BasketHAR is a publicly released multimodal dataset of professional basketball training activities captured with inertial sensors, physiological signals, and video, accompanied by a baseline alignment method.
URL https://www.sciencedirect.com/science/ article/pii/S1877050917312899
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A large-scale benchmark of 17 WHAR models across 30 datasets finds predictive performance has plateaued while efficiency favors compact neural models and random forests on the Pareto frontier.
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BasketHAR: A Multimodal Dataset for Human Activity Recognition and Sport Analysis in Basketball Training Scenarios
BasketHAR is a publicly released multimodal dataset of professional basketball training activities captured with inertial sensors, physiological signals, and video, accompanied by a baseline alignment method.