Design of a low-cost wireless surface EMG acquisition system

Fu, Jianting; Chen, Jian; Shi, Yizhi; Li, Yao

doi:10.1109/ner.2013.6696030

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…(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

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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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“…(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

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“…All in all, the function of the analog conditioning circuit is to amplify the original EMG signal 1,000 times and then signal processing by the second-order band-pass filtering with the range of 5–1,000 Hz. The schematic diagram of the signal conditioning circuit ( Fu et al, 2013 ) is shown in Figure 3 . The theoretical gain of the signal conditioning circuit is shown as follows:…”

Section: A Wireless Surface Emg Acquisition Systemmentioning

confidence: 99%

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors

Cao

Cai

et al. 2021

Front. Comput. Neurosci.

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Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very popular and successful because surface EMG is a cumulative bioelectric signal from the surface of the skin that can accurately and intuitively represent the force of the fingers. However, existing surface EMG-based methods still cannot fully satisfy the required recognition accuracy for artificial limb control as the lack of high-precision sensor and high-accurate recognition model. To address this issue, this study proposes a novel FGR model that consists of sensing and classification of surface EMG signals (SC-FGR). In the proposed SC-FGR model, wireless sensors with high-precision surface EMG are first developed for acquiring multichannel surface EMG signals from the forearm. Its resolution is 16 Bits, the sampling rate is 2 kHz, the common-mode rejection ratio (CMRR) is less than 70 dB, and the short-circuit noise (SCN) is less than 1.5 μV. In addition, a convolution neural network (CNN)-based classification algorithm is proposed to achieve FGR based on acquired surface EMG signals. The CNN is trained on a spectrum map transformed from the time-domain surface EMG by continuous wavelet transform (CWT). To evaluate the proposed SC-FGR model, we compared it with seven state-of-the-art models. The experimental results demonstrate that SC-FGR achieves 97.5% recognition accuracy on eight kinds of finger gestures with five subjects, which is much higher than that of comparable models.

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“…Rangkaian Instrumentasi EMG digunakan sebagai pemroses sinyal biolelektrik untuk mengetahui aktifitas sinyal yang disebabkan oleh aktifitas otot [8]…”

Section: B Rangkaian Instrumentasi Emgunclassified

Pengolahan Sinyal EMG Sebagai Perintah Kontrol Untuk Kursi Roda Elektrik

Sitanaya¹,

Tasripan²,

Arifin³

2019

JTITS

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Kursi roda elektrik merupakan salah satu alat bantu yang bisa digunakan untuk menunjang mobilitas seseorang yang mengalami kelumpuhan pada bagian kaki. Pada umumnya kontroller pada kursi roda elektrik menggunakan joystick. Tetapi penggunaan joystick sulit untuk orang yang telah lanjut usia dan orang yang menderita kuadriplegia. Oleh sebab itu dilakukan penelitian mengenai pengolahan sinyal elektromiografi (EMG) sebagai perintah kontrol untuk kursi roda elektrik. Sinyal EMG diakuisisi dengan menggunakan rangkaian instrumentasi EMG. Hasil sinyal yang didapatkan dari rangkaian instrumentasi lalu diproses oleh mikrokontroller dan dikirim ke laptop untuk memonitor sinyal otot dan memberikan perintah kontrol pada kursi roda elektrik. Hasil rata-rata persentase keberhasilan sistem untuk perintah kontrol maju sebesar 89.33 %, perintah kontrol belok kiri 97.33 %, belok kanan 97.33 %, dan berhenti 100 %. Kata kunci-Elektromiografi (EMG), Kelumpuhan, Kursi Roda Elektrik, Mikrokontroller.

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Design of a low-cost wireless surface EMG acquisition system

Cited by 11 publications

References 7 publications

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

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

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors

Pengolahan Sinyal EMG Sebagai Perintah Kontrol Untuk Kursi Roda Elektrik

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