Revolutionizing Safety: A Move Towards Personalized Protection
Moving past benchmarks to real-time individualized insights, technology is paving the way for workplaces that drive individualized safety for each worker.
- By Matthew Hart
- Oct 11, 2023
According to the Department of Commerce, women make up around 30 percent of manufacturing jobs today.1 While that representation is disproportionately low compared to the 47 percent of women in the overall workforce, it’s still a stark increase from the not too distant past when these jobs were overwhelmingly held by men. Optimistically, women’s participation in industrial sectors is also expected to continue increasing as automation makes physically intense job functions more accessible.
But while more women are clocking in today, we must recognize and address the ways the past is still lingering in the veins of the industry and restricting progress.
Occupational safety is an area where outdated data is informing far too many trainings and safety processes being implemented today. I still walk into warehouses and manufacturing facilities only to see benchmarks like “this weight is suitable for 75 percent of males” being used to inform all workers on what is safe to lift. If you’re a woman, or a man who doesn’t fit into the average 75 percent bucket, you’re out of luck.
Data that’s exclusive in nature is also integrated deeper into our industry’s safety fabric. Longstanding training methodologies and best practices are a result of decades of injury data from when these sectors were nearly exclusively male. Gender and sex aside, they also assume factors like height, weight, strength, fatigue, stress level and injury status. Put simply, they recommend the best practice for the most “typical” human, which isn’t very helpful on an individual basis.
When considering the effects of this at scale, the need to address the issue becomes even more apparent. Compared to men, women are more likely to develop at least one musculoskeletal disorder while performing job-related duties. As improving retention and reducing injuries remains so key to productivity and efficiency, we as an industry cannot afford to overlook equal safety attention for every worker.
An Industry Shift Towards Customized Safety
The workforce safety sector has seen significant innovation in the past decade, finding new applications for AI and advanced sensor technology to solve the greatest safety challenge: offering more personalized insights to individual workers and moving away from the “typical” human approach. We continue to see that every body has its unique tendencies, strengths and vulnerabilities and that nothing reduces injury rates as effectively as diagnosing and addressing risk on an individual basis.
Recently, my company carried out a comprehensive study to put the “typical” human safety approach to bed once and for all, and create an algorithm that can output personalized safety recommendations based on real-time data inputs.
Over a period of two years, we built our own massive dataset, recording more than 10,000 unique movements made by hundreds of men and women of varying ages, body types, stress levels and injury histories.
What we found is that every body truly is unique. There is no ideal formula for safety for male workers or for female workers, for old or for young, there are only safety insights for each individual and the way they are built and tend to move.
We took this new dataset and were able to formulate it into a universal measurement we call Movement Intensity, based on high-frequency Inertial Measurement Unit (IMU) data.
Put most simply, Movement Intensity measures how difficult a movement is for you to make. It accounts for factors like the jerkiness of your movement, the size of the load you’re carrying or the angle at which you’re doing so, and even physical conditions that can impact how ready you are to move that way – did you sleep well the night before, are you feeling extra fatigued or stressed, or is an illness or disease impacting your ability to move. Two people could each lift a box of the same size and weight and come out with significantly different Movement Intensities, because they each have unique factors to their body, impacting how difficult the movement is for them to make.
Using this Movement Intensity data, we trained a neural network to process over 100 different elements of individual movement patterns. We’ve then applied the neural network to process the data our wearables collect in real time. As a result, workers using our wearables at partner companies like IKEA and DHL are getting real-time custom alerts, letting them know when they’re making a movement that’s strenuous for their unique body (not for 75 percent of males), and are putting themselves at greater risk for injury so they can stop it before it happens.
The ROI for Inclusive Safety
This personalized approach isn’t just better for workers. Of course, reducing time off due to injuries and workers’ compensation claims impacts the bottom line, but the data custom safety tools like wearables provide today, also inform other productivity factors for managers.
Understanding who in the facility has trouble bending safely, and who is able to output at high levels without much risk of injury can directly inform the job functions they should hold to maximize overall shift performance. Data finding that everyone is moving safely consistently while reaching high outputs, or alternatively, trends in unsafe movements, could directly correlate to the quota or bonus threshold you’re using which could be adjusted to optimize high performance against reduced injuries.
For too long there’s been a perception that safety and performance are inherently at odds. In reality, leveraging data to inform safety actually allows us to better understand where that boundary lies between maximized performance and minimized injuries.
This article originally appeared in the October 2023 issue of Occupational Health & Safety.