Digital Sports #DigitalSports
Robotic Sports System, Robotic Player, Simulator
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
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
Finding weakness of a batsman from database of description of every ball he faced and the strokes offered.
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