Local discriminant bases and their applications
Journal of Mathematical Imaging and Vision - Special issue on mathematical imaging
SNAP: A Sensor-Network Asynchronous Processor
ASYNC '03 Proceedings of the 9th International Symposium on Asynchronous Circuits and Systems
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
BitSNAP: Dynamic Significance Compression for a Low-Energy Sensor Network Asynchronous Processor
ASYNC '05 Proceedings of the 11th IEEE International Symposium on Asynchronous Circuits and Systems
Design Considerations for Ultra-Low Energy Wireless Microsensor Nodes
IEEE Transactions on Computers
RTAS '11 Proceedings of the 2011 17th IEEE Real-Time and Embedded Technology and Applications Symposium
Low Power Tiered Wake-up Module for Lightweight Embedded Systems Using Cross Correlation
BSN '11 Proceedings of the 2011 International Conference on Body Sensor Networks
Observing Recovery from Knee-Replacement Surgery by Using Wearable Sensors
BSN '11 Proceedings of the 2011 International Conference on Body Sensor Networks
A Low Power Wake-Up Circuitry Based on Dynamic Time Warping for Body Sensor Networks
BSN '11 Proceedings of the 2011 International Conference on Body Sensor Networks
ICCPS '11 Proceedings of the 2011 IEEE/ACM Second International Conference on Cyber-Physical Systems
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Body sensor networks (BSNs) are considered a great example for cyber-physical systems due to their close coupling with human body. Activity monitoring is one of the numerous applications of BSNs. Continuous and real-time monitoring of human activities has many applications in healthcare and wellness domains. BSNs utilizing light-weight wearable computers and equipped with inertial sensors are highly suitable for real-time activity monitoring. However, power requirement is a major obstacle for miniaturization of these wearable systems, due to the need for sizable batteries, and also limits the life time of the system. In this paper, we propose a low-power programmable signal processing architecture for dynamic and periodic activity monitoring applications which utilizes the properties of the physical world (i.e., human body movements) to reduce the power consumption of the system. The significant power reduction is achieved by performing signal processing in a tiered-fashion and removing the signals that are not of interest as early as possible. Our proposed architecture uses wavelet decomposition and is favorable for the discrimination of periodic activities. The experimental results show that our architecture achieves 75.7% power saving while maintaining 96.9% sensitivity in the detection of target actions, compared with the scenario where the signal processing is not performed in tiered-fashion. This creates opportunities to enable the next generation of self-powered wearable computers.