Occupational health and safety practitioners who want to determine if an occupational task could cause injury often find it challenging.
Tools are expensive and not widely accessible, and many don’t have the proper training in biomechanics and ergonomics required to make an objective, accurate assessment.
Mina Salehi Sedeh experienced this firsthand as a former safety engineer who performed these types of assessments in the field – until she returned to college to earn a PhD in environmental and occupational health from Oregon State.
Mina is using her experience, and an open-source, deep learning-based software package created at Stanford University as the basis of her work.
The current software, Open Cap, is primarily used to assess athletes’ performance and patients’ recovery from injury, and Mina is using it as a foundation to create a new model that will assess workers and their complex movements.
Even though the technology sounds complicated, the laboratory and equipment she uses are simple. A laptop, three iPhones and the AI-based software package is all that’s required, in addition to an inquisitive mind and determination to reduce injury and improve workers’ health.
Of course, she also needs people to mimic workers’ movements, which involves musculoskeletal simulation and modeling to gather data.
In the end, the work not only will fuel her dissertation but will also result in an accessible, affordable, accurate, non-invasive way to assess the physical risks workers face in doing their jobs.