Automatic Detect Incorrect Lifting Posture with the Pose Estimation Model

• Published in: Life (Basel, Switzerland) Vol. 15; no. 3; p. 358
• Main Authors: Hsu, Gee-Sern Jison, Wu, Jie Syuan, Huang, Yin-Kai Dean, Chiu, Chun-Chieh, Kang, Jiunn-Horng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.02.2025; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Back pain; Backache; Biomechanics; Boxes; camera; Cameras; Classification; Datasets; Deep learning; Deformities; Environmental factors; Ergonomics; Feasibility studies; Feature extraction; Health aspects; Hoisting; Injury prevention; lifting posture; Long short-term memory; Low back pain; Machine learning; markerless system; Medical research; Motion capture; Musculoskeletal diseases; occupational back injury; Occupational health; Occupational safety; Pose estimation; Posture; Risk factors; Smartphones; Standard deviation; Work-related injuries; Workplaces; Taiwan; markerless system; lifting posture; occupational back injury; camera; artificial intelligence; pose estimation
• ISSN: 2075-1729; 2075-1729
• DOI: 10.3390/life15030358

Background: Occupational low back pain (LBP) is a pervasive health issue that significantly impacts productivity and contributes to work-related musculoskeletal disorders (WMSDs). Inadequate lifting postures are a primary, modifiable risk factor associated with LBP, making early detection of unsafe practices crucial to mitigating occupational injuries. Our study aims to address these limitations by developing a markerless, smartphone-based camera system integrated with a deep learning model capable of accurately classifying lifting postures. Material and Method: We recruited 50 healthy adults who participated in lifting tasks using correct and incorrect postures to build a robust dataset. Participants lifted boxes of varying sizes and weights while their movements were recorded from multiple angles and heights to ensure comprehensive data capture. We used the OpenPose algorithm to detect and extract key body points to calculate relevant biomechanical features. These extracted features served as inputs to a bidirectional long short-term memory (LSTM) model, which classified lifting postures into correct and incorrect categories. Results: Our model demonstrated high classification accuracy across all datasets, with accuracy rates of 96.9% for Tr, 95.6% for the testing set, and 94.4% for training. We observed that environmental factors, such as camera angle and height, slightly influenced the model’s accuracy, particularly in scenarios where the subject’s posture partially occluded key body points. Nonetheless, these variations were minor, confirming the robustness of our system across different conditions. Conclusions: This study demonstrates the feasibility and effectiveness of a smartphone camera and AI-based system for lifting posture classification. The system’s high accuracy, low setup cost, and ease of deployment make it a promising tool for enhancing workplace ergonomics. This approach highlights the potential of artificial intelligence to improve occupational safety and underscores the relevance of affordable, scalable solutions in the pursuit of healthier workplaces.