Automatic emotion perception using eye movement information for E-Healthcare systems.
Journal article
Authors | Yang 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. |
Keywords | emotion recognition; eye movement; adolescence; healthcare |
Year | 2018 |
Journal | Sensors |
Publisher | MDPI |
Digital Object Identifier (DOI) | https://doi.org/10.3390/s18092826 |
Web address (URL) | http://hdl.handle.net/10545/623027 |
hdl:10545/623027 | |
Publication dates | 31 Aug 2018 |
Publication process dates | |
Deposited | 11 Oct 2018, 09:13 |
Contributors | Anhui University and University of Derby |
File | File Access Level Open |
File | File Access Level Open |
https://repository.derby.ac.uk/item/924x4/automatic-emotion-perception-using-eye-movement-information-for-e-healthcare-systems
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