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 ~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.

Step Detection Algorithm

PEOPLE

Denys Matthies

Suranga Nanayakkara

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 ~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.

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 ~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.

Step Detection Algorithm

PEOPLE

Denys Matthies

Suranga Nanayakkara

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 ~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.