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Development of an awakening behavior detection system using a neural network
Author(s) -
Satoh Hironobu,
Takeda Fumiaki,
Shiraishi Yuhki,
Ikeda Rie
Publication year - 2011
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.10296
Subject(s) - falling (accident) , lying , artificial neural network , sitting , artificial intelligence , computer science , simulation , computer vision , engineering , psychology , medicine , pathology , psychiatry , radiology
We have developed a behavior detection system using a neural network (NN). The system detects dangerous behaviors such as nearly falling out of bed and actual falling out of bed. For detection, the system uses pictures captured by a web camera. The system classifies subjects' behavior into five states using a NN. The five states are “lying in bed,” “starting to sit up in bed,” “sitting in bed,” “almost falling from the bed,” and “having fallen from the bed.” Then, we define states of almost falling and having fallen from the bed as dangerous behavior. The states of lying in bed, starting to sit up in bed, and sitting in bed are defined as safe behavior. We propose a final detection rule by which the system classifies their behavior into two states. Finally, the detection ability of the system is evaluated. From the experiment's results, the detection rate of dangerous behavior is shown to be 81.7% using the proposed system. © 2011 Wiley Periodicals, Inc. Electron Comm Jpn, 94(2): 42–50, 2011; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/ecj.10296