The detection of vision problems in early childhood can prevent neurodevelopmental disorders such as amblyopia. However, accurate clinical assessment of visual function in young children is challenging. optokinetic nystagmus (OKN) is a reflexive sawtooth motion of the eye that occurs in response to drifting stimuli, that may allow for objective measurement of visual function in young children if appropriate child-friendly eye tracking techniques are available. In this paper, we present offline tools to detect the presence and direction of the optokinetic reflex in children using consumer grade video equipment. Our methods are tested on video footage of children (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$N = 5$ \end{document} children and 20 trials) taken as they freely observed visual stimuli that induced horizontal OKN. Using results from an experienced observer as a baseline, we found the sensitivity and specificity of our OKN detection method to be 89.13% and 98.54%, respectively, across all trials. Our OKN detection results also compared well (85%) with results obtained from a clinically trained assessor. In conclusion, our results suggest that OKN presence and direction can be measured objectively in children using consumer grade equipment, and readily implementable algorithms.
Introduction:Methamphetamine is a powerful psychostimulant that causes significant neurological impairments with long-lasting effects and has provoked serious international concerns about public health. Denial of drug abuse and drug craving are two important factors that make the diagnosis and treatment extremely challenging. Here, we present a novel and rapid noninvasive method with potential application for differentiation and monitoring methamphetamine abuse.Methods:Visual stimuli comprised a series of images with neutral and methamphetamine-related content. A total of 10 methamphetamine abusers and 10 age-gender matched controls participated in the experiments. Event-related potentials (ERPs) were recorded and compared using a time window analysis method. The ERPs were divided into 19 time windows of 100 ms with 50 ms overlaps. The area of positive sections below each window was calculated to measure the differences between the two groups.Results:Significant differences between two groups were observed from 250 to 500 ms (P300) in response to methamphetamine-related visual stimuli and 600 to 800 ms in response to neutral stimuli.Conclusion:This study presented a novel and noninvasive method based on neural correlates to discriminate healthy individuals from methamphetamine drug abusers. This method can be employed in treatment and monitoring of the methamphetamine abuse.
The use of human hand gestures as a natural interface tool has motivated researchers to conduct research in the modeling, analyzing and recognition of various hand movements. In particular, human computer intelligent interaction has been a focus for research in vision-based gesture recognition. In this work, we introduce a 3D hand model reconstruction method which offers flexible and elaborate representation of hand gestures. We used 20 landmarked points on tips and joints of the fingers and calculated the 3D coordinates of these points through a stereo vision system. Our results show that such reconstruction provides a precise 3D hand model only to be influenced by intrinsic and extrinsic camera parameter estimation errors. As our proposed 3D reconstruction method requires only 20 points, it is rated among very fast algorithms suitable for realtime hand gesture recognition applications.
Abstract-The presence of untreated visual disorders in early childhood can result in abnormal visual cortex development (amblyopia). However, accurate clinical assessment of visual function in young children is highly challenging. Reflexive eye movements may allow for precise measurement of visual functions such as resolution acuity in young children if age appropriate, clinically acceptable, quantitative eye tracking techniques can be developed. Children do not tolerate chinrests or head mounted eye-tracking equipment, therefore we have developed a method to measure and compensate for unrestrained head motion that may facilitate detection of eye movements. We implemented an automatic feature-based algorithm to track features on the face in pre-recorded videos. These data were used to "lock" the head to its initial position. Secondly, we implemented a single un-calibrated camera method to estimate the 3D movements of the head. The method was tested using video footage from five children who observed visual stimuli designed to induce horizontal optokinetic nystagmus (a reflexive sawtooth motion of the eye consisting of pursuit and saccadic eye movements). The children's heads were unrestrained, thereby exhibiting natural movement within the video. Markers placed on participants' faces were manually segmented to yield ground truth data. The standard deviation of head movement improved from (18.6676, 8.9088) to (1.8828, 1.4282) pixels after stabilization. The average mean square error (MSE) between the manual and automatic stabilization methods was 7.7494 pixels. The percentage error for 3D pose estimation was 0.2428 %. Stabilization of the eyes (relative to the head) was achieved. In conclusion, our initial results suggest that head movement stabilization is possible as a post processing step which could significantly facilitate the monitoring of eye movements in children. Furthermore automated methods could improve the monitoring of neurodevelopmental disorders that manifest through head movement.
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