Basketball Shooting Performance Analysis Using Multi-Modal Wearable and Mobile Sensing in Semi-Naturalistic Settings

Sixuan Wu, Alexander Hoelzemann, Marius Bock, Kristof Van Laerhoven, Thomas Ploetz, Alexander T. Adams

Abstract

Wearable devices have become efficient tools for sports performance analysis. Professional systems heavily rely on the high-tech setup, which are expensive and privacy-invasive for amateur players. This paper addresses the gap between advanced professional systems and limited consumer options by proposing a low-cost, privacy-preserving approach for basketball shot detection and outcome prediction. We leverage accelerome-ter data from wrist-worn smartwatches, combined with audio recordings, to develop a system capable of identifying shot movements and predicting shot outcomes. The shot detection was achieved by a ID CNN model through accelerometer data and outcome classification was achieved by an audio classification model. We evaluated the system on 6 participants, and the macro F1 score for shot outcome classification in data streams are 81.53% and 78.07% on dominant hand and non-dominant hand, respectively. Our system opens up explorations in other domains, including medical or industrial activity recognition, where similar approaches can be applied.

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Bibtex

@inproceedings{wuBasketballShootingPerformance2024,
author={Wu, Sixuan and Hoelzemann, Alexander and Bock, Marius and Van Laerhoven, Kristof and Ploetz, Thomas Ploetz and Adams, Alexander T.},
title = {Basketball Shooting Performance Analysis Using Multi-Modal Wearable and Mobile Sensing in Semi-Naturalistic Settings},
booktitle = {IEEE 20th International Conference on Body Sensor Networks (BSN)},
  	year={2024},
  	url = {https://doi.org/10.1109/BSN63547.2024.10780653}, }