MU

Dissertations - Thesis

GAIT analysis based on AGE & GENDER detection using Transfer Learning

Institute

ENGINEERING

Stream

Computer Engineering

Course

M.E./M.Tech.

Author/s

Charmy Vora

Supervisiors

Prof. Munindra Lunagaria

Co-Supervisiors

Year

April, 2023

Abstract

Gaits can be created and analyzed using ever-improving techniques. An observable dataset based on gait can be analyzed for insights is becoming highly significant. Even though there has been a demand for gait analysis for a very long time, the concept of gait datasets that are based on inertial sensors is a relatively new one. Therefore, the majority of machine learning studies on the gait dataset rely on images. The most difficult parts of using gait analysis to determine someone's identification and determining their gender and age. This study summarizes and compares the most recent research and studies that using gait analysis to determine gender and age. Furthermore, the proposed approach for detecting gender and age based on gait analysis includes pre-trained models as well as tuned ML models that maximize a model's performance to find improved accuracy for age and gender detection in gait analysis without over-fitting or causing too much variance


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