Compressed sensing for energy-efficient wireless telemonitoring: Challenges and opportunities

Abstract

Abstract-As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices. However, the non-sparseness of biosignals presents a major challenge to compressed sensing. This study proposes and evaluates a spatio-temporal sparse Bayesian learning algorithm, which has the desired ability to recover such non-sparse biosignals. It exploits both temporal correlation in each individual biosignal and inter-channel correlation among biosignals from different channels. The proposed algorithm was used for compressed sensing of multichannel electroencephalographic (EEG) signals for estimating vehicle drivers' drowsiness. Results showed that the drowsiness estimation was almost unaffected even if raw EEG signals (containing various artifacts) were compressed by 90%.

Sparse Bayesian Learning for non-Gaussian sources

“…If we tried to do this by exhaustively checking every possible subset of sources (assuming that at most N − 1 sources are active), then we would have to check O(M N−1 ) possibilities, as shown by equation (3).…”

“…In particular, compressed sensing methods have found use in wireless monitoring systems, where the reduced power draw by transmitting significantly less data is important due to the potential of significantly improving battery life [3]. However, it has been shown that in this case, a significant improvement may be achieved only if the sensing matrix is a binary matrix, and so not all methods are suitable.…”

Spatiotemporal Sparse Bayesian Learning With Applications to Compressed Sensing of Multichannel Physiological Signals

“…It is worth pointing out that most CS algorithms may not be used for energy-efficient wireless telemonitoring especially ambulatory monitoring, due to several challenges [20]- [22].…”

“…Thus, the collected physiological signals are inevitably contaminated by strong artifacts caused by muscle movement and electrode motion. As a result, even a sparse signal can become nonsparse in the time domain and also non-sparse in transformed domains [20]. The non-sparsity seriously degrades CS algorithms' performance, resulting in their failure [6].…”

“…Some preliminary results were published in [20]. The Matlab code of the proposed algorithm can be downloaded at https://sites.google.com/site/researchbyzhang/stsbl.…”