Golf Shot Detection Using Wearable Motion and Machine Learning
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Solution Overview
Problem
Existing golf performance monitoring technologies rely on sensors attached to golf clubs or manual user input to detect golf shots, lacking an automated and sensor-free method for determining when a golf shot occurs.
Innovation Solution
A system utilizing trained machine learning models to predict golf shots based on user location, positional data, and various inputs such as user profile, swing detection, and environmental conditions, without requiring sensors on golf clubs or manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are attached to golf clubs to detect shots, then shot detection accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the shot detection function from the golf club itself and relocates it to the golfer's wearable device. By using accelerometers and gyroscopes on the person rather than on the club, the system eliminates the need for complex club-mounted sensors while maintaining detection capability through body movement analysis during the swing.
Solution Approach 2:
The patent introduces an intermediary approach by using the golfer's body movements as a proxy for club movements. The wearable sensors detect body motion patterns that indirectly indicate shot occurrence, eliminating the need for direct club sensors while maintaining detection accuracy through the intermediary relationship between body motion and club motion.
2Device complexity
If manual user input is required to indicate shots, then system complexity is reduced, but productivity and automation level decrease
Solution Approach 1:
The system performs self-service by automatically detecting shots through wearable sensors that continuously monitor body movements. The machine learning model processes sensor data autonomously to identify shots without requiring manual input from the user, enabling automated shot detection while maintaining relatively simple system architecture.
Solution Approach 2:
The patent replaces manual mechanical input with automated electronic sensing and machine learning processing. Instead of requiring physical button presses or manual inputs, the system uses accelerometers, gyroscopes, and ML algorithms to automatically detect and identify shots, significantly improving automation while keeping the overall system simple.
3Measurement precision
If multiple sensor types are used to detect shots, then measurement precision is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent merges multiple sensor types (accelerometers and gyroscopes) into a unified wearable system that processes data through machine learning. By combining these sensors and using ML to integrate their signals, the system achieves high measurement precision while managing complexity through software-based data fusion rather than hardware complexity.
Data Source
AI summary
A method for predicting whether a user executed a golf shot includes (i) receiving a selection of a machine learning model from a plurality of machine learning models, each of the plurality of machine learning models being trained to predict whether the golf shot occurred, (ii) receiving inputs relevant to the machine learning model that was selected; and (iii) predicting, using the machine learning model that was selected, whether the user executed the golf shot using the inputs relevant to the machine learning model that was selected.