Abstract
This study presents a novel experimental paradigm for collecting Electromyography (EMG) data from continuous movement sequences and a Deep Neural Network (DNN) learning framework for segmenting movements from these signals. Unlike prior research focusing on individual movements, this approach characterizes human motion as continuous sequences. The DNN framework comprises a segmentation module for time point level labeling of EMG data and a transfer module predicting movement transition time points. These outputs are integrated based on defined rules. Experimental results reveal an impressive capacity to accurately segment movements, evidenced by segmentation metrics (accuracy: \(88.3\%\); Dice coefficient: \(82.9\%\); mIoU: \(72.7\%\)). This innovative approach to time point level analysis of continuous movement sequences via EMG signals offers promising implications for future studies of human motor functions and the advancement of human-machine interaction systems.
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
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016), pp. 265–283 (2016)
van Amsterdam, B., et al.: Gesture recognition in robotic surgery with multimodal attention. IEEE Trans. Med. Imaging 41(7), 1677–1687 (2022)
Blankertz, B., et al.: The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans. Biomed. Eng. 51(6), 1044–1051 (2004)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
Cisnal, A., Pérez-Turiel, J., Fraile, J.C., Sierra, D., de la Fuente, E.: Robhand: a hand exoskeleton with real-time EMG-driven embedded control quantifying hand gesture recognition delays for bilateral rehabilitation. IEEE Access 9, 137809–137823 (2021)
Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015)
Côté-Allard, U., et al.: Deep learning for electromyographic hand gesture signal classification using transfer learning. IEEE Trans. Neural Syst. Rehabil. Eng. 27(4), 760–771 (2019)
De Luca, C.J., Gilmore, L.D., Kuznetsov, M., Roy, S.H.: Filtering the surface EMG signal: movement artifact and baseline noise contamination. J. Biomech. 43(8), 1573–1579 (2010)
Henin, S., et al.: Learning hierarchical sequence representations across human cortex and hippocampus. Sci. Adv. 7(8), eabc4530 (2021)
Inam, S., et al.: A brief review of strategies used for EMG signal classification. In: Proceedings of 2021 International Conference on Artificial Intelligence (ICAI), pp. 140–145. IEEE (2021)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the International Conference on Machine Learning, pp. 448–456 (2015)
Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J. Neural Eng. 15(5), 056013 (2018)
Lee, M.H., et al.: EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. GigaScience 8(5), giz002 (2019)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3523–3542 (2022). https://doi.org/10.1109/TPAMI.2021.3059968
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38(11), 5391–5420 (2017)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Tieleman, T., Hinton, G., et al.: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Netw. Mach. Learn. 4(2), 26–31 (2012)
Voytek, B., et al.: Oscillatory dynamics coordinating human frontal networks in support of goal maintenance. Nat. Neurosci. 18(9), 1318–1324 (2015)
Xiang, T.Y., et al.: Quantitative movement analysis using scaled information implied in monocular videos. IEEE Trans. Med. Rob. Bionics 5, 88–99 (2023). https://doi.org/10.1109/TMRB.2023.3240285
Xiong, D., Zhang, D., Zhao, X., Zhao, Y.: Deep learning for EMG-based human-machine interaction: a review. IEEE/CAA J. Automatica Sinica 8(3), 512–533 (2021)
Zhou, X.H., et al.: Surgical skill assessment based on dynamic warping manipulations. IEEE Trans. Med. Rob. Bionics 4(1), 50–61 (2022)
Zhou, X.H., et al.: Learning skill characteristics from manipulations. IEEE Trans. Neural Netw. Learn. Syst. (2022). https://doi.org/10.1109/TNNLS.2022.3160159
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 62003343, Grant 62222316, Grant U1913601, Grant 62073325, Grant U20A20224, and Grant U1913210; in part by the Beijing Natural Science Foundation under Grant M22008; in part by the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) under Grant 2020140; in part by the CIE-Tencent Robotics X Rhino-Bird Focused Research Program.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xiang, Ty. et al. (2024). A DNN-Based Learning Framework for Continuous Movements Segmentation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14449. Springer, Singapore. https://doi.org/10.1007/978-981-99-8067-3_30
Download citation
DOI: https://doi.org/10.1007/978-981-99-8067-3_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8066-6
Online ISBN: 978-981-99-8067-3
eBook Packages: Computer ScienceComputer Science (R0)