Skip to main content Skip to main navigation


WristRotate - A Personalized Motion Gesture Delimiter for Wrist-Worn Devices

Frederic Kerber; Philipp Schardt; Markus Löchtefeld
In: Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia. International Conference on Mobile and Ubiquitous Multimedia (MUM-15), November 30 - December 2, Linz, Austria, ISBN 978-1-4503-3605-5/15/11, ACM, 2015.


In this paper, we present WristRotate, a personalized motion gesture delimiter that enables separation of non-relevant motion from gesture input. In an extensive data collection, we acquired 435.1 hours of smartwatch acceleration data during everyday usage. We implemented a gesture recognition system based on Dynamic Time Warping to partition a stream of accelerometer readings to identify possible gestures and to classify them accordingly. Through our analysis, we were able to identify a gesture that is (1) uncommon in daily life; (2) quick and easy to execute and (3) easily and reliably detectable. The gesture is executed by simply rotating the lower arm and wrist outwards and back inwards (twice).