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

Abdul Saboor*, Triin Kask, Alar Kuusik, Muhammad Mahtab Alam, Yannick Le Moullec, Imran Khan Niazi, Ahmed Zoha, Rizwan Ahmad

*Kontaktforfatter

Publikation: Bidrag til tidsskriftReview (oversigtsartikel)peer review

62 Citationer (Scopus)
167 Downloads (Pure)

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.
OriginalsprogEngelsk
Artikelnummer3022818
TidsskriftIEEE Access
Vol/bind8
Sider (fra-til)167830-167864
Antal sider35
ISSN2169-3536
DOI
StatusUdgivet - 8 sep. 2020

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