基于超声主动传感的微手势识别系统

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摘要翻译：
本文提出了一种基于超声主动传感的微手势识别系统和方法。该系统采用微动态手势进行识别，实现人机交互(HCI)。该系统由超声主动传感、脉冲雷达信号处理和基于机器学习的时序模式识别三部分组成。我们采用低频(300kHz)超声主动传感获得高分辨率的距离-多普勒图像特征。利用高质量的序列距离-多普勒特征，提出了一种基于状态转移的隐马尔可夫手势识别模型。该方法利用符号化的距离-多普勒特征实现了近90%的识别准确率，并显著降低了计算复杂度和功耗。此外，为了获得更高的分类精度，我们采用了端到端的神经网络模型，获得了96.32%的识别准确率。除了离线分析之外，还发布了一个实时原型来验证我们的方法在现实世界中的应用潜力。
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英文标题：
《Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing》
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作者：
Yu Sang, Laixi Shi, Yimin Liu
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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一级分类：Computer Science        计算机科学
二级分类：Human-Computer Interaction        人机交互
分类描述：Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
包括人为因素、用户界面和协作计算。大致包括ACM学科课程H.1.2和所有H.5中的材料，除了H.5.1，它更有可能以多媒体作为主要学科领域。
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英文摘要：
  In this paper, we propose a micro hand gesture recognition system and methods using ultrasonic active sensing. This system uses micro dynamic hand gestures for recognition to achieve human-computer interaction (HCI). The implemented system, called hand-ultrasonic gesture (HUG), consists of ultrasonic active sensing, pulsed radar signal processing, and time-sequence pattern recognition by machine learning. We adopt lower frequency (300 kHz) ultrasonic active sensing to obtain high resolution range-Doppler image features. Using high quality sequential range-Doppler features, we propose a state-transition-based hidden Markov model for gesture recognition. This method achieves a recognition accuracy of nearly 90\% by using symbolized range-Doppler features and significantly reduces the computational complexity and power consumption. Furthermore, to achieve higher classification accuracy, we utilize an end-to-end neural network model and obtain a recognition accuracy of 96.32\%. In addition to offline analysis, a real-time prototype is released to verify our method's potential for application in the real world.
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PDF链接：
https://arxiv.org/pdf/1712.00216

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关键词：Applications Optimization Recognition Application interaction 特征 Doppler 距离 实现 active