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结合 MediaPipe 和 YOLOv5 的运动范围评估系统，用于评估脊柱疾病和冻结肩。

Combined MediaPipe and YOLOv5 range of motion assessment system for spinal diseases and frozen shoulder.

机构信息

School of Mathematical Information, Shaoxing University, Shaoxing, China.

Department of AOP Physics, University of Oxford, Oxford, UK.

出版信息

Sci Rep. 2024 Jul 10;14(1):15879. doi: 10.1038/s41598-024-66221-8.

DOI:10.1038/s41598-024-66221-8
PMID:38982140
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11233611/
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

脊柱疾病和冻结肩是亚洲人群中常见的健康问题。早期评估和治疗对于防止病情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。为了实现运动范围的高效、实时和准确评估，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，将卷积注意力模块（CBAM）引入到 YOLOv5 目标检测模型中，能够增强特征信息的提取，抑制背景干扰，提高模型的泛化能力。为了满足大规模计算的要求，采用了客户端/服务器（C/S）框架结构。客户端上传图像数据后，可快速获得评估结果，为患者提供了一种方便实用的解决方案。此外，还开发了一个“摘杨梅”游戏作为辅助治疗方法，为患者提供有趣的康复训练。

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