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A Head Tracking Method for Improved Eye Movement Detection in Children

机译:一种用于改善儿童眼运动检测的头部跟踪方法

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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 chin-rests 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 neuro-developmental disorders that manifest through head movement.
机译:儿童早期未经治疗的视觉疾病的存在可能导致视觉皮质发育异常(弱视)。但是,准确对幼儿视觉功能的临床评估高度挑战性。反射眼球运动可以允许精确测量视觉功能,例如幼儿的分辨率,如果年龄适当,可以开发临床上可接受的,定量的眼睛跟踪技术。儿童不忍住下巴休息或头戴式追踪设备,因此我们开发了一种测量和补偿无限制的头部运动的方法,这可能有助于检测眼球运动。我们实现了一种基于自动特征的算法,可以在预先录制的视频中跟踪面部的功能。这些数据用于将头部“锁定”到其初始位置。其次,我们实施了一种未校准的摄像机方法来估计头部的3D运动。使用来自五个儿童的视频素材测试了该方法,这些播放观察了视觉刺激,设计用于诱导水平视网膜震颤(由追求和扫视眼球运动的眼睛的反射锯齿运动)。儿童的头部是无拘无束的,从而在视频中表现出自然运动。手动分割放置在参与者面上的标记以屈服于地面真理数据。头部运动的标准偏差从稳定后的(1.8828,1.4282)改善(1.8828,1.4282)像素。手动和自动稳定方法之间的平均平均方误差(MSE)为7.7494像素。 3D姿势估计的百分比误差为0.2428%。实现了眼睛的稳定(相对于头部)。总之,我们的初始结果表明,头部运动稳定性是可以显着促进儿童眼动力监测的后处理步骤。此外,自动化方法可以改善通过头部运动的神经发育障碍的监测。

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