Recognizing Bedside Events Using Thermal and Ultrasonic Readings.
Recognizing Bedside Events Using Thermal and Ultrasonic Readings.
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DOI：
10.3390/s17061342
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发表时间：
2017-06-09
期刊：
影响因子：
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通讯作者：
Jim T
中科院分区：
文献类型：
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作者：
Asbjørn D;Jim T
在养老院、寄宿护理机构和医院中，跌倒通常发生在靠近床的地方。大多数识别跌倒的方法都使用摄像头，这对隐私构成了挑战，或者连接在床上或身体上的传感器设备来识别床边事件和床边跌倒。我们使用从安装在天花板上的80 × 60热阵列和超声波传感器设备收集的数据。这种方法使监视活动成为可能，同时以非侵入性的方式保护隐私。我们评估了三种不同的方法来识别一个人的位置和姿势。使用10秒浮动图像规则/基于过滤器的方法识别床边事件，识别床边跌倒的准确率为98.62%。入床和出床事件的识别准确率分别为98.66%和96.73%。
Falls in homes of the elderly, in residential care facilities and in hospitals commonly occur in close proximity to the bed. Most approaches for recognizing falls use cameras, which challenge privacy, or sensor devices attached to the bed or the body to recognize bedside events and bedside falls. We use data collected from a ceiling mounted 80 × 60 thermal array combined with an ultrasonic sensor device. This approach makes it possible to monitor activity while preserving privacy in a non-intrusive manner. We evaluate three different approaches towards recognizing location and posture of an individual. Bedside events are recognized using a 10-second floating image rule/filter-based approach, recognizing bedside falls with 98.62% accuracy. Bed-entry and exit events are recognized with 98.66% and 96.73% accuracy, respectively.