Authors:
Kizito Nkurikiyeyezu
;
Kana Shoji
;
Anna Yokokubo
and
Guillaume Lopez
Affiliation:
Graduate School of Science and Engineering, Aoyama Gakuin University, 5-10-1 Fuchinobe, Chuo-ku, Sagamihara-shi, 252-5258 Kanagawa and Japan
Keyword(s):
Personalized Thermal Comfort, Work Stress, Heart Rate Variability, Smart Buildings, Humanized Computing, Affective Computing.
Related
Ontology
Subjects/Areas/Topics:
Affective Computing
;
Biomedical Engineering
;
Health Information Systems
;
Human-Machine Interfaces
;
Informatics in Control, Automation and Robotics
;
Pattern Recognition and Machine Learning
;
Robotics and Automation
Abstract:
Work stress and thermal discomfort are some of the hurdles that office workers face every day. Office workers experience a periodic work stress because work is long and mentally challenging. At the same time, current thermal comfort provision technologies are inefficient and consume a large amount of energy. In our previous works, we proposed an efficient thermal comfort provision system that is based on a person's heart rate variability (HRV). However, because work stress can also affect the person's HRV, this paper investigates the possibility to distinguish HRV changes that are due to thermal discomfort from changes that emanate from work stress. We conducted experiments on subjects taking Advanced Trail Making Test (ATMT) and observed that stress alters HRV and that it is possible to distinguish stressed and non-stressed subjects with a 100% accuracy. We validated our method on the multimodal SWELL knowledge work (SWELL-KW) stress dataset and achieved similar results (99.25% accu
racy and 99.75% average recall). Further analysis suggests that, although both thermal comfort and work stress affect HRV, their effect is perhaps non-overlapping, and that the two can be distinguished with a near-perfect accuracy. These results indicate that it could be possible to design an automatic and unobtrusive system that delivers thermal comfort and predicts work stress based on people's HRV
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