论文标题
朝着老年护理的实时嗜睡检测
Towards Real-time Drowsiness Detection for Elderly Care
论文作者
论文摘要
本文的主要重点是制定概念证明，以从视频中提取嗜睡信息，以帮助自己生活。为了量化打哈欠，眼睑和头部随着时间的流动，我们从捕获的视频中提取了3000张图像，以训练和测试与OpenCV库集成的深度学习模型。眼睑和嘴巴的分类精度在94.3％-97.2％之间。从带有3D坐标叠加的视频的视觉检查视觉检查，表明在收集的数据（偏航，滚动和俯仰）中显示清晰的时空图案。嗜睡信息作为时间表的提取方法适用于其他环境，包括支持隐私保护的增强教练，体育康复以及与医疗保健中的大数据平台的整合。
The primary focus of this paper is to produce a proof of concept for extracting drowsiness information from videos to help elderly living on their own. To quantify yawning, eyelid and head movement over time, we extracted 3000 images from captured videos for training and testing of deep learning models integrated with OpenCV library. The achieved classification accuracy for eyelid and mouth open/close status were between 94.3%-97.2%. Visual inspection of head movement from videos with generated 3D coordinate overlays, indicated clear spatiotemporal patterns in collected data (yaw, roll and pitch). Extraction methodology of the drowsiness information as timeseries is applicable to other contexts including support for prior work in privacy-preserving augmented coaching, sport rehabilitation, and integration with big data platform in healthcare.
