基于自我中心视频和YOLOv8模型的流体摄入动作检测
IEEE journal of biomedical and health informatics
|March 6, 2025
概括
这项研究引入了一种基于视觉的系统,使用可穿戴摄像头准确监测老年人摄入液体,帮助预防脱水. 该系统在检测饮用行为和容器相互作用方面表现出高准确性.
科学领域:
- 老年学是一门学科.
- 计算机视觉 计算机视觉
- 生物医学工程 生物医学工程
背景情况:
- 脱水对老年人来说是一个重要的健康风险,需要改进监测.
- 目前的液体摄入量监测方法往往是手动的,缺乏连续的客观数据.
- 基于视觉的第一人称方法为不引人注目的水分跟踪提供了一种新的解决方案.
研究的目的:
- 开发和评估一种基于视觉的系统,用于使用可穿戴摄像头准确检测液体摄入.
- 从第一人称的角度创建一个全面的饮酒和非饮酒活动数据集.
- 评估物体检测和动作识别模型的性能,用于水分监测.
主要方法:
- 从使用可穿戴相机的36名参与者收集了17个小时的饮酒和15个小时的非饮酒活动的数据集.
- 利用YOLOv8模型检测与饮酒相关的物体并分析它们的位置和大小.
- 开发了一种机制来识别手-容器相互作用和动作动作检测的运动.
主要成果:
- 对象检测模型实现了mAP@50>0.97和F1得分>0.95.
- 动作检测实现了0.917的F1得分,在干扰活动下降到0.863.
- 观察到高时间精度,饮酒开始/结束检测延迟为0.24s / 0.04s.
结论:
- 以自我为中心,基于视觉的液体摄入检测是可行的和准确的.
- 开发的系统和数据集为老年人高级水分监测提供了基础.
- 这项技术在各种现实世界的背景下,在预防脱水方面具有潜在的应用.
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