Wireless Mobile Communication and Healthcare. 6th International Conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings

Research Article

Fall Detection with Orientation Calibration Using a Single Motion Sensor

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  • @INPROCEEDINGS{10.1007/978-3-319-58877-3_31,
        author={Shuo Yu and Hsinchun Chen},
        title={Fall Detection with Orientation Calibration Using a Single Motion Sensor},
        proceedings={Wireless Mobile Communication and Healthcare. 6th International Conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings},
        proceedings_a={MOBIHEALTH},
        year={2017},
        month={6},
        keywords={Fall detection Sensor orientation calibration Machine learning},
        doi={10.1007/978-3-319-58877-3_31}
    }
    
  • Shuo Yu
    Hsinchun Chen
    Year: 2017
    Fall Detection with Orientation Calibration Using a Single Motion Sensor
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-319-58877-3_31
Shuo Yu1,*, Hsinchun Chen1,*
  • 1: University of Arizona
*Contact email: shuoyu@email.arizona.edu, hchen@eller.arizona.edu

Abstract

Falls are a major threat for senior citizens living independently. Sensor technologies and fall detection algorithms have emerged as a reliable, low-cost solution for this issue. We proposed a sensor orientation calibration algorithm to better address the uncertainty issue faced by fall detection algorithms in real world applications. We conducted controlled experiments of simulated fall events and non-fall activities on student subjects. We evaluated our proposed algorithm using sequence matching based machine learning approaches on five different body positions. The algorithm achieved an F-measure of 90 to 95% in detecting falls. Sensors worn as necklace pendants or in chest pockets performed best.