Machine Learning
On using existing time-use study data for ubiquitous computing applications
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Sustained logging and discrimination of sleep postures with low-level, wrist-worn sensors
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Monitoring body positions and movements during sleep using WISPs
WH '10 Wireless Health 2010
How to log sleeping trends? a case study on the long-term capturing of user data
EuroSSC'10 Proceedings of the 5th European conference on Smart sensing and context
Predicting sleeping behaviors in long-term studies with wrist-worn sensor data
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
Improving activity recognition without sensor data: a comparison study of time use surveys
Proceedings of the 4th Augmented Human International Conference
Towards never-ending learning from time series streams
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast, Accurate Event Classification on Resource-Lean Embedded Sensors
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Already up? using mobile phones to track & share sleep behavior
International Journal of Human-Computer Studies
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Long-term sleep monitoring of patients has been identified as a useful tool to observe sleep trends manifest themselves over weeks or months for use in behavioral studies. In practice, this has been limited to coarse-grained methods such as actigraphy, for which the levels of activity are logged, and which provide some insight but have simultaneously been found to lack accuracy to be used for studying sleeping disorders. This paper presents a method to automatically detect the user's sleep at home on a long-term basis. Inertial, ambient light, and time data tracked from a wrist-worn sensor, and additional night vision footage is used for later expert inspection. An evaluation on over 4400 hours of data from a focus group of test subjects demonstrates a high re-call night segment detection, obtaining an average of 94%. Further, a clustering to visualize reoccurring sleep patterns is presented, and a myoclonic twitch detection is introduced, which exhibits a precision of 74%. The results indicate that long-term sleep pattern detections are feasible.