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- Title
Skill Level Classification in Basketball Free-Throws Using a Single Inertial Sensor.
- Authors
Guo, Xiaoyu; Brown, Ellyn; Chan, Peter P. K.; Chan, Rosa H. M.; Cheung, Roy T. H.
- Abstract
Wearable sensors are an emerging technology, with growing evidence supporting their application in sport performance enhancement. This study utilized data collected from a tri-axial inertial sensor on the wrist of ten recreational and eight professional basketball players while they performed free-throws, to classify their skill levels. We employed a fully connected convolutional neural network (CNN) for the classification task, using 64% of the data for training, 16% for validation, and the remaining 20% for testing the model's performance. In the case of considering a single parameter from the inertial sensor, the most accurate individual components were upward acceleration (AX), with an accuracy of 82% (sensitivity = 0.79; specificity = 0.84), forward acceleration (AZ), with an accuracy of 80% (sensitivity = 0.78; specificity = 0.83), and wrist angular velocity in the sagittal plane (GY), with an accuracy of 77% (sensitivity = 0.73; specificity = 0.79). The highest accuracy of the classification was achieved when these CNN inputs utilized a stack-up matrix of these three axes, resulting in an accuracy of 88% (sensitivity = 0.87, specificity = 0.90). Applying the CNN to data from a single wearable sensor successfully classified basketball players as recreational or professional with an accuracy of up to 88%. This study represents a step towards the development of a biofeedback device to improve free-throw shooting technique.
- Subjects
CONVOLUTIONAL neural networks; BIOFEEDBACK training; MOTION capture (Human mechanics); TECHNOLOGICAL innovations; ANATOMICAL planes; ANGULAR velocity; WRIST
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 9, p5401
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app13095401