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首页> 外文期刊>Telemedicine and e-health: the official journal of the American Telemedicine Association >Automatic Detection of Confusion in Elderly Users of a Web-Based Health Instruction Video
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Automatic Detection of Confusion in Elderly Users of a Web-Based Health Instruction Video

机译：基于网络的健康指导视频中老年人的混乱自动检测

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Background:Because of cognitive limitations and lower health literacy, many elderly patients have difficulty understanding verbal medical instructions. Automatic detection of facial movements provides a nonintrusive basis for building technological tools supporting confusion detection in healthcare delivery applications on the Internet.Materials and Methods:Twenty-four elderly participants (70-90 years old) were recorded while watching Web-based health instruction videos involving easy and complex medical terminology. Relevant fragments of the participants' facial expressions were rated by 40 medical students for perceived level of confusion and analyzed with automatic software for facial movement recognition.Results:A computer classification of the automatically detected facial features performed more accurately and with a higher sensitivity than the human observers (automatic detection and classification, 64% accuracy, 0.64 sensitivity; human observers, 41% accuracy, 0.43 sensitivity). A drill-down analysis of cues to confusion indicated the importance of the eye and eyebrow region.Conclusions:Confusion caused by misunderstanding of medical terminology is signaled by facial cues that can be automatically detected with currently available facial expression detection technology. The findings are relevant for the development of Web-based services for healthcare consumers.
机译：背景：由于认知限制和较低的健康素养，许多老年患者难以理解口头医疗指导。面部动作的自动检测为构建支持Internet上医疗服务应用中的混乱检测的技术工具提供了非干扰性的基础。材料和方法：收看了二十四名老人参与者（70-90岁），同时观看了基于Web的健康指导视频涉及简单而复杂的医学术语。 40名医学生对参与者面部表情的相关片段进行了评估，以评估他们的困惑程度，并使用自动软件进行面部运动识别分析。结果：自动检测到的面部特征的计算机分类比计算机进行的分类更准确，灵敏度更高。观察者（自动检测和分类，准确度64％，灵敏度0.64;观察者，准确度41％，灵敏度0.43）。深入分析导致混淆的线索表明了眼睛和眉毛区域的重要性。这些发现与为医疗保健消费者开发基于Web的服务有关。

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