eZiGait: Toward an AI Gait Analysis And Sssistant System

Graham McCalmont, Philip Morrow, Huiru Zheng, Anas Samara, Sara Yasaei, Haiying / HY Wang, Sally I McClean

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)


Objectively assessing gait function in lower limb rehabilitation remains a challenge in healthcare. This paper proposed the framework of AI gait analysis and assessment system eZiGait, which is based on seamless smart insoles. The preliminary study of activity recognition using eZiGait is presented. Walking data for five types of activities including slow walking, normal walking, fast walking, climbing upstairs, and walking down stairs have been investigated. Three classifiers were used, including artificial neural network (ANN), k-nearest neighbour (KNN) and random forest, to classify the five exercises. Results shows that a classification accuracy of 80% can be achieved with the ANN or 70% with KNN and random forest. This demonstrates that simple features extracted from smart insoles can be used to classify different types of exercise. This provides for potential development of an AI gait analysis and assistant system to support lower limb rehabilitation at hospital, community or at home using state-of-the-art smart insoles and mobile technologies.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Bioinformatics and Biomedicine(BIBM 2018)
Place of PublicationMadrid, Spain
ISBN (Electronic) 978-1-5386-5488-0
ISBN (Print)978-1-5386-5489-7
Publication statusPublished (in print/issue) - 3 Dec 2018


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