Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-Worn Inertial Sensors

TL;DR

This paper introduces a comprehensive benchmark dataset of wrist-worn inertial sensor data for basketball activity recognition, enabling improved analysis, training, and personal tracking of basketball-related movements across diverse players and settings.

Contribution

The paper provides a novel, publicly available dataset capturing basketball activities from wrist sensors across different countries, skill levels, and game types, along with baseline classification results.

Findings

Dataset includes data from 24 players across USA and Germany.

Baseline deep learning models achieve promising activity recognition accuracy.

Dataset reveals cultural and skill-level variability in basketball movements.

Abstract

We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications toward game analysis, guided training, and personal physical activity tracking. The dataset was recorded for two teams from separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist, during both repetitive basketball training sessions and full games. Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels, since the participants were heterogeneous in terms of prior basketball experience. We illustrate the dataset's features in several time-series analyses and report on a baseline classification performance study with two state-of-the-art deep learning architectures.