An Activity Transition Based Fall Detection Model on Mobile Devices

  • Min Zhou
  • Shuangquan Wang
  • Yiqiang Chen
  • Zhenyu Chen
  • Zhongtang Zhao
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 182)

Abstract

Fall detection is a hot research issue in the field of pervasive computing and human-computer interaction. Its key difficulty is to build a fall detection model which obtains high detection accuracy and low false alarm rate simultaneously. In this paper, we propose a fall detection model based on activity transition. Firstly, our method segments continuous activity data based on activity recognition sequence, then extracts features from transition data between adjacent activities to build a fall detection model. Employing this model, we can detect the fall through recognizing abnormal activity transition. Tested on the real-world activity data set, our algorithm outperforms traditional methods.

Keywords

Fall detection Activity transition Extreme learning machine One-class SVM Accelerometer Gyroscope 

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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Min Zhou
    • 1
    • 2
  • Shuangquan Wang
    • 2
  • Yiqiang Chen
    • 2
  • Zhenyu Chen
    • 2
  • Zhongtang Zhao
    • 2
  1. 1.College of Information EngineeringXiangtan UniversityXiangtanChina
  2. 2.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

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