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，能够检测此类运动相关活动的系统可应用于比赛分析、导引训练及个人运动追踪等场景。该数据集采集自两个国家（美国与德国）的两支球队，共24名球员在篮球重复训练和全场比赛期间佩戴腕部惯性传感器。该数据集的特殊之处在于，由于数据在两个国家采集，文化差异导致的比赛规则与风格差异带来了固有变异性，同时参与者过往篮球经验各异，体现了不同运动技能水平。我们通过多项时间序列分析展示了数据集特征，并报告了采用两种前沿深度学习架构的基线分类性能研究。