A Novel Fuzzy Rough Nearest Neighbors Emotion Recognition Approach Based on Multimodal Wearable Biosensor Network
Due to the diversity and complexity of emotional bioinformation, most emotion recognition studies relied to ponderous medical grade electroencephalography (EEG) measuring devices. The emotion recognition scenarios were limited to hospitals or laboratories. It is hard to directly apply these research achievements into the emotion monitoring in daily-life. In this paper, a novel emotion recognition approach based on multimodal wearable biosensor network is investigated. In order to facilitate emotion monitoring in daily-life, a multimodal wearable biosensor network is constructed. The multimodal bio-signals are acquired by wearable biosensor measuring nodes, and then transmitted to sink node through wireless communication technologies. According to the fuzzy and rough characteristics of human emotions, the fuzzy rough nearest neighbors (FRNN) algorithm is introduced to classify different emotions. By considering the fuzzy thresholds of EEG concentration, a novel FRNN emotion recognition approach is proposed. The proposed method narrows the classification range of samples with significant concentration and reduces the disturbance of noisy samples, so that the high accuracy (65.6%) and fast speed were achieved in wearable scenario. Experiments verified the effectiveness of the proposed approach.