Learning Based Falling Detection Using Multiple Doppler Sensors

Shoichiro Tomii, Tomoaki Ohtsuki

Graduate School of Science and Technology, Keio University, Yokohama, Japan.

DOI: 10.4236/ait.2013.32A005                

Abstract

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.

Keywords

Falling Detection; Doppler Sensor; Cepstrum Analysis; SVM; k-NN

Conflicts of Interest

The authors declare no conflicts of interest.

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