An Inertial Pen with Dynamic Time Warping Recognizer for Handwriting and Gesture Recognition
International Journal of Engineering Trends and Technology (IJETT) | |
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© 2016 by IJETT Journal | ||
Volume-35 Number-11 |
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Year of Publication : 2016 | ||
Authors : L.M.MerlinLivingston, P.Deepika, M.Benisha |
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DOI : 10.14445/22315381/IJETT-V35P302 |
Citation
L.M.MerlinLivingston, P.Deepika, M.Benisha"An Inertial Pen with Dynamic Time Warping Recognizer for Handwriting and Gesture Recognition", International Journal of Engineering Trends and Technology (IJETT), V35(11),506-510 May 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
This paper presents an inertial-sensorbased digital pen (inertial pen) and its associated dynamic time warping (DTW)-based recognition algorithm for handwriting and gesturer recognition. Users hold the inertial pen to write numerals or English lowercase letters and make hand gestures with their preferred handheld style and speed. The inertial signals generated by hand motions are wirelessly transmitted to a computer for online recognition. The proposed DTW-based recognition algorithm includes the procedures of inertial signal acquisition; signal preprocessing, motion detection, template selection, and recognition. We integrate signals collected from an accelerometer, a gyroscope, and a magnetometer into a quaternionbased complementary filter for reducing the integral errors caused by the signal drift or intrinsic noise of the gyroscope, which might reduce the accuracy of the orientation estimation. Furthermore, we have developed minimal intra-class to maximal inter-class based template selection method (min-max template selection method) for a DTW recognizer to obtain a superior class separation for improved recognition. Experimental results have successfully validated theeffectiveness of the DTW-based recognition algorithm for online handwriting and gesture recognition using the inertial pen.
References
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Keywords
Inertial pen, dynamic time warping, quaternion-based complementary filter, handwriting recognition,Gesture recognition.