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.
Highlights
Today’s healthcare systems are effective for an individual measurement from the human body, but are not integrated into comprehensive Body Area Networks (BANs), wherein simultaneous biomedical sensors work on a test subject
Wearable wireless Surface Electromyography (sEMG) sensors based on the analog-Compressed Sensing (CS) theory aim to establish low power and low sampling rate algorithms for the long-term recording of the electrical activity produced by muscles, which are very useful for treatment and diagnostic purposes as well as for detection of various pathologies
Today’s healthcare systems are based on wired/fixed sensors because in many cases biomedical sensors for medical monitoring are not yet wireless. This creates the need for the implementation of new wireless bio-sensors for long-term recording and monitoring of bio-signals
Summary
Today’s healthcare systems are effective for an individual measurement from the human body, but are not integrated into comprehensive Body Area Networks (BANs), wherein simultaneous biomedical sensors work on a test subject. There is a need for increasing patient mobility because in many cases biomedical sensors for medical monitoring are not wireless yet [1]. This creates the need for the implementation of new wireless healthcare systems with a common architecture and the capacity to handle multiple wearable wireless bio-sensors, monitoring different body signals, with different requirements. Using wireless healthcare systems based on low-power wearable wireless bio-sensors has two aspects [1]. The use of new wireless technological solutions enables individually based, multi-parameter monitoring at home. Patients with chronic diseases, as well as a constantly growing number of seniors, will profit from treatment and medical monitoring at home or workplace
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