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Sensors 2014, 14(12), 24305-24328; doi:10.3390/s141224305

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

Electrical and Computer Engineering Department, Ryerson University, 350 Victoria Street, Toronto, ON M5B2K3, Canada
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Received: 11 October 2014 / Revised: 29 November 2014 / Accepted: 5 December 2014 / Published: 17 December 2014
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
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Abstract

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. View Full-Text
Keywords: sEMG bio-signal; compressed sensing; random sensing dictionary; reconstruction process; sparsity sEMG bio-signal; compressed sensing; random sensing dictionary; reconstruction process; sparsity
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Balouchestani, M.; Krishnan, S. Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing. Sensors 2014, 14, 24305-24328.

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