Systematic Literature Review: Recognition of Human Gait Cycle Using Machine Learning Approach

Systematic Literature Review: Recognition of Human Gait Cycle Using Machine Learning Approach

This paper aims to summarise the studies on the human gait cycle analysis that applied an Artificial Intelligent Algorithm (AI) based on inertial sensor data, verifying whether it can support the clinical evaluation. This study focuses on the research on the main databases, particularly from the yea...

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Published in:2021 6th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2021
Main Author: Kamaruzaman F.F.A.; Izhar C.A.A.; Fauzilan A.S.; Setumin S.; Hussain Z.; Abdullah M.F.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136505986&doi=10.1109%2fICRAIE52900.2021.9703983&partnerID=40&md5=0c48a5e24d3bafaefa89f055a98ee12d
id 2-s2.0-85136505986
spelling 2-s2.0-85136505986
Kamaruzaman F.F.A.; Izhar C.A.A.; Fauzilan A.S.; Setumin S.; Hussain Z.; Abdullah M.F.
Systematic Literature Review: Recognition of Human Gait Cycle Using Machine Learning Approach
2022
2021 6th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2021


10.1109/ICRAIE52900.2021.9703983
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136505986&doi=10.1109%2fICRAIE52900.2021.9703983&partnerID=40&md5=0c48a5e24d3bafaefa89f055a98ee12d
This paper aims to summarise the studies on the human gait cycle analysis that applied an Artificial Intelligent Algorithm (AI) based on inertial sensor data, verifying whether it can support the clinical evaluation. This study focuses on the research on the main databases, particularly from the year 2015 to 2021. Fifteen studies were identified that have met the inclusion criteria. This paper also discussed the Machine Learning (ML) approach applied to classify and predict the gait cycle. The ML algorithm proposed are SVM, MC and ANN. Features such as swing and stance are the most selected features for healthy subjects, extracted from ground reaction force (GRF) during gait. © 2021 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper
author Kamaruzaman F.F.A.; Izhar C.A.A.; Fauzilan A.S.; Setumin S.; Hussain Z.; Abdullah M.F.
spellingShingle Kamaruzaman F.F.A.; Izhar C.A.A.; Fauzilan A.S.; Setumin S.; Hussain Z.; Abdullah M.F.
Systematic Literature Review: Recognition of Human Gait Cycle Using Machine Learning Approach
author_facet Kamaruzaman F.F.A.; Izhar C.A.A.; Fauzilan A.S.; Setumin S.; Hussain Z.; Abdullah M.F.
author_sort Kamaruzaman F.F.A.; Izhar C.A.A.; Fauzilan A.S.; Setumin S.; Hussain Z.; Abdullah M.F.
title Systematic Literature Review: Recognition of Human Gait Cycle Using Machine Learning Approach
title_short Systematic Literature Review: Recognition of Human Gait Cycle Using Machine Learning Approach
title_full Systematic Literature Review: Recognition of Human Gait Cycle Using Machine Learning Approach
title_fullStr Systematic Literature Review: Recognition of Human Gait Cycle Using Machine Learning Approach
title_full_unstemmed Systematic Literature Review: Recognition of Human Gait Cycle Using Machine Learning Approach
title_sort Systematic Literature Review: Recognition of Human Gait Cycle Using Machine Learning Approach
publishDate 2022
container_title 2021 6th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2021
container_volume
container_issue
doi_str_mv 10.1109/ICRAIE52900.2021.9703983
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136505986&doi=10.1109%2fICRAIE52900.2021.9703983&partnerID=40&md5=0c48a5e24d3bafaefa89f055a98ee12d
description This paper aims to summarise the studies on the human gait cycle analysis that applied an Artificial Intelligent Algorithm (AI) based on inertial sensor data, verifying whether it can support the clinical evaluation. This study focuses on the research on the main databases, particularly from the year 2015 to 2021. Fifteen studies were identified that have met the inclusion criteria. This paper also discussed the Machine Learning (ML) approach applied to classify and predict the gait cycle. The ML algorithm proposed are SVM, MC and ANN. Features such as swing and stance are the most selected features for healthy subjects, extracted from ground reaction force (GRF) during gait. © 2021 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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