Medical Gesture Recognition Method Based on Improved Lightweight Network
Source abstract
Abstract
Surgery is a compelling application field for collaborative control robots. This paper proposes a gesture recognition method applied to a medical assistant robot delivering instruments to collaborate with surgeons to complete surgeries. The key to assisting the surgeon in passing instruments in the operating room is the ability to recognize the surgeon’s hand gestures accurately and quickly. Existing gesture recognition techniques suffer from poor recognition accuracy and low rate. To address the existing shortcomings, we propose an improved lightweight convolutional neural network called E-MobileNetv2. The ECA module is added to the original MobileNetv2 network model to obtain more useful features by computing the information interactions between the current channel and the adjacent channels and between the current channel and the distant channels in the feature map. We add R6-SELU activation function to enhance the network’s ability to extract features. By adjusting the shrinkable hyper-parameters, the number of parameters of the network is reduced to improve the recognition speed. The improved network model achieves excellent performance on both the self-built dataset Gesture_II and the public dataset Jester. The recognition accuracy of the improved model is 96.82%, which is 3.17 % higher than that of the original model, achieving an increase in accuracy and recognition speed.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Hand Gesture Recognition Systems · Robot Manipulation and Learning · Robotics and Automated Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronics and Information, Xi’an Polytechnic University, Xi’an 710600, China
Xi’an Polytechnic University Branch of Shaanxi Artificial Intelligence Joint Laboratory, Xi’an Polytechnic University, Xi’an 710600, China
School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China
Funding
National Natural Science Foundation of China · Key Research and Development Program of Shaanxi · Innovation Capability Support Program of Shaanxi · Applied Technology Research and development project in Beilin District of Xi’an
References (34)
Show source-provided references
1. Lio-a personal robot assistant for human-robot interaction and care applications10.1109/LRA.2020.3007462 ↗
2. Towards skill transfer via learning-based guidance in human-robot interaction: An application to orthopaedic surgical drilling skill10.1007/s10846-019-01082-2 ↗
3. Bai, L., Yang, J., Chen, X., Sun, Y., and Li, X. (2019). Medical robotics in bone fracture reduction surgery: A review. Sensors, 19.10.3390/s19163593 ↗
4. Locally operated assistant manipulators with selectable connection system for robotically assisted laparoscopic solo surgery10.1007/s11548-021-02338-9 ↗
5. Egocentric-vision based hand posture control system for reconnaissance robots10.1007/s10846-016-0440-2 ↗
6. Research and Application of Multifeature Gesture Recognition in Human-Computer Interaction Based on Virtual Reality Technology10.1155/2021/3603693 ↗
7. Evaluating the Performance of Machine Learning Algorithms in Gaze Gesture Recognition Systems10.1109/ACCESS.2021.3136153 ↗
8. Gabor filter-based hand-pose angle estimation for hand gesture recognition under varying illumination10.1016/j.eswa.2010.11.016 ↗
9. Tarvekar, M.P. (2018, January 14–15). Hand gesture recognition system for touch-less car interface using multiclass support vector machine. Proceedings of the 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.10.1109/ICCONS.2018.8663003 ↗
10. Rahim, M.A., Miah, A.S.M., Sayeed, A., and Shin, J. (2020, January 21–23). Hand Gesture Recognition Based on Optimal Segmentation in Human-Computer Interaction. Proceedings of the 2020 3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII), Kaohsiung, Taiwan.10.1109/ICKII50300.2020.9318870 ↗
11. Guo, X., Xu, W., Tang, W.Q., and Wen, C. (2019, January 25–27). Research on optimization of static gesture recognition based on convolution neural network. Proceedings of the 2019 4th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), Hohhot, China.10.1109/ICMCCE48743.2019.00095 ↗
12. Deep Reinforcement Learning for a Humanoid Robot Soccer Player10.1007/s10846-021-01333-1 ↗
13. Deep learning in vision-based static hand gesture recognition10.1007/s00521-016-2294-8 ↗
14. Gesture recognition based on CNN and DCGAN for calculation and text output10.1109/ACCESS.2019.2901930 ↗
15. Molchanov, P., Gupta, S., Kim, K., and Pulli, K. (2015, January 4–8). Multi-sensor system for driver’s hand-gesture recognition. Proceedings of the 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, Slovenia.10.1109/FG.2015.7163132 ↗
16. ElBadawy, M., Elons, A., Shedeed, H.A., and Tolba, M. (2017, January 5–7). Arabic sign language recognition with 3d convolutional neural networks. Proceedings of the 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.10.1109/INTELCIS.2017.8260028 ↗
17. Carreira, J., and Zisserman, A. (2017, January 21–26). Quo vadis, action recognition? a new model and the kinetics dataset. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.502 ↗
18. Koller, O., Ney, H., and Bowden, R. (2016, January 27–30). Deep hand: How to train a cnn on 1 million hand images when your data is continuous and weakly labelled. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.412 ↗
19. Offline signature verification using deep learning convolutional neural network (CNN) architectures GoogLeNet inception-v1 and inception-v310.1016/j.procs.2019.11.147 ↗
20. Self-Selection Salient Region-Based Scene Recognition Using Slight-Weight Convolutional Neural Network10.1007/s10846-021-01421-2 ↗
21. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13–19). Ghostnet: More features from cheap operations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00165 ↗
22. Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18–23). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00716 ↗
23. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv.
24. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18–23). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
25. Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2018, January 8–14). Shufflenet v2: Practical guidelines for efficient cnn architecture design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01264-9_8 ↗
26. Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50× fewer parameters and <0.5 MB model size. arXiv.
27. Sun, K., Li, M., Liu, D., and Wang, J. (2018). Igcv3: Interleaved low-rank group convolutions for efficient deep neural networks. arXiv.
28. Xiang, Q., Wang, X., Li, R., Zhang, G., Lai, J., and Hu, Q. (2019, January 22–24). Fruit image classification based on Mobilenetv2 with transfer learning technique. Proceedings of the 3rd International Conference on Computer Science and Application Engineering, Sanya, China.10.1145/3331453.3361658 ↗
29. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13–19). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.01155 ↗
30. Ioffe, S., and Szegedy, C. (2015, January 6–11). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France.
31. Nair, V., and Hinton, G.E. (2010, January 21–24). Rectified linear units improve restricted boltzmann machines. Proceedings of the ICML, Haifa, Israel.
32. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
33. Li, Z., Liu, F., Yang, W., Peng, S., and Zhou, J. (2021). A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst.10.1109/TNNLS.2021.3084827 ↗
34. Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S. (2017). Self-normalizing neural networks. Advances in Neural Information Processing Systems 30, Curran Associates Inc.