NDLI: Low power programmable architecture for periodic activity monitoring

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Low power programmable architecture for periodic activity monitoring

Content Provider	ACM Digital Library
Author	Jafari, Roozbeh Bidmeshki, Mohammad-Mahdi
Abstract	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.
Starting Page	81
Ending Page	88
Page Count	8
File Format	PDF
ISBN	9781450319966
DOI	10.1145/2502524.2502536
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-04-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Body sensor networks Power optimization Signal processing Wearable computing Activity monitoring
Content Type	Text
Resource Type	Article

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