Low-power ASIC suitable for miniaturized wireless EMG systems

Author:

Kledrowetz Vilem1,Prokop Roman1,Fujcik Lukas1,Pavlik Michal1,Háze Jiří1

Affiliation:

1. Department of Microelectronics , Brno University of Technology , Technická 3058/10, 616 00 Brno , Czech Republic

Abstract

Abstract Nowadays, the technology advancements of signal processing, low-voltage low-power circuits and miniaturized circuits have enabled the design of compact, battery-powered, high performance solutions for a wide range of, particularly, biomedical applications. Novel sensors for human biomedical signals are creating new opportunities for low weight wearable devices which allow continuous monitoring together with freedom of movement of the users. This paper presents the design and implementation of a novel miniaturized low-power sensor in integrated circuit (IC) form suitable for wireless electromyogram (EMG) systems. Signal inputs (electrodes) are connected to this application-specific integrated circuit (ASIC). The ASIC consists of several consecutive parts. Signals from electrodes are fed to an instrumentation amplifier (INA) with fixed gain of 50 and filtered by two filters (a low-pass and high-pass filter), which remove useless signals and noise with frequencies below 20 Hz and above 500 Hz. Then signal is amplified by a variable gain amplifier. The INA together with the reconfigurable amplifier provide overall gain of 50, 200, 500 or 1250. The amplified signal is then converted to pulse density modulated (PDM) signal using a 12-bit delta-sigma modulator. The ASIC is fabricated in TSMC0.18 mixed-signal CMOS technology.

Publisher

Walter de Gruyter GmbH

Reference8 articles.

1. [1] J. Bronzino and D. Peterson, Biomedical Engineering Fundamentals, Boca Raton: CRC Press, https://doi.org/10.1201/b15482.10.1201/b15482

2. [2] Adinstruments “Trigno EMG Sensors., [Accessed September 25 2019], https://www.adinstruments.com/products/trigno-emg-sensors.

3. [3] Shenzhen Fangwei Network Technology Co Ltd., “Wave plus 8/16/32ch, [Accessed September 25 2019], http://www.hanix.net/En/Products/info/id/125.html.

4. [4] Shenzhen Fangwei Network Technology Co Ltd., Najarian Kayvan: Biomedical signal image processing, 2nd ed, Boca Raton: Taylor & Francis/CRC Press, 2012, ISBN 978-1-4398-7033-4.

5. [5] M. Magno, L. Benini, C. Spagnol, and E. Popovici, “Wearable low power dry surface wireless sensor node for healthcare monitoring application, 2013 IEEE 9th International Conference on Wireless Mobile Computing Networking Communications (WiMob), Lyon, pp. 189-195, 2013.10.1109/WiMOB.2013.6673360

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