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Acceleration Signal Categorization Using Support Vector Machines

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Abstract

Large sensor installations are becoming prominent as the cost of sensors drop and new methods are developed for structural health monitoring, fall detection, building occupancy, etc. Large amounts of data could be quickly captured, especially for measurements of high sampling rate such as acceleration signals. Methods to quickly triage records for further analysis can be used to drastically reduce the amount of data to be process. This paper studies the use of Support Vector Machines to classify floor vibration signals to determine signals of interest. Four kernels and three signal metrics were explored in this research using a human activity dataset containing over 500,000 acceleration records. Results show that the Radial Basis Function using a Dispersion Ratio metric can be used to identify signals of interest effectively.

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Acknowledgements

This work is partially supported by a grant from the University of South Carolina Magellan Scholar Program, and additional partial support is provided by a grant from the Alzheimer’s Association (ETAC-10-174499).

This work was supported in part by VISN 7, US Department of Veterans Affairs. Also, William Jennings Bryan Dorn VAMC facilitated the conduct of the study in the hospital and patient homes. The contents do not represent the views of the US Department of Veterans Affairs or the US Government.

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant Number 1450810. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. Advanced Smart Systems and Evaluation Technologies, LLC, 1400 Laurel Street, Suite 1B, Columbia, SC, 29201, USA

    B. T. Davis

  2. Structural Dynamics and Intelligent Infrastructure (SDII) Laboratory, Department of Civil and Environmental Engineering, University of South Carolina, 300 Main Street, Columbia, SC, 29201, USA

    J. M. Caicedo

  3. Senior Care Primary Practice, Palmetto Health-USC Medical Group, 3010 Farrow Road Suite 300, Columbia, SC, 29203, USA

    V. A. Hirth

  4. William Jennings Bryan Dorn Veteran’s Administration Medical Center, 6439 Garners Ferry Road, Columbia, SC, 29209, USA

    B. M. Easterling

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  1. B. T. Davis
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  2. J. M. Caicedo
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  3. V. A. Hirth
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  4. B. M. Easterling
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Correspondence to B. T. Davis.

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Davis, B.T., Caicedo, J.M., Hirth, V.A. et al. Acceleration Signal Categorization Using Support Vector Machines. Exp Tech 43, 359–368 (2019). https://doi.org/10.1007/s40799-019-00318-y

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