Paper Reading AI Learner

Eye Tracking for Tele-robotic Surgery: A Comparative Evaluation of Head-worn Solutions

2023-10-18 21:31:45
Regine Büter, Roger D. Soberanis-Mukul, Paola Ruiz Puentes, Ahmed Ghazi, Jie Ying Wu, Mathias Unberath

Abstract

Purpose: Metrics derived from eye-gaze-tracking and pupillometry show promise for cognitive load assessment, potentially enhancing training and patient safety through user-specific feedback in tele-robotic surgery. However, current eye-tracking solutions' effectiveness in tele-robotic surgery is uncertain compared to everyday situations due to close-range interactions causing extreme pupil angles and occlusions. To assess the effectiveness of modern eye-gaze-tracking solutions in tele-robotic surgery, we compare the Tobii Pro 3 Glasses and Pupil Labs Core, evaluating their pupil diameter and gaze stability when integrated with the da Vinci Research Kit (dVRK). Methods: The study protocol includes a nine-point gaze calibration followed by pick-and-place task using the dVRK and is repeated three times. After a final calibration, users view a 3x3 grid of AprilTags, focusing on each marker for 10 seconds, to evaluate gaze stability across dVRK-screen positions with the L2-norm. Different gaze calibrations assess calibration's temporal deterioration due to head movements. Pupil diameter stability is evaluated using the FFT from the pupil diameter during the pick-and-place tasks. Users perform this routine with both head-worn eye-tracking systems. Results: Data collected from ten users indicate comparable pupil diameter stability. FFTs of pupil diameters show similar amplitudes in high-frequency components. Tobii Glasses show more temporal gaze stability compared to Pupil Labs, though both eye trackers yield a similar 4cm error in gaze estimation without an outdated calibration. Conclusion: Both eye trackers demonstrate similar stability of the pupil diameter and gaze, when the calibration is not outdated, indicating comparable eye-tracking and pupillometry performance in tele-robotic surgery settings.

Abstract (translated)

目的:来自眼动追踪和脉搏计量的指标在认知负荷评估方面具有潜力,可能在远程机器人手术中通过用户特定反馈提高培训和患者安全性。然而,与日常情况相比,当前的眼动追踪解决方案在远程机器人手术中的效果尚不确定,因为近距离交互导致极端瞳孔角和遮挡。为了评估现代眼动追踪解决方案在远程机器人手术中的有效性,我们比较了 Tobii Pro 3 眼镜和 Pupil Labs Core,评估了它们与 da Vinci 研究工具(dVRK)集成时的瞳孔直径和 gaze stability。方法:研究方案包括九个点位的眼动校准,然后使用 dVRK 进行选取和放置任务,接着重复三次。在最后一次校准之后,用户观看一个 3x3 的 AprilTags 网格,专注于每个标记物 10 秒钟,以评估 dVRK 屏幕位置下的 gaze stability,使用 L2 范数进行度量。不同的眼动校准评估了由于头部运动导致的校准时间衰减。瞳孔直径稳定性通过从瞳孔直径的 FFT 进行评估。用户使用带头戴的眼动追踪系统完成此日常任务。结果:从十名用户收集的数据表明,瞳孔直径稳定性相当。瞳孔直径的高频分量显示相似的振幅。尽管如此,Tobii 眼镜在时间上的 gaze stability 相比 Pupil Labs 更高,尽管两种眼追踪器在没有过时的校准时都产生了一个类似 4cm 的误差。结论:当校准不过时的情况下,两种眼追踪器表现出相似的瞳孔直径和 gaze 稳定性,表明在远程机器人手术环境中,它们的眼动追踪和脉搏计量性能相当。

URL

https://arxiv.org/abs/2310.13720

PDF

https://arxiv.org/pdf/2310.13720.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot