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Journal of Sensors
Volume 2016, Article ID 3692876, 10 pages
http://dx.doi.org/10.1155/2016/3692876
Research Article

Handwriting Recognition in Free Space Using WIMU-Based Hand Motion Analysis

Graduate School of Advanced Imaging Science, Multimedia & Film, Chung-Ang University, No. 221 Heukseok-Dong, Dongjak-Gu, Seoul 156-756, Republic of Korea

Received 31 December 2015; Revised 18 May 2016; Accepted 5 June 2016

Academic Editor: Stefania Campopiano

Copyright © 2016 Shashidhar Patil et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Erol, G. Bebis, M. Nicolescu, R. D. Boyle, and X. Twombly, “Vision-based hand pose estimation: a review,” Computer Vision and Image Understanding, vol. 108, no. 1-2, pp. 52–73, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Mitra and T. Acharya, “Gesture recognition: a survey,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 37, no. 3, pp. 311–324, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Berman and H. Stern, “Sensors for gesture recognition systems,” IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, vol. 42, no. 3, pp. 277–290, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Benbasat and J. Paradiso, “An inertial measurement framework for gesture recognition and applications,” in Gesture and Sign Language in Human-Computer Interaction, I. Wachsmuth and T. Sowa, Eds., vol. 2298, pp. 9–20, Springer, Berlin, Germany, 2002. View at Google Scholar
  5. J. K. Oh, C. Sung-Jung, B. Won-Chul et al., “Inertial sensor based recognition of 3-D character gestures with an ensemble classifiers,” in Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition (IWFHR-9 2004), pp. 112–117, Tokyo, Japan, October 2004. View at Publisher · View at Google Scholar
  6. S. Zhou, Z. Dong, W. J. Li, and C. P. Kwong, “Hand-written character recognition using MEMS motion sensing technology,” in Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM '08), pp. 1418–1423, IEEE, Xi'an, China, August 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. R. Xu, S. Zhou, and W. J. Li, “MEMS accelerometer based nonspecific-user hand gesture recognition,” IEEE Sensors Journal, vol. 12, no. 5, pp. 1166–1173, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Akl, C. Feng, and S. Valaee, “A novel accelerometer-based gesture recognition system,” IEEE Transactions on Signal Processing, vol. 59, no. 12, pp. 6197–6205, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Y. Liu, L. Zhong, J. Wickramasuriya, and V. Vasudevan, “uWave: accelerometer-based personalized gesture recognition and its applications,” Pervasive and Mobile Computing, vol. 5, no. 6, pp. 657–675, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. S.-D. Choi, A. S. Lee, and S.-Y. Lee, “On-line handwritten character recognition with 3D accelerometer,” in Proceedings of the IEEE International Conference on Information Acquisition (ICIA '06), pp. 845–850, IEEE, Weihai, China, August 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. J.-S. Wang, Y.-L. Hsu, and C.-L. Chu, “Online handwriting recognition using an accelerometer-based pen device,” in Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering, pp. 229–232, 2013.
  12. J.-S. Wang and F.-C. Chuang, “An accelerometer-based digital pen with a trajectory recognition algorithm for handwritten digit and gesture recognition,” IEEE Transactions on Industrial Electronics, vol. 59, no. 7, pp. 2998–3007, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Kratz, M. Rohs, and G. Essl, “Combining acceleration and gyroscope data for motion gesture recognition using classifiers with dimensionality constraints,” in Proceedings of the 18th International Conference on Intelligent User Interfaces (IUI '13), pp. 173–178, Santa Monica, Calif, USA, March 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. Y.-L. Hsu, C.-L. Chu, Y.-J. Tsai, and J.-S. Wang, “An inertial pen with dynamic time warping recognizer for handwriting and gesture recognition,” IEEE Sensors Journal, vol. 15, no. 1, pp. 154–163, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. J.-S. Wang, Y.-L. Hsu, and J.-N. Liu, “An inertial-measurement-unit-based pen with a trajectory reconstruction algorithm and its applications,” IEEE Transactions on Industrial Electronics, vol. 57, no. 10, pp. 3508–3521, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. K. Liu, C. Chen, R. Jafari, and N. Kehtarnavaz, “Fusion of inertial and depth sensor data for robust hand gesture recognition,” IEEE Sensors Journal, vol. 14, no. 6, pp. 1898–1903, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. S. L. Zhou, F. Fei, G. L. Zhang et al., “2D human gesture tracking and recognition by the fusion of MEMS inertial and vision sensors,” IEEE Sensors Journal, vol. 14, no. 4, pp. 1160–1170, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. X. Zhang, X. Chen, Y. Li, V. Lantz, K. Wang, and J. Yang, “A framework for hand gesture recognition based on accelerometer and EMG sensors,” IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, vol. 41, no. 6, pp. 1064–1076, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Junker, O. Amft, P. Lukowicz, and G. Tröster, “Gesture spotting with body-worn inertial sensors to detect user activities,” Pattern Recognition, vol. 41, no. 6, pp. 2010–2024, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  20. C. Amma, M. Georgi, and T. Schultz, “Airwriting: a wearable handwriting recognition system,” Personal and Ubiquitous Computing, vol. 18, no. 1, pp. 191–203, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. H.-K. Lee and J. H. Kim, “An HMM-based threshold model approach for gesture recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 10, pp. 961–973, 1999. View at Publisher · View at Google Scholar · View at Scopus
  22. C. Zhu and W. Sheng, “Wearable sensor-based hand gesture and daily activity recognition for robot-assisted living,” IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, vol. 41, no. 3, pp. 569–573, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. S. Kim, G. Park, S. Yim et al., “Gesture-recognizing hand-held interface with vibrotactile feedback for 3D interaction,” IEEE Transactions on Consumer Electronics, vol. 55, no. 3, pp. 1169–1177, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Chen, G. AlRegib, and B.-H. Juang, “Feature processing and modeling for 6D motion gesture recognition,” IEEE Transactions on Multimedia, vol. 15, no. 3, pp. 561–571, 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. M. H. Ko, G. West, S. Venkatesh, and M. Kumar, “Using dynamic time warping for online temporal fusion in multisensor systems,” Information Fusion, vol. 9, no. 3, pp. 370–388, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. D.-W. Kim, J. Lee, H. Lim, J. Seo, and B.-Y. Kang, “Efficient dynamic time warping for 3D handwriting recognition using gyroscope equipped smartphones,” Expert Systems with Applications, vol. 41, no. 11, pp. 5180–5189, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Vikram, L. Li, and S. Russell, “Handwriting and gestures in the air, recognizing on the fly,” in Proceedings of the CHI, p. 21, Paris, France, April-May 2013.
  28. S. Patil, H. R. Chintalapalli, D. Kim, and Y. Chai, “Inertial sensor-based touch and shake metaphor for expressive control of 3D virtual avatars,” Sensors, vol. 15, no. 6, pp. 14435–14457, 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. http://www2.ece.gatech.edu/6DMG/Air-handwriting.html.