Zhang Liangyu, Chen Junxin, Ma Chenfei, Liu Xiufang, Xu Lisheng
College of Medicine and Biological Information Engineering, Northeastern University, 195 Innovation Road, Shenyang 110169, China.
Edinburgh Neuroprosthetics Laboratory, School of Informatics, The University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK.
Micromachines (Basel). 2022 Oct 15;13(10):1748. doi: 10.3390/mi13101748.
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.
便携式和智能硬件需求的快速增长给信号采样、传输和存储资源带来了巨大压力。作为一种新兴的信号采集技术，压缩感知（CS）在低成本无线传感器网络中具有广阔的应用前景。为了降低能耗并保持对高采样率肌电图（EMG）信号更长的采集持续时间，本文全面分析了使用EMG的压缩感知方法。针对52种普通小波稀疏基和五种广泛应用的重构算法在不同压缩水平下的性能进行了公平比较。实验结果表明，db2小波基能够对EMG信号进行稀疏处理，从而使压缩后的EMG信号能够被正确重构，这得益于其在大多数压缩比下较低的均方根失真百分比（PRD）值。此外，通过比较，基追踪（BP）重构算法能够提供更高效的重构过程和更好的重构性能。实验记录和对比分析筛选出了适用于EMG信号的稀疏基和重构算法，为进一步的实际应用提供了前期实验，也为未来的学术研究提供了基准。
