The Conference Assistant: Combining Context-Awareness with Wearable Computing
ISWC '99 Proceedings of the 3rd IEEE International Symposium on Wearable Computers
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Validated caloric expenditure estimation using a single body-worn sensor
Proceedings of the 11th international conference on Ubiquitous computing
Using wearable activity type detection to improve physical activity energy expenditure estimation
Proceedings of the 12th ACM international conference on Ubiquitous computing
Accelerometer Placement for Posture Recognition and Fall Detection
IE '11 Proceedings of the 2011 Seventh International Conference on Intelligent Environments
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Monitoring human energy expenditure is important in many health and sport applications, since the energy expenditure directly reflects the level of physical activity. The actual energy expenditure is unpractical to measure; hence, the field aims at estimating it by measuring the physical activity with accelerometers and other sensors. Current advanced estimators use a context-dependent approach in which a different regression model is invoked for different activities of the user. In this paper, we go a step further and use multiple contexts corresponding to multiple sensors, resulting in an ensemble of models for energy expenditure estimation. This provides a multi-view perspective, which leads to a better estimation of the energy. The proposed method was experimentally evaluated on a comprehensive set of activities where it outperformed the current state-of-the-art.