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A Haptics feedback based-LSTM predictive model for pericardiocentesis therapy using public introperative data
conference contribution
posted on 2017-01-01, 00:00 authored by Seyedamin Khatami, Yonghang Tai, Abbas KhosraviAbbas Khosravi, Lei WeiLei Wei, Mohsen Moradi DalvandMohsen Moradi Dalvand, J Peng, Saeid NahavandiProposing 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.
History
Event
Neural Information Processing. International Conference (24th : 2017 : Guangzhou, China)Volume
10638Series
Lecture Notes in Computer SciencePagination
810 - 818Publisher
SpringerLocation
Guangzhou, ChinaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2017-11-14End date
2017-11-18ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319701387ISBN-10
3319701398Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2017, Springer International Publishing AGTitle of proceedings
ICONIP 2017 : Proceedings of the Neural Information Processing International ConferenceUsage metrics
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