@inproceedings{76b922e52a284cbdbeae0555af40764e,
title = "Fall detection with orientation calibration using a single motion sensor",
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.",
keywords = "Fall detection, Machine learning, Sensor orientation calibration",
author = "Shuo Yu and Hsinchun Chen",
note = "Funding Information: This study was supported by USA NSF SES-1314631, DUE-1303362, and STTR-1622788. Also, the authors thank Cathy Larson for the proofreading and suggestions. Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.; 6th International Conference on Wireless Mobile Communication and Healthcare, MobiHealth 2016 ; Conference date: 14-11-2016 Through 16-11-2016",
year = "2017",
doi = "10.1007/978-3-319-58877-3_31",
language = "English (US)",
isbn = "9783319588766",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer-Verlag",
pages = "233--240",
editor = "Giovanna Rizzo and Paolo Perego and Giuseppe Andreoni",
booktitle = "Wireless Mobile Communication and Healthcare - 6th International Conference, MobiHealth 2016, Proceedings",
}