Advertisement

Classification of Human Walking Patterns through Singular Value Decomposition Projection

Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

Sensing of structural vibration provides a rich source of information that can be used in structural health monitoring and impact/fault localization among other applications. In this paper, acceleration measurements from vibration sensors (accelerometers), installed in an operational smart building (Virginia Tech’s Goodwin Hall), are used to classify footsteps of different kinds from building occupants. Goodwin Hall is a 160,000 square foot five story building instrumented with over 200 accelerometers mounted to the building’s structure. Singular value decomposition (SVD) projection is used to classify measured data into categories seeded with training data. Contrary to the black box machine learning approach, the SVD framework allows classification parameters to be easily modified and their effects visualized to be understood. Better understanding of the classification problem and its dominant parameters will allow the development of more accurate and robust algorithms for classification of a wide variety of signals.

Keywords

Gait Singular value decomposition Classification Smart infrastructure User identification 

Notes

Acknowledgements

Dr. Tarazaga would like to acknowledge the financial support of the John R. Jones Faculty Fellowship.

The authors wish to acknowledge the support as well as the collaborative efforts provided by our sponsors, VTI Instruments, PCB Piezotronics, Inc.; Dytran Instruments, Inc.; and Oregano Systems. The authors are particularly appreciative for the support provided by the College of Engineering at Virginia Tech through Dean Richard Benson and Associate Dean Ed Nelson as well as VT Capital Project Manager, Todd Shelton, and VT University Building Official, William Hinson. The authors would also like to acknowledge Gilbane, Inc. and in particular, David Childress and Eric Hotek. We are especially thankful to the Student Engineering Council (SEC) at Virginia Tech and their financial commitment to this project. The work was conducted under the patronage of the Virginia Tech Smart Infrastructure Laboratory and its members.

References

  1. 1.
    Farshidi, A.: The new retail experience and its unaddressed privacy concerns: how RFID and mobile location analytics are collecting customer information. J. Law. Technol. Internet. 7(1), 15 (2016)Google Scholar
  2. 2.
    Kasarda, M.E., Tarazaga, P.A., Embree, M., Gugercin, S., Woolard, A., Joyce, B.S., Hamilton, J.: Detection and identification of firearms upon discharge using floor-based accelerometers. IMAC XXXIV A Conference and Exposition on Structural Dynamics, Orlando, Florida, January 25–28 (2016)Google Scholar
  3. 3.
    Agarwal, Y., Balaji, B., Gupta, R., Lyles, J., Wei, M., Weng, T.: Occupancy-driven energy management for smart building automation. In: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, pp. 1–6. ACM, New York (2010)Google Scholar
  4. 4.
    Pan, S. et al.: BOES: Building occupancy estimation system using sparse ambient vibration monitoring. in Proc. SPIE, San Diego, CA, USA, Art. no. 90611O (2014)Google Scholar
  5. 5.
    Bales, D.B., Tarazaga, P.A., Kasarda, M.E., Batra, D., Woolard, A.G., Poston, J.D., Malladi, V.V.N.S.: Gender classification of walkers via underfloor accelerometer measurements. IEEE Internet Things J. (2016).  https://doi.org/10.1109/JIOT.2016.2582723
  6. 6.
    Poston, J.D., Michael Buehrer, R., Tarazaga, P.A.: Indoor footstep localization from structural dynamics instrumentation. Mech. Syst. Signal Process. 88, 224–239 (2017)CrossRefGoogle Scholar

Copyright information

© The Society for Experimental Mechanics, Inc. 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringVirginia Tech Smart Infrastructure Lab (VTSIL)BlacksburgUSA

Personalised recommendations