SmartMove: A smartwatch algorithm to distinguish between high- and low-amplitude motions as well as doffed-states by utilizing noise and sleep

In this paper, we describe a self adapting algorithm for smart watches to define individual transitions between motion intensities. The algorithm enables for a distinction between high-amplitude motions (e.g. walking, running, or simply moving extremities) low-amplitude motions (e.g. human microvibrations, and heart rate) as well as a general doffed-state. A prototypical implementation for detecting all three motion types was tested with a wrist-worn acceleration sensor. Since the aforementioned motion types are user-specific, SmartMove incorporates a training module based on a novel actigraphy-based sleep detection algorithm, in order to learn the specific motion types. In addition, our proposed sleep algorithm enables for reduced power consumption since it samples at a very low rate. Furthermore, the algorithm can identify suitable timeframes for an inertial sensor-based detection of vital-signs (e.g. seismocardiography or ballistocardiography).