Can Foundation Models Predict Fitness for Duty?
This work addresses the problem of monitoring alertness for safety-critical work environments, but it appears incremental as it applies existing foundation models to a new application area.
The paper tackles the challenge of predicting fitness for duty using biometric iris images, by leveraging foundation models to overcome data scarcity issues in training AI models for alertness detection.
Biometric capture devices have been utilised to estimate a person's alertness through near-infrared iris images, expanding their use beyond just biometric recognition. However, capturing a substantial number of corresponding images related to alcohol consumption, drug use, and sleep deprivation to create a dataset for training an AI model presents a significant challenge. Typically, a large quantity of images is required to effectively implement a deep learning approach. Currently, training downstream models with a huge number of images based on foundational models provides a real opportunity to enhance this area, thanks to the generalisation capabilities of self-supervised models. This work examines the application of deep learning and foundational models in predicting fitness for duty, which is defined as the subject condition related to determining the alertness for work.