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This paper studies sports injury risks and prevention, specifically focusing on figure skating. USFSA STARS science and injury biomechanics are also illustrated. Modern 3D-motion techniques were introduced to help develop sports strength training curriculum -- sports injury failure modes and injury prevention stretching techniques were analyzed and used to develop a cohesive curriculum. Clustering Principal Component Analysis was utilized to understand the different clustering methods that can help select the appropriate clustering algorithms to discover more injury insights. Statistical data visualization tools were used to provide more correlation and causation patterns on understanding the injury mechanisms. Using results from our statistical analysis, the appropriate injury prevention program was developed for figure skaters.
This paper adopts the STEAMS (Science, Technology, Engineering, Artificial Intelligence, Math, Statistics) methodology. The objective of this paper is to introduce the benefits of integrating all 6 “STEAMS” elements, especially in the era of Big Data. There are three core visions of “STEAMS” methodology: (1) replace “Art” with “Artificial Intelligence”, (2) separate “Statistics” from “Math,” and (3) integrate all six “STEAMS” elements. Adding the “Artificial Intelligence” element can trigger and enhance the effectiveness of sports science and mathematical algorithms. SPARQ science and injury biomechanics are also illustrated. Modern 3D-motion techniques were introduced to help develop sports strength training curriculum. Sports injury failure modes and injury prevention stretching techniques are common problem-solving methods in the engineering field. The popular Artificial Intelligence tools used were clustering Principal Component Analysis. Understanding the different clustering methods can help select the appropriate clustering algorithms to discover more injury insights. Statistical data visualization tools provide more correlation and causation patterns on understanding the injury mechanisms. This interdisciplinary STEAMS approach is a very powerful technique to help prevent injuries.
This paper adopts STEAMS (Science, Technology, Engineering, Artificial Intelligence, Math, Statistics) methodology. The objectives of this paper are to introduce the benefits of integrating all 6 “STEAMS” elements, especially living in the Big Data World. NBA Draft Position case study was demonstrated to present this novel “STEAMS” concept as compared to current “STEM” or” STEAM” approach. There are three core visions of this “STEAMS” methodology: (1) replace “Art” with “Artificial Intelligence”, (2) separate “Statistics” from “Math”, and (3) integrate all six “STEAMS” elements. Adding the “Artificial Intelligence” element can trigger and enhance the effectiveness of “Sports” Science Research and “Math” algorithms. Separating the “Statistics” element can conduct more effective risk management and draw practical conclusions. Due to the previous two benefits, integrating all 6 “STEAMS” elements is becoming a natural critical thinking way for most scientists and engineers striving in the modern Big Data era. Several techniques are used to help determine the NBA Player position and identify the similar NBA players for benchmarking objective. It’s critical and urgent for educators and teachers to migrate from their traditional STEM approach to the new “STEAMS” approach to educate our next generations in their early school learning and career development.
This paper adopts STEAMS (Science, Technology, Engineering, Artificial Intelligence, Math, Statistics) methodology. The objectives of this paper are to introduce the benefits of integrating all 6 “STEAMS” elements, especially living in the Big Data World. There are three core visions of this “STEAMS” methodology: (1) replace “Art” with “Artificial Intelligence”, (2) separate “Statistics” from “Math”, and (3) integrate all six “STEAMS” elements. Adding the “Artificial Intelligence” element can trigger and enhance the effectiveness of “Sports” Science Research and “Math” algorithms. Basketball Sport case study was demonstrated to present this novel “STEAMS” concept as compared to current “STEM”/” STEAM” approach. Basketball SPARQ science and injury biomechanics are illustrated. Modern 3D-Motion techniques were introduced to help develop Sports Strength training curriculum. Sports injury failure modes and injury-preventive stretching techniques are common engineering problem solving thinking. Clustering and Principal Component Analysis are popular Artificial Intelligence analytics. Understanding the clustering distance math could help select the appropriate clustering algorithms to discover the injury insights. Statistical data visualization tools could provide more correlation and causation patterns on understanding the Sports Injury mechanisms. This interdisciplinary STEAMS approach is very powerful on the Sports Science and Injury research.
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