This paper presents a method of pose estimation with camera localization based on a genetic algorithm to derive the joint angle using an inverse kinematics model. In recent years, some open-source libraries for human skeleton tracking that can be implemented on smartphones have been released. Such software can detect joint positions of a skeleton from a 2D camera image; however, the 3D posture cannot be obtained. We therefore propose a method to estimate joint angles by using skeleton data. In the proposed approach, we use a multi-island genetic algorithm to maintain the diversity of the population and design a multi-objective function to improve the robustness. Moreover, structured learning based on topological mapping is implemented in the proposed method to enhance searching efficiency. In an experiment, the proposed method reduced the effect of outliers caused by misdetection. In addition, the structured learning was effective in decreasing the difference between skeleton data and the estimated poses.

1. [1] Y. Shiroishi, K. Uchiyama, and N. Suzuki, “Society 5.0: For Human Security and Well-Being,” Computer, Vol.51, Issue 77, pp. 91-95, 2018. https://doi.org/10.1109/MC.2018.3011041
2. [2] M. Kitsuregawa, “Transformational Role of Big Data in Society 5.0,” Proc. of IEEE Int. Conf. on Big Data 2018 (Big Data 2018), 2018. https://doi.org/10.1109/BigData.2018.8621989
3. [3] H. Viswanathan and E. M. Preben, “Communications in the 6G Era,” IEEE Access, Vol.8, pp. 57063-57074, 2020. https://doi.org/10.1109/ACCESS.2020.2981745
4. [4] F. Ardilla et al., “Topological Twin for Mobility Support Robots,” Proc. of IEEE 8th World Forum on Internet of Things, 2022.
5. [5] Y. Liu et al., “A novel cloud-based framework for the elderly healthcare services using digital twin,” IEEE Access, Vol.7, pp. 49088-49101, 2019. https://doi.org/10.1109/ACCESS.2019.2909828
6. [6] M. M. Alam et al., “A survey on the roles of communication technologies in IoT-based personalized healthcare applications,” IEEE Access, Vol.6, pp. 36611-36631, 2018. https://doi.org/10.1109/ACCESS.2018.2853148
7. [7] A. Bandura, “Self-efficacy mechanism in human agency,” American Psychologist, Vol.37, No.2, pp. 122-147, 1982. https://doi.org/10.1037/0003-066X.37.2.122
8. [8] S. Sun et al., “Health Promotion Using Smart Device Interlocked Robot Partners for Elderly People,” Proc. of Joint 8th Int. Conf. on Soft Computing and Intelligent Systems and 17th Int. Symp. on Advanced Intelligent Systems (SCIS & ISIS 2016), 2016. https://doi.org/10.1109/SCIS-ISIS.2016.0073
9. [9] T. Obo et al., “Hybrid Approach for Lower Limb Joint Angle Estimation Using Genetic Algorithm and Feed-forward Neural Network,” Proc. of the 2020 IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC2020), 2020. https://doi.org/10.1109/SMC42975.2020.9283281
10. [10] Z. Cao et al., “Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 7291-7299, 2017.
11. [11] G. Papandreou et al., “PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model,” V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss (Eds.), “Computer Vision – ECCV 2018,” pp. 269-286, Springer International Publishing, 2018. https://doi.org/10.1007/978-3-030-01264-9_17
12. [12] J. Liu et al., “Feature Boosting Network for 3D Pose Estimation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.42, No.2, pp. 494-501, 2020. https://doi.org/10.1109/TPAMI.2019.2894422
13. [13] S. Sharma et al., “Monocular 3D human pose estimation by generation and ordinal ranking,” Proc. of the IEEE/CVF Int. Conf. on Computer Vision, pp. 2325-2334, 2019. https://doi.org/10.1109/ICCV.2019.00241
14. [14] Y. Kudo et al., “Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations,” arXiv:1803.08244, 2018.
15. [15] J. Martinez et al., “A simple yet effective baseline for 3D human pose estimation,” Proc. of the IEEE Int. Conf. on Computer Vision, pp. 2659-2668, 2017.
16. [16] D. Mehta et al., “VNect: Real-Time 3D Human Pose Estimation with a Single RGB Camera,” ACM Trans. on Graphics, Vol.36, Issue 4, 2017. https://doi.org/10.1145/3072959.3073596
17. [17] F. Wanget al., “A real-time human imitation system,” Proc. of the 10th World Congress on Intelligent Control and Automation (WCICA), pp. 3692-3697, 2012. https://doi.org/10.1109/WCICA.2012.6359088
18. [18] M. Mitchell, “An Introduction to Genetic Algorithms,” MIT Press, 1998.
19. [19] T. Mantere and J. T. Alander, “Evolutionary software engineering, a review,” Applied Soft Computing, Vol.5, No.3, pp. 315-331, 2005. https://doi.org/10.1016/j.asoc.2004.08.004
20. [20] E. Alba and J. M. Troya, “A Survey of Parallel Distributed Genetic Algorithms,” Complexity, Vol.4, No.4, pp. 31-52, 1999.
21. [21] D. Whitley, “A genetic algorithm tutorial,” Statistics and Computing, Vol.4, No.2, pp. 65-85, 1994. https://doi.org/10.1007/BF00175354
22. [22] T. Kohonen, “Self-Organization and Associative Memory,” Springer-Verlag, 1984.
23. [23] B. Fritzke, “Growing cell structures – A self-organizing network for unsuper vised and supervised learning,” Neural Networks, Vol.7, Issue 9, pp. 1441-1460, 1994. https://doi.org/10.1016/0893-6080(94)90091-4
24. [24] B. Fritzke, “A growing neural gas network learns topologies,” Advances in Neural Information Processing Systems, Vol.7, pp. 625-632, 1994.