Florian Hartmann1, Alexander Schlaefer. 1. Medical Robotics Group, Institute for Robotics and Cognitive Systems, University of Lübeck, Lübeck, Germany.
Abstract
PURPOSE: Today's highly technical operating rooms lead to fairly complex surgical workflows where the surgeon has to interact with a number of devices, including the operating room light. Hence, ideally, the surgeon could direct the light without major disruption of his work. We studied whether a gesture tracking-based control of an automated operating room light is feasible. METHODS: So far, there has been little research on control approaches for operating lights. We have implemented an exemplary setup to mimic an automated light controlled by a gesture tracking system. The setup includes a articulated arm to position the light source and an off-the-shelf RGBD camera to detect the user interaction. We assessed the tracking performance using a robot-mounted hand phantom and ran a number of tests with 18 volunteers to evaluate the potential of touch-less light control. RESULTS: All test persons were comfortable with using the gesture-based system and quickly learned how to move a light spot on flat surface. The hand tracking error is direction-dependent and in the range of several centimeters, with a standard deviation of less than 1 mm and up to 3.5 mm orthogonal and parallel to the finger orientation, respectively. However, the subjects had no problems following even more complex paths with a width of less than 10 cm. The average speed was 0.15 m/s, and even initially slow subjects improved over time. Gestures to initiate control can be performed in approximately 2 s. Two-thirds of the subjects considered gesture control to be simple, and a majority considered it to be rather efficient. CONCLUSIONS: Implementation of an automated operating room light and touch-less control using an RGBD camera for gesture tracking is feasible. The remaining tracking error does not affect smooth control, and the use of the system is intuitive even for inexperienced users.
PURPOSE: Today's highly technical operating rooms lead to fairly complex surgical workflows where the surgeon has to interact with a number of devices, including the operating room light. Hence, ideally, the surgeon could direct the light without major disruption of his work. We studied whether a gesture tracking-based control of an automated operating room light is feasible. METHODS: So far, there has been little research on control approaches for operating lights. We have implemented an exemplary setup to mimic an automated light controlled by a gesture tracking system. The setup includes a articulated arm to position the light source and an off-the-shelf RGBD camera to detect the user interaction. We assessed the tracking performance using a robot-mounted hand phantom and ran a number of tests with 18 volunteers to evaluate the potential of touch-less light control. RESULTS: All test persons were comfortable with using the gesture-based system and quickly learned how to move a light spot on flat surface. The hand tracking error is direction-dependent and in the range of several centimeters, with a standard deviation of less than 1 mm and up to 3.5 mm orthogonal and parallel to the finger orientation, respectively. However, the subjects had no problems following even more complex paths with a width of less than 10 cm. The average speed was 0.15 m/s, and even initially slow subjects improved over time. Gestures to initiate control can be performed in approximately 2 s. Two-thirds of the subjects considered gesture control to be simple, and a majority considered it to be rather efficient. CONCLUSIONS: Implementation of an automated operating room light and touch-less control using an RGBD camera for gesture tracking is feasible. The remaining tracking error does not affect smooth control, and the use of the system is intuitive even for inexperienced users.
Authors: Juan P Wachs; Helman I Stern; Yael Edan; Michael Gillam; Jon Handler; Craig Feied; Mark Smith Journal: J Am Med Inform Assoc Date: 2008 May-Jun Impact factor: 4.497