Implementation and Evaluation of a Low-Power Sound-Based User Activity Recognition System
ISWC '04 Proceedings of the Eighth International Symposium on Wearable Computers
Trading off Prediction Accuracy and Power Consumption for Context-Aware Wearable Computing
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Power and Size Optimized Multi-Sensor Context Recognition Platform
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Power and accuracy trade-offs in sound-based context recognition systems
Pervasive and Mobile Computing
A framework for the automated generation of power-efficient classifiers for embedded sensor nodes
Proceedings of the 5th international conference on Embedded networked sensor systems
Using rhythm awareness in long-term activity recognition
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Automatic feature selection for context recognition in mobile devices
Pervasive and Mobile Computing
Energy-accuracy trade-off for continuous mobile device location
Proceedings of the 8th international conference on Mobile systems, applications, and services
Energy-efficient rate-adaptive GPS-based positioning for smartphones
Proceedings of the 8th international conference on Mobile systems, applications, and services
An energy-efficient quality adaptive framework for multi-modal sensor context recognition
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
SpeakerSense: energy efficient unobtrusive speaker identification on mobile phones
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Investigation of Context Prediction Accuracy for Different Context Abstraction Levels
IEEE Transactions on Mobile Computing
KNOWME: An Energy-Efficient Multimodal Body Area Network for Physical Activity Monitoring
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on CAPA'09, Special Section on WHS'09, and Special Section VCPSS' 09
Energy-Efficient Continuous Activity Recognition on Mobile Phones: An Activity-Adaptive Approach
ISWC '12 Proceedings of the 2012 16th Annual International Symposium on Wearable Computers (ISWC)
Energy-Efficient Activity Recognition Using Prediction
ISWC '12 Proceedings of the 2012 16th Annual International Symposium on Wearable Computers (ISWC)
Towards Collaborative Group Activity Recognition Using Mobile Devices
Mobile Networks and Applications
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Energy storage is quickly becoming the limiting factor in mobile pervasive technology. We introduce a novel method for activity recognition which leverages the predictability of human behavior to conserve energy by dynamically selecting sensors. We further present a taxonomy of existing approaches to dynamically reducing consumption while maintaining recognition rates. The novel algorithm conserves energy by quantifying activity-sensor dependencies and using prediction methods to identify likely future activities. The approach is implemented and simulated using two activity recognition data sets, and the effects of the novel method are evaluated in terms of recognition rates, energy consumption, and prediction rates. The results indicate that switching off sensors only significantly affects prediction under extreme conditions and that these effects can be counteracted by adjusting system parameters. Large savings in energy can be achieved at very low cost, for example, recognition losses of 1.5 pp with 84.8 % energy savings for the first data set, and 2.8 pp and 89.9 % for the second.