Using IoT Smart Basketball and Wristband Motion Data to Quantitatively Evaluate Action Indicators for Basketball Shooting

Abstract

Traditional approaches to improving basketball players’ shooting skills rely on coaches’ experience in adjusting players’ biomechanical motions. However, such an approach cannot provide specific instructions or facilitate immediate feedback for improvement of the shooting motion. In this article, a method is presented to quantitatively evaluate four key action indicators of shooting basketballs using a machine‐learning model based on Bayesian optimization of a light gradient boosting machine (LightGBM). Important motion data for the model are collected by micro‐inertial measurement units embedded in a wrist motion sensor and an internet of things (IoT) smart basketball. Basketball shooting motion data are collected from 16 subjects and used for model training and data testing, and four important action indicators that influence the shot quality are selected for quantitative assessment. The LightGBM model is then developed for the regression prediction of the four action indicators of shooting. In the results, it is indicated that for an individual player, the highest correlation scores of the four indexes range from 97.6% to 99.3%. The proposed approach for quantitatively assessing shooting indexes can provide objective and data‐based guidance to improve players’ shooting performance. Foreseeably, the prediction model can be embedded into a chip of a wearable device to evaluate the real‐time shot quality quantitatively.