AI vision processing recognition for reduction of musculoskeletal injury risk in industry

Injuries and illness in the workplace are commonplace. 41% of such incidents can be attributed to musculoskeletal injury (498,000 in the UK alone last year), and results in 6.9m workdays being lost in the UK every year across sectors including logistics, construction, aviation and agriculture. This is a cost that has to be shouldered by the employer and, in across Europe and US alone exceeds £100B per annum.

The most effective control that employers can implement are eliminating or reducing the need for workers to even have to make movements that put them at risk of a musculoskeletal injury. However, they often can be the most expensive and thus analysis must be done to ensure that the correct risk control is designed and implemented that will provide the business with an ROI.

Currently this analysis is primarily done through assessments undertaken by safety professionals. These assessments are usually done 'by eye', with the professional watching a person undertake a task, measure (or guess) the angles and positions the person undertakes, and then creates a report with recommendations on how that particular task could be improved.

Common solutions include introducing new tooling, automating higher-risk tasks, or redesigning how the task flows to reduce risk. However, by relying on the person to make these measurements and judgements, the wrong conclusions are often made and it's very difficult to scale this across large workplaces.

As part of a previous InnovateUK feasibility study, Soter Analytics developed a vision recognition software that identifies the movements a person makes and categorises the risk of each movement. While developed to solve a different, internal challenge, Soter's customer base have requested the software to be used to identify task risk.

Currently, there are no solutions able to accurately and scalable identify and measure the inherent workplace and task risk. Risk assessments are done 'by eye' by safety professionals. This leads to subjective assessments and the lack of scalability leads to a small amount of assessments being done, reducing the reliability and accuracy of workplace improvement initiatives.

SoterTask will be the new product developed during this project, to create a software based, vision processing AI solution to solve this problem.

Soter's management team and board have approved the project, providing funding can be secured, and estimate an 18X ROI over the first 5-years of commercialisation.