Researchers have designed a system of wearable sensors that relies on machine learning to monitor workers for signs of physical strain and tiredness. The goal is that using new devices to help prevent accidents and injuries. To measure fatigue and physical health, researchers developed an interconnected array of six wearable sensors placed across a wearer’s torso and arms. These were coupled with two depth cameras to measure joint movements and an HD webcam to analyze movement intensity, repetition, and diminished strength over time.
Once enabled, these devices continuously monitored heart rate, skin temperature, and locomotion patterns. But given that there are no widely accepted, universal biomarker metrics for fatigue, researchers relied on the wearer’s self-reported perceived exertion levels on a 0-10 scale that they then entered into a machine learning model. Once trained, this model was then used to predict a user’s fatigue levels in real-time to provide a more nuanced understanding of the subject’s physical state than past studies, according to researchers.
More information: