Back

Title
  • en Privacy-Preserved Behavior Analysis and Fall Detection by an Infrared Ceiling Sensor Network
Creator
Accessrights open access
Rights
Subject
  • Other en behavior analysis
  • Other en fall detection
  • Other en privacy-preserved
  • Other en ceiling sensor network
  • Other en infrared sensors
  • NDC 548
Description
  • Abstract en An infrared ceiling sensor network system is reported in this study to realize behavior analysis and fall detection of a single person in the home environment. The sensors output multiple binary sequences from which we know the existence/non-existence of persons under the sensors. The short duration averages of the binary responses are shown to be able to be regarded as pixel values of a top-view camera, but more advantageous in the sense of preserving privacy. Using the "pixel values" as features, support vector machine classifiers succeeded in recognizing eight activities (walking, reading, etc.) performed by five subjects at an average recognition rate of 80.65%. In addition, we proposed a martingale framework for detecting falls in this system. The experimental results showed that we attained the best performance of 95.14% (F1 value), the FAR of 7.5% and the FRR of 2.0%. This accuracy is not sufficient in general but surprisingly high with such low-level information. In summary, it is shown that this system has the potential to be used in the home environment to provide personalized services and to detect abnormalities of elders who live alone.
Publisher en MDPI
Date
    Issued2012-12
Language
  • eng
Resource Type journal article
Version Type VoR
Identifier HDL http://hdl.handle.net/2115/51701
Relation
  • isIdenticalTo DOI https://doi.org/10.3390/s121216920
Journal
    • PISSN 1424-8220
      • en Sensors
      • Volume Number12 Issue Number12 Page Start16920 Page End16936
File
Oaidate 2023-07-26