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量化坐姿：使用人体模型对计算机视觉和可穿戴传感器（姿势实验室）进行的初步可行性研究。

Quantifying sitting posture: A pilot feasibility study of computer vision and wearable sensors (Posture Lab) using a manikin model.

作者信息

Vorapojpisut Supachai, Sansuk Suphawit, Yindee Phoomtai, Panich Darawadee, Puengtanom Vinitha, Saadprai Sairag

机构信息

Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand.

Faculty of Allied Health Sciences, Thammasat University, Pathumthani, Thailand.

出版信息

Wearable Technol. 2025 Jun 16;6:e27. doi: 10.1017/wtc.2025.10005. eCollection 2025.

DOI:10.1017/wtc.2025.10005
PMID:40529117
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12170950/
Abstract

Posture-related musculoskeletal issues among office workers are a significant health concern, mainly due to long periods spent in static positions. This research presents a Posture Lab which is a workplace-based solution through an easy-to-use posture monitoring system, allowing employees to assess their posture. The Posture Lab focuses on two key aspects: Normal Head Posture (NHP) versus Forward Head Posture (FHP) measurement and thoracic spine kyphosis. Craniovertebral (CA) and Shoulder Angles (SA) quantify NHP and FHP. The Kyphosis Angle (KA) is for measuring normal thoracic spine and kyphosis. To measure these angles, the system uses computer vision technology with ArUco markers detection via a webcam to analyze head positions. Additionally, wearable accelerometer sensors measure kyphosis by checking the angles of inclination. The framework includes a web-based user interface for registration and specialized desktop applications for different measurement protocols. A RESTful API enables system communication and centralized data storage for reporting. The Posture Lab serves as an effective tool for organizations to evaluate employee postures and supports early intervention strategies, allowing timely referrals to healthcare providers if any potential musculoskeletal issues are identified. The Posture Lab has also shown medium to very high correlations with standard 2D motion analysis methods - Kinovea - for CA, SA, and KA in FHP with kyphosis measurements ( = 0.607, 0.704, and 0.992) and shown high to very high correlations in NHP with normal thoracic spine measurements ( = 0.809, 0.748, and 0.778), with significance at < .01, utilizing the Pearson correlation coefficient.

摘要

办公室职员中与姿势相关的肌肉骨骼问题是一个重大的健康问题，主要原因是长时间处于静态姿势。本研究提出了一个姿势实验室，这是一种基于工作场所的解决方案，通过一个易于使用的姿势监测系统，让员工能够评估自己的姿势。姿势实验室关注两个关键方面：正常头部姿势（NHP）与前伸头部姿势（FHP）的测量以及胸椎后凸。颅颈（CA）和肩角（SA）量化NHP和FHP。后凸角（KA）用于测量正常胸椎和后凸情况。为了测量这些角度，该系统使用计算机视觉技术，通过网络摄像头检测ArUco标记来分析头部位置。此外，可穿戴加速度计传感器通过检查倾斜角度来测量后凸。该框架包括一个用于注册的基于网络的用户界面和针对不同测量协议的专用桌面应用程序。一个RESTful API实现系统通信和集中数据存储以进行报告。姿势实验室是组织评估员工姿势的有效工具，并支持早期干预策略，如果发现任何潜在的肌肉骨骼问题，可及时转诊至医疗服务提供者。姿势实验室还显示出与标准二维运动分析方法——Kinovea——在FHP伴后凸测量中的CA、SA和KA方面具有中等至非常高的相关性（ = 0.607、0.704和0.992），在NHP与正常胸椎测量方面具有高至非常高的相关性（ = 0.809、0.748和0.778），在 <.01水平上具有显著性，采用皮尔逊相关系数。

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