Dissertation/Thesis Abstract

Computer Vision-based Estimation of Body Mass Distribution, Center of Mass, and Body Types: Design and Comparative Study
by Gautam, Kumar, M.S., California State University, Long Beach, 2018, 106; 10838305
Abstract (Summary)

Body mass distribution and center of mass (CoM) are important topics in the field of human biomechanics and the healthcare industry. Increasing global obesity has led researchers to measure body parameters. This project focuses on developing an automatic computer vision approach to calculate the body mass distribution and CoM, as well as identify body types with a minimum setup cost.

In this project, a 3-D calibrated experimental setup was devised to take images of four male subjects in three views: front view, left side view, and right side view. First, a method was devised to separate the human subject from the background. Second, a novel approach was developed to find the CoM, percentage body mass distribution, and body types using two models: Simulated Skeleton Model (SSM) and Simulated Skeleton Matrix (SSMA). The CoM using this method was 94.36% of the CoM calculated with a reaction board experiment. Total body mass using this method was 96.6% of the total body mass calculated with the weighing balance. This project has three components: (1) finding the body mass distribution and comparing the results with the weighing balance, (2) finding the CoM and comparing the results with the reaction board experiment, and (3) offering new ways to conceptualize the three body types that are ectomorph, endomorph, and mesomorph with ratings in the range of 0 to 5.

Indexing (document details)
Advisor: Druzgalski, Christopher
Commitee: Ary, James, Haggerty, Kevin
School: California State University, Long Beach
Department: Electrical Engineering
School Location: United States -- California
Source: MAI 58/01M(E), Masters Abstracts International
Subjects: Electrical engineering, Artificial intelligence, Computer science
Keywords: And body types: design and comparative study, Body mass distribution, Body types, Center of mass, Center of mass of human, Classification of body types, Computer vision, Computer vision-based estimation of body mass distribution, Machine learning, Precentage body mass distribution
Publication Number: 10838305
ISBN: 9780438301498
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