IEEE J Biomed Health Inform. 2013 Jan;17(1):53-9. doi: 10.1109/TITB.2012.2220374. Epub 2012 Sep 21.
In this paper, a feasibility study where small wireless devices are used to classify some typical users positions in the bed is presented. Wearable wireless low-cost commercial transceivers operating at 2.4 GHz are supposed to be widely deployed in indoor settings and on peoples bodies in tomorrows pervasive computing environments. The key idea of this work is to leverage their presence by collecting the received signal strength (RSS) measured among fixed devices, deployed in the environment, and the wearable one. The RSS measurements are used to classify a set of users positions in the bed, monitoring the activities of patients unable to make the desirable bodily movements. The collected data are classified using both support vector machine and K-nearest neighbour methods, in order to recognize the different users position, and thus supporting the bedsores issue.
本文提出了一项可行性研究，即在未来的普及计算环境中，使用小型无线设备对床上的一些典型用户位置进行分类。可穿戴的、低成本的商用无线收发器，工作在 2.4GHz 频段，有望在室内环境中和人体上广泛部署。这项工作的核心思想是利用它们的存在，通过收集固定设备之间的接收信号强度（RSS）测量值，并利用穿戴设备进行收集。RSS 测量值用于对床上的一组用户位置进行分类，监测无法进行理想身体运动的患者的活动。使用支持向量机和 K-最近邻方法对收集到的数据进行分类，以识别不同的用户位置，从而支持褥疮问题。
