence states for VR applications.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:emotion ; valence ; EMG ; VR ; virtual Reality ; faceteq ; affect detection ; facial expression ; classification
Group:Bournemouth University Business School
ID Code:31022
Deposited By: Symplectic RT2
Deposited On:20 Jul 2018 14:31
Last Modified:14 Mar 2022 14:12


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