Abstract
Acoustic gesture recognition based on the Doppler effect has garnered much research attention. The accuracy of gesture recognition and potential false positives are the main factors that limit the widespread use of gestures. To this end, we propose a novel gesture classification method based on the acoustic Doppler effect that does not require any custom hardware, simply a speaker and one microphone on a laptop. An effective sound field is built by a high frequency sound wave from the speaker, and the wave reflected by hand motion is captured by the microphone. We design a set with five features, three of them are stable and invariant to different people, so even new users can operate our system with zero start-up cost and no training. The remaining two features are highly correlated with the velocity and the range to computer of the gestures, which can reduce the potential false positives in detection. Besides, a classifier is designed depending on multistage decision rules to identify the 11 kinds of defined gestures. The experiment result about user experience feedback of HCI shows that our system has good usability performance. And the numerical experiments with 10 users show that our system can not only keep less potential false positives, but also achieve a classification accuracy of up to 99.09%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ai, H., Men, Y., Han, L., Li, Z., Liu, M.: High precision gesture sensing via quantitative characterization of the doppler effect. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 973–978. IEEE (2016)
Asadzadeh, P., Kulik, L., Tanin, E.: Gesture recognition using RFID technology. Pers. Ubiquit. Comput. 16(3), 225–234 (2012)
Aumi, M.T.I., Gupta, S., Goel, M., Larson, E., Patel, S.: Doplink: using the doppler effect for multi-device interaction. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 583–586. ACM (2013)
Bevan, N., Curson, I.: Methods for measuring usability. In: Howard, S., Hammond, J., Lindgaard, G. (eds.) Human-Computer Interaction INTERACT 1997. ITIFIP, pp. 672–673. Springer, Boston, MA (1997). https://doi.org/10.1007/978-0-387-35175-9_126
Cabral, M.C., Morimoto, C.H., Zuffo, M.K.: On the usability of gesture interfaces in virtual reality environments. In: Proceedings of the 2005 Latin American Conference on Human-Computer Interaction, pp. 100–108. ACM (2005)
Chen, K.Y., Ashbrook, D., Goel, M., Lee, S.H., Patel, S.: Airlink: sharing files between multiple devices using in-air gestures. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 565–569. ACM (2014)
Fu, B., Karolus, J., Grosse-Puppendahl, T., Hermann, J., Kuijper, A.: Opportunities for activity recognition using ultrasound doppler sensing on unmodified mobile phones. In: Proceedings of the 2nd international Workshop on Sensor-based Activity Recognition and Interaction, p. 8. ACM (2015)
Gupta, S., Morris, D., Patel, S., Tan, D.: Soundwave: using the doppler effect to sense gestures. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1911–1914. ACM (2012)
Jeong, J., Jang, Y.: Max-min hand cropping method for robust hand region extraction in the image-based hand gesture recognition. Soft Comput. 19(4), 815–818 (2015)
Kellogg, B., Talla, V., Gollakota, S.: Bringing gesture recognition to all devices. NSDI 14, 303–316 (2014)
Molchanov, P., Gupta, S., Kim, K., Kautz, J.: Hand gesture recognition with 3D convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–7 (2015)
Nielsen, M., Störring, M., Moeslund, T.B., Granum, E.: A procedure for developing intuitive and ergonomic gesture interfaces for HCI. In: Camurri, A., Volpe, G. (eds.) GW 2003. LNCS (LNAI), vol. 2915, pp. 409–420. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24598-8_38
Paramonov, P., Sutula, N.: Simplified scoring methods for HMM-based speech recognition. Soft Comput. 20(9), 3455–3460 (2016)
Pittman, C., Wisniewski, P., Brooks, C., LaViola Jr, J.J.: Multiwave: doppler effect based gesture recognition in multiple dimensions. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1729–1736. ACM (2016)
Pittman, C.R., LaViola Jr, J.J.: Multiwave: complex hand gesture recognition using the doppler effect. In: Proceedings of the 43rd Graphics Interface Conference, pp. 97–106. Canadian Human-Computer Communications Society (2017)
Qifan, Y., Hao, T., Xuebing, Z., Yin, L., Sanfeng, Z.: Dolphin: ultrasonic-based gesture recognition on smartphone platform. In: 2014 IEEE 17th International Conference on Computational Science and Engineering (CSE), pp. 1461–1468. IEEE (2014)
Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev. 43(1), 1–54 (2015)
Seddon, N., Bearpark, T.: Observation of the inverse doppler effect. Science 302(5650), 1537–1540 (2003)
Suk, H.I., Sin, B.K., Lee, S.W.: Hand gesture recognition based on dynamic bayesian network framework. Pattern Recogn. 43(9), 3059–3072 (2010)
Xiao, Q., Siqi, L.: Motion retrieval based on dynamic Bayesian network and canonical time warping. Soft Comput. 21(1), 267–280 (2017)
Xiao, Q., Song, R.: Motion retrieval based on motion semantic dictionary and HMM inference. Soft Comput. 21(1), 255–265 (2017)
Acknowledgment
We thank the participants for participating the user study. This work is partially supported by The National Key Research and Development Program of China (2016YFB0502201).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Ai, H., Han, L., Wang, Y., Liao, L. (2018). Accurate Acoustic Based Gesture Classification with Zero Start-Up Cost. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11336. Springer, Cham. https://doi.org/10.1007/978-3-030-05057-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-05057-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05056-6
Online ISBN: 978-3-030-05057-3
eBook Packages: Computer ScienceComputer Science (R0)