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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Ensemble Learning Based Gender Recognition from Physiological Signals
verfasst von : Huiling Zhang, Ning Guo, Guangyuan Liu, Junhao Hu, Jiaxiu Zhou, Shengzhong Feng, Yanjie Wei
Erschienen in: Big Data – BigData 2018
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Abstract
Gender recognition based on facial image, body gesture and speech has been widely studied. In this paper, we propose a gender recognition approach based on four different types of physiological signals, namely, electrocardiogram (ECG), electromyogram (EMG), respiratory (RSP) and galvanic skin response (GSR). The core steps of the experiment consist of data collection, feature extraction and feature selection & classification. We developed a wrapper method based on Adaboost and sequential backward selection for feature selection and classification. Through the data analysis of 234 participants, we obtained a recognition accuracy of 91.1% with a subset of 12 features from ECG/EMG/RSP/GSR, 82.3% with 11 features from ECG only, 80.8% with 5 features from RSP only, indicating the effectiveness of the proposed method. The ECG, EMG, RSP, GSR signals are collected from human wrist, face, chest and fingers respectively, hence the method proposed in this paper can be easily applied to wearable devices.