Smartwatches enable spatial user input, namely for the continuous tracking of physical activity and relevant health parameters. Additionally, smartwatches are experiencing greater social acceptability, even among the elderly. While step counting is an essential parameter to calculate the user's spatial activity, current detection algorithms are insufficient for calculating steps when using a rollator, which is a common walking aid for elderly people. Through a pilot study conducted with eight different wrist-worn smart devices, an overall recognition of ~10% was achieved. 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. This paper thus introduces an improved step counting algorithm for wrist-worn accelerometers. This new algorithm was first evaluated through a controlled study and achieved promising results with an overall recognition of ~85%. As a follow-up, a preliminary field study with randomly selected elderly people who used rollators resulted in similar detection rates of ~83%. To conclude, this research will expectantly contribute to greater step counting precision in smart wearable technology.