Paradigm Shift in Continuous Signal Pattern Classification: Mobile Ride Assistance System for two-wheeled Mobility Robots

TL;DR

This paper presents a novel smartphone-based ride assistance system for two-wheeled robots, achieving near-perfect real-time pattern recognition through an innovative training framework that reduces human intervention.

Contribution

It introduces a new framework for training pattern classifiers with high discriminative power, eliminating manual pattern labeling, for mobile ride assistance applications.

Findings

Almost 100% recognition accuracy in real-time classification

Effective training dictionary with high discriminative capacity

Validated on hand gestures for robotic wheelchair control

Abstract

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.