Abstract
Detection of long-lasting stress in its early stage may allow prevention of irreversible health damages, but most of today’s stress detectors are not sufficiently convenient for long-term use. Some detectors rely on wearable physiological devices, known to have high abandonment rate, whereas other systems collect only behavioural data, but require each user to report occurrences of his/her stress during at least a month because behavioural patterns notably vary between individuals. Therefore, behaviour-based stress detection models should be learned separately for each person, and in existing systems such learning is typically supervised. In contrast, this work proposes a person-specific stress monitoring system, requiring no efforts from end users. This system acquires users’ motion trajectories via in-office depth cameras and employs a novel unsupervised method for analysis of these trajectories, based on discrete Hidden Markov Models. In 10-months-long real life study the proposed system correctly recognised the most stressful working periods of the monitored subjects and pointed out the most stress-prone persons.
This work was supported by the Finnish Funding Agency for Technology and Innovation (Tekes) and VTT Technical Research Centre of Finland (ITEA 3 15008 ESTABLISH project).
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Acknowledgment
This research has been conducted as a part of the ITEA 3 15008 ESTABLISH project. We thank test subjects for their efforts to provide stress labels during a long study.
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Vildjiounaite, E., Huotari, V., Kallio, J., Kyllönen, V., Mäkelä, SM., Gimel’farb, G. (2019). Detection of Prolonged Stress in Smart Office. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 857. Springer, Cham. https://doi.org/10.1007/978-3-030-01177-2_90
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