Engaging in safe manual handling is crucial for protecting workers from the pain and suffering that occurs with work-related musculoskeletal disorders (WRMSDs). These injuries cost the individual, the organization and society. Workplaces are disrupted due to lost time; organizations suffer increased costs due to injury management and employees’ personal and family lives are affected in negative proportions.
Workplace injury prevention services, strategies, programs all assist for employee safety and injury prevention in the workplace and organizations should consistently source the best offerings for their business and their people.
Proactive Not Reactive Using Data
Outcome: Materially safer workplace which reduces the need for employees to perform at-risk movements.
Moving into the proactive not reactive space is a prevention strategy that is more likely going to produce better results for your workers.
We know that typically, workplace injuries result in extensive observation and investigation using subjective tools which are intensive and synthetic. This approach is reactive.
An approach to supporting injury prevention strategies is using data. Manual handling movement data collected from sensors worn by the workers provides opportunity to foster a proactive approach to injury. Seeking the source of the problem releases insight into any potential requirements for redesign of workplace environments and employee training.
Back injuries don’t just happen, they happen very slowly, a lot of stress is put on the body, it gets weaker over time, and eventually the pain starts, the pain gets worse and suddenly, you’ve got an injury. This might take years and years. The data can provide timelines that can be measured, predicted and intervention strategies can be put in place to avoid these injuries from occurring.
Data showing risk insights can include metrics that can be used directly, or to start a conversation and capture feedback, comments, thoughts straight from workers. Algorithms will capture all concerns to help those at imminent or future risk so procedures can be put in place to prevent workplace injuries.
Types of metrics that can assist with preventing workplace injuries using data insights can be:
- Quality of movement
- Frequency – how many times
- Duration – how long
- Psychology of worker
- Time patterns of exposure and recovery
- Type of interaction – a key indicator for safety culture
- Weight of exertion
- Fatigue management
- High risk areas
Individualized Training via Machine Learning
Algorithms have potential to not only evaluate exposure for individuals by producing data but depending on the configuration, can recognize their personal capabilities. For example, the weight of an object can be picked up and how difficult it is for the worker to lift. With continuous collection of data and by way of machine learning, it can determine what would be the ‘norm’ of exposure for the individual user and if, on any given day, this is exceeded, one could see a prediction of fatigue.
When it comes to fatigue management, this is a superpower, especially if a user has an existing injury, is feeling unwell or has stress, the algorithms will recognize, and recommendations or warnings can be given. Workers start to be conscious of when they are feeling fatigued and a new pattern of behavior through awareness is ignited. The more information the machine has about the user’s movements the more accurate it will be.
Preventing manual handling injuries in the workplace by using data and machine learning for proactive risk management is the best gift that you can provide your workers. Evaluating the level of exposure and capturing this information can improve the safety of workers in ways that reach beyond the original methods.
If there is the ability for individual musculoskeletal systems to be kept safe by way of AI-driven algorithms providing objective data, then change can provide lasting outcomes. By looking at the safety issues for one person and not a standard, organizations can benefit from fewer injuries, more accurate diagnosis of environmental issues and an overall increase in worker health.
Using AI-driven algorithms based on data collected from workers is not a stand-alone solution but is a powerful tool to help within the safety ecosystem to greatly impact workers personal safety.
About Soter Analytics
Soter Analytics is a global safety science company producing AI-supported wearable solutions that reduce the risk of ergonomic injuries in the workplace. Soter wearables are widely used in logistics, manufacturing, healthcare and other industries, helping leading companies to prevent up to 55% of back & shoulder musculoskeletal injuries.
To see how Soter Analytics can help you improve behaviour, engage employees to self-manage their training and prevent workplace ergonomic injuries, simply Book a FREE DEMO today.