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

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.