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.
Highlights
E-Healthcare systems, an effective and timely communication mode between patients, doctors, nurses, and other healthcare professionals, has been a research hotspot in the field of intelligent perception and healthcare for several years [1,2,3]
This study presented an emotion recognition method combining EOG and eye movement video
To improve the performance of emotion perception, we further explored two fusion strategies to integrate the time/frequency features, saccades features, fixation features, and pupil diameters
Summary
E-Healthcare systems, an effective and timely communication mode between patients, doctors, nurses, and other healthcare professionals, has been a research hotspot in the field of intelligent perception and healthcare for several years [1,2,3]. The existing E-Healthcare systems focus mainly on the acquisition and recording of information associated with physical health conditions (e.g., body temperature, saturation of pulse oxygen, respiratory rate, heart rate, etc.), while ignoring the emotional health aspect. Researchers have carried out a series of studies on the automatic acquisition and analysis of emotional states [8,9,10,11]. The commonly used emotional information acquisition methods are mainly divided into contact-free and contact two ways. Contact-free methods mainly refer to speech, facial expressions, postures, etc. Such methods have the advantages of simple signal acquisition and causing no discomfort to the subjects.
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