Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Critical-Path based Low-Energy Scheduling Algorithms for Body Area Network Systems
RTCSA '07 Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Energy conservation in wireless sensor networks: A survey
Ad Hoc Networks
Mercury: a wearable sensor network platform for high-fidelity motion analysis
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Measuring foot pronation using RFID sensor networks
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
A human probe for measuring walkability
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Transmission power control in body area sensor networks for healthcare monitoring
IEEE Journal on Selected Areas in Communications - Special issue on body area networking: Technology and applications
Energy optimization in wireless medical systems using physiological behavior
WH '10 Wireless Health 2010
Energy and Cost Reduction in Localized Multisensory Systems through Application-Driven Compression
DCC '12 Proceedings of the 2012 Data Compression Conference
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Traditional optimization methods for large multisensory networks often use sensor array reduction and sampling techniques that attempt to reduce energy while retaining full predictability of the raw sensed data. For systems such as medical sensor networks, raw data prediction is unnecessary, rather, only relevant semantics derived from the raw data are essential. We present a new method for sensor fusion, array reduction, and subsampling that reduces both energy and cost through semantics-driven system configuration. Using our method, we reduce the energy requirements of a medical shoe by a factor of 17.9 over the original system configuration while maintaining semantic relevance.