PDF(Pubmed)
Abstract：
Spinal diseases and frozen shoulder are prevalent health problems in Asian populations. Early assessment and treatment are very important to prevent the disease from getting worse and reduce pain. In the field of computer vision, it is a challenging problem to assess the range of motion. In order to realize efficient, real-time and accurate assessment of the range of motion, an assessment system combining MediaPipe and YOLOv5 technologies was proposed in this study. On this basis, Convolutional Block Attention Module (CBAM) is introduced into the YOLOv5 target detection model, which can enhance the extraction of feature information, suppress background interference, and improve the generalization ability of the model. In order to meet the requirements of large-scale computing, a client/server (C/S) framework structure is adopted. The evaluation results can be obtained quickly after the client uploads the image data, providing a convenient and practical solution. In addition, a game of \"Picking Bayberries\" was developed as an auxiliary treatment method to provide patients with interesting rehabilitation training.
摘要：
脊柱疾病和肩周炎是亚洲人群中普遍存在的健康问题。早期评估和治疗对于防止疾病恶化和减轻疼痛非常重要。在计算机视觉领域,评估运动范围是一个具有挑战性的问题。为了实现高效,实时准确的运动范围评估，本研究提出了一种结合MediaPipe和YOLOv5技术的评估系统。在此基础上,将卷积块注意力模块(CBAM)引入到YOLOv5目标检测模型中,可以增强特征信息的提取，抑制背景干扰，提高了模型的泛化能力。为了满足大规模计算的要求,采用客户端/服务器(C/S)框架结构。客户端上传图像数据后，可以快速获得评估结果，提供了方便实用的解决方案。此外,开发了“采摘杨梅”游戏作为一种辅助治疗方法，为患者提供有趣的康复训练。
