標題: Data Compression by Temporal and Spatial Correlations in a Body-Area Sensor Network: A Case Study in Pilates Motion Recognition
作者: Wu, Chun-Hao
Tseng, Yu-Chee
資訊工程學系
Department of Computer Science
關鍵字: Body-area sensor network;data compression;inertial sensor;pervasive computing;wireless sensor network
公開日期: 1-十月-2011
摘要: We consider a body-area sensor network (BSN) consisting of multiple small, wearable sensor nodes deployed on a human body to track body motions. Concerning that human bodies are relatively small and wireless packets are subject to more serious contention and collision, this paper addresses the data compression problem in a BSN. We observe that, when body parts move, although sensor nodes in vicinity may compete strongly with each other, the transmitted data usually exist some levels of redundancy and even strong temporal and spatial correlations. Unlike traditional data compression approaches for large-scale and multihop sensor networks, our scheme is specifically designed for BSNs, where nodes are likely fully connected and overhearing among sensor nodes is possible. In our scheme, an offline phase is conducted in advance to learn the temporal and spatial correlations of sensing data. Then, a partial ordering of sensor nodes is determined to represent their transmission priorities so as to facilitate data compression during the online phase. We present algorithms to determine such partial ordering and discuss the design of the underlying MAC protocol to support our compression model. An experimental case study in Pilates exercises for patient rehabilitation is reported. The results show that our schemes reduce more than 70 percent of overall transmitted data compared with previous approaches.
URI: http://dx.doi.org/10.1109/TMC.2010.264
http://hdl.handle.net/11536/19015
ISSN: 1536-1233
DOI: 10.1109/TMC.2010.264
期刊: IEEE TRANSACTIONS ON MOBILE COMPUTING
Volume: 10
Issue: 10
起始頁: 1459
結束頁: 1472
顯示於類別:期刊論文


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  1. 000293968000008.pdf