  The great success of wearables and smartphone apps for provision of extensive
physical workout instructions boosts a whole industry dealing with consumer
oriented sensors and sports equipment. But with these opportunities there are
also new challenges emerging. The unregulated distribution of instructions
about ambitious exercises enables unexperienced users to undertake demanding
workouts without professional supervision which may lead to suboptimal training
success or even serious injuries. We believe, that automated supervision and
realtime feedback during a workout may help to solve these issues. Therefore we
introduce four fundamental steps for complex human motion assessment and
present SensX, a sensor-based architecture for monitoring, recording, and
analyzing complex and multi-dimensional motion chains. We provide the results
of our preliminary study encompassing 8 different body weight exercises, 20
participants, and more than 9,220 recorded exercise repetitions. Furthermore,
insights into SensXs classification capabilities and the impact of specific
sensor configurations onto the analysis process are given.
