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Point Anomaly Detection: Application to Freezing of Gait Monitoring

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Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings

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Abstract

This chapter describes details of settings and experiment reports for a point anomaly detection application. First, a dataset of freezing of gait (FoG) in patients with advanced Parkinson’s disease is explained. Then, specific classification settings for FoG detection are provided. Final sections are results of feature ranking and performance comparisons with existing methods.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia

    Thuy T. Pham

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  1. Thuy T. Pham
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Correspondence to Thuy T. Pham .

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Pham, T.T. (2019). Point Anomaly Detection: Application to Freezing of Gait Monitoring . In: Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-98675-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-98675-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98674-6

  • Online ISBN: 978-3-319-98675-3

  • eBook Packages: EngineeringEngineering (R0)

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