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

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.

A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

“…There are several basic limitations associated with this study that need further development to provide the wearable sEMG system for clinical purposes. (1) Although we obtained a stable acquisition system, Balouchestni et al [ 76 ] designed a system to recover the original bio-signals with good level of accuracy and SNR greater than 95.8 dB. Therefore, the circuit architecture optimization are still needed.…”

“…sEMG monitoring systems are suitable for wearable wireless applications that require small size, excellent mobility, low power consumption, and high transmission rates [ 76 ]. The most common systems were based on rigid PCBs [ 44 , 50 , 77 ].…”

Robust compressive sensing algorithm for wireless surface electromyography applications

“…However, certain conditions must be met to guarantee accuracy of the reconstruction process [17,18]. To grantee the success of the reconstruction process, random sensing matrix must obey the following conditions: (1) Matrix has Restricted Isometry Property (RIP) condition; (2) Matrix exhibits high degree of incoherence with the sparsity matrix , where coherence measures the largest correlation between any row of and column of .…”

“…To grantee the success of the reconstruction process, random sensing matrix must obey the following conditions: (1) Matrix has Restricted Isometry Property (RIP) condition; (2) Matrix exhibits high degree of incoherence with the sparsity matrix , where coherence measures the largest correlation between any row of and column of . The less coherence between and , the fewer the measurements M needed to recover the signal; (3) Matrix consists of independent and identically distributed (iid) random measurements [18]. Fig.…”

A Time-Domain Analog Spatial Compressed Sensing Encoder for Multi-Channel Neural Recording

“…When the number of non-zero elements of s is , vector v is K -sparse on the basis B . If v has sparsity on the arbitrary basis B , v can be represented by using vector ( ), which has fewer dimensions, as where is a sensing matrix for an incoherent sampling [ 37 ] . It is known that Bernoulli matrix in which all the entries are either +1 or −1 can be used as the sensing matrix [ 28 ].…”