Optimized Limited Memory and Warping LCSS for Online Gesture Recognition or Overlearning?

Baptiste Lemarcis, Valère Plantevin, Bruno Bouchard, Bob-Antoine-Jerry Ménélas

2017

Abstract

In this paper, we present and evaluate a new algorithm for online gesture recognition in noisy streams. This technique relies upon the proposed LM-WLCSS (Limited Memory and Warping LCSS) algorithm that has demonstrated its efficiency on gesture recognition. This new method involves a quantization step (via the KMeans clustering algorithm). This transforms new data to a finite set. In this way, each new sample can be compared to several templates (one per class) and gestures are rejected based on a previously trained rejection threshold. Then, an algorithm, called SearchMax, find a local maximum within a sliding window and output whether or not the gesture has been recognized. In order to resolve conflicts that may occur, another classifier could be completed. As the K-Means clustering algorithm, needs to be initialized with the number of clusters to create, we also introduce a straightforward optimization process. Such an operation also optimizes the window size for the SearchMax algorithm. In order to demonstrate the robustness of our algorithm, an experiment has been performed over two different data sets. However, results on tested data sets are only accurate when training data are used as test data. This may be due to the fact that the method is in an overlearning state.

References

  1. Altun, K., Barshan, B. & Tunçel, O. 2010. CoAltun, K., Barshan, B. & Tunçel, O. 2010. Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognition, 43, 3605-3620.
  2. Bahrepour, M., Meratnia, N. & Havinga, P. J. M. Sensor fusion-based event detection in Wireless Sensor Networks. Mobile and Ubiquitous Systems: Networking & Services, MobiQuitous, 2009. MobiQuitous 7809. 6th Annual International, 13-16 July 2009 2009. 1-8.
  3. Banos, O., Damas, M., Pomares, H. & Rojas, I. 2012. On the use of sensor fusion to reduce the impact of rotational and additive noise in human activity recognition. Sensors, 12, 8039-8054.
  4. Ben-David, A. 2007. A lot of randomness is hiding in accuracy. Engineering Applications of Artificial Intelligence, 20, 875-885.
  5. Chen, C. & Shen, H. 2014. Improving Online Gesture Recognition with Warping LCSS by Multi-Sensor Fusion. In: Wong, W. E. & Zhu, T. (eds.) Computer Engineering and Networking. Springer International Publishing.
  6. Dardas, N. H. & Georganas, N. D. 2011. Real-Time Hand Gesture Detection and Recognition Using Bag-ofFeatures and Support Vector Machine Techniques. Instrumentation and Measurement, IEEE Transactions on, 60, 3592-3607.
  7. Gamage, N., Kuang, Y. C., Akmeliawati, R. & Demidenko, S. 2011. Gaussian Process Dynamical Models for hand gesture interpretation in Sign Language. Pattern Recognition Letters, 32, 2009-2014.
  8. Guiry, J. J., Van De Ven, P. & Nelson, J. 2014. Multi-sensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices. Sensors, 14, 5687- 5701.
  9. Hartmann, B. & Link, N. Gesture recognition with inertial sensors and optimized DTW prototypes. Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on, 10-13 Oct. 2010 2010. 2102-2109.
  10. Hyeon-Kyu, L. & Kim, J. H. 1999. An HMM-based threshold model approach for gesture recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 21, 961-973.
  11. Karantonis, D. M., Narayanan, M. R., Mathie, M., Lovell, N. H. & Celler, B. G. 2006. Implementation of a realtime human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Transactions on Information Technology in Biomedicine, 10, 156-167.
  12. Kiliboz, N. Ç. & Güdükbay, U. 2015. A hand gesture recognition technique for human-computer interaction. Journal of Visual Communication and Image Representation, 28, 97-104.
  13. Long-Van, N.-D., Roggen, D., Calatroni, A. & Troster, G. Improving online gesture recognition with template matching methods in accelerometer data. Intelligent Systems Design and Applications (ISDA), 2012 12th International Conference on, 27-29 Nov. 2012 2012. 831-836.
  14. Nguyen-Dinh, L.-V., Calatroni, A., Tr, G., #246 & Ster 2014a. Towards a unified system for multimodal activity spotting: challenges and a proposal. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. Seattle, Washington: ACM.
  15. Nguyen-Dinh, L. V., Calatroni, A. & Tröster, G. 2014b. Robust online gesture recognition with crowdsourced annotations. Journal of Machine Learning Research, 15, 3187-3220.
  16. Quinlan, J. R. 2014. C4. 5: programs for machine learning, Elsevier.
  17. Reyes, M., Dominguez, G. & Escalera, S. Featureweighting in dynamic timewarping for gesture recognition in depth data. Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, 6-13 Nov. 2011 2011. 1182-1188.
  18. Roggen, D., Cuspinera, L., Pombo, G., Ali, F. & NguyenDinh, L.-V. 2015. Limited-Memory Warping LCSS for Real-Time Low-Power Pattern Recognition in Wireless Nodes. In: Abdelzaher, T., Pereira, N. & Tovar, E. (eds.) Wireless Sensor Networks. Springer International Publishing.
  19. Rung-Huei, L. & Ming, O. A real-time continuous gesture recognition system for sign language. Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on, 14-16 Apr 1998 1998. 558-567.
  20. Vlachos, M., Hadjieleftheriou, M., Gunopulos, D. & Keogh, E. 2003. Indexing multi-dimensional timeseries with support for multiple distance measures. Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. Washington, D.C.: ACM.
  21. Witten, I. H. & Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems), Morgan Kaufmann Publishers Inc.
  22. Zappi, P., Roggen, D., Farella, E., Tr, G., #246, Ster & Benini, L. 2012. Network-Level Power-Performance Trade-Off in Wearable Activity Recognition: A Dynamic Sensor Selection Approach. ACM Trans. Embed. Comput. Syst., 11, 1-30.
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Paper Citation


in Harvard Style

Lemarcis B., Plantevin V., Bouchard B. and Ménélas B. (2017). Optimized Limited Memory and Warping LCSS for Online Gesture Recognition or Overlearning? . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: HUCAPP, (VISIGRAPP 2017) ISBN 978-989-758-229-5, pages 108-115. DOI: 10.5220/0006151001080115


in Bibtex Style

@conference{hucapp17,
author={Baptiste Lemarcis and Valère Plantevin and Bruno Bouchard and Bob-Antoine-Jerry Ménélas},
title={Optimized Limited Memory and Warping LCSS for Online Gesture Recognition or Overlearning?},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: HUCAPP, (VISIGRAPP 2017)},
year={2017},
pages={108-115},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006151001080115},
isbn={978-989-758-229-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: HUCAPP, (VISIGRAPP 2017)
TI - Optimized Limited Memory and Warping LCSS for Online Gesture Recognition or Overlearning?
SN - 978-989-758-229-5
AU - Lemarcis B.
AU - Plantevin V.
AU - Bouchard B.
AU - Ménélas B.
PY - 2017
SP - 108
EP - 115
DO - 10.5220/0006151001080115