Hand Gesture Recognition Using Three-Dimensional Electrical Impedance Tomography
Hand Gesture Recognition Using Three-Dimensional Electrical Impedance Tomography
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DOI：
10.1109/tcsii.2020.3006430
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发表时间：
2020-09-01
影响因子：
4.4
通讯作者：
Demosthenous, Andreas
中科院分区：
文献类型：
--
作者：
Jiang, Dai;Wu, Yu;Demosthenous, Andreas
本文介绍了一种用于手势识别的16电极电阻抗断层成像（EIT）系统。该系统的硬件是基于集成电路，包括一个12位的高频谱纯度电流舵DAC在0.18 μ m CMOS技术，电流驱动器和仪表放大器在0.35 μ m CMOS技术实现。测试了2D和3D EIT电极布置的手势识别。结果表明，使用机器学习算法，当电极放置在单个腕带上时，可以从测量的生物阻抗数据中区分出8个手势，准确度为97.9%，并且对于3D EIT测量，相同数量的电极分布在两个腕带上，准确度为99.5%。特别是3D EIT在区分具有相似肌肉收缩的手势的能力方面表现出显着的优越性。
This brief presents a 16-electrode electrical impedance tomography (EIT) system for hand gesture recognition. The hardware of the system is based on integrated circuits including a 12-bit high spectral purity current-steering DAC implemented in 0.18 mu m CMOS technology, a current driver and an instrumentation amplifier in 0.35 mu m CMOS technology. Both 2D and 3D EIT electrode arrangements were tested for hand gesture recognition. It is shown that using machine learning algorithms, eight hand gestures can be distinguished from the measured bio-impedance data with an accuracy of 97.9% when the electrodes are placed on a single wristband, and an accuracy of 99.5% with the same number of electrodes distributed on two wristbands for 3D EIT measurement. In particular 3D EIT demonstrated significant superiority in its ability to discriminate between gestures with similar muscle contractions.