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A Deep Learning-Based Model for Tactile Understanding on Haptic Data Percutaneous Needle Treatment

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收录情况: ◇ CPCI(ISTP) ◇ EI

机构: [a]Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC [b]Urology Surgery Department, Yunnan First People’s Hospital, Kunming, China
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关键词: Residual networks Sequence classification Tactile understanding Time series

摘要:
Tactile understanding during surgery is essential in medical simulation. To improve a remote surgical operation one step further, in this paper, we develop a sequence classification technique, categorising different tissues, evaluating on biomechanics data. The importance of the proposed model is emphasised when problems such as a delay is occurring during simulation. Monitoring, predicting, and understanding the sense of tissue which is supposed to be involved in operation is vital during surgery. To achieve this, different deep structural techniques are investigated to find the effect of deep features for tactile and kinaesthetic understanding. The experimental results reveal that residual networks outperform others with respect to different terms. The results are accurate and fast which enables the technique to perform in real-time. © 2017, Springer International Publishing AG.

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第一作者机构: [a]Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC
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