Classifying Human Activity Patterns from Smartphone Collected GPS data: a Fuzzy Classification and Aggregation Approach.
Classifying Human Activity Patterns from Smartphone Collected GPS data: a Fuzzy Classification and Aggregation Approach.
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DOI：
10.1111/tgis.12181
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发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
Lin G
中科院分区：
文献类型：
--
作者：
Wan N;Lin G
智能手机已经成为一种有前途的设备，用于监测环境健康研究中的人类活动。然而，智能手机测量的GPS数据的定位精度下降和不一致性限制了其对人类活动模式进行分类的有效性。本研究提出了一种模糊分类方案，用于区分智能手机收集的GPS数据的人类活动模式。具体而言，采用模糊逻辑推理，以克服位置不确定性的影响，通过估计的概率不同的活动类型的单个GPS点。在此基础上，开发了一种分段聚合方法来推断活动模式，同时调整点属性的不确定性。所提出的方法的验证进行了一个方便的样本的基础上，三个科目不同类型的智能手机。结果表明期望的准确度（例如，活性鉴定高达96%）。附录中提供了两个例子，说明如何将拟议的方法应用于环境卫生研究。研究人员可以根据不同的研究主题调整这个方案。
Smartphones have emerged as a promising type of equipment for monitoring human activities in environmental health studies. However, degraded location accuracy and inconsistency of smartphone-measured GPS data have limited its effectiveness for classifying human activity patterns. This study proposes a fuzzy classification scheme for differentiating human activity patterns from smartphone-collected GPS data. Specifically, a fuzzy logic reasoning was adopted to overcome the influence of location uncertainty by estimating the probability of different activity types for single GPS points. Based on that approach, a segment aggregation method was developed to infer activity patterns, while adjusting for uncertainties of point attributes. Validations of the proposed methods were carried out based on a convenient sample of three subjects with different types of smartphones. The results indicate desirable accuracy (e.g., up to 96% in activity identification) with use of this method. Two examples were provided in the appendix to illustrate how the proposed methods could be applied in environmental health studies. Researchers could tailor this scheme to fit a variety of research topics.