Step Detection for Rollator Users has until now been a problem for Smartwatches. Current algorithms work insufficiently for calculating steps when using a rollator, which is a common walking aid for elderly people (overall recognition of ~10% of all steps). This is because characteristic motions utilized by step counting algorithms are poorly reflected at the user’s wrist when pushing a rollator. This issue is also present among other spatial activities such as pushing a pram, a bike, and a shopping cart. We introduce an improved step counting algorithm for wrist-worn accelerometers, which achieves recognition rates of of ~85% in the lab and similar detection rates of ~83% in the field with elderly people. This research was published at the Symposium on Spatial User Interaction (SUI ’18) and at the International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR '18). This research was awarded the Best Short Paper at the SUI '18 in Berlin, Germany.