What is it about?
The research paper explores an innovative vision-based method (SVBM) that utilizes computer vision to identify, analyse, and train manual lifting human poses in the construction industry. The study highlights SVBM's capability to observe relevant events without requiring additional attachments to the human body. It emphasizes the practical implications of SVBM in preventing posture-related hazardous behaviours and its potential to diagnose and prevent musculoskeletal disorders. The research acknowledges the limitations of the literature review methodology and emphasizes the need to address trust, privacy, and psychological issues. Overall, SVBM represents a significant advancement in human pose detection and provides valuable contributions to enhancing occupational health and safety in the construction sector.
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Photo by Josue Isai Ramos Figueroa on Unsplash
Why is it important?
The theoretical and practical implications involve personalized and tailored predictions, allowing SVBM to offer customized recommendations based on each worker's unique physical traits and work environment. This helps prevent most posture-related hazardous behaviours before a critical injury occurs. The theoretical implications also encompass mimicking human poses and conducting lab-based analyses without needing sensors that may alter natural working positions. SVBM can assist researchers in developing more accurate data and theoretical models that closely reflect real-life scenarios.
Perspectives
In a work environment, detecting human poses and calculating joint angles is vital for the early identification of musculoskeletal disorders. Traditional methods rely on location data from wearables and controlled motion sensors, but this paper introduces innovative computer vision and deep learning techniques using mobile and embedded cameras. These methods allow for manual handling pose deduction and analysis of angles, neckline, and torso line in real construction work environments without needing attachments to the human body.
Mahesh Babu Purushothaman
Auckland University of Technology
Read the Original
This page is a summary of: Smart vision-based analysis and error deduction of human pose to reduce musculoskeletal disorders in construction, Smart and Sustainable Built Environment, August 2023, Emerald,
DOI: 10.1108/sasbe-02-2023-0037.
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