Au Lawrence K, Bui Alex A T, Batalin Maxim A, Xu Xiaoyu, Kaiser William J
Electrical Engineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:2228-32. doi: 10.1109/IEMBS.2011.6090422.
Advancement in wireless health sensor systems has triggered rapidly expanding research in continuous activity monitoring for chronic disease management or promotion and assessment of physical rehabilitation. Wireless motion sensing is increasingly important in treatments where remote collection of sensor measurements can provide an in-field objective evaluation of physical activity patterns. The well-known challenge of limited operating lifetime of energy-constrained wireless health sensor systems continues to present a primary limitation for these applications. This paper introduces CARER, a software system that supports a novel algorithm that exploits knowledge of context and dynamically schedules sensor measurement episodes within an energy consumption budget while ensuring classification accuracy. The sensor selection algorithm in the CARER system is based on Partially Observable Markov Decision Process (POMDP). The parameters for the POMDP algorithm can be obtained through standard maximum likelihood estimation. Sensor data are also collected from multiple locations of the subjects body, providing estimation of an individual's daily activity patterns.
无线健康传感器系统的进步引发了对用于慢性病管理或促进及评估身体康复的连续活动监测的研究迅速扩展。在远程收集传感器测量数据可提供身体活动模式现场客观评估的治疗中，无线运动传感变得越来越重要。能量受限的无线健康传感器系统运行寿命有限这一众所周知的挑战，仍然是这些应用的主要限制。本文介绍了CARER，这是一个支持一种新颖算法的软件系统，该算法利用上下文知识并在能耗预算内动态安排传感器测量时段，同时确保分类准确性。CARER系统中的传感器选择算法基于部分可观测马尔可夫决策过程（POMDP）。POMDP算法的参数可通过标准最大似然估计获得。还从受试者身体的多个位置收集传感器数据，以估计个人的日常活动模式。
