Latest research trends in gait analysis using wearable sensors and machine learning: a systematic review

Saboor, A., Kask, T., Kuusik, A., Alam, M. M., Le Moullec, Y., Niazi, I. K., Zoha, A. and Ahmed, R. (2020) Latest research trends in gait analysis using wearable sensors and machine learning: a systematic review. IEEE Access, 8, pp. 167830-167864. (doi: 10.1109/ACCESS.2020.3022818)

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Abstract

Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.

Item Type:Articles
Additional Information:This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Program under Grant 668995, in part by the European Union Regional Development Fund through the framework of the Tallinn University of Technology Development Program 2016–2022, under Grant 2014-2020.4.01.16-0032, and in part by the Estonian Research Council under Grant PUT-PRG 424.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zoha, Dr Ahmed
Authors: Saboor, A., Kask, T., Kuusik, A., Alam, M. M., Le Moullec, Y., Niazi, I. K., Zoha, A., and Ahmed, R.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Access
Publisher:IEEE
ISSN:2169-3536
ISSN (Online):2169-3536
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in IEEE Access 8:167830-167864
Publisher Policy:Reproduced under a Creative Commons license

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