Computer Science: Fully Funded EPSRC and RCNDE PhD Scholarship: Real Time Tracking of Cognitive Load to Assess Human Factors During Inspections

Computer Science: Fully Funded EPSRC and RCNDE PhD Scholarship: Real Time Tracking of Cognitive Load to Assess Human Factors During Inspections

Many industries are relocating workers from hazardous environments to supervisory rooms, where they need to engage with automated systems to perform their work. A critical challenge in human-automated systems interaction lies in effectively managing human factors such as cognitive load (CL) and emotions such as stress to ensure optimal task execution. However, currently, measuring CL involves bulky, expensive techniques such as functional magnetic resonance imaging and magnetoencephalography, limiting their practicality. Recent progress in wearable design helps non-invasively measure CL and stress by synchronously analysing physiological indicators such as Pupillometry, heart-rate variability, and skin conductivity. Regardless, previous work involved wearing eye trackers and heart monitors, which may not be suitable for real-time inspection setups. Besides, the tasks utilised to collect data for estimating CL were superficial and consequently, CL measures were task dependent. 

Thus, there is a clear need to develop less invasive and robust task-independent measures for CL. This need is particularly relevant in non-destructive evaluation (NDE), where automation is increasing, and human factors significantly contribute to uncertainties in inspection results. This project will explore corrective methods to enhance inspection reliability by developing robust measure of CL and correlating it with operator performance. With automated systems generating vast volumes of inspection data and data analysis tasks becoming more complex, addressing human factors becomes increasingly vital for ensuring the overall dependability of inspections.