Pantelopoulos Alexandros, Bourbakis Nikolaos
Assistive Technologies Research Center, Wright State University, Dayton, Ohio 45435, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:320-3. doi: 10.1109/IEMBS.2009.5333786.
The deployment of Wearable Health Monitoring Systems (WHMS) can potentially enable ubiquitous and continuous monitoring of a patient's physiological parameters. Moreover by incorporating multiple biosensors in such a system a comprehensive estimation of the user's health condition can possibly be derived. In this paper we present a Stochastic Petri Net (SPN) model of a multi-sensor WHMS along with a corresponding simulation framework implemented in Java. The proposed model is built on top of a previously published multisensor data fusion strategy, which has been expanded in this work to take into account synchronization issues and temporal dependencies between the measured bio-signals.
可穿戴健康监测系统(WHMS)的部署有可能实现对患者生理参数的普遍和持续监测。此外,通过在这样一个系统中集成多个生物传感器,有可能得出对用户健康状况的全面评估。在本文中,我们提出了一种多传感器WHMS的随机Petri网(SPN)模型以及一个用Java实现的相应模拟框架。所提出的模型是基于先前发表的多传感器数据融合策略构建的,在这项工作中该策略已得到扩展,以考虑测量的生物信号之间的同步问题和时间依赖性。