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

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    Authors
    Yang Wang
    Zhao Iv
    Yongjun Zheng
    Affiliation
    Anhui University
    University of Derby
    Issue Date
    2018-08-31
    
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    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.
    Citation
    Wang, Y., Iv, Z., Zheng, Y. (2018) 'Automatic emotion perception using eye movement information for E-Healthcare systems.' Sensors, 18(9), 2826; doi. 10.3390/s18092826.
    Publisher
    MDPI
    Journal
    Sensors
    URI
    http://hdl.handle.net/10545/623027
    DOI
    10.3390/s18092826
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.3390/s18092826
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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