Towards robust framework for on-line human activity reporting using accelerometer readings

Abstract

This paper investigates subsequent matching approach and feature-based classification for activity recognition using accelerometer readings. Recognition is done by similarity measure based on Dynamic Time Warping (DTW) on each acceleration axis. Ensemble method is proposed and comparative study is executed showing better and more stable results. Our scenario assumes that activity is recognized with very small latency. Results shows that hybrid approach is promising for activity reporting, i.e. different walking patterns, using of tools. The proposed solution is designed to be a part of decision support in fire and rescue actions at the fire ground. © 2014 Springer International Publishing.