Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing

Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process.

[1]  Wen Gao,et al.  A Motion-Aligned Auto-Regressive Model for Frame Rate Up Conversion , 2010, IEEE Transactions on Image Processing.

[2]  Yuri I. Abramovich,et al.  Two-Dimensional Multivariate Parametric Models for Radar Applications—Part I: Maximum-Entropy Extensions for Toeplitz-Block Matrices , 2008, IEEE Transactions on Signal Processing.

[3]  Charles Soussen,et al.  Joint K-Step Analysis of Orthogonal Matching Pursuit and Orthogonal Least Squares , 2011, IEEE Transactions on Information Theory.

[4]  Tzyy-Ping Jung,et al.  Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning , 2012, IEEE Transactions on Biomedical Engineering.

[5]  Pierre Vandergheynst,et al.  Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes , 2011, IEEE Transactions on Biomedical Engineering.

[6]  Wei Chen,et al.  A novel pedestrian dead reckoning algorithm using wearable EMG sensors to measure walking strides , 2010, 2010 Ubiquitous Positioning Indoor Navigation and Location Based Service.

[7]  Sridhar P. Arjunan,et al.  Computation and Evaluation of Features of Surface Electromyogram to Identify the Force of Muscle Contraction and Muscle Fatigue , 2014, BioMed research international.

[8]  Xiaodai Dong,et al.  Multiple Access and Data Reconstruction in Wireless Sensor Networks Based on Compressed Sensing , 2013, IEEE Transactions on Wireless Communications.

[10]  Alejandro Ribeiro,et al.  Distributed maximum a posteriori probability estimation of dynamic systems with wireless sensor networks , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Gennian Ge,et al.  Deterministic Sensing Matrices Arising From Near Orthogonal Systems , 2014, IEEE Transactions on Information Theory.

[12]  Yonina C. Eldar,et al.  Xampling: Signal Acquisition and Processing in Union of Subspaces , 2009, IEEE Transactions on Signal Processing.

[13]  Venkatesh Saligrama,et al.  Dynamic Thresholding for Distributed Multiple Hypotheses Testing , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.

[14]  Bhaskar D. Rao,et al.  Recovery of block sparse signals using the framework of block sparse Bayesian learning , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  Hong Liu,et al.  EMG pattern recognition and grasping force estimation: Improvement to the myocontrol of multi-DOF prosthetic hands , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  K. Raahemifar,et al.  Low Power Wireless Body Area Networks with Compressed sensing theory , 2012, 2012 IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS).

[17]  Zheng Liu,et al.  A multitask recovery algorithm for block-sparse signals , 2013, 2013 International Conference on Wireless Communications and Signal Processing.

[18]  Xiaojing Huang,et al.  Energy-Efficient Distributed Data Storage for Wireless Sensor Networks Based on Compressed Sensing and Network Coding , 2013, IEEE Transactions on Wireless Communications.

[19]  Bhaskar D. Rao,et al.  Nested Sparse Bayesian Learning for block-sparse signals with intra-block correlation , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Yao Li,et al.  Design of a low-cost wireless surface EMG acquisition system , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[21]  白伦,et al.  Auto-Regressive Models of Non-Stationary Time Series with Finite Length , 2005 .

[22]  Yuri I. Abramovich,et al.  Two-Dimensional Multivariate Parametric Models for Radar Applications—Part II: Maximum-Entropy Extensions for Hermitian-Block Matrices , 2008, IEEE Transactions on Signal Processing.

[23]  P. Brigidi,et al.  The Three Genetics (Nuclear DNA, Mitochondrial DNA, and Gut Microbiome) of Longevity in Humans Considered as Metaorganisms , 2014, BioMed research international.

[24]  Mohammadreza Balouchestani,et al.  Advanced K-means clustering algorithm for large ECG data sets based on a collaboration of compressed sensing theory and K-SVD approach , 2016, Signal Image Video Process..

[25]  Essam A. Rashed,et al.  Adaptive thresholding for robust iterative image reconstruction from limited views projection data , 2011, 2011 IEEE Nuclear Science Symposium Conference Record.

[26]  Xiaojing Huang,et al.  Random circulant orthogonal matrix based Analog Compressed Sensing , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[27]  Vladimir Stojanovic,et al.  A signal-agnostic compressed sensing acquisition system for wireless and implantable sensors , 2010, IEEE Custom Integrated Circuits Conference 2010.

[28]  M. V. C. Costa,et al.  Effect of the amount of sub-bands in the performance of discrete wavelet transform based dynamic S-EMG signals encoder , 2014, 2014 Pan American Health Care Exchanges (PAHCE).

[29]  Daibashish Gangopadhyay,et al.  Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors , 2012, IEEE Transactions on Biomedical Circuits and Systems.

[30]  Panagiotis K. Artemiadis,et al.  An EMG-Based Robot Control Scheme Robust to Time-Varying EMG Signal Features , 2010, IEEE Transactions on Information Technology in Biomedicine.

[31]  Yonina C. Eldar,et al.  Xampling: Analog Data Compression , 2010, 2010 Data Compression Conference.

[32]  T. Kalker,et al.  Maximum A Posteriori Estimation of Time Delay , 2007, 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing.

[33]  Jian Li,et al.  Compressed Sensing via Sparse Bayesian Learning and Gibbs Sampling , 2009, 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop.

[34]  Sabine Van Huffel,et al.  Multi-sparse signal recovery for compressive sensing , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[35]  Kaamran Raahemifar,et al.  New sampling approach for wireless ECG systems with compressed sensing theory , 2013, 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[36]  Erik Vavrinsky,et al.  Design of EMG wireless sensor system , 2011, 2011 International Conference on Applied Electronics.

[37]  Mohammadreza Balouchestani,et al.  Fast clustering algorithm for large ECG data sets based on CS theory in combination with PCA and K-NN methods , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[38]  Vladimir Stojanovic,et al.  Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors , 2012, IEEE Journal of Solid-State Circuits.

[39]  Kongqiao Wang,et al.  A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[40]  Chien-Mo James Li,et al.  Column parity and row selection (CPRS): a BIST diagnosis technique for multiple errors in multiple scan chains , 2005, IEEE International Conference on Test, 2005..

[41]  Tao Zhu,et al.  A novel hybrid motion detection algorithm based on dynamic thresholding segmentation , 2010, 2010 IEEE 12th International Conference on Communication Technology.

[42]  E. Hughes,et al.  A wireless surface electromyography system , 2007, Proceedings 2007 IEEE SoutheastCon.

[43]  Hongbin Li,et al.  Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals , 2013, IEEE Transactions on Signal Processing.

[44]  Alejandro Ribeiro,et al.  D-MAP: Distributed Maximum a Posteriori Probability Estimation of Dynamic Systems , 2013, IEEE Transactions on Signal Processing.

[45]  Daibashish Gangopadhyay,et al.  Compressive sampling of EMG bio-signals , 2011, 2011 IEEE International Symposium of Circuits and Systems (ISCAS).

[46]  Y. Makino,et al.  EMG sensor integration based on Two Dimensional Communication , 2008, 2008 5th International Conference on Networked Sensing Systems.