Touchless computer interfaces in hospitals: A review <s> Radiologists 29 users <s> The use of doctor-computer interaction devices in the operation room (OR) requires new modalities that support medical imaging manipulation while allowing doctors' hands to remain sterile, supporting their focus of attention, and providing fast response times. This paper presents "Gestix," a vision-based hand gesture capture and recognition system that interprets in real-time the user's gestures for navigation and manipulation of images in an electronic medical record (EMR) database. Navigation and other gestures are translated to commands based on their temporal trajectories, through video capture. "Gestix" was tested during a brain biopsy procedure. In the in vivo experiment, this interface prevented the surgeon's focus shift and change of location while achieving a rapid intuitive reaction and easy interaction. Data from two usability tests provide insights and implications regarding human-computer interaction based on nonverbal conversational modalities. <s> BIB001 </s> Touchless computer interfaces in hospitals: A review <s> Radiologists 29 users <s> Computerized medical systems play a vital role in the operating room, however, sterility requirements and interventional workflow often make interaction with these devices challenging for surgeons. Typical solutions, such as delegating physical control of keyboard and mouse to assistants, add an undesirable level of indirection. We present a touchless, gesture-based interaction framework for the operating room that lets surgeons define a personalized set of gestures for controlling arbitrary medical computerized systems. Instead of using cameras for capturing gestures, we rely on a few wireless inertial sensors, placed on the arms of the surgeon, eliminating the dependence on illumination and line-of-sight. A discriminative gesture recognition approach based on kernel regression allows us to simultaneously classify performed gestures and to track the relative spatial pose within each gesture, giving surgeons fine-grained control of continuous parameters. An extensible software architecture enables a dynamic association of learned gestures to arbitrary intraoperative computerized systems. Our experiments illustrate the performance of our approach and encourage its practical applicability. <s> BIB002
The iPad was found to be the most usable, and the Kinect the least usable, with tasks taking nearly 4 times as long with the Kinect. MRI navigation BIB001 Surgeons 1 operating procedure The system was found to be easy to use, responsive and fast to train with. Gesturerecognition accuracy of 96%. Locating an aortic stent and navigating to bifurcation BIB002 Biomedical engineers