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Source Recovery for Body Sensor Network

机译:身体传感器网络的源恢复

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摘要

To accurately capture clinically relevant episodes with Body Sensor Networks (BSNs), multi-sensor fusion is essential for extracting intrinsic physiological and contextual information. Due to the heterogeneous nature of the sensors compounded by the mixture of signals across different sensor channels, this process can be practically difficult. The purpose of this paper is to describe the use of source separation for BSN based on Independent Component Analysis (ICA). We demonstrate how this can be used in practical BSN experiments when the number of sensing channels is limited.
机译:为了准确地捕获具有身体传感器网络(BSNS)的临床相关发作,多传感器融合对于提取内在生理和上下文信息至关重要。由于通过不同传感器通道的信号混合物复合的传感器的异质性,但该过程实际上是困难的。本文的目的是描述基于独立分量分析(ICA)的BSN源分离的使用。我们展示当传感通道的数量有限时,如何在实际的BSN实验中使用。

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