Notes On “Digital Sports” & Sports Science [1]

Digital Sports    #DigitalSports

Applications Areas

Robotic Sports System, Robotic Player, Simulator

Cricket

Batting against a Machine that throws a ball with adjustable speed and spin / swing

3D video of bowler running towards the batsman and the machine throws the ball from a hole – feels like a real match; sound of crowds cheering

Table Tennis

Baseball

Tennis

Robocup

People playing not video games, rather with a Robot in the real world

More realistic than Kinect-like experience

Data Analytics Applied to Sports

Player Analysis, Match Analysis Software

Cricket

Finding weakness of a batsman from database of description of every ball he faced and the strokes offered.

Computer Vision

Machine Learning, Statistics, Big Data

Soccer: Match Analysis

Machine learning & Statistical Analysis, Image Processing, Computer Vision (e.g., finding weakness of a batsman in cricket)

Application of Data Science / Visualization techniques to Team play Analysis (e.g., Soccer)

Sensors for feedback, performance analysis (e.g., in Tennis, Golf, etc.)

Mechanical Engineering (e.g., Batting practice in cricket)

Enumerating all the skills, helping others learn those skills (make the skills automatic – so that players can perform them without thinking) Systematic acquisition of skills.

Learning a specific Athletic skill:

Biomechanics. Image processing.

How close to learning the skill? Feedback from Biomechanical Images – Movement of Muscle and bone (e.g. while learning the skill of “Serving” in Tennis).

 

Sports Psychology. Flow State. Optimum performance. Stress Management. Resilience. Visualization.

Neural representation of over-lapping motor skills

Low level motor skills.

High level motor skills consisting of low level skills.

Hierarchical representation in the brain.




Researchers working in Neural Representation of Motor Learning / Motor Skills

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