Abstract
Feature selection for surface electromyography based hand motion recognition has been seen to retrieve an optimal or quasi-optimal feature subset for classification. This work aims to consider the influence of channel, feature and window length simultaneously with an emphasis on the multiple segmentation. The bacterial memetic algorithm is applied to select the feature candidates from time domain and autoregressive coefficients, which is measured by the inter-day hand motion recognition accuracy. The evaluation is conducted on a case study of 3 able-bodied subjects performing 9 hand motions in consecutive 7 days with 4 different window lengths adopted for the electromyographic data segmentation. Classification in combination with the multi-length windowed feature selection achieved an improved recognition accuracy in comparison with using solely the single-length windowed features in inter-day scenarios and indicated that complementary information to full length segmentation resides in the sub-windows, thus providing feasible feature combinations for conventional pattern recognition based solutions to prosthetic control.
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
Similar content being viewed by others
References
Ji, X., Wang, C., Ju, Z.: A new framework of human interaction recognition based on multiple stage probability fusion. Appl. Sci. 7(6), 567 (2017)
Fang, Y., Hettiarachchi, N., Zhou, D., Liu, H.: Multi-modal sensing techniques for interfacing hand prostheses: a review. IEEE Sens. J. 15(11), 6065–6076 (2015)
Xue, Y., Ju, Z., Xiang, K., Chen, J., Liu, H.: Multimodal human hand motion sensing and analysis-a review. IEEE Trans. Cogn. Develop. Syst. (2018)
Atzori, M., Cognolato, M., Müller, H.: Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands. Front. Neurorobot. 10, 9 (2016)
Zhai, X., Jelfs, B., Chan, R.H., Tin, C.: Self-recalibrating surface emg pattern recognition for neuroprosthesis control based on convolutional neural network. Front. Neurosci. 11, 379 (2017)
Scheme, E., Englehart, K.: Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. J. Rehab. Res. Develop. 48(6), 643 (2011)
Phinyomark, A., Quaine, F., Charbonnier, S., Serviere, C., Tarpin-Bernard, F., Laurillau, Y.: EMG feature evaluation for improving myoelectric pattern recognition robustness. Exper. Syst. Appl. 40(12), 4832–4840 (2013)
Oskoei, M.A., Hu, H.: GA-based feature subset selection for myoelectric classification. In: 2006 IEEE International Conference on Robotics and Biomimetics, pp. 1465–1470. IEEE (2006)
Khushaba, R.N., Al-Jumaily, A.: Channel and feature selection in multifunction myoelectric control. In: 2007 IEEE 29th Annual International Conference of the Engineering in Medicine and Biology Society, EMBS 2007, pp. 5182–5185. IEEE (2007)
Al-Timemy, A.H., Bugmann, G., Escudero, J., Outram, N.: Classification of finger movements for the dexterous hand prosthesis control with surface electromyography. IEEE J. Biomed. Health Inform. 17(3), 608–618 (2013)
Al-Angari, H.M., Kanitz, G., Tarantino, S., Cipriani, C.: Distance and mutual information methods for emg feature and channel subset selection for classification of hand movements. Biomed. Signal Process. Control 27, 24–31 (2016)
Adewuyi, A.A., Hargrove, L.J., Kuiken, T.A.: Evaluating emg feature and classifier selection for application to partial-hand prosthesis control. Front. Neurorobot. 10, 15 (2016)
Smith, L.H., Hargrove, L.J., Lock, B.A., Kuiken, T.A.: Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay. IEEE Trans. Neural Syst. Rehab. Eng. 19(2), 186–192 (2011)
Zhou, D., Fang, Y., Botzheim, J., Kubota, N., Liu, H.: Bacterial memetic algorithm based feature selection for surface emg based hand motion recognition in long-term use. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7. IEEE (2016)
Hudgins, B., Parker, P., Scott, R.N.: A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 40(1), 82–94 (1993)
Botzheim, J., Cabrita, C., Kóczy, L.T., Ruano, A.: Fuzzy rule extraction by bacterial memetic algorithms. Int. J. Intell. Syst. 24(3), 312–339 (2009)
Fang, Y., Liu, H., Li, G., Zhu, X.: A multichannel surface emg system for hand motion recognition. Int. J. Human. Robot. 12(02), 1550011 (2015)
Englehart, K., Hudgins, B.: A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 50(7), 848–854 (2003)
Acknowledgments
The authors would like to acknowledge support from DREAM project of EU FP7-ICT (grant no. 611391), and National Natural Science Foundation of China (grant no. 51575338 and 51575412).
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
Zhou, D., Fang, Y., Ju, Z., Liu, H. (2018). Multi-length Windowed Feature Selection for Surface EMG Based Hand Motion Recognition. In: Chen, Z., Mendes, A., Yan, Y., Chen, S. (eds) Intelligent Robotics and Applications. ICIRA 2018. Lecture Notes in Computer Science(), vol 10984. Springer, Cham. https://doi.org/10.1007/978-3-319-97586-3_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-97586-3_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97585-6
Online ISBN: 978-3-319-97586-3
eBook Packages: Computer ScienceComputer Science (R0)