Design of flexible polyimide-based serpentine EMG sensor for AI-enabled fatigue detection in construction

Abstract

Physical fatigue and musculoskeletal disorders are critical health issues for construction workers, stemming from repetitive motions, heavy lifting, and awkward postures. These factors compromise worker well-being, productivity, and safety while increasing the risk of accidents and long-term health problems. Recent advancements in wearable health monitoring technology offer potential solutions, but current sensors encounter significant challenges in the dynamic construction environment. These include inadequate skin contact, increased contact impedance, and vulnerability to motion artifacts all of which degrade signal quality and reduce the accuracy of fatigue detection. This paper develops a fractal-based, flexible sensor for enhanced adaptability and accurate fatigue estimation. Finite element analysis compared five space-filling designs, with the serpentine curve exhibiting the highest contact area and lowest strain, making it the preferred choice for fabrication. Evaluations demonstrated significant improvements in signal-to-noise ratio (SNR) and motion artifact reduction, with the newly developed sensor achieving a 37 % to 59 % SNR improvement over commercial electrodes across different muscle groups. The developed flexible sensor was integrated with a fatigue-detecting framework based on a vision transformer model which provided an average accuracy of 87 % for fatigue detection. The developed sensor significantly enhances EMG signal quality and reliability, promising improved health monitoring and safety for construction workers.