Context-aware fall detection using inertial sensors and time-of-flight transceivers.
Context-aware fall detection using inertial sensors and time-of-flight transceivers.
复制标题
使用惯性传感器和飞行时间收发器进行情境感知跌倒检测。
DOI：
10.1109/embc.2016.7590766
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Jacobs,PeterG
中科院分区：
文献类型：
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作者：
Shastry,MaheshC;Asgari,Meysam;Wan,EricA;Leitschuh,Joseph;Preiser,Nicholas;Folsom,Jon;Condon,John;Cameron,Michelle;Jacobs,PeterG
福尔斯的自动检测对于使老年人能够在家中安全地独立生活更长时间是重要的。当前的自动跌倒检测系统通常使用定位在身体上的惯性传感器来设计，如果存在运动的突然变化，则惯性传感器生成警报。这些惯性传感器不提供有关被监视人员的上下文的信息，并且容易出现误报，从而限制其持续使用。我们描述了一种跌倒检测系统，该系统由可穿戴惯性测量单元（IMU）和RF飞行时间（ToF）收发器组成，该收发器与位于整个家庭中的其他ToF信标一起工作。ToF测距使系统能够跟踪人在家中移动时的位置。我们描述并展示了三种机器学习算法的结果，这些算法将上下文相关的位置信息与基于IMU的跌倒检测相结合，以更深入地了解福尔斯发生的位置，并提高跌倒检测的特异性。用于定位福尔斯的信标能够在模拟的家庭环境中准确地跟踪到0.39米以内的特定航路点。三种算法中的每一种都在有和没有基于上下文的假警报检测的情况下对由3名志愿者受试者在模拟家庭中完成的模拟福尔斯跌倒进行评估。当包括上下文时，假阳性率降低了50%。
Automatic detection of falls is important for enabling people who are older to safely live independently longer within their homes. Current automated fall detection systems are typically designed using inertial sensors positioned on the body that generate an alert if there is an abrupt change in motion. These inertial sensors provide no information about the context of the person being monitored and are prone to false positives that can limit their ongoing usage. We describe a fall-detection system consisting of a wearable inertial measurement unit (IMU) and an RF time-of-flight (ToF) transceiver that ranges with other ToF beacons positioned throughout a home. The ToF ranging enables the system to track the position of the person as they move around a home. We describe and show results from three machine learning algorithms that integrate context-related position information with IMU based fall detection to enable a deeper understanding of where falls are occurring and also to improve the specificity of fall detection. The beacons used to localize the falls were able to accurately track to within 0.39 meters of specific waypoints in a simulated home environment. Each of the three algorithms was evaluated with and without the context-based false alarm detection on simulated falls done by 3 volunteer subjects in a simulated home. False positive rates were reduced by 50% when including context.