AI wearable monitors to cut workers' comp claims

Injury prevention will in future take a data-driven approach and incorporate smart wearable technology, machine learning and predictive analytics, UK-based firm Soter Analytics says.

Soter says wearable technology driven by artificial intelligence (AI) allows a proactive approach, addresses reducing ergonomic risks and minimises workers’ compensation claim costs.

“Insurers and companies alike would benefit from the cost savings in minimising back and shoulder injuries at work,” Soter said.

Only around 12% of manufacturers and construction firms have adopted predictive analytics technology yet as many as 3% of workers suffer from a back or shoulder injury each year, costing tens of billions.

“Traditional methods of preventing these injuries are time consuming, costly and have been proven to be largely ineffective in creating any real change,” Soter said. “Coaching workers to self-correct their movements in real-time and avoid ergonomic injuries, is stimulating behavioural change.”

AI-driven wearable technology assists workers to learn more about their movements, understand them, and help reduce their risk of injury permanently.

Soter Analytics says its small and light SoterCoach wearable device monitors the behaviour of workers and alerts of any hazardous movements that could lead to back or shoulder injuries. The sensor ups awareness of any hazardous movements such as twisting and coaches the worker on how to avoid these, and a dashboard displays data for employers to spot trends.

“With predictive safety modelling, it is possible to anticipate potential safety hazards through the interpretation of data. This is a powerful lens that can turn a usually reactive approach to workplace safety into a strong proactive one.”

AI collects and evaluates data to identify patterns, producing best data analytics and the ability to make predictions as algorithms detect the quality of movements performed. The safety of any given movement can be assessed against characteristics including velocity, jerkiness and bend angle.

“The AI can easily identify the risks of injury from particular movements due to contributing factors such as fatigue, stress, pre-existing injury or distraction,” Soter said.