Evaluation of an accelerometer-based monitor for detecting bed net use and human entry/exit using a machine learning algorithm.
Evaluation of an accelerometer-based monitor for detecting bed net use and human entry/exit using a machine learning algorithm.
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DOI：
10.1186/s12936-022-04102-z
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发表时间：
2022-03-12
期刊：
影响因子：
3
通讯作者：
Krezanoski PJ
中科院分区：
文献类型：
--
作者：
Koudou GB;Monroe A;Irish SR;Humes M;Krezanoski JD;Koenker H;Malone D;Hemingway J;Krezanoski PJ
分发长效驱虫蚊帐是防治疟疾的主要战略之一。改进疟疾预防方案需要了解接受长效驱虫蚊帐的家庭的使用模式，但对自我报告的长效驱虫蚊帐使用情况进行标准的横断面调查所能提供的信息是有限的。本研究旨在评估一种基于加速计的方法的性能，该方法用于测量一系列长效驱虫蚊帐使用行为，作为在更长时间内进行更细粒度长效驱虫蚊帐使用监测的概念证明。本研究于2018年5月至7月在英国利物浦在受控条件下进行。一个加速度计固定在LLIN的侧板上，参与者进行了五种LLIN使用行为：（1）展开网;（2）进入展开的网;（3）静静躺着，好像睡着了;（4）从网下出来;（5）折叠网。R中的randomForest包是一种有监督的非线性分类算法，用于在标记的加速度计数据的20秒历元上训练模型。使用总体准确性、灵敏度和特异性、受试者工作曲线和曲线下面积（AUC）在验证数据集中比较模型。在验证数据集中，五类模型的总体准确率为82.9%，进入净值的灵敏度为0.681，退出为0.632，净下跌为0.733，净上涨为0.800。简化的四类模型，结合进入/退出一个网络到一个类别的准确性为94.8%，并增加了净下降（0.756）和净上升（0.829）的敏感性。进一步简化的三类模型，识别睡眠，净上升和组合的净下降/进入/退出类别的准确性为96.2%（483/502），净下降的AUC为0.997，净上升的AUC为0.987。检测成人进入/退出的模型比儿童的模型更准确（87.8% vs 70.0%; p < 0.001），并且具有更高的AUC（p = 0.03）。了解如何使用长效驱虫蚊帐对于规划疟疾预防方案至关重要。基于加速度计的系统为研究LLIN的使用提供了一种有前途的新方法。进一步探索加速度计放置、测量频率和其他机器学习方法的工作可能会使这些方法在未来更加准确。在线版本包含补充材料，可通过10.1186/s12936-022-04102-z获得。
Distribution of long-lasting insecticidal bed nets (LLINs) is one of the main control strategies for malaria. Improving malaria prevention programmes requires understanding usage patterns in households receiving LLINs, but there are limits to what standard cross-sectional surveys of self-reported LLIN use can provide. This study was designed to assess the performance of an accelerometer-based approach for measuring a range of LLIN use behaviours as a proof of concept for more granular LLIN-use monitoring over longer time periods. This study was carried out under controlled conditions from May to July 2018 in Liverpool, UK. A single accelerometer was affixed to the side panel of an LLIN and participants carried out five LLIN use behaviours: (1) unfurling a net; (2) entering an unfurled net; (3) lying still as if sleeping; (4) exiting from under a net; and, (5) folding up a net. The randomForest package in R, a supervised non-linear classification algorithm, was used to train models on 20-s epochs of tagged accelerometer data. Models were compared in a validation dataset using overall accuracy, sensitivity and specificity, receiver operating curves and the area under the curve (AUC). The five-category model had overall accuracy of 82.9% in the validation dataset, a sensitivity of 0.681 for entering a net, 0.632 for exiting, 0.733 for net down, and 0.800 for net up. A simplified four-category model, combining entering/exiting a net into one category had accuracy of 94.8%, and increased sensitivity for net down (0.756) and net up (0.829). A further simplified three-category model, identifying sleeping, net up, and a combined net down/enter/exit category had accuracy of 96.2% (483/502), with an AUC of 0.997 for net down and 0.987 for net up. Models for detecting entering/exiting by adults were significantly more accurate than for children (87.8% vs 70.0%; p < 0.001) and had a higher AUC (p = 0.03). Understanding how LLINs are used is crucial for planning malaria prevention programmes. Accelerometer-based systems provide a promising new methodology for studying LLIN use. Further work exploring accelerometer placement, frequency of measurements and other machine learning approaches could make these methods even more accurate in the future. The online version contains supplementary material available at 10.1186/s12936-022-04102-z.
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影响因子：
3
作者：
Monroe, April;Moore, Sarah;Killeen, Gerry F.
通讯作者：
Killeen, Gerry F.
DOI：
10.4269/ajtmh.2007.76.1100
发表时间：
2007-06-01
影响因子：
3.3
作者：
Skarbinski, Jacek;Massaga, Julius J.;Kachur, S. Patrick
通讯作者：
Kachur, S. Patrick
影响因子：
3
作者：
Githinji S;Herbst S;Kistemann T;Noor AM
通讯作者：
Noor AM
影响因子：
11.8
作者：
Toé KH;Jones CM;N'Fale S;Ismail HM;Dabiré RK;Ranson H
通讯作者：
Ranson H
影响因子：
3
作者：
Hakizimana, Emmanuel;Cyubahiro, Beatus;Karema, Corine
通讯作者：
Karema, Corine