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.

翻译：我们提出一个用于评估腕戴式传感器人体物理活动识别方法的基准数据集，其特定场景为篮球训练、练习和比赛。篮球活动非常适合通过腕戴式惯性传感器进行测量，能够检测此类与运动相关活动的系统可应用于比赛分析、指导训练和个人身体活动追踪。该数据集记录了来自两个不同国家（美国和德国）球队的24名球员数据，球员在重复性篮球训练课和完整比赛中均佩戴腕戴式惯性传感器。该数据集的显著特征包括：因数据在两个国家采集而产生的文化差异（体现在比赛规则和风格上）带来的固有方差，以及因参与者在篮球经验方面存在异质性而呈现的不同运动技能水平。我们通过多项时间序列分析展示了数据集特征，并报告了采用两种最新深度学习架构的基准分类性能研究。