The more you do something, the better you get at it. This is a simple truth of life – experience comes with time. However, in the world of construction experience can also mean that you know how to protect yourself better from wear and tear injuries. Scientists say that AI systems can be used to spread this knowledge easier.
Scientists from the University of Waterloo used artificial intelligence to gain some insight into how construction workers follow the rules of ergonomics to reduce work-related injuries. They found that older workers don’t typically follow these rules that are taught to novices. Instead they are developing their own ways of moving and working. More experienced brick layers are quicker, safer and their work is of a higher quality. However, their methods are not taught to novices – they have to figure it out by themselves.
For example, brick layers bend their backs less when they have to pick up something heavy. They also swing blocks rather than lift them. Meanwhile master masons are incredible at working fast, while maintaining high levels of workplace safety. Scientists analysed data from brick layers from various experience levels to determine how their workplace safety and efficiency differs. Researchers put sensors on the bodies of workers to see how they are moving and found their motions are faster and more accurate. Scientists also analysed the work of master masons – sensors recorded their motions while AI system analysed the data and identified patterns in body positions and movements. Carl Haas, one of the leaders of the research, said: “Skilled masons work in ways we can show are safer, but we don’t quite understand yet how they manage to do that. Now we need to understand the dynamics”.
You may think that work-related injuries in construction sites are typically caused by falling objects or people, but many injuries are actually minor. They are related to simple wear and tear of the human body. While novices are trained to avoid these injuries, many brick layers fall out of their jobs because they start noticing effects in has on their bodies. Furthermore, a lot of master workers have to finish working early because of work-related injuries. It is not good for these people, not good for the industry and economy as a whole.
Hopefully, scientists will find ways to introduce this knowledge into training programs. Youth in the industry can benefit greatly from the experience of the masters. Now scientists should see how this AI-analysed data can be introduced into training programs.
Source: University of Waterloo