Making Sense of Big (Kinematic) Data: A Comprehensive Analysis of Movement Parameters in a Diverse Population
OBJECTIVE: The purpose of this study was to determine how effective measurement of biomechanical movement using 3D collinear resistance loads reflects the comparison of muscles in active individuals and dynamically visualizing muscles involved. METHODS: This was a retrospective data-driven study which involved analyzing pre-recorded database(s). Active individuals involved in the study ranged from age 8 to 83 years old. Given the area of focus being collinear resistance load the core measure for evaluation was power. To evaluate power the collection of data was curated with a General Power Test. A series of 4 exercises conducted for each participant across multiple sessions. These exercises measured and recorded based on 6 distinct biometric movements: explosiveness, velocity, power, deceleration, breaking, consistency, endurance, and range of motion to extend analysis and furthermore develop data visualization. Participants that were not involved in more than 3 consistent tests were excluded. With that criteria, the remaining records and sports involved included baseball, football, golf, soccer, and field hockey. Results: reduce the groups of athletes and their sports to the top 5 sports to avoid diluting the population, accuracy and precision results was implemented to ensure reliability within the model. Though the model with the highest accuracy rate was Naïve Bayes and Fast Large Margin at 58.3%. Discussion: Performance metrics compared with different active individuals can indicate significance of opposing and related sport mechanics for muscle improvements. The connection to muscles and exercises affecting the muscles through the use and monitoring of these modalities may serve in the future for therapeutic or muscle development.