Research on the Optimization of the College Physical Examination Mode Based on the Human Body Size Measurement Algorithm Using Images

高校传统体检模式长期面临效率低、误差率高、数据管理粗放等痛点。本研究以计算机视觉技术为核心，提出基于图像的人体特征点提取与尺寸测量算法，构建非接触式智能化体检系统。通过融合图像增强、特征点定位、多模态数据融合等技术，实现身高、臂围、臂长等指标的自动化测量。在某高校试点中，系统使单日检测量提升300%，关键指标误差率降低至0.5 cm以下，并打通健康数据全链条管理。研究结果为高校体检数字化转型提供了可复制的技术路径与管理范式，对推动智慧校园建设具有重要实践价值。The traditional physical examination mode in colleges and universities has long faced the pain points of low efficiency, high error rate and extensive data management. This study takes computer vision technology as the core, proposes an image-based human feature point extraction and size measurement algorithm, and constructs a non-contact intelligent physical examination system. By integrating image enhancement, feature point location and multi-modal data fusion, the automatic measurement of height, arm circumference and arm length is realized. In the pilot project of a university, the system can increase the single-day detection volume by 300%, reduce the error rate of key indicators to below 0.5 cm, and get through the whole chain management of health data. The research results provide a reproducible technical path and management paradigm for the digital transformation of physical examination in colleges and universities, and have important practical value for promoting the construction of smart campus.