Smart Media Journal (스마트미디어저널)

THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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A Fall Detection Technique using Features from Multiple Sliding Windows

  • Pant, Sudarshan (Dept. of Multimedia Engineering, Graduate School, Mokpo National University) ;
  • Kim, Jinsoo (Graduate School of Computer Information Security, Chonbuk National University) ;
  • Lee, Sangdon (Dept. of Multimedia Engineering, College of Engineering, Mokpo National University)
  • Received : 2018.08.30
  • Accepted : 2018.11.17
  • Published : 2018.12.31
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Abstract

In recent years, falls among elderly people have gained serious attention as a major cause of injuries. Falls often lead to fatal consequences due to lack of prompt response and rescue. Therefore, a more accurate fall detection system and an effective feature extraction technique are required to prevent and reduce the risk of such incidents. In this paper, we proposed an efficient feature extraction technique based on multiple sliding windows and validated it through a series of experiments using supervised learning algorithms. The experiments were conducted using the public datasets obtained from tri-axial accelerometers. The results depicted that extraction of the feature from adjacent sliding windows led to high accuracy in supervised machine learning-based fall detection. Also, the experiments conducted in this study suggested that the best accuracy can be achieved by keeping the window size as small as 2 seconds. With the kNN classifier and dataset from wearable sensors, the experiments achieved accuracy rates of 94%.

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References

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