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International Conference on Engineering Applications of Neural Networks

EANN 2017: Engineering Applications of Neural Networks pp 609–619Cite as

A Random Forest Method to Detect Parkinson’s Disease via Gait Analysis

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 744))

Abstract

Remote care and telemonitoring have become essential component of current geriatric medicine. Intelligent use of wireless sensors is a major issue in relevant computational studies to realize these concepts in practice. While there has been a growing interest in recognizing daily activities of patients through wearable sensors, the efforts towards utilizing the streaming data from these sensors for clinical practices are limited. Here, we present a practical application of clinical data mining from wearable sensors with a particular objective of diagnosing Parkinson’s Disease from gait analysis through a sets of ground reaction force (GRF) sensors worn under the foots. We introduce a supervised learning method based on Random Forests that analyze the multi-sensor data to classify the person wearing these sensors. We offer to extract a set of time-domain and frequency-domain features that would be effective in distinguishing normal and diseased people from their gait signals. The experimental results on a benchmark dataset have shown that proposed method can significantly outperform the previous methods reported in the literature.

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Acknowledgement

This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under the Project 115E451.

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Authors and Affiliations

  1. Department of Computer Engineering, Başkent University, Ankara, Turkey

    Koray Açıcı, Çağatay Berke Erdaş, Tunç Aşuroğlu & Hasan Oğul

  2. Department of Neurology, Başkent University, Ankara, Turkey

    Münire Kılınç Toprak

  3. Department of Electrical and Electronics Engineering, Başkent University, Ankara, Turkey

    Hamit Erdem

Authors
  1. Koray Açıcı
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  2. Çağatay Berke Erdaş
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  3. Tunç Aşuroğlu
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  4. Münire Kılınç Toprak
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  5. Hamit Erdem
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  6. Hasan Oğul
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Corresponding author

Correspondence to Çağatay Berke Erdaş .

Editor information

Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Giacomo Boracchi

  2. Democritus University of Thrace, University Campus, Xanthi, Greece

    Lazaros Iliadis

  3. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Univesity of Ioannina, Ioannina, Greece

    Aristidis Likas

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Açıcı, K., Erdaş, Ç.B., Aşuroğlu, T., Toprak, M.K., Erdem, H., Oğul, H. (2017). A Random Forest Method to Detect Parkinson’s Disease via Gait Analysis. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_51

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  • DOI: https://doi.org/10.1007/978-3-319-65172-9_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65171-2

  • Online ISBN: 978-3-319-65172-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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