Learning Based Falling Detection Using Multiple Doppler Sensors  

作　　者：Shoichiro Tomii Tomoaki Ohtsuki 

机构地区：[1]Graduate School of Science and Technology, Keio University, Yokohama, Japan

出　　处：《Advances in Internet of Things》2013年第2期33-43,共11页物联网（英文）

摘　　要：Automated falling detection is one of the important tasks in this ageing society. Such systems are supposed to have little interference on daily life. Doppler sensors have come to the front as useful?devices to detect human activity without using any wearable sensors. The conventional Doppler sensor based falling detection mechanism uses the features of only one sensor. This paper presents falling detection using multiple Doppler sensors. The resulting data from sensors are combined or selected to find out the falling event. The combination method, using three sensors, shows 95.5% accuracy of falling detection. Moreover, this method compensates the drawbacks of mono Doppler sensor which encounters problems when detecting movement orthogonal to irradiation directions.Automated falling detection is one of the important tasks in this ageing society. Such systems are supposed to have little interference on daily life. Doppler sensors have come to the front as useful?devices to detect human activity without using any wearable sensors. The conventional Doppler sensor based falling detection mechanism uses the features of only one sensor. This paper presents falling detection using multiple Doppler sensors. The resulting data from sensors are combined or selected to find out the falling event. The combination method, using three sensors, shows 95.5% accuracy of falling detection. Moreover, this method compensates the drawbacks of mono Doppler sensor which encounters problems when detecting movement orthogonal to irradiation directions.

关 键 词：FALLING DETECTION DOPPLER Sensor CEPSTRUM Analysis SVM K-NN 

分 类 号：R73[医药卫生—肿瘤]