Manually Acquiring Targets From Multiple Viewpoints Using Video Feedback.

OBJECTIVE: The effect of camera viewpoint was studied when performing visually obstructed psychomotor targeting tasks. BACKGROUND: Previous research in laparoscopy and robotic teleoperation found that complex perceptual-motor adaptations associated with misaligned viewpoints corresponded to degraded performance in manipulation. Because optimal camera positioning is often unavailable in restricted environments, alternative viewpoints that might mitigate performance effects are not obvious. METHODS: A virtual keyboard-controlled targeting task was remotely distributed to workers of Amazon Mechanical Turk. The experiment was performed by 192 subjects for a static viewpoint with independent parameters of target direction, Fitts' law index of difficulty, viewpoint azimuthal angle (AA), and viewpoint polar angle (PA). A dynamic viewpoint experiment was also performed by 112 subjects in which the viewpoint AA changed after every trial. RESULTS: AA and target direction had significant effects on performance for the static viewpoint experiment. Movement time and travel distance increased while AA increased until there was a discrete improvement in performance for 180°. Increasing AA from 225° to 315° linearly decreased movement time and distance. There were significant main effects of current AA and magnitude of transition for the dynamic viewpoint experiment. Orthogonal direction and no-change viewpoint transitions least affected performance. CONCLUSIONS: Viewpoint selection should aim to minimize associated rotations within the manipulation plane when performing targeting tasks whether implementing a static or dynamic viewing solution. Because PA rotations had negligible performance effects, PA adjustments may extend the space of viable viewpoints. APPLICATIONS: These results can inform viewpoint selection for visual feedback during psychomotor tasks.

Real-time respiration rate measurement from thoracoabdominal movement with a consumer grade camera.

In this paper, we propose a novel computer vision technique to measure respiration rate by counting the periodic thoracoabdominal motion in real-time using an inexpensive consumer grade camera. We compute the component of optical flow parallel to the image gradient at each pixel, which is a computationally inexpensive operation. Then, we find a principal flow field by gathering information over many frames. Subsequently, in each frame, we compute the component of flow along this principal flow field to capture the thoracoabdominal motion. Our method is very simple, easy to implement and needs no specialized hardware. This method is computationally very efficient and can be easily implemented in mobile devices. We demonstrate the efficacy of our method on real world datasets and compare the results with those obtained using impedance pneumography.