SmartSen:智能传感,用于增强基于手机的交互式CPS的实时活动识别

Huan Li, Qinghua Yu, K. Ramamritham, Xiaotao Liu
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引用次数: 0

摘要

使用移动设备增强用户与CPS的交互对医疗保健应用程序有许多潜在的好处。最近的研究着眼于如何使用智能手机识别人类活动,以表明健康状况。但是很少有人关注实时自动识别个人的活动习惯。当然,在设计这种网络物理识别系统时,必须考虑智能手机的能量限制。在本文中,我们提出了一种基于预测的智能传感策略,该策略节能且实时工作。利用现实世界现象的时间相关性,提出了一种基于k阶马尔可夫链的自适应预测算法,避免了连续感知,从而实现了显著的节能。预测结果在线实时分析,以确保系统能够跟踪个人的行为模式,并及时响应行为变化。使用我们的原型进行的实际实验表明,这种面向识别的CPS系统不仅可以实现节能,而且可以实时收敛到具有高个体识别精度的稳态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SmartSen: smart sensing for enhancing real-time activity recognition in phone-based interactive CPS
Using mobile devices to enhance users interaction with CPS has many potential benefits for healthcare applications. Recent research has looked at how to recognize human activities using smartphones, to indicate health status. But little attention has been paid to automatically identify the activity habits of individuals in real-time. Of course, the energy constraint in smartphones must be considered during the design of such cyber-physical recognition systems. In this paper, we propose a prediction-based smart sensing strategy that is energy efficient and works in real-time. By making use of the temporal correlation property of real-world phenomena, an adaptive k-order Markov chain based prediction algorithm is proposed to avoid continuous sensing so that significant energy savings can be achieved. The prediction results are analyzed online in real-time, to ensure that the system can track an individual's behavior pattern and provide timely response to changes in behavior. Real-world experiments using our prototype show that such recognition oriented CPS systems can not only achieve energy savings, but also converge to steady state with high individual recognition accuracy, in a real-time manner.
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