A Wearable Wireless Armband Sensor for High-Density Surface Electromyography Recording

被引:0
|
作者
Tam, S. [1 ]
Bilodeau, G. [1 ]
Brown, J. [1 ]
Gagnon-Turcotte, G. [1 ]
Campeau-Lecours, A. [2 ]
Gosselin, B. [1 ]
机构
[1] Univ Laval, Dept Comp & Elect Engn, Quebec City, PQ G1V 0A6, Canada
[2] Univ Laval, Dept Mech Engn, Quebec City, PQ G1V 0A6, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
OF-THE-ART;
D O I
10.1109/embc.2019.8857750
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
This paper presents a portable and modular wireless multichannel sensor system for high-density surface electromyography (HD-sEMG) signals acquisition. Featuring low-power and high-quality off-the-shelf components such as the Intan Technologies RHD2132 digital electrophysiology interface chip, the current iteration of the proposed sensor system allows the recording of 32 surface electromyography (sEMG) channels, each at a sampling rate of 1 kHz, and a sample resolution of 16 bits. It features the RHD2132's typical input-referred noise of 2.4 mu V-rms, with <15% variation with amplifier bandwidth as specified by the manufacturer, and a total power consumption of 49.5 mW. Data is sent in real-time to a base station using a 2.4-GHz industrial, scientific and medical (ISM) wireless link. Along with the recording platform, the integrated sensor system includes a dry surface electrodes array prototype directly built on a printed circuit board. Intended for complex muscles activity patterns detection on the forearm, the flexible 32 surface electrodes array is designed to be placed flat or to fit a curved area like the forearm in a hand gestures recognition prosthetic system. In such applications, this device will offer improved prosthesis control scheme intuitiveness and ease-of-use. Among other core features of the system are its compact, light-weight and easy to install physical design. The complete system fits on a 2 by 6.5 cm(2) printed circuit board mounted on a 7.6 by 11.8 cm(2) electrodes array. HD-sEMG user forearm output data collected with the system is presented with a proposed frequency-time-space cross-domain preprocessing method for visualization of HD-EMG data and building training datasets.
引用
收藏
页码:6040 / 6044
页数:5
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