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Human locomotion activity recognition using spectral analysis and convolutional neural networks

Ji, Ze and Amer, Ahmad 2021. Human locomotion activity recognition using spectral analysis and convolutional neural networks. International Journal of Manufacturing Research 16 , 4. 10.1504/IJMR.2021.10039081
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Abstract

To enable a collaborative robot or exoskeleton robot to better support humans more safely and efficiently, understanding human behaviours is an essential enabling technology for the machines to make decisions on optimal control strategies. This work introduces an algorithm for classification of human locomotion activities using inertial data captured with Inertial Measurement Unit (IMU) to support the control of robots, exoskeletons and many other applications. The proposed approach, to recognise human locomotion activities and gait events, includes two main steps: 1) applying spectral analysis on the inertial signals to transform the data into time-frequency representation; and 2) classify the time-frequency data of an image to be recognised using Convolutional Neural Networks (CNN). There are six activities considered in the work. The highest accuracy in classification with a sub-set of 3 classes is 99% demonstrating the promising result in being applicable for real applications.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Inderscience
ISSN: 1750-0591
Date of First Compliant Deposit: 17 January 2020
Date of Acceptance: 4 January 2020
Last Modified: 21 Dec 2021 16:17
URI: https://orca.cardiff.ac.uk/id/eprint/128727

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