摘要翻译：
使用智能手机和可穿戴传感技术对症状进行客观、无创和远程临床测试具有相当大的潜力。然而，这种技术所能达到的临床精确度高度依赖于将有用的传感器数据从无关的或混淆的传感器数据中分离出来。在受控的临床实验室环境之外使用数字传感器监测患者症状会带来各种实际挑战，如不可避免的和意外的用户行为。这些行为经常违反临床测试协议的假设，这些协议是为了探索特定的症状而设计的。这种违规行为在实验室外经常发生，并可能影响后续数据分析和科学结论的准确性。同时，传感器数据采集过程结束后手工整理本身具有主观性、费力、易出错等特点。为了解决这些问题，我们报告了一个统一的算法框架，用于自动传感器数据质量控制，它可以识别传感器数据中足够可靠的部分，以供进一步分析。对于不同的传感器数据类型（例如加速度计、数字音频），作为该框架的特例的算法检测传感器数据在多大程度上符合各种临床测试的测试协议的假设。该方法足够广泛，可以应用于大量临床测试，我们展示了它在行走、平衡和基于智能手机的语音测试中的性能，这些测试旨在监测帕金森病的症状。
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英文标题：
《A unified algorithm framework for quality control of sensor data for
  behavioural clinimetric testing》
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作者：
Reham Badawy, Yordan P. Raykov and Max A. Little
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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：
  The use of smartphone and wearable sensing technology for objective, non-invasive and remote clinimetric testing of symptoms has considerable potential. However, the clinimetric accuracy achievable with such technology is highly reliant on separating the useful from irrelevant or confounded sensor data. Monitoring patient symptoms using digital sensors outside of controlled, clinical lab settings creates a variety of practical challenges, such as unavoidable and unexpected user behaviours. These behaviours often violate the assumptions of clinimetric testing protocols, where these protocols are designed to probe for specific symptoms. Such violations are frequent outside the lab, and can affect the accuracy of the subsequent data analysis and scientific conclusions. At the same time, curating sensor data by hand after the collection process is inherently subjective, laborious and error-prone. To address these problems, we report on a unified algorithmic framework for automated sensor data quality control, which can identify those parts of the sensor data which are sufficiently reliable for further analysis. Algorithms which are special cases of this framework for different sensor data types (e.g. accelerometer, digital audio) detect the extent to which the sensor data adheres to the assumptions of the test protocol for a variety of clinimetric tests. The approach is general enough to be applied to a large set of clinimetric tests and we demonstrate its performance on walking, balance and voice smartphone-based tests, designed to monitor the symptoms of Parkinson's disease.
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PDF链接：
https://arxiv.org/pdf/1711.07557