Emotion Detection Using EEG and ECG Signals from Wearable Textile Devices for Elderly People

• Published in: Journal of Textile Engineering Vol. 66; no. 6; pp. 109 - 117
• Main Authors: ZENG, Fangmeng, LIN, Yitao, SIRIARAYA, Panote, CHOI, Dongeun, KUWAHARA, Noriaki
• Format: Journal Article
• Language: English
• Published: Osaka The Textile Machinery Society of Japan 15.12.2020; Japan Science and Technology Agency
• Subjects: Classification; Deep learning; Dementia; ECG; EEG; Elderly people; Electrocardiography; Electroencephalography; Emotion; Emotions; Machine learning; Neural networks; Older people; Teaching methods; Wear; Wearable technology; Wearable textile devices
• ISSN: 1346-8235; 1880-1986
• DOI: 10.4188/jte.66.109

The global population is ageing; exacerbating a range of age-related health problems, like dementia. In the late stage of dementia, patients often are unable to find words to express their feelings; causing serious challenges in healthcare. Our aim is to detect the emotions of elderly patients using physiological signals - electroencephalogram (EEG) and electrocardiogram (ECG) - using deep learning neural networks. However, most EEG and ECG monitoring devices are uncomfortable and not suitable for daily wear by elderly people. For this study, a prior experiment was conducted on 5 healthy elderly subjects for binary classification of positive and negative emotions: EEG and ECG data were collected from the subjects, using our own designed wearable textile devices while they watch selected stimuli. We propose an end-to-end deep learning method - Long short-term memory (LSTM) - to detect emotion from raw clean signals after removing noises and baseline wander. LSTM can learn features from raw data directly and achieve binary emotion classification with an accuracy of 76.67% with EEG signals, 75.00% with ECG signals, and 95.00% with EEG and ECG signals, respectively. This proposed system for detecting emotion by deep learning method using our userfriendly and easy-to-wear textile devices offer great prospects for use in everyday care situations and dementia care.