This paper presents an improved tactile sensor using a piezoelectric bimorph able to differentiate soft materials with similar mechanical characteristics. The final aim is to develop intelligent surgical tools for brain tumour resection using integrated sensors in order to improve tissue tumour delineation and tissue differentiation. The bimorph sensor is driven using a random phase multisine and the properties of contact between the sensor's tip and a certain load are evaluated by means of the evaluation of the nonparametric FRF. An analysis of the nonlinear contributions is presented to show that the use of a linear model is feasible for the measurement conditions. A series of gelatine phantoms were tested. The tactile sensor is able to identify minimal differences in the consistency of the measured samples considering viscoelastic behaviour. A variance analysis was performed to evaluate the reliability of the sensors and to identify possible error sources due to inconsistencies in the preparation method of the phantoms. The results of the variance analysis are discussed showing that ability of the proposed tactile sensor to perform high quality measurements.
This contribution presents a tactile resonance sensor able to differentiate soft materials with similar mechanical characteristics. The intended application of this research work is to assist neurosurgeons by developing intraoperative tactile tools to improve tissue tumour delineation and tissue differentiation. The proposed solution introduces the use of a piezoelectric biomorph where mechanical vibrations are used to excite mechanically soft biological tissues or tissue phantoms by the use of multisines as excitation signal.
The surgeon’s tactile sense is -next to the visual aspect of the operative situs -crucial for the surgical procedure and strategy. Tactile perception of tissue consistency will be experienced by the surgeon either by direct manual palpation or transmitted by a surgical instrument in a tool-to-tissue interaction. This sensor modality, in combination with an eye-minded surgical technique, supports the delineation of tissue pathologies differing in consistency, as nodules, tissue maceration or tumour entities. Even different tissue compartments, textures, or surface structures of healthy tissue may be differentiated by direct or indirect tactile sense, ascribing it to a crucial influence for the surgical course.
With the introduction and the enhancements of the modern, so called less or minimal invasive surgical techniques, as laparoscopic surgery, neuroendoscopy [1], or more recently the NOTES (Natural Orifice Transluminal Endocsopic Surgery) technology [2], the surgeon’s tactile sense is considerably constrained. For instance, due to the friction of the trocars at the entry ports at the abdominal or thoracic wall, transduction of tissue consistency is widely or even completely damped. Moreover, in robotic guided soft tissue surgery a tactile sensor modality is a priori not available and -if necessary -has therefore to be artificially emulated.
Reproducing the human sense of touch is a challenging task, due to the fact that human tactile sensing is a highly complex function composed of different sensory qualities. Next to nociceptors and thermoreceptors, a set of mechanoreceptors with different adapting rates enable the recognition of several object’s properties like size, position, stiffness, vibration, texture, and roughness. Post processing of psychophysically information is important because it influences absolute sensory threshold, at which a stimulus becomes perceivable and it modulates the difference threshold, allowing discrimination of different stimulus strengths known as the just noticeable difference (JND). Furthermore, human sense of touch is subjected to habituation and adaptation [3].
Focussing on tissue consistency, a correct description of the mechanical behaviour of tissues could roughly be simulated by their elastic behaviour using a single parameter or element (e.g. purely elastic spring). For this task, more accurate models comprising viscoelastic properties should be used. These can be represented, for instance, by a standard viscoelastic solid model built up of two springs and a viscous damper element [4]. Even more, in order to provide a better description of the observed material behaviour on biological tissues, the implementation of hyper-viscoelastic constitutive models is frequently used, in particular for finite element calculations [5].
For preparatory and first experimental measurements, tissue phantoms are preferred to be used to mimic mechanical properties of biological tissues. The use of phantoms instead of real tissue presents several advantages, like a higher stability and the possibility to obtain compositions with controllable gradients [6]. The report of [7] compared the properties of tissue phantoms made of rubberlike hardened liquid plastics and porcine skin gelatine. For simulating tissue elasticity, individually, rubberlike hardened liquid plastics exhibit good characteristics and the capability of a long-term use. However, plastic phantoms lacked in a linear relation of concentration (i.e. the ratio of regular base liquid plastic to a softener solution) to their elastic modulus. Porcine skin gelatine gel preparations exhibit better tissue like characteristics where similar viscoelastic properties can be mimicked. Furthermore, it is reported in literature that identification of their mechanical parameters can be done using a discrete 1-D viscoelastic model [8].
Several tactile sensor applications have been proposed for medical purposes. According to their type of application, these can be classified as: contact or non-contact sensors. With regard to their sensing principle, a proper classification can be done as follow: resistive, piezoelectric, capacitive, optical or resonant sensors. Furthermore, tactile sensors can be typified by their time resolution, their dynamical range or the spatial resolution and their construction principle (i.e. point or matrix array) [9]. A thorough review of the state of the art of tactile sensors and their
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