Fall classification by machine learning using mobile phones.
Fall classification by machine learning using mobile phones.
复制标题
使用手机通过机器学习进行秋季分类。
DOI：
10.1371/journal.pone.0036556
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Jayaraman A
中科院分区：
文献类型：
--
作者：
Albert MV;Kording K;Herrmann M;Jayaraman A
跌倒预防是医疗保健的重要组成部分;福尔斯是老年人常见的损伤来源，并与显著的死亡率和发病率水平相关。自动检测福尔斯可以允许对潜在的紧急情况做出快速响应;此外，了解跌倒的原因或方式可以有益于预防研究或更有针对性的紧急响应。本研究的目的是展示技术，不仅可靠地检测跌倒，而且自动分类的类型。我们让15名受试者戴着移动的手机和先前验证过的专用加速计，模拟四种不同类型的跌倒--左右侧摔、前倒和后滑。9名受试者也佩戴了该装置10天，以提供与模拟福尔斯进行比较的数据。我们将五个机器学习分类器应用于大型时间序列特征集以检测福尔斯。支持向量机和正则化逻辑回归能够以98%的准确率识别跌倒，并以99%的准确率对跌倒类型进行分类。这项工作展示了当前的机器学习方法如何简化跌倒相关研究中的预防数据收集，以及如何提高对福尔斯潜在伤害的快速反应。
Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls–left and right lateral, forward trips, and backward slips–while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls.
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