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A Mobile Solution Based on Soft Computing for Fall Detection

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Mobile Solutions and Their Usefulness in Everyday Life

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Falling is an important health risk, especially for the elderly people. This situation prevents individuals from living independently. Automatic and high-accuracy detection of the falls will contribute in preventing the negative situations that may occur. In this study, a mobile solution with a new architecture for the detection of falls is presented. For this purpose, motion sensor data have been collected simultaneously from smartwatch and smartphone with Android operating system. Data sets for both smartwatch and smartphone have been created by labeling the falls and actions which are not falling in the data. The performances of Decision Tree, Naive Bayes, and k-Nearest Neighbor (kNN) methods have been tested on these data sets, and the kNN method has given the best result on two data sets. Accordingly, the kNN method is used for classification in the developed Android-based mobile solution. In addition, it is aimed to detect and prevent actions that could lead to bad results by monitoring the heart rate of the user with the built-in heart rate monitor on the smartwatch.

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Acknowledgments

This study is supported by Muğla Sıtkı Koçman University Scientific Research Projects under the grant number 016-061.

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Authors and Affiliations

  1. Faculty of Technology, Information Systems Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey

    Serkan Ballı & Musa Peker

  2. Faculty of Engineering, Department of Computer Engineering, Ege University, İzmir, Turkey

    Ensar Arif Sağbaş

Authors
  1. Serkan Ballı
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  2. Ensar Arif Sağbaş
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  3. Musa Peker
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Corresponding author

Correspondence to Ensar Arif Sağbaş .

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Editors and Affiliations

  1. Applied Research Centre for Digital Transformation - ARC4DigiT, Polytechnic Institute of Viana do Castelo, Viana do Castelo, Portugal

    Sara Paiva

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Ballı, S., Sağbaş, E.A., Peker, M. (2019). A Mobile Solution Based on Soft Computing for Fall Detection. In: Paiva, S. (eds) Mobile Solutions and Their Usefulness in Everyday Life. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-93491-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-93491-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93490-7

  • Online ISBN: 978-3-319-93491-4

  • eBook Packages: EngineeringEngineering (R0)

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