By monitoring workers' vital signs and movements in real-time, researchers have developed a powerful system to predict fatigue, offering companies a cutting-edge solution for reducing injuries and improving job performance. Study: Wearable network for multilevel physical fatigue prediction in manufacturing workers . Image Credit: UNIKYLUCKK / Shutterstock In a recent study published in the journal PNAS Nexus , researchers explored using multimodal wearable sensors combined with machine learning to measure real-time fatigue among manufacturing workers.
Their findings provide important insights into the physical challenges of factory work through monitoring vital signs and motion, with implications for improving work conditions and productivity. Background Task-specific fatigue indicators: The study revealed that different tasks impact the body in varied ways, with left arm movement and heart rate variability playing a more significant role in fatigue prediction for certain manufacturing tasks. There are high costs of fatigue in the manufacturing industry, with the cost of health-related productivity loss estimated to be $136 billion per year in the United States.
High levels of fatigue have also been reported among workers in Sweden, Japan, the European Union, and Canada, with 90% of shift workers reporting regular fatigue, sleepiness, higher injury risk, accidents, and conditions such as chronic fatigue syndrome or musculoskeletal disorders. Fatigue is difficult to monitor as.