Abstract: Emotions are fundamental to human life and can impact elderly healthcare encounters between caregiver and patient. Detecting emotions by monitoring the physical signals with wearable smart devices offers new promises for care support. While there are multiple studies on wearable devices, few of these pertain to soft electroencephalogram (EEG) caps designed for long-time wear by elderly people. In this study, a 4-channel textile cap was designed with dry electrodes held by an ultra-soft gel holder, while fashion and ergonomic design features were introduced to enhance wearability and comfort. The dry-electrode textile cap performed highly for monitoring EEG signals; closely matching the wet electrodes equipment. All participants reported positive feedback stating that the textile cap was softer, lighter, and more comfortable than other devices. A cumulative contribution rate of 72.199% for two factors (materials properties factor and design pattern factor) was achieved using the principal factor method (PFA), which are influencing the usability of the wearable devices. An average emotion classification accuracy of 81.32% was obtained from 5 healthy elderly subjects. It was thus concluded that the proposed method provides a stable monitoring and comfortable user experience for users, and can be used to detect emotions for elderly people with good results in the future.

Fangmeng ZENG, Panote Siriaraya, Dongeun Choi and Noriaki Kuwahara, “Textile EEG Cap using Dry-Comb Electrodes for Emotion Detection of Elderly People” International Journal of Advanced Computer Science and Applications(IJACSA), 11(4), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110409

@article{ZENG2020,
title = {Textile EEG Cap using Dry-Comb Electrodes for Emotion Detection of Elderly People},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110409},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110409},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {4},
author = {Fangmeng ZENG and Panote Siriaraya and Dongeun Choi and Noriaki Kuwahara}
}