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Two-dimensional discrete feature based spatial attention CapsNet For sEMG signal recognition

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Abstract

Deep learning frameworks(such as deep convolutional networks) require data to have a regular shape. However, discrete features extracted from heterogeneous data cannot be collected in a regular shape to convolute. In this article, a Two-Dimensional Discrete Feature Based Spatial Attention CapsNet(TDACAPS) is proposed to convert one-dimensional discrete features into two-dimensional structured data through Cartesian Product for surface electromyogram(sEMG) signal recognition. sEMG signal varies from person to person is the main signal source of prosthetic control. Our model transforms multi-angle discrete features into structured data to find the inherent law of sEMG signal. Due to uneven information distribution of structured data, this model combines capsule network with attention mechanism to place emphasis on abundant information regions and reduce ancillary information loss. Extensive experiments show our model yields an improvement for sEMG signal recognition of almost 3% than capsule network and other neural networks under different conditions. Our attention mechanism that employs overlapping pooling to search feature map weight is preferable to the squeeze-and-excitation module, convolutional block attention module and others. Moreover, we validate that our model has great expansibility on Wine Quality Dataset and Breast Cancer Wisconsin.

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References

  1. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  2. Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Advances in neural information processing systems, pp 3856–3866

  3. Hinton GE, Sabour S, Frosst N (2018) Matrix capsules with EM routing

  4. Peng H, Li J, Gong Q, Wang S, He L, Li B, Wang L, Yu PS (2019) Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification. arXiv:1906.04898

  5. Zhang N, Deng S, Sun Z, Chen X, Zhang W, Chen H (2018) Attention-based capsule networks with dynamic routing for relation extraction. arXiv:1812.11321

  6. Deng F, Shengliang P, Chen X, Shi Y, Yuan T, Shengyan Pu (2018) Hyperspectral image classification with capsule network using limited training samples. Sensors 18(9):3153

    Article  Google Scholar 

  7. Salamon J, Bello JP (2017) Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process Lett 24(3):279–283

    Article  Google Scholar 

  8. Li L, Qin B, Ren W, Liu T (2017) Document representation and feature combination for deceptive spam review detection. Neurocomputing 254:33–41

    Article  Google Scholar 

  9. Ibrahim AFT, Gannapathy VR, Chong LW, Isa ISM (2016) Analysis of electromyography (EMG) signal for human arm muscle: a review. In: Advanced computer and communication engineering technology, Springer, pp 567–575

  10. Chen X, Xu Z, Zhao Z-Y, Yang J-H, Lantz V, Wang K-Q (2007) Multiple hand gesture recognition based on surface EMG signal. In: 2007 1St international conference on bioinformatics and biomedical engineering, IEEE, pp 506–509

  11. Phinyomark A, Phukpattaranont P, Limsakul C (2012) Feature reduction and selection for EMG signal classification. Expert Syst Appl 39(8):7420–7431

    Article  Google Scholar 

  12. Phinyomark A, Limsakul C, Phukpattaranont P (2009) A novel feature extraction for robust EMG pattern recognition. arXiv:0912.3973

  13. Rechy-Ramirez EJ, Hu H (2011) Stages for Developing Control Systems using EMG and EEG signals: A survey. School of Computer Science and Electronic Engineering, University of Essex, pp 1744–8050

  14. Chowdhury RH, Reaz MBI, Alauddin M, Ali BM, Bakar AAA, Chellappan K, Chang TG (2013) Surface electromyography signal processing and classification techniques. Sensors 13(9):12431–12466

    Article  Google Scholar 

  15. Geethanjali P, Ray KK, Vivekananda Shanmuganathan P (2009) Actuation of prosthetic drive using EMG signal. In: TENCON 2009-2009 IEEE Region 10 conference, IEEE, pp 1–5

  16. Naik GR, Al-Timemy AH, Nguyen HT (2015) Transradial amputee gesture classification using an optimal number of sEMG sensors: an approach using ICA clustering. IEEE Trans Neural Syst Rehabil Eng 24(8):837–846

    Article  Google Scholar 

  17. Oskoei MA, Hu H (2008) Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans Biomed Eng 55(8):1956–1965

    Article  Google Scholar 

  18. Gokgoz E, Subasi A (2015) Comparison of decision tree algorithms for EMG signal classification using DWT. Biomed Signal Process Control 18:138–144

    Article  Google Scholar 

  19. He Y, Fukuda O, Bu N, Okumura H, Yamaguchi N (2018) Surface emg pattern recognition using long short-term memory combined with multilayer perceptron. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp 5636–5639

  20. Allard UC, Nougarou F, Fall CL, Giguère P, Gosselin C, Laviolette F, Gosselin B (2016) A convolutional neural network for robotic arm guidance using sEMG based frequency-features. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp 2464–2470

  21. Cote-Allard U, Fall CL, Campeau-Lecours A, Gosselin C, Laviolette F, Gosselin B (2017) Transfer learning for sEMG hand gestures recognition using convolutional neural networks. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp 1663–1668

  22. Ding Z, Yang C, Tian Z, Yi C, Fu Y, Jian F (2018) sEMG-based gesture recognition with convolution neural networks. Sustainability 10(6):1865

    Article  Google Scholar 

  23. Hu Y, Wong Y, Wei W, Du Y, Kankanhalli M, Geng W (2018) A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition. PloS One 13(10):1–18

  24. Na D, Liu L-Z, Yu X-J, Li Q, Yeh S-C (2019) Classification of multichannel surface-electromyography signals based on convolutional neural networks. J Ind Inf Integration 15:201–206

    Google Scholar 

  25. Tsinganos P, Cornelis B, Cornelis J, Jansen B, Skodras A (2019) Improved gesture recognition based on sEMG signals and TCN. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1169–1173

  26. Jie H, Li S, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7132–7141

  27. Woo S, Park J, Lee J-Y, Kweon IS (2018) cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 3–19

  28. Cortez P, Cerdeira A, Almeida F, Matos T, Reis J (2009) Modeling wine preferences by data mining from physicochemical properties. Decis Support Syst 47(4):547–553

    Article  Google Scholar 

  29. Nick Street W, Wolberg WH, Mangasarian OL (1993) Nuclear feature extraction for breast tumor diagnosis. In: Biomedical Image Processing and Biomedical Visualization, vol 1905, International Society for Optics and Photonics, pp 861–870

  30. Itti L, Koch C (2001) Computational modelling of visual attention. Nat Rev Neurosci 2(3):194

    Article  Google Scholar 

  31. Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis &, Machine Intelligence, 20(11):1254–1259

  32. Rensink RA (2000) The dynamic representation of scenes. Visual Cognition 7(1-3):17–42

    Article  Google Scholar 

  33. Madani K, Kachurka V, Sabourin C, Amarger V, Golovko V, Rossi L (2018) A human-like visual-attention-based artificial vision system for wildland firefighting assistance. Appl Intell 48(8):2157–2179

    Article  Google Scholar 

  34. Mnih V, Heess N, Graves A (2014) Recurrent models of visual attention. In: Advances in Neural Information Processing Systems, pp 2204–2212

  35. Xing W, Zhikang D, Guo Y, Fujita H (2019) Hierarchical attention based long short-term memory for Chinese lyric generation. Appl Intell 49(1):44–52

    Article  Google Scholar 

  36. Liu T, Yu S, Xu B, Yin H (2018) Recurrent networks with attention and convolutional networks for sentence representation and classification. Appl Intell 48(10):3797–3806

    Article  Google Scholar 

  37. Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell: Neural image caption generation with visual attention. In: International conference on machine learning, pp 2048–2057

  38. Jaderberg M, Simonyan K, Zisserman A (2015) Spatial transformer networks. In: Advances in neural information processing systems, pp 2017–2025

  39. Sharma S, Kiros R, Salakhutdinov R (2015)

  40. Liu H, Feng J, Qi M, Jiang J, Yan S (2017) End-to-end comparative attention networks for person re-identification. IEEE Trans Image Process 26(7):3492–3506

    Article  MathSciNet  Google Scholar 

  41. Chorowski JK, Bahdanau D, Serdyuk D, Cho K, Bengio Y (2015) Attention-based models for speech recognition. In: Advances in Neural Information Processing Systems, pp 577–585

  42. Zeyer A, Irie K, Schlüter R, Ney H (2018) Improved training of end-to-end attention models for speech recognition. arXiv:1805.03294

  43. Park J, Woo S, Lee J-Y, Kweon IS (2018) Bam:, Bottleneck attention module. arXiv:1807.06514

  44. Pei W, Dibeklioğlu H, Baltrušaitis T, Tax DMJ (2019) Attended end-to-end architecture for age estimation from facial expression videos. IEEE Transactions on Image Processing

  45. Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(1-4):131–156

    Article  Google Scholar 

  46. Gehler P, Nowozin S (2009) On feature combination for multiclass object classification. In: 2009 IEEE 12Th international conference on computer vision, IEEE, pp 221–228

  47. Papaxanthos L, Llinares-López F, Bodenham D, Borgwardt K (2016) Finding significant combinations of features in the presence of categorical covariates. In: Advances in neural information processing systems, pp 2279–2287

  48. Abualigah L, Shehab M, Alshinwan M, Alabool H (2019) Salp swarm algorithm: a comprehensive survey. Neural Comput Applic, pp 1–21

  49. Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381

    Article  Google Scholar 

  50. Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer

  51. Zhang J, Zheng L, Zheng L, Ge J (2018) Recognition of comparative sentences from online reviews based on multi-feature item combinations. In: International conference on intelligent computing, Springer, pp 182–193

  52. Abualigah LM, Khader TA (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. Jo Supercomput 73(11):4773–4795

    Article  Google Scholar 

  53. Zang Z, Wang W, Song Y, Lu L, Li W, Wang Y, Zhao Y (2019) Hybrid deep neural network scheduler for Job-Shop problem based on convolution Two-Dimensional transformation. Computational Intelligence and Neuroscience, 2019

  54. Zagoruyko S, Komodakis N (2016) Paying more attention to attention:, Improving the performance of convolutional neural networks via attention transfer. arXiv:1612.03928

  55. Übeyli ED (2007) Implementing automated diagnostic systems for breast cancer detection. Expert Syst Appl 33(4):1054–1062

    Article  Google Scholar 

  56. Appalasamy P, Mustapha A, Rizal ND, Johari F, Mansor AF (2012) Classification-based data mining approach for quality control in wine production. J Appl Sci 12(6):598–601

    Article  Google Scholar 

  57. Marcano-Cedeño A, Quintanilla-Domínguez J, Andina D (2011) WBCD Breast cancer database classification applying artificial metaplasticity neural network. Expert Syst Appl 38(8):9573–9579

    Article  Google Scholar 

  58. Lee S, Park J, Kang K (2015) Assessing wine quality using a decision tree. In: 2015 IEEE International symposium on systems engineering (ISSE), IEEE, pp 176–178

  59. Bonyadi MR, Tieng QM, Reutens DC (2018) Optimization of distributions differences for classification. IEEE Trans Meural Metw Learn Syst 30(2):511–523

    MathSciNet  Google Scholar 

  60. Bhardwaj A, Tiwari A (2015) Breast cancer diagnosis using genetically optimized neural network model. Expert Syst Appl 42(10):4611–4620

    Article  Google Scholar 

  61. Aličković E, Subasi A (2017) Breast cancer diagnosis using GA feature selection and Rotation Forest. Neural Comput Applic 28(4):753–763

    Article  Google Scholar 

  62. Liu J, Li X, Li G, Zhou P (2014) EMG feature assessment for myoelectric pattern recognition and channel selection: a study with incomplete spinal cord injury. Med Eng Phys 36(7):975–980

    Article  Google Scholar 

  63. Khezri M, Jahed M (2009) An exploratory study to design a novel hand movement identification system. Comput Bio Med 39(5):433–442

    Article  Google Scholar 

  64. Li Y, Tian Y, Chen W (2010) Multi-pattern recognition of sEMG based on improved BP neural network algorithm. In: Proceedings of the 29th Chinese Control Conference, IEEE, pp 2867–2872

  65. Atzori M, Cognolato M, Müller H (2016) Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands. Front Neurorobotics 10:9

    Article  Google Scholar 

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Acknowledgements

This work is supported by National Natural Science Foundation of China (No. 61873240).

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China

    Guoqi Chen, Wanliang Wang, Zheng Wang, Zelin Zang & Weikun Li

  2. Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, UK

    Honghai Liu

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  1. Guoqi Chen
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Correspondence to Wanliang Wang.

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Chen, G., Wang, W., Wang, Z. et al. Two-dimensional discrete feature based spatial attention CapsNet For sEMG signal recognition. Appl Intell 50, 3503–3520 (2020). https://doi.org/10.1007/s10489-020-01725-0

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