An Inertial Pen with Dynamic Time Warping Recognizer for Handwriting and Gesture Recognition

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2016 by IJETT Journal
Volume-35 Number-11
Year of Publication : 2016
Authors : L.M.MerlinLivingston, P.Deepika, M.Benisha
DOI :  10.14445/22315381/IJETT-V35P302


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. published by seventh sense research group

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.


[1] K. Altun, B. Barshan, and O. Tunçel, ?Comparative study on classifying human activities with miniature inertial and magnetic sensors, PatternRecognit., vol. 43, no. 10, pp. 3605–3620, 2010.
[2] A. Akl, C. Feng, and S. Valaee, ?A novel accelerometerbased gesture recognition system, IEEE Trans. Signal Process., vol. 59, no. 12, pp. 6197–6205, Dec. 2011.
[3] W. C. Bang, W. Chang, K. H. Kang, E. S. Choi, A. Potanin, and D. Y. Kim, ?Self-contained spatial input device for wearable computers, in Proc. IEEE Int. Conf. Wearable Comput., Oct. 2003, pp. 26–34.
[4] G. Bailador, C. Sanchez-Avila, J. Guerra-Casanova, and A. de Santos Sierra, ?Analysis of pattern recognition techniques for in-air signature biometrics, Pattern Recognit., vol. 44, nos. 10–11, pp. 2468–2478, 2011.
[5] C. M. N. Brigante, N. Abbate, A. Basile, A. C. Faulisi, and S. Sessa, ?Towards miniaturization of a MEMS-based wearable motion capture system, IEEE Trans. Ind. Electron., vol. 58, no. 8, pp. 3234–3241, Aug. 2011.
[6] M. J. Caruso, ?Application of magnetoresistive sensors in navigation systems, in Proc. SAE, 1997, pp. 15–21.
[7] R. Xu, S. Zhou, and W. J. Li, ?MEMS accelerometer based nonspecificuser hand gesture recognition, IEEE Sensors J., vol. 12, no. 5, pp. 1166–1173, May 2012.
[8] Z. Dong, C. Wejinya, and W. J. Li, ?An optical-tracking calibration method for MEMS-based digital writing instrument, IEEE Sensors J., vol. 10, no. 10, pp. 1543– 1551, Oct. 2010.

Inertial pen, dynamic time warping, quaternion-based complementary filter, handwriting recognition,Gesture recognition.