An Unobtrusive Stress Recognition System for the Smart Office

Bin Yu, Biyong Zhang, Pengcheng An, Lisheng Xu, Mengru Xue, Jun Hu

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

2 Citaten (Scopus)

Samenvatting

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.

Originele taal-2Engels
Titel2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's1326-1329
Aantal pagina's4
ISBN van elektronische versie9781538613115
DOI's
StatusGepubliceerd - jul 2019
Evenement41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Duitsland
Duur: 23 jul 201927 jul 2019

Congres

Congres41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
LandDuitsland
StadBerlin
Periode23/07/1927/07/19

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