  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.
