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Human Gait Recognition

(Analysis and Classification)

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Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019) (ICCBI 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 49))

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Abstract

Gait State Analysis refers to the way in which an individual walks. With every individual having a unique walking pattern, it can be used as a biometric in many domains like sports, medicine, and many more. The gait state analysis can be used as a biometric without any contact with the individual, making it a very versatile methods for many purposes. Gait recognition has two major steps, first the data is to be collected and stored, then from the parameters calculated using that data, mechanisms to identify an individual are applied. The first step can be done by using two methods: Wearable Sensors and Non-Wearable Sensors. Types of Wearable sensors explained are: Floor Sensors and Image Processing Sensors; Types of Non-Wearable sensors explained are: Force and pressure sensors, Sensing Fabric and Electromagnetic Tracking System. Once the data is collected and stored, it can be used for the recognition of an individual by means of ANN (Artificial Neural Network Deep Learning), Inductive Machine Learning, and Fuzzy Logic mechanisms.

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Author information

Authors and Affiliations

  1. Department of IT, Mukesh Patel School of Technology Management and Engineering, NMIMS (deemed-to-be) University, Mumbai, India

    Vaishnavi Ahuja & Rejo Mathew

Authors
  1. Vaishnavi Ahuja
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  2. Rejo Mathew
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Corresponding author

Correspondence to Vaishnavi Ahuja .

Editor information

Editors and Affiliations

  1. Department of CSE, Vaigai College of Engineering, Melur, Tamil Nadu, India

    A. Pasumpon Pandian

  2. Department of Business Administration, The Gerald Schwartz School of Business, StFX University, Antigonish, NS, Canada

    Ram Palanisamy

  3. Electrical and Computer Engineering, University of Applied Sciences, Egaleo, Attiki, Greece

    Klimis Ntalianis

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Ahuja, V., Mathew, R. (2020). Human Gait Recognition. In: Pandian, A., Palanisamy, R., Ntalianis, K. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). ICCBI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-030-43192-1_37

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