To reduce the occurrence of SS, a degree of peripheral vision of the external world should be provided. Furthermore, users and designers should be aware that head movement behavior may be affected by HMD characteristics.

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This work introduces and evaluates an automated intra-retinal segmentation method for spectral-domain optical coherence (SD-OCT) retinal images. While quantitative assessment of retinal features in SD-OCT data is important, manual segmentation is extremely time-consuming and subjective. We address challenges that have hindered prior automated methods, including poor performance with diseased retinas relative to healthy retinas, and data smoothing that obscures image features such as small retinal drusen. Our novel segmentation approach is based on the iterative adaptation of a weighted median process, wherein a three-dimensional weighting function is defined according to image intensity and gradient properties, and a set of smoothness constraints and pre-defined rules are considered. We compared the segmentation results for 9 segmented outlines associated with intra-retinal boundaries to those drawn by hand by two retinal specialists and to those produced by an independent state-of-the-art automated software tool in a set of 42 clinical images (from 14 patients). These images were obtained with a Zeiss Cirrus SD-OCT system, including healthy, early or intermediate AMD, and advanced AMD eyes. As a qualitative evaluation of accuracy, a highly experienced third independent reader blindly rated the quality of the outlines produced by each method. The accuracy and image detail of our method was superior in healthy and early or intermediate AMD eyes (98.15% and 97.78% of results not needing substantial editing) to the automated method we compared against. While the performance was not as good in advanced AMD (68.89%), it was still better than the manual outlines or the comparison method (which failed in such cases). We also tested our method's performance on images acquired with a different SD-OCT manufacturer, collected from a large publicly available data set (114 healthy and 255 AMD eyes), and compared the data quantitatively to reference standard markings of the internal limiting membrane and inner boundary of retinal pigment epithelium, producing a mean unsigned positioning error of 6.04 ± 7.83µm (mean under 2 pixels). Our automated method should be applicable to data from different OCT manufacturers and offers detailed layer segmentations in healthy and AMD eyes.

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The effect of viewing a display of the "real-world" via an HMD on simulator sickness was investigated. We hypothesized that simulator sickness would increase as time performing a task wearing an HMD increased. Also, we predicted that viewing a "real-world" display via an HMD compared to a control of not using an HMD would result in greater sickness. Participants made 200 head movements to look at eight different objects during two within-subjects conditions: (1) wearing an HMD and viewing a video display of the room; and (2) not wearing an HMD. Sickness scores were greater when viewing the room through the HMD and increased as time on task increased in both conditions. These findings suggested that characteristics of the HMD as well as task performance may have contributed to simulator sickness.

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Heart rate variability (HRV) is traditionally analyzed while a subject is in a controlled environment, such as at rest in a clinic, where it can be used as a medical indicator. This paper concerns analyzing HRV outside of controlled environments, such as on an actively moving person. We describe automated methods for inter-heartbeat interval (IBI) error detection and correction. We collected 124,998 IBIs from 18 subjects, undergoing a variety of active motions, for use in evaluating our methods. Two human graders manually labeled each IBI, evaluating 10% of the IBIs as having an error, which is a far greater error percentage than has been examined in any previous study. Our automated method had a 96% agreement rate with the two human graders when they themselves agreed, with a 49% rate of matching specific error corrections and a 0.01% false alarm rate.

No abstract