On construction sites and associated industries, the repetitive nature of tasks and the heavy loads lifted on site can lead to musculoskeletal injuries. The University of Wollongong is undertaking a project that aims to develop a warning system to alert workers to excessive loads and help prevent injuries
The University of Wollongong (UOW) is focusing on ways to prevent musculoskeletal injuries in construction workers repeatedly lifting heavy loads through the development of a cutting edge wearable technology.
It is seeking to enhance the occupational health and safety of construction workers by looking at advancing current wearable sensor-based monitoring methods coupled with the state-of-the-art biomechanics and human movement analysis.
Funded by UOW’s Global Seed Fund, the Telstra-UOW Hub for Artificial Intelligence of Things’ Solutions Seed Funding Program, and funds from ARC Steel Research Hub’s SME Engagement Program, UOW is developing technology that could predict and warn workers of potential injury or elevated levels of musculoskeletal loads.
Combining four UOW Faculties (Faculty of Engineering and Information Sciences; Faculty of Arts, Social Sciences and Humanities, Faculty of Business and Law and Faculty of Science, Medicine, and Health), the multidisciplinary team is focused on developing a warning system that could work together with a wearable device.
The aim is to make repetitive and physically demanding jobs safer.
“When a worker gets injured, not only is that person impacted, but their family also gets impacted and the community gets impacted,” UOW structural engineering lecturer Aziz Ahmed says.
“We want to avoid or minimise injury and long-term health issues .”
Completing a pilot study in 2021, it was found that using machine and deep learning algorithms could predict body movements and loads quite accurately – using only five sensors.
“This was a good outcome for us because the fewer sensors you need to predict these forces, the more user-friendly our technology will be,” Ahmed says.
Planning to run a proof-of-concept study in an emulated construction scenario in early 2024, Ahmed says his team will be able to develop artificial intelligence-based programs to correlate sensor data to the muscle and joint forces.
“When we are confident that our code is working well, we’ll take this setup to the site, and do the same tests in a live environment – where the workers are actually working on real life tasks,” Ahmed says.
The project will involve motion capture technology, recording an individual’s movements and providing information on where a person is in a certain space.
“If you’ve seen sci-fi movies, they have these green screens where the stunt actors wear these reflective markers on their body – essentially that’s camera-based motion capture technology and it’s very accurate,” UOW biomechatronics lecturer Manish Sreenivasa says.
“In addition to motion, what we want to do is record the forces that a person is putting on the environment, or the environment is putting on them.”
Computer models can then be used to estimate both the joint load on an individual during different tasks and what muscle force was being applied to make a specific motion.
Looking to the future, Ahmed expects that the deep-learning algorithms will be able to predict joint force and muscle force without requiring motion capture.
“The deep-learning algorithm will be our main invention, integrated into software which will be installed on a mobile. That app will be able to give someone warnings based on the predicted forces on joints and muscles,” Ahmed says.
UOW believes this technology could be applied to any industry where someone’s body undergoes repetitive stress load.
“Our system has the potential for a lot of applications, it could be used in any of those places where you are doing highly physical, repetitive work,” Sreenivasa says.
For more information on UOW’s warning system technology, visit: Development of wearable sensors for the occupational health and safety improvement of construction workers
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