Automatic emotion perception using eye movement information for E-Healthcare systems.

Journal article


Yang Wang, Zhao Iv and Yongjun Zheng 2018. Automatic emotion perception using eye movement information for E-Healthcare systems. Sensors. https://doi.org/10.3390/s18092826
AuthorsYang Wang, Zhao Iv and Yongjun Zheng
Abstract

Facing the adolescents and detecting their emotional state is vital for promoting rehabilitation therapy within an E-Healthcare system. Focusing on a novel approach for a sensor-based E-Healthcare system, we propose an eye movement information-based emotion perception algorithm by collecting and analyzing electrooculography (EOG) signals and eye movement video synchronously. Specifically, we extract the time-frequency eye movement features by firstly applying the short-time Fourier transform (STFT) to raw multi-channel EOG signals. Subsequently, in order to integrate time domain eye movement features (i.e., saccade duration, fixation duration, and pupil diameter), we investigate two feature fusion strategies: feature level fusion (FLF) and decision level fusion (DLF). Recognition experiments have been also performed according to three emotional states: positive, neutral, and negative. The average accuracies are 88.64% (the FLF method) and 88.35% (the DLF with maximal rule method), respectively. Experimental results reveal that eye movement information can effectively reflect the emotional state of the adolescences, which provides a promising tool to improve the performance of the E-Healthcare system.

Keywordsemotion recognition; eye movement; adolescence; healthcare
Year2018
JournalSensors
PublisherMDPI
Digital Object Identifier (DOI)https://doi.org/10.3390/s18092826
Web address (URL)http://hdl.handle.net/10545/623027
hdl:10545/623027
Publication dates31 Aug 2018
Publication process dates
Deposited11 Oct 2018, 09:13
ContributorsAnhui University and University of Derby
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https://repository.derby.ac.uk/item/924x4/automatic-emotion-perception-using-eye-movement-information-for-e-healthcare-systems

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