Performance Analysis of Electromyogram Signal Compression Sampling in a Wireless Body Area Network

Abstract

The rapid growth in demand for portable and intelligent hardware has caused tremendous pressure on signal sampling, transfer, and storage resources. As an emerging signal acquisition technology, compressed sensing (CS) has promising application prospects in low-cost wireless sensor networks. To achieve reduced energy consumption and maintain a longer acquisition duration for high sample rate electromyogram (EMG) signals, this paper comprehensively analyzes the compressed sensing method using EMG. A fair comparison is carried out on the performances of 52 ordinary wavelet sparse bases and five widely applied reconstruction algorithms at different compression levels. The experimental results show that the db2 wavelet basis can sparse EMG signals so that the compressed EMG signals are reconstructed properly, thanks to its low percentage root mean square distortion (PRD) values at most compression ratios. In addition, the basis pursuit (BP) reconstruction algorithm can provide a more efficient reconstruction process and better reconstruction performance by comparison. The experiment records and comparative analysis screen out the suitable sparse bases and reconstruction algorithms for EMG signals, acting as prior experiments for further practical applications and also a benchmark for future academic research.

A signal-adaptive measurement matrix construction algorithm for compressed sensing of sEMG data

“…For each frame, x a is calculated according to equation (8). If the current frame is the first one or absolute change rate between x th (n) and x th (n−1) exceeds ε, the vector p is updated according equation ( 9), and the new measurement matrix is constructed according to equation (10). If n > 1 and the absolute change rate of x th is bellow ε, the vector p and matrix Φ are not changed.…”

“…Therefore, CS is widely regarded as an effective means of bio-signal compression in WBSNs. Zhang et al proposed a CS-based WBSNs scheme for the remote transmission and real-time monitoring of sEMG signals [10]. Elmantawi et al introduced a novel 96-channel application specific integrated circuit (ASIC) designed using 0.35 µm CMOS technology [11].…”

“…Balouchestani and Krishnan proposed a CS scheme to improve the reconstruction accuracy through dictionary optimization with a supervised approach [20]. Zhang et al compared the performances of 52 ordinary wavelet sparse bases and five widely applied reconstruction algorithms at different compression levels [10]. Wu et al proposed an approximated l 0 (AL0) norm-based method to search the solution via the gradient descent method to recover the diaphragmatic electromyogram (EMGdi) signals, along with a novel wavelet threshold denoising technique designed to eliminate ECG interference from EMGdi recordings [21].…”

“…Performance comparison of the proposed method and other methods with Gaussian[11,29], Bernoulli[10,17,21] and DBBD matrices[19]. (a) average, (b) standard deviation.…”

Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN

“…Many studies [3][4][5][6][7][8][9][10][11][12][13][14] were conducted in an effort to realize energy saving and efficiency improvement for WBAN data transmission by using various methods or technologies, such as developing new energy-saving sensors [3,4], enhancing the performance of existed data transmission schemes [5][6][7], improving channel environment [8][9][10] or compressing the amount of data to be transmitted [11][12][13][14]. Usually, most studies adopted one of the above methods to improve the quality of services (QoS) of WBAN.…”