Abstract
Electromyography (EMG) is well suited for capturing static hand features involving relatively long and stable muscle activations. At the same time, inertial sensing can inherently capture dynamic features related to hand rotation and translation. This paper introduces a hand gesture recognition wristband based on combined EMG and IMU signals. Preliminary testing was performed on four healthy subjects to evaluate a classification algorithm for identifying four surface pressing gestures at two force levels and eight air gestures. Average classification accuracy across all subjects was 88% for surface gestures and 96% for air gestures. Classification accuracy was significantly improved when both EMG and inertial sensing was used in combination as compared to results based on either single sensing modality.
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
Preview
Unable to display preview. Download preview PDF.
References
Georgi, M., Amma, C., Schultz, T.: Recognizing hand and finger gestures with IMU based motion and EMG based muscle activity sensing. In: Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, pp. 99–108 (2015)
Trindade, P., Lobo, J., Barreto, J.P.: Hand gesture recognition using color and depth images enhanced with hand angular pose data. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 71–76 (2012)
Van Den Bergh, M., Carton, D., De Nijs, R., Mitsou, N., Landsiedel, C., Kuehnlenz, K., Wollherr, D., Van Gool, L., Buss, M.: Real-time 3D hand gesture interaction with a robot for understanding directions from humans. In: IEEE International Workshop on Robot and Human Interactive Communication, pp. 357–362 (2011)
Rowe, J.B., Friedman, N., Bachman, M., Reinkensmeyer, D.J.: The Manumeter: a non-obtrusive wearable device for monitoring spontaneous use of the wrist and fingers. In: IEEE International Conference on Rehabilitation Robotics, pp. 1–6 (2013)
Jeong, E., Lee, J., Kim, D.: Finger-gesture recognition glove using velostat ( ICCAS 2011). In: 11th International Conference on Control, Automation and Systems. Number Iccas, pp. 206–210 (2011)
Ko, S., Bang, W.: A Measurement System for 3D Hand-Drawn Gesture with a PHANToM TM Device. Journal of Information Processing Systems 6(3), 347–358 (2010)
Muthulakshmi, M.: Mems Accelerometer Based Hand Gesture Recognition. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 2(5), 1886–1892 (2013)
Wang, J.S., Chuang, F.C.: An accelerometer-based digital pen with a trajectory recognition algorithm for handwritten digit and gesture recognition. IEEE Transactions on Industrial Electronics 59(7), 2998–3007 (2012)
Benbasat, A.Y., Paradiso, J.A.: An inertial measurement framework for gesture recognition and applications. In: Wachsmuth, I., Sowa, T. (eds.) GW 2001. LNCS (LNAI), vol. 2298, pp. 9–20. Springer, Heidelberg (2002)
Hartmann, B., Link, N.: Gesture recognition with inertial sensors and optimized DTW prototypes. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 2102–2109 (2010)
Wu, J., Pan, G., Zhang, D., Qi, G., Li, S.: Gesture recognition with a 3-d accelerometer. In: Zhang, D., Portmann, M., Tan, A.-H., Indulska, J. (eds.) UIC 2009. LNCS, vol. 5585, pp. 25–38. Springer, Heidelberg (2009)
Samadani, A.A., Kuli, D.: Hand gesture recognition based on surface electromyography. In: 36th Annual International Conference of the IEEE Engineering in Medicince and Biology Society, pp. 4196–4199 (2014)
Kim, J., Mastnik, S., André, E.: EMG-based hand gesture recognition for realtime biosignal interfacing. In: Proceedings of the 13th International Conference on Intelligent user Interfaces, IUI 2008, vol. 39, p. 30 (2008)
Saponas, T.S., Tan, D.S., Morris, D., Balakrishnan, R.: Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces. In: Proceeding of the Twentysixth Annual CHI Conference on Human Factors in Computing Systems, CHI 2008, p. 515 (2008)
He, J., Zhang, D., Zhu, X.: Adaptive pattern recognition of myoelectric signal towards practical multifunctional prosthesis control. In: Su, C.-Y., Rakheja, S., Liu, H. (eds.) ICIRA 2012, Part I. LNCS, vol. 7506, pp. 518–525. Springer, Heidelberg (2012)
Jiang, N., Dosen, S., Müller, K.R., Farina, D.: Myoelectric control of artificial limbs-is there a need to change focus. IEEE Signal Process. Mag. 29(5), 148–152 (2012)
Farina, D., Jiang, N., Rehbaum, H., Holobar, A., Graimann, B., Dietl, H., Aszmann, O.: The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges. IEEE Trans. Neural Syst. Rehabil. Eng. 22(4), 797 (2014)
Young, A., Kuiken, T., Hargrove, L.: Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses. Journal of Neural Engineering 11(5), 056021 (2014)
Wolf, M.T., Assad, C., Stoica, A., You, K., Jethani, H., Vernacchia, M.T., Fromm, J., Iwashita, Y.: Decoding static and dynamic arm and hand gestures from the JPL biosleeve. In: IEEE Aerospace Conference Proceedings (2013)
Li, Y., Chen, X., Tian, J., Zhang, X., Wang, K., Yang, J.: Automatic recognition of sign language subwords based on portable accelerometer and EMG sensors. In: International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction on - ICMI-MLMI 2010, p. 1 (2010)
Zhang, X., Chen, X., Li, Y., Lantz, V., Wang, K., Yang, J.: A framework for hand gesture recognition based on accelerometer and EMG sensors. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 41(6), 1064–1076 (2011)
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. 1–4 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Huang, Y. et al. (2015). Preliminary Testing of a Hand Gesture Recognition Wristband Based on EMG and Inertial Sensor Fusion. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2015. Lecture Notes in Computer Science(), vol 9244. Springer, Cham. https://doi.org/10.1007/978-3-319-22879-2_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-22879-2_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22878-5
Online ISBN: 978-3-319-22879-2
eBook Packages: Computer ScienceComputer Science (R0)