Badawy Reham, Raykov Yordan P, Evers Luc J W, Bloem Bastiaan R, Faber Marjan J, Zhan Andong, Claes Kasper, Little Max A
School of Engineering and Applied Sciences, Aston University, Birmingham B4 7ET, UK.
Institute for Computing and Information Sciences, Radboud University, 6525 EC Nijmegen, The Netherlands.
Sensors (Basel). 2018 Apr 16;18(4):1215. doi: 10.3390/s18041215.
The use of wearable sensing technology for objective, non-invasive and remote clinimetric testing of symptoms has considerable potential. However, the accuracy achievable with such technology is highly reliant on separating the useful from irrelevant sensor data. Monitoring patient symptoms using digital sensors outside of controlled, clinical lab settings creates a variety of practical challenges, such as recording unexpected user behaviors. These behaviors 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 affect the accuracy of the subsequent data analysis and scientific conclusions. 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 that are sufficiently reliable for further analysis. Combining both parametric and nonparametric signal processing and machine learning techniques, we demonstrate that across 100 subjects and 300 clinimetric tests from three different types of behavioral clinimetric protocols, the system shows an average segmentation accuracy of around 90%. By extracting reliable sensor data, it is possible to strip the data of confounding factors in the environment that may threaten reproducibility and replicability.
将可穿戴传感技术用于对症状进行客观、非侵入性和远程临床测量测试具有相当大的潜力。然而，这种技术所能达到的准确性高度依赖于从无关传感器数据中分离出有用数据。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，例如记录意外的用户行为。这些行为常常违反临床测量测试协议的假设，而这些协议旨在探测特定症状。在实验室之外，此类违规行为很常见，会影响后续数据分析和科学结论的准确性。为解决这些问题，我们报告了一种用于自动传感器数据质量控制的统一算法框架，该框架可以识别传感器数据中足够可靠以便进一步分析的部分。结合参数化和非参数化信号处理以及机器学习技术，我们证明，在来自三种不同类型行为临床测量协议的100名受试者和300次临床测量测试中，该系统的平均分割准确率约为90%。通过提取可靠的传感器数据，可以去除环境中可能威胁可重复性和可复制性的混杂因素的数据。
