| CPC G06F 18/00 (2023.01) [A63B 60/46 (2015.10); A63B 2102/20 (2015.10); A63B 2220/34 (2013.01); A63B 2220/40 (2013.01); A63B 2220/833 (2013.01); A63B 2225/20 (2013.01)] | 11 Claims |
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1. A system for detecting a sweet spot on a bat, the system comprising:
a sensor device operably coupled to a rear surface of the bat, wherein the sensor device comprises a plurality of sensors 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 within a configurable time period in one of real-time and near real-time, and wherein the sensor device is communicatively coupled to a user device and a sweet spot detection server via a network;
a non-transitory, computer-readable storage medium configured to store the plurality of event-based data elements and computer program instructions defined by a plurality of modules embedded in one of the sensor device, the user device, and the sweet spot detection server;
at least one processor operably and communicatively coupled to the non-transitory, computer-readable storage medium and configured to execute the computer program instructions defined by the plurality of modules; and
the plurality of modules comprising:
a data extraction module configured to extract and store the plurality of event-based data elements received from the plurality of sensors of the sensor device;
a data tagging module configured to aggregate and tag each of the plurality of event-based data elements received from the plurality of sensors, based on a position of each of the plurality of shots at each of the plurality of regions on the bat where a ball is hit; and
a sweet spot detection module configured to:
process the plurality of regions on the bat as a first region, a second region, a third region, and a fourth region, wherein the first region is located proximal to a handle and a shoulder of the bat, the second region is located proximal to the shoulder of the bat, the third region is located proximal to a toe of the bat, and the fourth region is located at the toe of the bat;
classify each of the plurality of shots into a first part, a second part, and a third part, wherein the first part lies in a time duration before the ball hits the bat and when the bat gains momentum till the ball is hit or missed, the second part is a time instance when the ball collides with the bat, and the third part lies in a time duration when the bat loses momentum and attempts to return to rest;
determine responses produced by the bat at the each of the first region, the second region, the third region, and the fourth region during and after each of the plurality of shots is hit, by analysing the plurality of event-based data elements; and
detect and distinguish a sweet spot shot from a non-sweet spot shot and an edge shot among the plurality of shots based on determined responses; and
wherein said sweet spot detection module is further configured to determine the responses produced by the bat at each of the first region, the second region, the third region, and the fourth region, by:
computing a resultant acceleration of the bat during and after each of the plurality of shots for each of the first region, the second region, the third region, and the fourth region;
computing a change in the resultant acceleration of the bat after each of the plurality of shots for each of the first region, the second region, the third region, and the fourth region using a statistical metric, and wherein the statistical metric is standard deviation; and
analysing a deviation in the resultant acceleration versus a deviation in the change in the resultant acceleration of the bat associated with each of the plurality of shots for each of the first region, the second region, the third region, and the fourth region;
and wherein the sweet spot detection module is configured to analyse the deviation in the resultant acceleration between two axes of the bat associated with each of the plurality of shots for each of the first region, the second region, the third region, and the fourth region by generating one or more decision boundaries as a classifier for clustering the plurality of shots and classifying each of the plurality of shots as the sweet spot shot, the non-sweet spot shot, and the edge shot, said sweet spot detection module further configured to cluster the plurality of shots into two parts in each of the regions by generating said one or more decision boundaries as the classifier.
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