Date of Award
Master of Arts (M.A.)
Health, Exercise, and Sport Sciences
First Committee Member
Mark Van Ness
Second Committee Member
Mahdi Mehdi Khazaeli
The purpose of this study was to determine how kinematic, big data can be evaluated using computational, comprehensive analysis of movement parameters in a diverse population.
Retrospective data was collected, cleaned, and reviewed for further analysis of biomechanical movement in an active population using 3D collinear resistance loads. The active sample of the population involved in the study ranged from age 7 to 82 years old and respectively identified as active in 13 different sports. Moreover, a series of exercises were conducted by each participant across multiple sessions. Exercises were measured and recorded based on 6 distinct biometric movement parameters: explosiveness, velocity, power, deceleration, braking, consistency, endurance, and range of motion. Analysis and data visualization portrayed how 3D collinear resistance load impacted specific muscles and performance metrics.
The model with the highest accuracy rate was Naive Bayes and Fast Large Margin at 58.3% for future predictions considering impact for specific muscles, movement parameters, and performance metric data. The data visualization involved a proof-of-concept human-computer interface and presented each component in relation to one another within the active population database, movement parameters, and performance metrics.
Understanding the findings regarding 3D collinear resistance sets a precedence for future development for the active population and research in the sports analytics field. Additionally, the visual proof of concept interface promotes future development for a diverse, active population.
Nunis, Naomi Wilma. (2023). Making Sense of Big (Kinematic) Data: A Comprehensive Analysis of Movement Parameters in a Diverse Population. University of the Pacific, Thesis. https://scholarlycommons.pacific.edu/uop_etds/4177
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