Identification of Cognitive Workload during Surgical Tasks with Multimodal Deep Learning

In operating Rooms (ORs), activities are usually different from other typical working environments. In particular, surgeons are frequently exposed to multiple psycho-organizational constraints that may cause negative repercussions on their health and performance. This is commonly attributed to an increase in the associated Cognitive Workload (CWL) that results from dealing with unexpected and repetitive tasks, as well as large amounts of information and potentially risky cognitive overload. In this paper, a cascade of two machine learning approaches is suggested for the multimodal recognition of CWL in a number of four different surgical tasks. First, a model based on the concept of transfer learning is used to identify if a surgeon is experiencing any CWL. Secondly, a Convolutional Neural Network (CNN) uses this information to identify different types of CWL associated to each surgical task. The suggested multimodal approach consider adjacent signals from electroencephalogram (EEG), functional near-infrared spectroscopy (fNIRS) and pupil eye diameter. The concatenation of signals allows complex correlations in terms of time (temporal) and channel location (spatial). Data collection is performed by a Multi-sensing AI Environment for Surgical Task $\&$ Role Optimisation platform (MAESTRO) developed at HARMS Lab. To compare the performance of the proposed methodology, a number of state-of-art machine learning techniques have been implemented. The tests show that the proposed model has a precision of 93%.