OS Lab, Department of Computer Engineering, Kyung Hee University, Yongin-Si, 446-701, Korea.
Sensors (Basel). 2011;11(12):11581-604. doi: 10.3390/s111211581. Epub 2011 Dec 12.
Ubiquitous Life Care (u-Life care) is receiving attention because it provides high quality and low cost care services. To provide spontaneous and robust healthcare services, knowledge of a patient's real-time daily life activities is required. Context information with real-time daily life activities can help to provide better services and to improve healthcare delivery. The performance and accuracy of existing life care systems is not reliable, even with a limited number of services. This paper presents a Human Activity Recognition Engine (HARE) that monitors human health as well as activities using heterogeneous sensor technology and processes these activities intelligently on a Cloud platform for providing improved care at low cost. We focus on activity recognition using video-based, wearable sensor-based, and location-based activity recognition engines and then use intelligent processing to analyze the context of the activities performed. The experimental results of all the components showed good accuracy against existing techniques. The system is deployed on Cloud for Alzheimer's disease patients (as a case study) with four activity recognition engines to identify low level activity from the raw data captured by sensors. These are then manipulated using ontology to infer higher level activities and make decisions about a patient's activity using patient profile information and customized rules.
无处不在的生活护理（u-Life care）受到关注，因为它提供高质量、低成本的护理服务。要提供自发且强大的医疗保健服务，需要了解患者的实时日常生活活动。具有实时日常生活活动的上下文信息有助于提供更好的服务并改善医疗保健服务的提供。现有的生活护理系统的性能和准确性不可靠，即使服务数量有限。本文提出了一种人类活动识别引擎（HARE），该引擎使用异构传感器技术监测人类健康和活动，并在云平台上智能处理这些活动，以低成本提供改进的护理。我们专注于使用基于视频的、基于可穿戴传感器的和基于位置的活动识别引擎进行活动识别，然后使用智能处理来分析所执行活动的上下文。针对现有的技术，所有组件的实验结果都显示出了很好的准确性。该系统部署在云平台上，针对阿尔茨海默病患者（作为案例研究）使用四个活动识别引擎，从传感器捕获的原始数据中识别低级别活动。然后使用本体对其进行操作，以根据患者档案信息和定制规则推断出更高层次的活动，并对患者的活动做出决策。
