Swing Analysis System Neural Network Autodetection
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Solution Overview
Problem
Current training methods for athletes engaging in swinging motions, such as baseball or golf, rely on manual observation and feedback, which is often delayed and less effective than receiving real-time, quantitative feedback.
Innovation Solution
A swing analysis system that includes user input devices, a motion capture system, and data processing devices capable of autodetecting swing phases and generating performance metrics, using trained neural networks to analyze motion data and force measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation and feedback methods are used for swing training, then the system complexity is low, but the feedback precision and timeliness deteriorate
Solution Approach 1:
The swing analysis system divides the swing motion into distinct phases (address, backswing, downswing, follow-through) and analyzes each phase separately using motion capture markers and force plates. This segmentation enables precise measurement of specific swing parameters while maintaining manageable system complexity through modular analysis.
Solution Approach 2:
The system replaces manual visual observation with automated optical motion capture technology and force sensing. Motion capture cameras track marker positions, and force plates measure ground reaction forces, substituting human judgment with precise instrumental measurement for objective swing analysis.
2Loss of time
If real-time motion capture and force measurement systems are implemented, then the feedback timeliness is improved, but the device complexity increases
Solution Approach 1:
Motion capture markers are pre-positioned on the athlete's body, and force plates are calibrated before the swing training session begins. This preliminary setup enables immediate data capture and analysis from the first swing, minimizing feedback delay without requiring complex real-time adjustment mechanisms.
Solution Approach 2:
The system implements closed-loop feedback by continuously capturing motion and force data, processing it through neural networks to generate swing phase detections and performance metrics, and providing real-time feedback to the athlete and coach. This feedback loop operates throughout the training session, enabling immediate technique adjustments.
3Measurement precision
If automated swing phase detection using neural networks is used, then the measurement precision is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system introduces neural networks as an intermediary layer between raw motion capture and force plate data and the final swing phase detection. The neural network processes complex multi-dimensional data from markers and force sensors, automatically identifying swing phases and transitions with high precision while shielding users from the computational complexity.
Solution Approach 2:
The system transforms physical swing parameters (marker coordinates, force plate readings) into meaningful swing phase classifications through neural network processing. This parameter transformation converts complex continuous data into discrete, interpretable swing phase labels that are easy to understand and act upon.
Data Source
AI summary
A swing analysis system is disclosed herein. The swing analysis system includes at least one user input device; a motion capture system comprising at least one motion capture device configured to detect the motion of at least one of: (i) one or more body segments of a subject, (ii) a head and/or face of the subject, (iii) a hand and/or fingers of the subject, and (iv) an object being manipulated by the subject, and generate output data; and at least one data processing device operatively coupled to the user input device and the motion capture system, the at least one data processing device being programmed to perform autodetection for the movement of the subject that is selected by the user by inputting the output data from the motion capture system into a trained neural network so that the movement being performed by the subject is able to be automatically determined.