||This work introduces an open benchmark dataset to investigate inertial sensor displacement effects in activity recognition. While sensor position displacements such as rotations and translations have been recognised as a key limitation for the deployment of wearable systems, a realistic dataset is lacking. We introduce a concept of gradual sensor displacement conditions, including ideal, self-placement of a user, and mutual displacement deployments. These conditions were analysed in the dataset considering 33 ﬁtness activities, recorded using 9 inertial sensor units from 17 participants. Our statistical analysis of acceleration features quantiﬁed relative effects of the displacement conditions. We expect that the dataset can be used to benchmark and compare recognition algorithms in the future.