Paradigm Shift in Continuous Signal Pattern Classification: Mobile Ride Assistance System for two-wheeled Mobility Robots
This provides a practical solution for enhancing mobility robot control, though it appears incremental as it builds on existing pattern classification methods.
The paper tackles real-time signal pattern classification for mobile ride assistance by developing a smartphone/tablet app with a novel training dictionary framework that eliminates human intervention, achieving nearly 100% recognition accuracy as verified in a robotic wheelchair steering study.
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.