Abstract
Proposing a robust and fast real-time medical procedure, operating remotely is always a challenging task, due mainly to the effect of delay and dropping of the speed of networks, on operations. If a further stage of prediction is properly designed on remotely operated systems, many difficulties could be tackled. Hence, in this paper, an accurate predictive model, calculating haptics feedback in percutaneous heart biopsy is investigated. A one-layer Long Short-Term Memory based (LSTM-based) Recurrent Neural Network, which is a natural fit for understanding haptics time series data, is utilised. An offline learning procedure is proposed to build the model, followed by an online procedure to operate on new experiments, remotely fed to the system. Statistical analyses prove that the error variation of the model is significantly narrow, showing the robustness of the model. Moreover, regarding computational costs, it takes 0.7 ms to predict a time step further online, which is quick enough for real-time haptic interaction.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Babaie, M., Kalra, S., Sriram, A., Mitcheltree, C., Zhu, S., Khatami, A., Rahnamayan, S., Tizhoosh, H.R.: Classification and retrieval of digital pathology scans: a new dataset. arXiv preprint (2017). arXiv:1705.07522
Breslow, N.: A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship. Biometrika 57(3), 579–594 (1970)
Duriez, C., Andriot, C., Kheddar, A.: A multi-threaded approach for deformable/rigid contacts with haptic feedback. In: Proceedings of 12th International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, 2004. HAPTICS 2004, pp. 272–279. IEEE (2004)
Gamboa, J.C.B.: Deep learning for time-series analysis. arXiv preprint (2017). arXiv:1701.01887
Gao, Y., Hendricks, L.A., Kuchenbecker, K.J., Darrell, T.: Deep learning for tactile understanding from visual and haptic data. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 536–543. IEEE (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Khatami, A., Babaie, M., Khosravi, A., Tizhoosh, H., Salaken, S.M., Nahavandi, S.: A deep-structural medical image classification for a radon-based image retrieval. In: 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–4. IEEE (2017)
Khatami, A., Khosravi, A., Lim, C.P., Nahavandi, S.: A wavelet deep belief network-based classifier for medical images. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9949, pp. 467–474. Springer, Cham (2016). doi:10.1007/978-3-319-46675-0_51
Khatami, A., Khosravi, A., Nguyen, T., Lim, C.P., Nahavandi, S.: Medical image analysis using wavelet transform and deep belief networks. Expert Syst. Appl. 86, 190–198 (2017)
Khatami, A., Mirghasemi, S., Khosravi, A., Lim, C.P., Nahavandi, S.: A new PSO-based approach to fire flame detection using K-Medoids clustering. Expert Syst. Appl. 68, 69–80 (2017)
Ortega, M., Redon, S., Coquillart, S.: A six degree-of-freedom god-object method for haptic display of rigid bodies with surface properties. IEEE Trans. Vis. Comput. Graph. 13(3), 458–469 (2007)
Otaduy, M.A., Lin, M.C.: Stable and responsive six-degree-of-freedom haptic manipulation using implicit integration. In: First Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, 2005. World Haptics 2005, pp. 247–256. IEEE (2005)
Picinbono, G., Lombardo, J.C.: Extrapolation: a solution for force feedback. In: International Scientific Workshop on Virtual Reality and Prototyping, pp. 117–125 (1999)
Picinbono, G., Lombardo, J.C., Delingette, H., Ayache, N.: Improving realism of a surgery simulator: linear anisotropic elasticity, complex interactions and force extrapolation. J. Vis. Comput. Animat. 13(3), 147–167 (2002)
Ruffaldi, E., Morris, D., Edmunds, T., Barbagli, F., Pai, D.K.: Standardized evaluation of haptic rendering systems. In: 2006 14th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 225–232. IEEE (2006)
Sung, J., Salisbury, J.K., Saxena, A.: Learning to represent haptic feedback for partially-observable tasks. arXiv preprint (2017). arXiv:1705.06243
Wu, J., Song, A., Li, J.: A time series based solution for the difference rate sampling between haptic rendering and visual display. In: 2006 IEEE International Conference on Robotics and Biomimetics. ROBIO 2006, pp. 595–600. IEEE (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Khatami, A. et al. (2017). A Haptics Feedback Based-LSTM Predictive Model for Pericardiocentesis Therapy Using Public Introperative Data. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_82
Download citation
DOI: https://doi.org/10.1007/978-3-319-70139-4_82
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70138-7
Online ISBN: 978-3-319-70139-4
eBook Packages: Computer ScienceComputer Science (R0)