A body sensor node SoC for ECG/EMG applications with compressed sensing and wireless powering

Tu, Yo-Hao; Yao, Kaisheng; Huang, Ming-Hao; Lin, Yu-Yun; Chi, Hao-Yu; Cheng, Po-Min; Tsai, Pei–Yun; Shiue, Muh-Tian; Liu, Chien-Nan Jimmy; Cheng, Kuo-Hsing; Fu, Jia‐Shiang

doi:10.1109/vlsi-dat.2017.7939668

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Experimental results revealed the superiority of the proposed approach. 39 Besides the application in ECG acquisition which is the primary concern of this article, CS is also applicable for compressed and lowcost acquisition of EEG, 40,41 EMG, 30,42 and PPG (photoplethysmogram) 43 in WBSN. For example, Baheti and Garudadri 43 developed a CS-based hardware system for noninvasive monitoring of pulse oximeter.…”

Section: Application Of Cs In Wbsnmentioning

confidence: 99%

Compressed sensing for electrocardiogram acquisition in wireless body sensor network: A comparative analysis

Chen

Xing

Zhang

et al. 2019

International Journal of Distributed Sensor Networks

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In the past decades, compressed sensing emerges as a promising technique for signal acquisition in low-cost sensor networks. For prolonging the monitoring duration of biosignals, compressed sensing is also exploited for simultaneous sampling and compression of electrocardiogram signals in the wireless body sensor network. This article presents a comprehensive analysis of compressed sensing for electrocardiogram acquisition. The performances of involved important factors, such as wavelet basis, overcomplete dictionaries, and the reconstruction algorithms, are comparatively illustrated, with the purpose to give data reference for practical applications. Drawn from a bulk of comparative experiments, the potential of compressed sensing in electrocardiogram acquisition is evaluated in different compression levels, while preferred sparsifying basis and reconstruction algorithm are also suggested. Relative perspectives and discussions are also given.

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Section: Application Of Cs In Wbsnmentioning

confidence: 99%

Compressed sensing for electrocardiogram acquisition in wireless body sensor network: A comparative analysis

Chen

Xing

Zhang

et al. 2019

International Journal of Distributed Sensor Networks

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“…Research has also been done in developing on-silicon system-on-chip such as [16], a highly integrated batteryless sensor node implemented in TSMC 0.18 µm CMOS technology for ECG/EMG applications with wireless powering and compressed sensing. The entire implementation contains the analog front-end (AFE), analog-to-digital converter (ADC), digital signal processor (DSP), power management, and an RF-to-DC rectifier and its size is of 1.7 mm × 2.5 mm.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Channel Electromyography, Electrocardiography and Inertial Wireless Sensor Module Using Bluetooth Low-Energy

Biagetti¹,

Crippa²,

Falaschetti³

et al. 2020

Electronics

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This paper proposes a wireless sensor device for the real-time acquisition of bioelectrical signals such as electromyography (EMG) and electrocardiography (ECG), coupled with an inertial sensor, to provide a comprehensive stream of data suitable for human activity detection, motion analysis, and technology-assisted nursing of persons with physical or cognitive impairments. The sensor is able to acquire up to three independent bioelectrical channels (six electrodes), each with 24 bits of resolution and a sampling rate up to 3.2 kHz, and has a 6-DoF inertial platform measuring linear acceleration and angular velocity. The bluetooth low-energy wireless link was chosen because it allows easy interfacing with many consumer electronics devices, such as smartphones or tablets, that can work as data aggregators, but also imposes data rate restrictions. These restrictions are investigated in this paper as well, together with the strategy we adopted to maximize the available bandwidth and reliability of the transmission within the limits imposed by the protocol.

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Low-Cost and Confidential ECG Acquisition Framework Using Compressed Sensing and Chaotic Systems for Wireless Body Area Network

Zhang

Chen

Zhang

et al. 2022

IEEE J. Biomed. Health Inform.

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A body sensor node SoC for ECG/EMG applications with compressed sensing and wireless powering

Cited by 3 publications

References 10 publications

Compressed sensing for electrocardiogram acquisition in wireless body sensor network: A comparative analysis

Compressed sensing for electrocardiogram acquisition in wireless body sensor network: A comparative analysis

A Multi-Channel Electromyography, Electrocardiography and Inertial Wireless Sensor Module Using Bluetooth Low-Energy

Low-Cost and Confidential ECG Acquisition Framework Using Compressed Sensing and Chaotic Systems for Wireless Body Area Network

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