In this study we describe the development of a ride assistance application which can be implemented on the widespread smart phones and tablet. The ride assistance application has a signal processing and pattern classification module which yield almost 100% recognition accuracy for real-time signal pattern classification. We introduce a novel framework to build a training dictionary with an overwhelming discriminating capacity which eliminates the need of human intervention spotting the pattern on the training samples. We verify the recognition accuracy of the proposed methodologies by providing the results of another study in which the hand posture and gestures are tracked and recognized for steering a robotic wheelchair.
@article{arxiv.1506.04810,
title = {Paradigm Shift in Continuous Signal Pattern Classification: Mobile Ride Assistance System for two-wheeled Mobility Robots},
author = {Ali Boyali and Naohisa Hashimoto and Osamu Matsumoto},
journal= {arXiv preprint arXiv:1506.04810},
year = {2015}
}
Comments
This paper introduce a training approach for continuous signal pattern classification and its application to braking state classification of a mobility robots. In our previous journal article, we didn't employ a training method thus this paper is an improvement of a previously published journal article