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.
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Acknowledgement
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.
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Yu, S., Chen, H. (2017). Fall Detection with Orientation Calibration Using a Single Motion Sensor. In: Perego, P., Andreoni, G., Rizzo, G. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 192. Springer, Cham. https://doi.org/10.1007/978-3-319-58877-3_31
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DOI: https://doi.org/10.1007/978-3-319-58877-3_31
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