Automatic Detect Incorrect Lifting Posture with the Pose Estimation Model
<b>Background:</b> 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 d...
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MDPI AG
2025-02-01
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| author | Gee-Sern Jison Hsu Jie Syuan Wu Yin-Kai Dean Huang Chun-Chieh Chiu Jiunn-Horng Kang |
| author_facet | Gee-Sern Jison Hsu Jie Syuan Wu Yin-Kai Dean Huang Chun-Chieh Chiu Jiunn-Horng Kang |
| author_sort | Gee-Sern Jison Hsu |
| collection | DOAJ |
| description | <b>Background:</b> 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. <b>Material and Method:</b> 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. <b>Results</b>: 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. <b>Conclusions:</b> 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. |
| format | Article |
| id | doaj-art-cff35b080e924a4385d68e5cafbb3a74 |
| institution | DOAJ |
| issn | 2075-1729 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj-art-cff35b080e924a4385d68e5cafbb3a742025-08-20T02:42:32ZengMDPI AGLife2075-17292025-02-0115335810.3390/life15030358Automatic Detect Incorrect Lifting Posture with the Pose Estimation ModelGee-Sern Jison Hsu0Jie Syuan Wu1Yin-Kai Dean Huang2Chun-Chieh Chiu3Jiunn-Horng Kang4Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanSchool of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, TaiwanDepartment of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei 11031, TaiwanDepartment of Physical Medicine and Rehabilitation, Wan Fang Hospital, Taipei Medical University, Taipei 11696, TaiwanDepartment of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei 11031, Taiwan<b>Background:</b> 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. <b>Material and Method:</b> 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. <b>Results</b>: 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. <b>Conclusions:</b> 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.https://www.mdpi.com/2075-1729/15/3/358occupational back injurylifting posturecameramarkerless systempose estimationartificial intelligence |
| spellingShingle | Gee-Sern Jison Hsu Jie Syuan Wu Yin-Kai Dean Huang Chun-Chieh Chiu Jiunn-Horng Kang Automatic Detect Incorrect Lifting Posture with the Pose Estimation Model Life occupational back injury lifting posture camera markerless system pose estimation artificial intelligence |
| title | Automatic Detect Incorrect Lifting Posture with the Pose Estimation Model |
| title_full | Automatic Detect Incorrect Lifting Posture with the Pose Estimation Model |
| title_fullStr | Automatic Detect Incorrect Lifting Posture with the Pose Estimation Model |
| title_full_unstemmed | Automatic Detect Incorrect Lifting Posture with the Pose Estimation Model |
| title_short | Automatic Detect Incorrect Lifting Posture with the Pose Estimation Model |
| title_sort | automatic detect incorrect lifting posture with the pose estimation model |
| topic | occupational back injury lifting posture camera markerless system pose estimation artificial intelligence |
| url | https://www.mdpi.com/2075-1729/15/3/358 |
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