Discovery of activity patterns using topic models
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Simulating the Potential Savings of Implicit Energy Management on a City Scale
DS-RT '08 Proceedings of the 2008 12th IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications
Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography
Pervasive '08 Proceedings of the 6th International Conference on Pervasive Computing
Review: The use of pervasive sensing for behaviour profiling - a survey
Pervasive and Mobile Computing
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
BadIdeas for usability and design of medicine and healthcare sensors
USAB'07 Proceedings of the 3rd Human-computer interaction and usability engineering of the Austrian computer society conference on HCI and usability for medicine and health care
Trimming the tree: tailoring adaptive huffman coding to wireless sensor networks
EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
Identification of relevant multimodal cues to enhance context-aware hearing instruments
Proceedings of the 6th International Conference on Body Area Networks
Detecting leisure activities with dense motif discovery
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition
Pattern Recognition Letters
CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living
Future Generation Computer Systems
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This paper introduces an encapsulated sensor node that is devised to monitor and record motion patterns over long, quotidian periods of time with potential application in psychological studies. Its design fuses different sensing modalities to allow efficient capturing of tilt and acceleration stimuli, as well as embedded algorithms that abstract from the raw sensory data to indicative features. By combining tilt switches and accelerometers with customized processing techniques, it is argued that a power-efficient yet information-rich approach is reached for the observation and logging of human motion-based activity.