Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
SATIRE: a software architecture for smart AtTIRE
Proceedings of the 4th international conference on Mobile systems, applications and services
Activity-based serendipitous recommendations with the Magitti mobile leisure guide
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An activity recognition system for mobile phones
Mobile Networks and Applications
Editorial: Hybrid learning machines
Neurocomputing
Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection
EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
Activity recognition using cell phone accelerometers
ACM SIGKDD Explorations Newsletter
Interacting activity recognition using hierarchical durational-state dynamic bayesian network
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
ICEC'07 Proceedings of the 6th international conference on Entertainment Computing
Towards the detection of unusual temporal events during activities using HMMs
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Context-aware mobile music recommendation for daily activities
Proceedings of the 20th ACM international conference on Multimedia
Complex activity recognition using context-driven activity theory and activity signatures
ACM Transactions on Computer-Human Interaction (TOCHI)
My act: an automatic daily caloric estimation based on physical activity data using smart phones
Proceedings of the 7th International Convention on Rehabilitation Engineering and Assistive Technology
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As smartphone users have been increased, studies using mobile sensors on smartphone have been investigated in recent years. Activity recognition is one of the active research topics, which can be used for providing users the adaptive services with mobile devices. In this paper, an activity recognition system on a smartphone is proposed where the uncertain time-series acceleration signal is analyzed by using hierarchical hidden Markov models. In order to address the limitations on the memory storage and computational power of the mobile devices, the recognition models are designed hierarchy as actions and activities. We implemented the real-time activity recognition application on a smartphone with the Google android platform, and conducted experiments as well. Experimental results showed the feasibility of the proposed method.