A computing-efficient algorithm for accelerometer-based real-time activity recognition systems

Authors:
Pejman Ghorbanzade;Ali Khaleghi;Ilangko Balasingham
Affiliations:
K. N. Toosi University of Technology, Tehran, Iran;K. N. Toosi University of Technology, Tehran, Iran;Norwegian University of Science and Technology, Trondheim, Norway
Venue:
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
Year:
2013

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Abstract

Considered as fundamental part of many pervasive applications, human Activity Recognition (AR) systems have recently attracted interest of the research community. One of the many challenges in developing reliable AR systems is accurate recognition of human daily physical activities while maintaining simplicity of the recognition algorithm, essential to meeting real-time functionality of AR systems as well as dealing with their processing ability constraint. In this paper, we propose a real-time computing-efficient AR algorithm for accelerometer-based AR systems. Evaluation of the proposed algorithm is conducted in a laboratory setting using a simple learning based AR system with single wearable triaxial accelerometer attached to human thigh or wrist. Simple sequential human gestures are shown to be recognized with an average recognition accuracy of 98.8% and 96% for ambulatory movements and hand gestures, respectively.