TITLE:
Learning Based Falling Detection Using Multiple Doppler Sensors
AUTHORS:
Shoichiro Tomii, Tomoaki Ohtsuki
KEYWORDS:
Falling Detection; Doppler Sensor; Cepstrum Analysis; SVM; k-NN
JOURNAL NAME:
Advances in Internet of Things,
Vol.3 No.2A,
June
19,
2013
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 usefuldevices 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.