基于YOLOv7的双模型图像识别系统,简化了老年人在移动卫生中的数据输入
Yu-Cheng Liu1, Wei-Ting Lee1, Chia-Chi Yin1
1National Yang Ming Chiao Tung University, Institute of Biomedical Informatics.
Studies in health technology and informatics
|August 8, 2025
概括
使用YOLOv7技术的新双系统模型显著改善了长期护理中老年用户的医疗设备数据输入,提高了效率92.5%. 这项创新旨在减轻用户负担,并确保准确的健康数据收集.
科学领域:
- 生物医学工程 生物医学工程
- 计算机视觉 计算机视觉
- 老年学是一门学科.
背景情况:
- 医疗设备的数据输入带来了挑战,特别是对老年用户来说.
- 在长期护理环境中,健康数据收集的效率和准确性至关重要.
研究的目的:
- 开发一个用于识别医疗设备价值的自动化系统.
- 为了提高数据输入的效率和老年用户的可访问性.
主要方法:
- 使用YOLOv7物体检测算法开发了一种双系统模型.
- 该系统在长期护理环境中使用内部照片进行了训练和验证.
主要成果:
- 基于YOLOv7的模型实现了92.5%的数据输入效率提高.
- 该系统在现实世界的实施中展示了实际的有效性.
结论:
- 开发的双系统模型提高了数据输入效率,并提高了老年用户的可访问性.
- 该系统有助于准确的健康数据收集,并有可能扩展到移动健康设备.
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