Stress Monitoring using Multimodal Bio-sensing Headset

被引:7
作者
Lee, Joong Hoon [1 ,6 ]
Gamper, Hannes [2 ]
Tashev, Ivan [2 ]
Dong, Steven [3 ]
Ma, Siyuan [4 ]
Remaley, Jacquelin [4 ]
Holbery, James D. [5 ,6 ]
Yoon, Sang Ho [4 ]
机构
[1] Korea Univ, KU KIST Grad Sch Converging Sci & Technol, Seoul, South Korea
[2] Microsoft Res, Redmond, WA USA
[3] Microsoft, Human Factors, Redmond, WA USA
[4] Microsoft, Appl Sci, Redmond, WA USA
[5] Tactual Labs, Redmond, WA USA
[6] Microsoft, Redmond, WA USA
来源
CHI'20: EXTENDED ABSTRACTS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS | 2020年
关键词
Wearable; Biometric Sensing; Stress detection; Machine learning; Convolutional Neural Network;
D O I
10.1145/3334480.3382891
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Exposure to continuous stress can have a negative impact on a person's mental and physical well-being. Stress monitoring and management, with the aim to analyze or mitigate the effects of stress, are an active area of research. A promising approach for detecting stress is by measuring bio-signals such as an electroencephalogram (EEG) or an electrocardiogram (ECG). In this study, we introduce a wearable in- and over-ear device that measures EEG and ECG signals simultaneously. The device is composed of dry and soft sensing electrodes which are conformally integrated on the surface of earbuds. We carried out a pilot study exposing test subjects to three standard stressors (stroop, memory search, and mental arithmetic) while measuring their EEG and ECG signals. Preliminary results indicate the feasibility of classifying various stress conditions using a convolutional neural network.
引用
收藏
页数:7
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