Development and application of an online detection system for intelligent assessment poor posture
YU Suli
LI Zhuoran
GOU Jianfeng
LIN Zhaotong
DING Siqi
TAO Chunjing
FAN Yubo
Abstract：To enhance the efficiency and accuracy of posture assessment and meet users'demands for early identification and cor-rection of poor posture,we developed an intelligent online detection system for poor posture assessment integrating WeChat mini-pro-grams with deep learning algorithms.Firstly,the front-end design was WeChat mini-program,which could provide functions such as image upload and display of posture detection results.Secondly,the back-end processing used the OpenPose algorithm to identify hu-man key points,and the X-Cobb model assessed the degree of scoliosis to achieve posture and spine detection.Finally,integrating the front-end and back-end parts,an intelligent online detection system for poor posture assessment was built.The results showed that the system successfully identified 11 common poor postures.Moreover,it achieved the upload of images and the return of assessment results within 20 s,significantly enhancing user convenience.This research can achieve effective detection and early intervention of poor pos-ture,and provide an innovative technical means for solving problems related to poor posture.
Keywords：Online monitoringPoor posturePosture recognitionScoliosisCobb angle measurement
Publication Date：2025-08-30
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：6( 209-214 )