Towards a social and context-aware multi-sensor fall detection and risk assessment platform
Towards a social and context-aware multi-sensor fall detection and risk assessment platform
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DOI：
10.1016/j.compbiomed.2014.12.002
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发表时间：
2015-09-01
影响因子：
7.7
通讯作者：
De Turck, F.
中科院分区：
文献类型：
--
作者：
De Backere, F.;Ongenae, F.;De Turck, F.
对于老年人来说，跌倒事件是改变生活的事件，导致退化甚至丧失自主性。当前的跌倒检测系统是不集成的，往往与未检测到的福尔斯和/或假alarm.In本文中，社会和上下文感知的多传感器平台，它集成了由过多的跌倒检测系统和传感器在家中的老人，通过使用基于云的解决方案，利用本体收集的信息。在本体中，静态和动态信息都被捕获，以模拟特定患者及其正式护理人员的情况。该集成的背景信息允许自动地和连续地评估老年人的跌倒风险，以更准确地检测福尔斯并识别假警报，并且自动地通知适当的护理者，例如，所提出的平台的主要优点是，多个跌倒检测系统和传感器可以被集成，因为它们可以被容易地插入，这可以基于患者的特定需求来完成。多个系统和传感器的组合使系统更可靠，精度更高。使用可视化工具对概念验证进行了测试，该工具可以更好地分析后端内的数据流，并使用配备了几种不同传感器的便携式测试台。(C)2014爱思唯尔有限公司版权所有。
For elderly people fall incidents are life-changing events that lead to degradation or even loss of autonomy. Current fall detection systems are not integrated and often associated with undetected falls and/or false alarms.In this paper, a social- and context-aware multi-sensor platform is presented, which integrates information gathered by a plethora of fall detection systems and sensors at the home of the elderly, by using a cloud-based solution, making use of an ontology. Within the ontology, both static and dynamic information is captured to model the situation of a specific patient and his/her (in)formal caregivers. This integrated contextual information allows to automatically and continuously assess the fall risk of the elderly, to more accurately detect falls and identify false alarms and to automatically notify the appropriate caregiver, e.g., based on location or their current task.The main advantage of the proposed platform is that multiple fall detection systems and sensors can be integrated, as they can be easily plugged in, this can be done based on the specific needs of the patient. The combination of several systems and sensors leads to a more reliable system, with better accuracy. The proof of concept was tested with the use of the visualizer, which enables a better way to analyze the data flow within the back-end and with the use of the portable testbed, which is equipped with several different sensors. (C) 2014 Elsevier Ltd. All rights reserved.