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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 reserach. 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.

Publication Date

Fall 11-15-2021




Gait Analysis; Machine Learning; Wearable Sensors; Embedded Machine Learning

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File Size

4000 KB


Faculty Mentor: Venkatesan Muthukumar, Ph.D.

A Review on the Usage of Machine Learning Methods in Gait Analysis and Possibility of a Portable Gait Analysis Device