An unconstrained Activity Recognition Method using Smart Phones

Abstract

In this paper, we provide human activity recognition performance rates, using accelerometer and gyroscope signals acquired using smart phones. Covering seven basic actions (walking, running, jumping, standing, ascending stairs, descending stairs, and standing up and sitting down as one action) and a complex action (getting in and out of a car), with more than 100 subjects in a database collected in different environments, we provide recognition results on the largest database in the published literature. Utilizing features (e.g. extrema, zero crossing rates$\dots $) extracted from time-windows (e.g. with a duration of 2 seconds), K-Star classifier led to the best performance among 6 classifiers tested, exceeding 98\% recognition accuracy. A detailed comparison with current approaches is provided, along with possible future research directions. The associated technology could be helpful for health-related monitoring of one's activities, generating automatic status feeds for social networking sites, and calculating precise/adaptive calorie intake needs for individuals.