Automatic emotion perception using eye movement information for E-Healthcare systems.
|Authors||Yang Wang, Zhao Iv and Yongjun Zheng|
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|
|Digital Object Identifier (DOI)||https://doi.org/10.3390/s18092826|
|Web address (URL)||http://hdl.handle.net/10545/623027|
|Publication dates||31 Aug 2018|
|Publication process dates|
|Deposited||11 Oct 2018, 09:13|
|Contributors||Anhui University and University of Derby|
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