Skip to main content
SpringerLink
Log in

A Real Time Gesture Recognition with Wrist Mounted Accelerometer

  • Conference paper
  • First Online:
Information Systems Design and Intelligent Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 340))

Abstract

This paper presents an acceleration based gesture recognition approach with wearable MEMS tri-axial accelerometer. In the application model, we have introduced frame based lookup table for gesture recognition. In accelerometer based gesture recognition concept, sensor data calibration plays an important aspect owing to their erroneous output due to zero-G error. In this work six-point based calibration of the sensor data is presented. The calibrated acceleration data so obtained from the sensor is represented in the form of frame-based signifier, to extract discriminative gesture information. It is observed that this procedure is always advantageous over conventional video image processing based gesture recognition that uses cameras and bulky computational algorithms. Thus, this accelerometer based gesture recognition not only reduces the hardware complexity but also minimizes the consumption of power by associated circuitry. Finally, this study helps us to develop a real time implementation of wearable gesture recognition device.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Montoliu, R., Blom, J., Gatica-Perez, D.: Discovering places of interest in everyday life from smartphone data. Multimed Tools Appl. 62(1), 179–307 (2013)

    Article  Google Scholar 

  2. Hürst, W., van Wezel, C.: Gesture-based interaction via finger tracking for mobile augmented reality. Multimed Tools Appl. 62(1), 233–258 (2012)

    Article  Google Scholar 

  3. Holzinger, A., Softic, S., Stickel, C., Ebner, M., Debevc, M., Hu, B.: Nintendo Wii remote controller in higher education: development and evaluation of a demonstrator kit for e-teaching. Comput. Inform. 29(4), 601–615 (2012)

    Google Scholar 

  4. Dong, Z., Wejinya, U.C., Li, W.J.: An optical-tracking calibration method for MEMS-based digital writing instrument. IEEE Sens. J. 10(10), 1543–1551 (2010)

    Google Scholar 

  5. Wang, J.S., Hsu, Y.L., Liu, J.N.: An inertial-measurement-unit-based pen with a trajectory reconstruction algorithm and its applications. IEEE Trans. Ind. Electron. 57(10), 3508–3521 (2010)

    Google Scholar 

  6. Schlömer, T., Poppinga, B., Henze, N., Boll, S.: Gesture recognition with a Wii controller. In: International Conference on Tangible and Embedded Interaction (TEI 2008), pp. 11–14. Bonn, 18–20 Feb 2008

    Google Scholar 

  7. Xu, R., Zhou, S., Li, W.J.: MEMS accelerometer based nonspecific-user hand gesture recognition. IEEE Sens. J. 12(5), 1166–1173 (2012)

    Google Scholar 

  8. Sawada, H., Hashimoto, S.: Gesture recognition using an accelerometer sensor and its application to musical performance control. Electron. Commun. Jpn. Part 3, 9–17 (2000)

    Google Scholar 

  9. Mäntylä, V.-M., Mäntyjärvi, J., Seppänen, T., Tuulari, E.: Hand gesture recognition of a mobile device user. In: Proceedings of the International IEEE Conference on Multimedia and Expo., pp. 281–284 (2000)

    Google Scholar 

  10. Mäntyjärvi, J., Kela, J., Korpipää, P., Kallio, S.: Enabling fast and effortless customization in accelerometer based gesture interaction. In: Proceedings of the 3rd International Conference on Mobile and Ubiquitous Multimedia (MUM 2004), pp. 25–31. ACM Press, New York, 27–29 Oct (2004)

    Google Scholar 

  11. Mäntylä, V.-M.: Discrete Hidden Markov Models with Application to Isolated User- Dependent Hand Gesture Recognition. VTT publications, Finland (2001)

    Google Scholar 

  12. Pei, M., Jia, Y., Zhu, S.C.: Parsing video events with goal inference and intent prediction. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 487–494. IEEE, New York (2011)

    Google Scholar 

  13. Ravi, N., Dandekar, N., Musore, P., Littman, M.: Activity recognition from accelerometer data. In: Proceedings of IAAI 2008, pp. 11–18 (2005)

    Google Scholar 

  14. Frigo, M., Johnson, S.G.: The design and implementation of FFTW3. Proc. IEEE 93(2) (2005)

    Google Scholar 

  15. Takahashi, M., Fujii, M., Naemura, M., Satoh, S.: Human gesture recognition system for tv viewing using time-of-flight camera. Multimed Tools Appl. 62(3), 761–783 (2013)

    Article  Google Scholar 

  16. Christanini, J., Taylor, J.S.: An Introduction to Support Vector Machines and Other Kernel Based Methods. Cambridge University Press, Cambridge (2000)

    Book  Google Scholar 

  17. Mitra, S., Acharya, T.: Gesture Recognition: a survey. IEEE Trans. Syst. Man, Cybern. Part C 37(3), 311–324 (2007)

    Google Scholar 

  18. Wang, D., Xiong, Z., Zhang, M.: An application oriented and shape feature based multi-touch gesture description and recognition method. Multimed Tools Appl. 58(3), 497–519 (2012)

    Article  MathSciNet  Google Scholar 

  19. Moghaddam, B., Yang, M.-H.: Learning gender with support faces. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 707–711 (2002)

    Article  Google Scholar 

  20. Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 130–136 (1997)

    Google Scholar 

  21. Cho, S.-J., Choi, E., Bang, W.-C., Yang, J., Sohn, J., Kim, D.Y., Lee, Y.-B., Kim, S.: Twostage recognition of raw acceleration signals for 3D-gesture-understanding cell phones. In: 10th International Workshop on Frontiers in Handwriting Recognition (2006)

    Google Scholar 

  22. Niezen, G., Hancke, G.P.: Gesture recognition as ubiquitous input for mobile phones. In: International Workshop on Devices that Alter Perception (DAP 2008), conjunction with Ubicomp 2008 (2008)

    Google Scholar 

  23. Liu, J., Wang, Z., Zhong, L., Wickramasuriya, J., Vasudevan, V.: uWave: accelerometer based personalized gesture recognition and its applications. In: IEEE PerCom 2009 (2009)

    Google Scholar 

  24. Peng, X., Bennamoun, M., Mian, A.S.: A training-free nose tip detection method from face range images. Pattern Recogn. 44(3), 544–558 (2011)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Heritage Institute of Technology, Kolkata, India

    Debjyoti Chowdhury & Madhurima Chattopadhyay

  2. Cisco Inc, Bangalore, India

    Soumya Jyoti Banerjee

  3. Wipro, Kolkata, India

    Krishnendu Sanyal

Authors
  1. Debjyoti Chowdhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soumya Jyoti Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Krishnendu Sanyal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Madhurima Chattopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Debjyoti Chowdhury .

Editor information

Editors and Affiliations

  1. University of Kalyanai, Kalyanai, West Bengal, India

    J. K. Mandal

  2. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  3. Dean, Faculty of Engineering, Technology, University of Kalyani, Kalyani, West Bengal, India

    Manas Kumar Sanyal

  4. Engineering and Technological Studies, University of Kalyani, Kalyani, West Bengal, India

    Partha Pratim Sarkar

  5. Department Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Anirban Mukhopadhyay

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer India

About this paper

Cite this paper

Chowdhury, D., Banerjee, S.J., Sanyal, K., Chattopadhyay, M. (2015). A Real Time Gesture Recognition with Wrist Mounted Accelerometer. In: Mandal, J., Satapathy, S., Kumar Sanyal, M., Sarkar, P., Mukhopadhyay, A. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 340. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2247-7_26

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2247-7_26

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2246-0

  • Online ISBN: 978-81-322-2247-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation