海报:基于相机的神经缺陷检测的初步研究

Yan Zhuang, M. Hassan, Chad M. Aldridge, Xuwang Yin, T. McMurry, A. Southerland, G. Rohde
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引用次数: 1

摘要

BANDIT -脑攻击神经缺陷识别工具-项目旨在开发一种自动工具来定量评估中风相关的神经缺陷,如面部无力和肢体漂移。在本文中,我们首先通过描述主要框架来介绍BANDIT项目,然后提出了一个在真实医院环境中进行的患者视频数据采集协议。我们还讨论了数据集中的固有偏差和变化,这可能会给算法设计带来挑战。从我们的研究中获得的经验和教训可能对在互联健康领域进行基于相机的研究的其他研究人员有益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Poster: A Pilot Study On Camera-based Neurological Deficit Detection
The BANDIT - Brain Attack Neurological Deficit Identification Tool - project aims at developing an automatic tool to quantitatively assess stroke-related neurological deficits such as facial weakness and limb drift. In this paper, we first introduce the BANDIT project by describing the main framework and then present a patient video data acquisition protocol that was conducted in a real-world hospital setting. We also discuss the inherent bias and variations within our dataset that may create challenges for the algorithm design. The experiences and lessons gained from our study could be beneficial for other researchers who conduct camera-based research in the connected health area.
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