Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Ankle Active Rehabilitation Strategies Analysis Based on the Characteristics of Human and Robotic Integrated Biomechanics Simulation Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Towards Finger Gestures and Force Recognition Based on Wrist Electromyography and Accelerometers

Authors : Bo Lv, Xinjun Sheng, Weichao Guo, Xiangyang Zhu, Han Ding

Published in: Intelligent Robotics and Applications

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Surface electromyography (EMG) is widely used in hand gesture recognition for human-computer interface (HCI). This paper presents a finger gesture recognition scheme at two level of plane pressing force through the fusion of wrist EMG and accelerometers (ACC). The classification algorithm is evaluated on eight healthy subjects for identifying five finger gestures at two plane pressing force level. Experimental results show that frequency domain (improved discrete Fourier, iDFT) feature is better than time domain (TD) feature for wrist EMG classification. Moreover, it indicates that the fusion of EMG and ACC achieved improved recognition performance (85.77%) for finger gestures at two level of plane pressing force when compared to that obtained using EMG (80.65%) or ACC (56.86%) solely.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Mobile Terminals Haptic Interface: A Vibro-Tactile Finger Device for 3D Shape Rendering
next chapter Man-Machine Interaction for an Unmanned Tower Crane Using Wireless Multi-Controller
Literature
1.
Kim, J., Kim, M., Kim, K.: Development of a wearable HCI controller through sEMG & IMU sensor fusion. In: 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 83–87. IEEE (2016)
2.
Mitra, S., Acharya, T.: Gesture recognition: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 37(3), 311–324 (2007)
3.
Chu, J.U., Moon, I., Lee, Y.J., Kim, S.K., Mun, M.S.: A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE/ASME Trans. Mechatron. 12(3), 282–290 (2007)CrossRef
4.
Mobasser, F., Eklund, J.M., Hashtrudi-Zaad, K.: Estimation of elbow-induced wrist force with EMG signals using fast orthogonal search. IEEE Trans. Biomed. Eng. 54(4), 683–693 (2007)CrossRef
5.
Hoozemans, M.J., Van Dieen, J.H.: Prediction of handgrip forces using surface EMG of forearm muscles. J. Electromyogr. Kinesiol. 15(4), 358–366 (2005)CrossRef
6.
Samadani, A.A., Kulic, D.: Hand gesture recognition based on surface electromyography. In: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 4196–4199. IEEE (2014)
7.
Chen, X., Zhang, X., Zhao, Z.Y., Yang, J.H., Lantz, V., Wang, K.Q.: Hand gesture recognition research based on surface EMG sensors and 2D-accelerometers. In: 2007 11th IEEE International Symposium on Wearable Computers, pp. 11–14. IEEE (2007)
8.
Xiong, A., Chen, Y., Zhao, X., Han, J., Liu, G.: A novel HCI based on EMG and IMU. In: 2011 IEEE International Conference on Robotics and Biomimetics, pp. 2653–2657. IEEE (2011)
9.
Haque, F., Nancel, M., Vogel, D.: Myopoint: pointing and clicking using forearm mounted electromyography and inertial motion sensors. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3653–3656. ACM, Berlin (2015)
10.
Georgi, M., Amma, C., Schultz, T.: Recognizing hand and finger gestures with IMU based motion and EMG based muscle activity sensing. In: BIOSIGNALS. pp. 99–108 (2015)
11.
Oyama, T., Choge, H., Karungaru, S., Tsuge, S., Mitsukura, Y., Fukumi, M.: Identification of wrist EMG signals using dry type electrodes. In: ICCAS-SICE, pp. 4433–4436. IEEE (2009)
12.
Nagar, A., Zhu, X.: Gesture control by wrist surface electromyography. In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops, pp. 556–561. IEEE (2015)
13.
Oyama, T., Mitsukura, Y., Karungaru, S.G., Tsuge, S., Fukumi, M.: Wrist EMG signals identification using neural network. In: 35th Annual Conference of IEEE Industrial Electronics (IECON 2009), pp. 4286–4290. IEEE (2009)
14.
Buchanan, T.S., Moniz, M.J., Dewald, J.P., Rymer, W.Z.: Estimation of muscle forces about the wrist joint during isometric tasks using an EMG coefficient method. J. Biomech. 26(4–5), 547–560 (1993)CrossRef
15.
Englehart, K., Hudgins, B.: A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 50(7), 848–854 (2003)CrossRef
16.
He, J., Zhang, D., Sheng, X., Meng, J., Zhu, X.: Improved discrete fourier transform based spectral feature for surface electromyogram signal classification. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6897–6900. IEEE (2013)
17.
Kaufmann, P., Englehart, K., Platzner, M.: Fluctuating EMG signals: investigating long-term effects of pattern matching algorithms. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6357–6360. IEEE (2010)
18.
Liu, H., Yuan, X., Tang, Q., Kustra, R.: An efficient method to estimate labelled sample size for transductive LDA (QDA/MDA) based on bayes risk. In: European Conference on Machine Learning. pp. 274–285. Springer, New York (2004)
19.
Posatskiy, A., Chau, T.: The effects of motion artifact on mechanomyography: A comparative study of microphones and accelerometers. J. Electromyogr. Kinesiol. 22(2), 320–324 (2012)CrossRef
20.
Guo, W., Sheng, X., Liu, H., et al.: Mechanomyography assisted myoeletric sensing for upper-extremity prostheses: a hybrid approach. IEEE Sens. J. 17(10), 3001–3011 (2017)CrossRef
Metadata
Title
Towards Finger Gestures and Force Recognition Based on Wrist Electromyography and Accelerometers
Authors
Bo Lv
Xinjun Sheng
Weichao Guo
Xiangyang Zhu
Han Ding
Copyright Year
2017
Book
Intelligent Robotics and Applications

Print ISBN: 978-3-319-65288-7

Electronic ISBN: 978-3-319-65289-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-65289-4_36

Premium Partner

    Image Credits