Mechanical models for soft human organs are necessary for a variety of medical applications, such as surgical planning, virtual reality surgery simulators, and for diagnostic purposes. An adequate quantitative description of the mechanical behaviour of human organs requires high quality experimental data to be acquired and analyzed. We present a novel technique for the acquisition of such data from soft tissues and its post processing to determine some parameters of the tissue's mechanical properties. A small tube is applied to the target organ and a weak vacuum is generated inside the tube according to a predefined pressure history. A video camera grabs images of the deformation profile of the aspirated tissue, and a pressure sensor measures the correspondent vacuum level. The images are processed and used to inform the fitting of uniaxial and continuum mechanics models. Whilst the aspiration test device has been designed to fulfill the requirements for in-vivo applications, for measurements obtained during open surgery, initial experiments performed on human cadaveric tissues demonstrate the ability to both differentiate between different organs and also between normal and diseased organs on the basis of the derived mechanical properties.
Abstract-This paper focuses on the study of a bio-inspired neural controller used to govern a mobile robot. The network's architecture is based on the understanding that neurophysiologists have obtained on the nervous system of some simple animals, like arthropods or invertebrates. The neuronal model mimics the behavior of the natural cells present in the animal, and elaborates the continuous signals coming from the robot's sensors. The output generated by the controller, after scaling, commands the wheel rotation and therefore the robot's linear and angular velocity. The mobile robot, thanks to the controller, presents different behaviors, like reaching a sonorous source, avoiding obstacles and finding the recharge stations. In the network architecture different modules, charged of different functionality, are regulated and coordinated using an inhibition mechanism. In order to test the control strategy and the neural architecture, we implemented the system in Matlab and finally in hardware using a dedicated dual processor board equipped with an ARM7TDMI micro-controller. Results show that the neural controller can govern the robot efficiently with performances comparable with those described about the animal.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations –citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.