Paper Info
Title
An Unobtrusive Stress Recognition System For The Smart Office
Abstract
This paper presents a novel approach to monitor office workers' behavioral patterns and heart rate variability. We integrated an EMFi sensor into a chair to measure the pressure changes caused by a user's body movements and heartbeat. Then, we employed machine learning methods to develop a classification model through which different work behaviors (body moving, typing, talking and browsing) could be recognized from the sensor data. Subsequently, we developed a BCG processing method to process the data recognized as 'browsing' and further calculate heart rate variability. The results show that the developed model achieved classification accuracies of up to 91% and the HRV could be calculated effectively with an average error of 5.77ms, By combining these behavioral and physiological measures, the proposed approach portrays work-related stress in a more comprehensive manner and could contribute an unobtrusive early stress detection system for future smart offices.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8856597
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Computer vision,Behavioral pattern,Heartbeat,Heart beat,Computer science,Heart rate variability,Feature extraction,Artificial intelligence,Stress recognition,Smart office,Wavelet transform
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Bin Yu1409.65
Biyong Zhang242.89
Pengcheng An385.88
Lisheng Xu417839.09
Mengru Xue543.48
jun hu619733.20