How Leading Companies Are Using AI Sensors for Safety
New advances in ergonomic training using sensors and biofeedback are forging a step change in manual handling injury reduction. Leveraging the power of artificial intelligence (AI) and machine learning (ML), coaching workers to self-correct their movements in real-time and avoid ergonomic injuries, is stimulating an engaging personalized pathway to behavioural change.
Soter Analytics is leading the way when it comes to developing miniaturized technology using AI and ML tomeasure the capability of humans and calculating how much we can endure.
Kenco Logistics, one of North America’s leading third-party logistics providers have been using the sensors in their warehouses and Miguel Trivino, CSP, director of environmental, health, and safety at Kenco Logistics says, “Nobody likes back pain, but proper lifting is a habit that many new associates have not yet developed. The Soter device gently yet persistently raises the level of awareness, building a good incentive to use better body mechanics”.
What is artificial intelligence (AI) and machine learning?
At a simple level of interpretation, AI can be described as the collection and evaluation of extraordinarily large data sets, commonly known as big data. It is also important to know that ‘machine learning’ algorithms are considered a subset of AI and can be defined as the ability for the machine to ‘learn’ from human behavior and improve its analysis by using the algorithms. The algorithms work by taking large sets of data to recognize patterns and then training the machine to make recommendations. With continual use, the repetitions enhance modifications, and the machine is then able to provide predictions (predictive analysis) about behavior based on input it receives.
How does it work andassist your workers?
Every time a person makes a movement, for example, lifting an object, the Soter device collects high-frequency Inertial Measurement Unit (IMU) data. This data is fed into a neural network which, based on a 2-year study, is trained to understand if the person finds the particular movement difficult or not.
Mr. Shawn Rush, Sr. Director, Environmental, Health & Safety Giant Eagle says,“The solution accurately detects and provides warnings for hazardous movements that have high potential to cause injury. As a result, we’ve seen the number of at-risk postures and movements cut roughly in half for the Team Members involved in the process”.
A worker may lift an item over a duration with correct posture but after some time, the quality of this movement can change. Contributing factors include fatigue, stress, pre-existing injury, distraction. The Soter device uses ML based on algorithms and picks up different qualities of a movement that quantify its safety and will alert the worker. Among many characteristics, it will consider the velocity, jerkiness and bend angle at completion of the movement.
