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M. Yu, S. M. Naqvi, A. Rhuma, and J. Chambers, "Fall Detection in a Smart Room by Using a Fuzzy One Class Support Vector Machine and Imperfect Training Data," in Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 1833-1836.

has been cited by the following article:

• TITLE: Falling-Incident Detection and Alarm by Smartphone with Multimedia Messaging Service (MMS)

  AUTHORS: Yi He, Ye Li, Chuan Yin

  KEYWORDS: Multimedia Messaging Service (MMS); Smartphone; Tri-Accelerometer; Falling-Incident Detection

  JOURNAL NAME: E-Health Telecommunication Systems and Networks, Vol.1 No.1, March 26, 2012

  ABSTRACT: This paper presents a system with real-time classification of human movements based on smartphone mounted on the waist. The built-in tri-accelerometer was utilized to collect the information of body motion. At the same time, the smartphone is able to classify the data for activity recognition. By our algorithm, body motion can be classified into five different patterns: vertical activity, lying, sitting or static standing, horizontal activity and fall. It alarms falling by Multimedia Messaging Service (MMS) with map of suspected fall location, GPS coordinate and time etc. If a fall was suspected, an automatic MMS would be sent to preset people. The major advantage of the proposed system is the novel application of smartphone which already have the necessary sensors and can monitor fall ubiquitously without any additional devices.