An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
On combining classifiers using sum and product rules
Pattern Recognition Letters
Activity and Location Recognition Using Wearable Sensors
IEEE Pervasive Computing
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
On using existing time-use study data for ubiquitous computing applications
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
What did you do today?: discovering daily routines from large-scale mobile data
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Activity Recognition from Accelerometer Data on a Mobile Phone
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Hierarchical ensemble of global and local classifiers for face recognition
IEEE Transactions on Image Processing
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
Discovering routines from large-scale human locations using probabilistic topic models
ACM Transactions on Intelligent Systems and Technology (TIST)
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
A unified framework for modeling and predicting going-out behavior
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Improving activity recognition without sensor data: a comparison study of time use surveys
Proceedings of the 4th Augmented Human International Conference
Prior knowledge of human activities from social data
Proceedings of the 2013 International Symposium on Wearable Computers
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Having mobile devices that are capable of finding out what activity the user is doing, has been suggested as an attractive way to alleviate interaction with these platforms, and has been identified as a promising instrument in for instance medical monitoring. Although results of preliminary studies are promising, researchers tend to use high sampling rates in order to obtain adequate recognition rates with a variety of sensors. What is not fully examined yet, are ways to integrate into this the information that does not come from sensors, but lies in vast data bases such as time use surveys. We examine using such statistical information combined with mobile acceleration data to determine 11 activities. We show how sensor and time survey information can be merged, and we evaluate our approach on continuous day-and-night activity data from 17 different users over 14 days each, resulting in a data set of 228 days. We conclude with a series of observations, including the types of activities for which the use of statistical data has particular benefits.