Adaptive event detection with time-varying poisson processes
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Discovering routines from large-scale human locations using probabilistic topic models
ACM Transactions on Intelligent Systems and Technology (TIST)
ICOST'10 Proceedings of the Aging friendly technology for health and independence, and 8th international conference on Smart homes and health telematics
Detecting Human Activity Profiles with Dirichlet Enhanced Inhomogeneous Poisson Processes
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Mining Sensor Streams for Discovering Human Activity Patterns over Time
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Learning time-based presence probabilities
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
PreHeat: controlling home heating using occupancy prediction
Proceedings of the 13th international conference on Ubiquitous computing
Health score prediction using low-invasive sensors
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Autonomous place naming system using opportunistic crowdsensing and knowledge from crowdsourcing
Proceedings of the 12th international conference on Information processing in sensor networks
A survey on ontologies for human behavior recognition
ACM Computing Surveys (CSUR)
Forecasting multi-appliance usage for smart home energy management
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Using time use with mobile sensor data: a road to practical mobile activity recognition?
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
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Living in society, to go out is almost inevitable for healthy life. There is increasing attention to it in many fields, including pervasive computing, medical science, etc. There are various factors affecting the daily going-out behavior such as the day of the week, the condition of one's health, and weather. We assume that a person has one's own rhythm or patterns of going out as a result of the factors. In this paper, we propose a non-parametric clustering method to extract one's rhythm of the daily going-out behavior and a prediction method of one's future presence using the extracted models. We collect time histories of going out/coming home (6 subjects, total 827 days). Experimental results show that our method copes with the complexity of patterns and flexibly adapts to unknown observation.