Y. Kim and H. Ling, “Human Activity Classification Based on Microdopplersignatures Using a Support Vector Machine,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No. 5, 2009, pp. 1328-1337. doi:10.1109/TGRS.2009.2012849

has been cited by the following article:

• 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.