Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Rhythm modeling, visualizations and applications
Proceedings of the 16th annual ACM symposium on User interface software and technology
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Healthcare of an organization: using wearable sensors and feedback system for energizing workers
Proceedings of the 16th Asia and South Pacific Design Automation Conference
Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
A life log collector integrated with a remote-controller for enabling user centric services
IEEE Transactions on Consumer Electronics
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Increasingly, longitudinal behavioral data captured by various sensors are being analyzed to improve workplace performance. In this paper, we analyze the correlation between the regularity of workers' behavior and their levels of stress. We used a 23-month behavioral dataset for 18 workers that recorded their use of PCs and their locations in the office. We found that the principal eigenbehaviors extracted from the dataset with PCA represented typical work behaviors such as overwork using a PC and routine times for meetings. We found that more than 80% of each of the 18 workers' individual behaviors could be reconstructed using nine principal eigenbehaviors. In addition, the deviation ranges for the reconstruction accuracies were significantly different for workers in different positions. We conducted the correlation analysis between work behaviors of the workers and their stress level. Our results show a significant negative correlation (r 0.69, p