Real-Time Video-Based Posture Monitoring Measurement of Back Angles Using YOLOv8 and Edge Detection for Strength Training

Pei-Hsuan Hsieh; Hyun Sub Choi; Hung-Yu Hsu

doi:10.46604/ijeti.2026.15942

Authors

Pei-Hsuan Hsieh Department of Computer Science, National Chengchi University, Taipei, Taiwan, ROC
Hyun Sub Choi Department of Computer Science, National Chengchi University, Taipei, Taiwan, ROC
Hung-Yu Hsu Department of Computer Science, National Chengchi University, Taipei, Taiwan, ROC

DOI:

https://doi.org/10.46604/ijeti.2026.15942

Keywords:

computer vision, YOLOv8, back angle measurement, strength training, sports injury prevention

Abstract

Workout-related lower back injuries are common during strength training and are often caused by improper posture, highlighting the need for real-time posture monitoring to support injury prevention and performance optimization. This study proposes a mobile-based computer vision approach for real-time quantification of back angles during workouts. The proposed method integrates YOLOv8 instance segmentation to isolate the trunk region and applies Canny edge detection for contour extraction. It then employs a geometric formulation to identify neck and back reference points for angle computation. This hybrid design enables robust trunk localization and stable angle estimation across dynamic exercise movements. The model is evaluated on six gym-recorded videos captured with a low-cost mobile camera, achieving a mean relative error of 6.49%, comparable to video-based biomechanical assessment methods. These findings indicate that the proposed approach provides an efficient and practical solution for real-time back-posture monitoring using mobile devices, supporting safer and higher-quality daily training.

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