Bat Sweet Spot Detection with Real-Time Vibration Sensors
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Solution Overview
Problem
Existing systems for analyzing bat shots in sports like cricket are not cost-effective, portable, and require substantial manual effort, making real-time feedback difficult, especially for identifying the sweet spot on bats of varying thicknesses and player builds.
Innovation Solution
A portable system using a sensor device with accelerometers, gyroscopes, and magnetometers mounted on the bat to record and analyze vibrations, combined with machine learning algorithms for real-time sweet spot detection and classification of shots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera systems are used to analyze bat shots, then measurement precision can be achieved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex camera systems with a simplified sensor-based system. Instead of using optical cameras to capture and analyze bat motion and impact, the invention uses accelerometers, gyroscopes, and magnetometers mounted on the bat to directly measure vibrations, rotation, and magnetic field changes during impact. This substitution of mechanical/optical systems with electronic sensors reduces device complexity while maintaining measurement precision for sweet spot detection.
Solution Approach 2:
The patent introduces an intermediary processing system that includes a microcontroller and machine learning algorithm. The sensor data is processed through this intermediary system which automatically identifies sweet spot impacts based on predefined characteristics. This intermediary layer simplifies the overall system by automating the analysis process that would otherwise require complex camera-based image processing and manual review.
2Measurement precision
If camera systems are used for shot analysis, then measurement precision is achieved, but loss of time increases due to manual analysis requirements
Solution Approach 1:
The system performs self-service analysis by automatically processing sensor data in real-time during the sporting event. The machine learning algorithm continuously monitors sensor outputs and automatically identifies sweet spot impacts without requiring external manual analysis. This self-service capability eliminates the time loss associated with post-event manual review while maintaining accurate shot analysis.
Solution Approach 2:
The patent implements real-time feedback by immediately analyzing sensor data during the sporting event and providing instant information about whether a sweet spot impact occurred. The system uses feedback from the sensor measurements to automatically trigger notifications or visual indicators on a display device, allowing players to receive immediate feedback without waiting for manual analysis completion.
3Device complexity
If conventional analysis methods are used, then equipment simplicity is maintained, but productivity decreases due to time-consuming manual analysis
Solution Approach 1:
The patent ensures continuity of useful action by continuously monitoring sensor data throughout the entire sporting event without interruption. The machine learning algorithm operates continuously to identify sweet spot impacts as they occur, eliminating downtime between analysis operations. This continuous processing dramatically increases productivity compared to batch processing methods while keeping the system relatively simple.
Solution Approach 2:
The invention replaces manual mechanical analysis processes with automated electronic processing. Instead of requiring manual review of camera footage, the system uses electronic sensors and computer algorithms to automatically analyze each impact in real-time. This substitution maintains system simplicity while dramatically improving analysis throughput and productivity.
4Device complexity
If general-purpose sensors are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent merges multiple types of sensors (accelerometers, gyroscopes, magnetometers) into a single integrated sensor system mounted on the bat. By combining these sensors to measure different aspects of impact (linear acceleration, rotational movement, magnetic field changes) and processing them together through machine learning, the system achieves high measurement precision without requiring each individual sensor to be overly complex or expensive.
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 real-time identification of sweet spot shots, providing instant feedback to players, independent of bat type and player build, enhancing training efficiency and accuracy.
Implementation Method 1
The sensor device comprises an accelerometer, a gyroscope, and a magnetometer
Implementation Method 2
The sensor device comprises an accelerometer, a gyroscope, and a magnetometer
Implementation Method 3
The sensor device comprises an accelerometer, a gyroscope, and a magnetometer
Implementation Method 4
configured to continuously record a plurality of event-based data elements associated with a plurality of shots on a plurality of regions on the bat
Data Source
AI summary
A system including a data extraction module (DEM), a data tagging module (DTM), and a sweet spot detection module (SSDM) and a method for detecting a sweet spot on a bat are provided. A sensor device including multiple sensors is coupled to a rear surface of the bat. The sensors continuously record event-based data elements associated with multiple shots on multiple regions on the bat within a configurable time period. The DEM extracts and stores the event-based data elements. The DTM aggregates and tags each event-based data element based on a position of each shot at each region on the bat and feedback data regarding shot types. The SSDM detects and distinguishes a sweet spot shot from a non-sweet spot shot and an edge shot based on responses produced by the bat at each region during and after each shot is hit using the event-based data elements.