A wireless surface electromyography system

Hughes, E.A.; Bell, Aleeta E.

doi:10.1109/secon.2007.342896

“…(4) limits on hardware memory and delay; (5) establishing ultra low power and low sampling rate wireless healthcare sEMG systems [11][12][13].…”

Section: Overview Of Compressed Sensing Theorymentioning

confidence: 99%

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

Balouchestani¹,

Krishnan²

2022

Preprint

0

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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.

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“…The need for compression of sEMG bio-signals arises in the following areas: (1) large sEMG database in the hospital; (2) long-term sEMG recording; (3) ambulatory (24 h) monitoring of sEMG bio-signals; (4) limits on hardware memory and delay; (5) establishing ultra low power and low sampling rate wireless healthcare sEMG systems [ 11 – 13 ].…”

Section: Overview Of Compressed Sensing Theorymentioning

confidence: 99%

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

Balouchestani

¹

,

Krishnan

²

2014

Sensors

View full text Add to dashboard Cite

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.

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“…These limits create the real need for the implementation of new wearable and wireless sEMG sensors that deliver healthcare services to anywhere and at anytime [3]. The sEMG bio-signals can be processed to detect medical abnormalities or to analyse the biomechanics of the human or animal movement [4,5]. After an exhaustive search, we did not find any new study that aims an effective random sampling-rate data acquisition algorithm for wireless sEMG systems.…”

Section: Introductionmentioning

confidence: 99%