We developed a simple, light, and cheap 3-D visualization device based on mixed reality that can be used by physicians to see preoperative radiological exams in a natural way. The system allows the user to see stereoscopic "augmented images," which are created by mixing 3-D virtual models of anatomies obtained by processing preoperative volumetric radiological images (computed tomography or MRI) with real patient live images, grabbed by means of cameras. The interface of the system consists of a head-mounted display equipped with two high-definition cameras. Cameras are mounted in correspondence of the user's eyes and allow one to grab live images of the patient with the same point of view of the user. The system does not use any external tracker to detect movements of the user or the patient. The movements of the user's head and the alignment of virtual patient with the real one are done using machine vision methods applied on pairs of live images. Experimental results, concerning frame rate and alignment precision between virtual and real patient, demonstrate that machine vision methods used for localization are appropriate for the specific application and that systems based on stereoscopic mixed reality are feasible and can be proficiently adopted in clinical practice.
This paper describes a novel mechatronic tool for arthroscopy, which is at the same time a smart tool for traditional arthroscopy and the main component of a system for computer-assisted arthroscopy. The mechatronic arthroscope has a cable-actuated servomotor-driven multi-joint mechanical structure, is equipped with a position sensor measuring the orientation of the tip and with a force sensor detecting possible contact with delicate tissues in the knee, and incorporates an embedded microcontroller for sensor signal processing, motor driving and interfacing with the surgeon and/or the system control unit. When used manually, the mechatronic arthroscope enhances the surgeon's capabilities by enabling him/her to easily control tip motion and to prevent undesired contacts. When the tool is integrated in a complete system for computer-assisted arthroscopy, the trajectory of the arthroscope is reconstructed in real time by an optical tracking system using infrared emitters located in the handle, providing advantages in terms of improved intervention accuracy. The computer-assisted arthroscopy system comprises an image processing module for segmentation and three-dimensional reconstruction of preoperative computer tomography or magnetic resonance images, a registration module for measuring the position of the knee joint, tracking the trajectory of the operating tools, and matching preoperative and intra-operative images, and a human-machine interface that displays the enhanced reality scenario and data from the mechatronic arthroscope in a friendly and intuitive manner. By integrating preoperative and intra-operative images and information provided by the mechatronic arthroscope, the system allows virtual navigation in the knee joint during the planning phase and computer guidance by augmented reality during the intervention. This paper describes in detail the characteristics of the mechatronic arthroscope and of the system for computer-assisted arthroscopy and discusses experimental results obtained with a preliminary version of the tool and of the system.
Interaction with machines is mediated by Human-Machine Interfaces (HMIs). Brainmachine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper a method is introduced for pointing out effective combinations of interfaces and devices for creating real-world applications. First, devices for domotics, rehabilitation and assistive robotics, and their requirements, in terms of throughput and latency, are described. Second, HMIs are classified and their performance described, still in terms of throughput and latency. Then device requirements are matched with performance of available interfaces. Simple rehabilitation and domotics devices can be easily controlled by means of BMI technology. Prosthetic hands and wheelchairs are suitable applications but do not attain optimal interactivity. Regarding humanoid robotics, the head and the trunk can be controlled by means of BMIs, while other parts require too much throughput. Robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for non-invasive BMIs. Combining smart controllers with BMIs could improve interactivity and boost BMI applications.
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