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Hand Gesture Recognition Using Three-Dimensional Electrical Impedance Tomography

机译:使用三维电阻断层扫描的手势识别

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摘要

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.
机译:本简要介绍了用于手势识别的16电极电阻断层扫描(EIT)系统。系统的硬件基于集成电路,包括在0.18μmCMOS技术,电流驱动器和仪表放大器中实现的12位高光谱纯度电流转向DAC。测试2D和3D EIT电极布置,用于手势识别。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与测量的生物阻抗数据区分开出97.9%的精度,并且具有相同数量的电极数为99.5%的精度分布在两个腕带上进行3D EIT测量。特别是3D EIT在其区分具有类似肌肉收缩的手势之间的能力中表现出显着的优势。

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