Title: Machine learning-based approach to identify human push recovery using GAIT analysis

Authors: S. Niranjani; Murugesan Punniyamoorthy; G. Lakshmi

Addresses: Department of Management Studies, National Institute of Technology, Tiruchirappalli-620015, India ' Department of Management Studies, National Institute of Technology, Tiruchirappalli-620015, India ' College of Management, SRM Institute of Science and Technology, Kattankulathur, Chennai – 603203, India

Abstract: The authors have performed empirical mode decomposition for a process to arrive at the human push recovery data using features that are obtained from intrinsic mode functions (IMFs). For the above purpose data related to leg joint angles (hip, knee and ankle) are collected. Three kinds of pushes were applied to analyse the recovery mechanism, in the field of robotics. The classification was performed using deep neural network (DNN) and other classification methods like KNN, naive Bayes, decision tree, random forest and support vector machine. The results have been compared to find the best method for further analysis. Use of five different classification technique and extraction of two additional features which improved the accuracy of the system are some of the unique features of this article.

Keywords: human push recovery; deep neural network; DNN; K nearest neighbour; decision tree; random forest; support vector machine; SVM; intrinsic mode functions; IMFs.

DOI: 10.1504/IJENM.2025.146312

International Journal of Enterprise Network Management, 2025 Vol.16 No.2, pp.147 - 161

Received: 06 May 2023
Accepted: 17 Mar 2024
Published online: 21 May 2025 *

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