In this article, we demonstrate the ability to recognize hand gestures in a noncontact wireless fashion using only incoherent light signals reflected from a human subject. Fundamentally distinguished from radar, lidar, and camera-based sensing systems, this sensing modality uses only a low-cost light source (e.g., LED) and a sensor (e.g., photodetector). The lightwave-based gesture recognition system identifies different gestures from the variations in light intensity reflected from the subject's hand within a short (20–35 cm) range. As users perform different gestures, scattered light forms unique, statistically repeatable, time-domain signatures. These signatures can be learned by repeated sampling to obtain the training model against which unknown gesture signals are tested and categorized. These time-domain variations of the lightwave signals reflected from hand are denoised, standardized, and then classified by using machine learning classification tools such as $K$-nearest neighbors and support vector machine. Performance evaluations have been conducted with eight gestures, five subjects, different distances and lighting conditions, and visible and infrared light sources. The results demonstrate the best hand gesture recognition performance of infrared sensing at 20 cm with an average of 96% accuracy. The developed gesture recognition system is low-cost, effective, and noncontact technology for numerous human–computer interaction applications.

中文翻译：

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使用反射可见光和红外光波信号进行手势识别

在本文中，我们展示了仅使用从人类对象反射的非相干光信号以非接触式无线方式识别手势的能力。从根本上区别于雷达、激光雷达和基于相机的传感系统，这种传感方式仅使用低成本光源（例如 LED）和传感器（例如光电探测器）。基于光波的手势识别系统根据从受试者手反射的光强度在较短（20-35 厘米）范围内的变化来识别不同的手势。当用户执行不同的手势时，散射光会形成独特的、统计上可重复的时域特征。这些签名可以通过重复采样来学习，以获得用于测试和分类未知手势信号的训练模型。这些从手反射的光波信号的时域变化经过去噪、标准化，然后使用机器学习分类工具（例如 $K$-最近邻和支持向量机）进行分类。性能评估已通过八个手势、五个对象、不同距离和光照条件以及可见光和红外光源进行。结果表明，红外感应在 20 cm 处的手势识别性能最佳，平均准确率为 96%。开发的手势识别系统是低成本、有效和非接触式技术，适用于众多人机交互应用。性能评估已通过八个手势、五个对象、不同距离和光照条件以及可见光和红外光源进行。结果表明，红外感应在 20 cm 处的手势识别性能最佳，平均准确率为 96%。开发的手势识别系统是低成本、有效和非接触式技术，适用于众多人机交互应用。性能评估已通过八个手势、五个对象、不同距离和光照条件以及可见光和红外光源进行。结果表明，红外感应在 20 cm 处的手势识别性能最佳，平均准确率为 96%。开发的手势识别系统是低成本、有效和非接触式技术，适用于众多人机交互应用。