HNSE-P1-6. A Review on the Usage of Machine Learning Methods Gait Analysis and Possibility of a Portable Gait Analysis Device

Hassan Adam1
Faculty Mentor: Venkatesan Muthukumar, Ph.D.1
1Howard R. Hughes College of Engineering, Department of Electrical and Computer Engineering

Gait analysis is a valuable tool for evaluating and monitoring an individual’s walking pattern, which is used to recognize movement-related irregularities. Lately, machine learning methods have been introduced in the processing of the gait analysis data to help monitor and analyze the data. Given the increased interest in the area, this paper will focus on two parts: one is analyzing and reviewing the latest Machine learning Methods and sensors used, and the second is the possibility of a portable device capable of measuring and processing an individual’s gait. The analysis of the Machine learning models and sensors papers illustrated that several algorithms and methods used had shown a possibility in helping to identify and monitor neurodegenerative disease, which is an excellent area for further research. Additionally, the second part of the study showed that a portable device capable of measuring and processing an individual’s gait is possible and would be capable of data processing onsite. However, that device would have a disadvantage over the conventional gait analysis.

This research was funded by the Southern Nevada Northern Arizona (SNNA) Louis Stokes Alliance for Minority Participation (LSAMP), which is housed within UNLV’s Center for Academic Enrichment and Outreach and supported by a grant (HRD – 1712523) from the National Science Foundation (NSF). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF. 


Nov 15 - 19 2021


All Day


HNSE: Poster Session 1
The Office of Undergraduate Research


The Office of Undergraduate Research


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