Automated Activity Recognition with Gait Positions Using Machine Learning Algorithms

Authors

  • Y. L. Ng Faculty of Arts and Science, University of Toronto, Canada
  • X. Jiang Grainger College of Engineering, University of Illinois at Urbana-Champaign, USA
  • Y. Zhang College of Letters and Science, University of California, USA
  • S. B. Shin Hankuk Academy of Foreign Studies, S. Korea
  • R. Ning Austin Preparatory School, USA
Volume: 9 | Issue: 4 | Pages: 4554-4560 | August 2019 | https://doi.org/10.48084/etasr.2952

Abstract

Exoskeletons are wearable devices for enhancing human physical performance and for studying actions and movements. They are worn on the body for additional power and load-carrying capacity. Exoskeletons can be controlled using signals from the muscles. In recent years, gait analysis has attracted increasing attention from fields such as animation, athletic performance analysis, and robotics. Gait patterns are unique, and each individual has his or her own distinct gait pattern characteristics. Gait analysis can monitor activity in sensitive areas. This paper uses various machine learning algorithms to predict the activity of subjects using exoskeletons. Here, localization data from the UIC machine learning repository are used to recognize activities with gait positions. The study also compares five machine learning methods and examines their efficiency and accuracy in activity prediction for three different subjects. The results for the various machine learning methods along with efficiency and accuracy results are discussed.

Keywords:

Exoskeletons, Gait analysis, Activity recognition, Machine learning algorithms

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https://archive.ics.uci.edu/ml/datasets/Localization+Data+for+Person+Activity

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How to Cite

[1]
Y. L. Ng, X. Jiang, Y. Zhang, S. B. Shin, and R. Ning, “Automated Activity Recognition with Gait Positions Using Machine Learning Algorithms”, Eng. Technol. Appl. Sci. Res., vol. 9, no. 4, pp. 4554–4560, Aug. 2019.

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