How AI-Driven Algorithms Improve an Individual’s Ergonomic Safety
With the use of AI-driven (Artificially Intelligent) algorithms, the pressure of personal worker safety is relieved from organizations and transferred to individuals. Workers are empowered by using personalised feedback and learning about their actions.
- By Toni-Louise Gianatti
- May 14, 2020
The physical demands of Material Manual Handling (MMH) workers are immense and are to be congratulated, handled with care, even. It is fair to say that employees are using their own invaluable asset (the body) to perform tasks that benefit organizations.
Musculoskeletal injuries at work cost the individual, the organization and society. According to the Bureau of Labour Statistics, in 2019, US companies lost more than $1 billion per week due to workplace injuries. Overexertion was the number one cause, relating to injuries from lifting, pushing, pulling, holding, carrying or throwing.1
When it comes to workplace manual handling and training, one-size-fits-all doesn’t always match, and there are considerable evidence-based reviews supporting the idea that the effectiveness of classroom training is limited, and the principles are not applied in the working environment.2 Traditional training also fails to address the compounding factors of lifting technique, posture, task repetition and intensity, which are often the cause of lower back pain onset and musculoskeletal disorders—a singular instance of poor manual handling.
Predictive analysis and AI are becoming the leading resource to help prevent injuries. Using big sets of personalized data to recognize and provide recommendations about how a worker is behaving can help to train the worker in a more personalized fashion.
What is Artificial Intelligence (AI)?
First, it is safe to say that AI is not just another technology trend. Gartner, a leading research and advisory company, is known for describing the process trends of emerging technologies. Gartner calls this the ‘hype cycle’ which is simply an explanation of the typical cycle of a new technology that comes onto the market and helps to distinguish hype from the real deal.3 The five stages look a little like this:
1. There is early proof of concept/product
2. The technologies first fulfil great expectations
3. The technologies begin to fail quickly
4. After time and research, the technologies rise again
5. Finally, the technologies meet initial expectations
Towards the end of 2018, all the technologies near the plateau stage (No. 5, meeting expectations) of the Gartner hype cycle had association with AI. This shows that AI is no longer a hype, especially also given that expenditure has increased 768 percent since 2016 and is set to reach $46 billion this year and $97.9 billion in 2023.7