The prevalence of minimally invasive surgical (MIS) procedures is on the rise due to the promise of fewer complications; however, these surgeries are more technically difficult and require greater training than their traditional counterparts. To counteract this, surgeons undergo more training and utilize surgical simulators to gain experience without risking patients' health. To increase the accuracy and, therefore, effectiveness of the surgical simulators, tissue property databases have been created, such as the Human Tissue Property Database created by the Center for Research in Education and Simulation Technologies, which test excised tissues postmortem for their mechanical properties [1]. Databases such as this have potential beyond their utilization in simulation and may serve as a platform for automated diagnostic systems capable of characterizing the health of tissues during surgical procedures [2]. Because of the importance to surgical simulation now and in the future of automated diagnostics, tissue property databases must better their accuracy to reflect the complex mechanical behavior of biological samples in vivo [2,3]. This upgrade requires the development of instrumentation capable of collecting data nondestructively in vivo, as well as nonintrusively to a surgical environment. A promising approach to collecting data nondestructively and non-intrusively during surgical procedures is the modification of current MIS tools to contain sensors, and occasionally actuators separate from the operating area [3-6]. Due to the small size of the operating component and the slender connection to the handle, it is not practical to include these additions to the tool anywhere except the handle. Tools, such as the motorized smart endoscopic grasper (MSEG) tool, created by the BioRobotics Lab at the University of Washington, utilize a strain gauge and encoder equipped motor in place of the handle to provide actuation to the jaw and record the stress and strain experienced by the tissue being grasped [6]. Previous efforts to calibrate the MSEG system by the Medical Robotics and Devices Lab at the University of Minnesota have focused on static loading at discrete locations rather than quasi-static loading recorded continuously over a range of the tool's motion [7]. This paper describes the latter method of calibration in order to provide more accurate interpretation of the friction and momentum within the system.
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