Golf Club Sensor Attachment for Swing Analysis
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Solution Overview
Problem
Existing golf swing analysis technologies lack a comprehensive system to detect and measure the entire golf swing, provide accurate statistics, simulate laser lines for swing plane analysis, and offer precise coaching using graphical and verbal feedback, while being lightweight and cost-effective.
Innovation Solution
A lightweight attachment to a golf club featuring a 3-axis accelerometer, gyroscope, computer memory, microprocessor, and transmitter communicates with a mobile device to display a graphical representation of the swing with comprehensive statistics, simulates laser lines, and corrects errors using RF and ultrasound technologies, integrating data from cameras and image recognition for precise analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive golf swing analysis system is implemented with multiple sensors and processing components, then measurement precision and analysis comprehensiveness are improved, but device complexity and weight increase
Solution Approach 1:
The patent combines multiple sensors (accelerometer, gyroscope, magnetometer) and processing components into a single integrated attachment device that fits on the golf club. This merging approach maintains comprehensive measurement capabilities while reducing the overall system complexity and making it portable.
Solution Approach 2:
The attachment device performs multiple functions including detecting linear acceleration, angular velocity, magnetic field orientation, and processing this data to provide comprehensive swing analysis. This multi-functionality eliminates the need for separate devices for each measurement type, reducing overall system complexity.
2Reliability
If error correction mechanisms using RF and ultrasound are added, then measurement reliability is improved, but device complexity and energy consumption increase
Solution Approach 1:
The error correction system uses periodic RF and ultrasound signals to establish reference frames and correct drift in sensor measurements. By using periodic rather than continuous correction, the system maintains high reliability while reducing energy consumption compared to continuous correction mechanisms.
3Measurement precision
If real-time video capture and image recognition are implemented, then measurement precision is improved, but processing time and energy consumption increase
Solution Approach 1:
The system captures video frames and performs image recognition processing during the swing execution rather than requiring separate post-processing time. This preliminary action approach provides immediate feedback while maintaining measurement precision, reducing the perceived processing time for the user.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables full analysis of the golf swing with precise error correction and comprehensive statistics, allowing golfers to improve their technique through interactive 3D animations and verbal feedback, while being lightweight and cost-effective.
Implementation Method 1
a three-axis accelerometer for generating linear acceleration data from the apparatus
Implementation Method 2
a three-axis gyroscope for generating angular velocity data from the apparatus
Implementation Method 3
Error correction is further accomplished by analyzing position data throughout the swing using ultrasonic and radio frequency pulses
Data Source
AI summary
Systems and methods for motion attribute recognition are defined using data stream pre-processing to orient, align and segment motion data before using non-parametric classification recognition to search a motion data exemplar database or using parametric classification recognition to find attributes by comparing pre-processed motion data with support vector machines. Results from the non-parametric classification recognition and the parametric classification recognition may be fused to produce a single result. Active learning and metric learning are used to improve searches of the database and comparisons to the support vector machines.