Improving the Validity of Lifelogging Physical Activity Measures in an Internet of Things Environment
Improving the Validity of Lifelogging Physical Activity Measures in an Internet of Things Environment
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DOI：
10.1109/cit/iucc/dasc/picom.2015.341
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Po Yang;Martin Hanneghan;J. Qi;Zhikun Deng;F. Dong;Dina Fan
中科院分区：
文献类型：
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作者：
Po Yang;Martin Hanneghan;J. Qi;Zhikun Deng;F. Dong;Dina Fan
最近，可穿戴设备和移动应用程序的流行使用使得在物联网（IoT）环境中有效捕获生活记录身体活动数据成为可能。从长远来看，有效收集身体活动指标有利于跨学科医疗保健研究以及临床医生、研究人员和患者的合作。然而，由于物联网环境中连接设备的异构性和多样化生活模式的快速变化，移动设备捕获的生活记录身体活动信息通常包含很多不确定性。在本文中，我们对现有的生活记录身体活动测量设备进行了全面回顾，并确定了物联网环境中这些活动测量的规则和不规则的不确定性。然后，我们通过定义与步行速度相关的得分（称为物理空间中的日常活动（DAPS））来预测不规则不确定性的分布。最后，我们提出了一种基于椭圆拟合模型的有效性改进方法，用于减少物联网环境中生活记录身体活动测量的不确定性。实验结果表明，该方法有效提高了医疗保健平台体力活动测量的有效性。
Recently, the popular use of wearable devices and mobile apps makes the effectively capture of lifelogging physical activity data in an Internet of Things (IoT) environment possible. The effective collection of measures of physical activity in the long term is beneficial to interdisciplinary healthcare research and collaboration from clinicians, researchers to patients. However, due to heterogeneity of connected devices and rapid change of diverse life patterns in an IoT environment, lifelogging physical activity information captured by mobile devices usually contains much uncertainty. In this paper, we provide a comprehensive review of existing life-logging physical activity measurement devices, and identify regular and irregular uncertainties of these activity measures in an IoT environment. We then project the distribution of irregular uncertainty by defining a walking speed related score named as Daily Activity in Physical Space (DAPS). Finally, we present an ellipse fitting model based validity improvement method for reducing uncertainties of life-logging physical activity measures in an IoT environment. The experimental results reflect that the proposed method effectively improves the validity of physical activity measures in a healthcare platform.