Optimal Probing Behavior


The implementation of tactile feedback for various medical applications can potentially increase the efficiency of the procedure and introduce better clinical outcomes. It is already an established fact, that providing sense of touch for the surgeon during minimally invasive surgery (MIS) would lead to better accuracy and quality of this type of surgery. Enabled tactile sensations would allow evaluating the mechanical conditions of internal organs and detecting malignant or benign formations, which are typically stiffer, in addition, the damage to internal organs could be minimised by using force control during manipulations.

Experimental results of tactile sensing performed on ex-vivo organs and tissues show high variability of the obtained data, which depends on non-homogeneity of tissue stiffness distribution and the resulting nonlinear temporal dynamics of indentation. But only robust and accurate systems can be used for surgical applications, what limits the use of artificial tactile sensors. Therefore, most of tactile surgical devices are still on the research and development stage.


In this research the focus is on the understanding the process of soft tissue tactile examination, and on the development of techniques, which can make the method feasible for real life applications. The approach taken towards the development of a tactile sensing device for MIS is different from standard routs researchers undertake. The aim is to study the requirements and important components of robust and accurate tactile examination process. We propose a hypothesis, that the detection and localization of hard formations can be significantly improved by using specially defined probing strategy. This implies that the reliability of detecting abnormal tissue sites depend not only on the sensitivity of the probe itself, but also the force/velocity control strategy too.

The primary challenge of this project is the understanding of the main principles of manual palpation and techniques people apply to detect hard embedded formations:

If you are interested in this project and want to know more, please contact Jelizaveta Konstantinova.