Detecting complex user activities using ensemble machine learning over inertial sensors data

Abstract

A computer implemented method of detecting complex user activities, comprising using processor(s) in each of a plurality of consecutive time intervals for: obtaining sensory data from wearable inertial sensor(s) worn by a user, computing an action score for continuous physical action(s) performed by the user, the continuous physical action(s) extending over multiple time intervals are indicated by repetitive motion pattern(s) identified by analyzing the sensory data, computing a gesture score for brief gesture(s) performed by the user, the brief gesture(s) bounded in a single basic time interval is identified by analyzing the sensory data, aggregating the action and gesture scores to produce an interval activity score of predefined activity(s) for a current time interval, adding the interval activity score to a cumulative activity score accumulated during a predefined number of preceding time intervals and identifying the predefined activity(s) when the cumulative activity score exceeds a predefined threshold.