摘要翻译：
在辅助生活和远程监控等领域，人类活动的检测和解释已经成为一个具有挑战性的医疗保健问题。除了依赖于可穿戴设备和摄像头系统的传统方法之外，基于WiFi的技术正在发展成为室内监控和活动识别的一个有前途的解决方案。这在一定程度上是由于WiFi在住宅环境中的普遍性质，如家庭和护理设施，以及基于WiFi的传感的不引人注目的性质。先进的信号处理技术可以使用商用现成(COTS)设备或定制硬件准确地提取WiFi信道状态信息(CSI)。这包括相位变化、频率偏移和信号电平。在本文中，我们描述了多普勒频移在WiFi CSI中的医疗应用，这种多普勒频移是由发生在信号覆盖区域的人类活动引起的。该技术被证明可以识别不同类型的人类活动和行为，非常适合在医疗保健中的应用。本文介绍了三个实验案例，以说明WiFi CSI多普勒感知在辅助生活和居家护理环境中的能力。我们还讨论了现实世界场景的潜在机会和实际挑战。
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英文标题：
《Exploiting WiFi Channel State Information for Residential Healthcare
  Informatics》
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作者：
Bo Tan, Qingchao Chen, Kevin Chetty, Karl Woodbridge, Wenda Li, Robert
  Piechocki
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最新提交年份：
2017
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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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英文摘要：
  Detection and interpretation of human activities have emerged as a challenging healthcare problem in areas such as assisted living and remote monitoring. Besides traditional approaches that rely on wearable devices and camera systems, WiFi based technologies are evolving as a promising solution for indoor monitoring and activity recognition. This is, in part, due to the pervasive nature of WiFi in residential settings such as homes and care facilities, and unobtrusive nature of WiFi based sensing. Advanced signal processing techniques can accurately extract WiFi channel status information (CSI) using commercial off-the-shelf (COTS) devices or bespoke hardware. This includes phase variations, frequency shifts and signal levels. In this paper, we describe the healthcare application of Doppler shifts in the WiFi CSI, caused by human activities which take place in the signal coverage area. The technique is shown to recognize different types of human activities and behaviour and be very suitable for applications in healthcare. Three experimental case studies are presented to illustrate the capabilities of WiFi CSI Doppler sensing in assisted living and residential care environments. We also discuss the potential opportunities and practical challenges for real-world scenarios.
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PDF链接：
https://arxiv.org/pdf/1712.03401