Abstract
Movement distribution analysis can reveal the body’s changes from training with rehabilitation robotic assistance, and the distribution result has been used to develop robot control scheme. However, movement distribution modeling and further validation of the control scheme remain a problem. In this study, we propose a generative adversarial network (GAN) to learn the distribution of human movement, which will be used to design the control scheme for a cable-driven robot later. We preliminary collect a movement dataset of ten healthy subjects following a circular training trajectory, and develop a GAN model based on WGAN-GP to learn the distribution of the dataset. The distribution of the generated data is close to that of the real dataset (Kullback-Leibler divergence = 0.172). Ergodicity is also used to measure the movement trajectories generated by our GAN model and that of the real dataset, and there is no significant difference (p = 0.342). The results show that the developed GAN model can capture the features of human movement distribution effectively. Future work will focus on conducting further experiments based on the proposed control scheme, integrating human movement distribution into the control of real cable-driven robot, recruiting subjects for robot training experiments and evaluation.
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC2001600, in part by the Guangdong Science and Technology Plan Project under Grant 2020B1212060077, and in part by the Shenzhen Science and Technology Plan Project Grant GJHZ20200731095211034.
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References
Stinear, C.M., Lang, C.E., Zeiler, S.: Advances and challenges in stroke rehabilitation. Lancet Neurol. 19(4), 348–360 (2020)
Lo, A.C., Guarino, P.D., Richards, L.G.: Robot-assisted therapy for long-term upper-limb impairment after stroke. N. Engl. J. Med. 362(19), 1772–1783 (2010)
Nordin, N., Xie, S.Q., Wünsche, B.: Assessment of movement quality in robot-assisted upper limb rehabilitation after stroke: a review. J. Neuroeng. Rehabil. 11(1), 1–23 (2014)
Fitzsimons, K., Acosta, A.M., Dewald, J.P.: Ergodicity reveals assistance and learning from physical human-robot interaction. Sci. Robot. 4(29), eaav6079 (2019)
Huang, F.C., Patton, J.L.: Individual patterns of motor deficits evident in movement distribution analysis. In: 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR), pp. 1–6. IEEE, Seattle (2013)
Wright, Z.A., Fisher, M.E., Huang, F.C.: Data sample size needed for prediction of movement distributions. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5780–5783. IEEE, Chicago (2014)
Huang, F.C., Patton, J.L.: Movement distributions of stroke survivors exhibit distinct patterns that evolve with training. J. Neuroeng. Rehabil. 13(1), 1–13 (2016)
Patton, J.L., Mussa-Ivaldi, F.A.: Robot-assisted adaptive training: custom force fields for teaching movement patterns. IEEE Trans. Biomed. Eng. 51(4), 636–646 (2004)
Wright, Z.A., Lazzaro, E., Thielbar, K.O.: Robot training with vector fields based on stroke survivors’ individual movement statistics. IEEE Trans. Neural Syst. Rehabil. Eng. 26(2), 307–323 (2017)
Patton, J.L., Aghamohammadi, N.R., Bittman M.F.: Error Fields: Robotic training forces that forgive occasional movement mistakes. PREPRINT (Version 1) available at Research Square (2022). https://doi.org/10.21203/rs.3.rs-1277924/v1
Goodfellow, I., Pouget-Abadie, J., Mirza, M.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27 (2014)
Wang, Z., Chai, J., Xia, S.: Combining recurrent neural networks and adversarial training for human movement synthesis and control. IEEE Trans. Visual Comput. Graphics 27(1), 14–28 (2019)
Zhao, R., Su, H., Ji, Q.: Bayesian adversarial human movement synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6225–6234. IEEE, Washington (2020)
Wang, J., Yan, S., Dai, B., Lin, D.: Scene-aware generative network for human movement synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12206–12215. IEEE (2021)
Nishimura, Y., Nakamura, Y., Ishiguro, H.: Human interaction behavior modeling using generative adversarial networks. Neural Netw. 132, 521–531 (2020)
Gulrajani, I., Ahmed, F., Arjovsky, M.: Improved training of wasserstein gans. Advances in Neural Information Processing Systems (NIPS), vol. 30. MIT Press, Los Angeles (2017)
Zi, B., Duan, B.Y., Du, J.L.: Dynamic modeling and active control of a cable-suspended parallel robot. Mechatronics 18(1), 1–12 (2008)
Li, Y., Ge, S.S.: Human–robot collaboration based on movement intention estimation. IEEE/ASME Trans. Mechatron. 19(3), 1007–1014 (2013)
Maurice, P., Huber, M.E., Hogan, N.: Velocity-curvature patterns limit human–robot physical interaction. IEEE Robot. Autom. Lett. 3(1), 249–256 (2017)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579–2605 (2008)
Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)
Mathew, G., Mezić, I.: Metrics for ergodicity and design of ergodic dynamics for multi-agent systems. Physica D 240(4–5), 432–442 (2011)
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Li, Z., Xie, C., Song, R. (2022). Generative Adversarial Network Based Human Movement Distribution Learning for Cable-Driven Rehabilitation Robot. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_4
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