Title: Data-driven cognitive load classification system using machine-learning algorithm
Subject: Computer science
Level: Basic/Advanced
Description: Sitting behind a wheel, i.e., driving requires a high degree of concentration and dynamic and complex activities e.g., visual, cognitive and manual tasks are involved during driving. The driver has to make strategic decision, monitor the roadway and surrounding environment as well as inside the vehicle system, process information and execute control level activities. All these activities impose workload and cognitive load on the driver.

Physiological measures are sensitive indices for detecting changes in mental workload. It is suggested in the literature that individuals devote their mental resources to keep up a given level of performance until the point at which their resources are exhausted. For the development of advanced safety system, it may be significant to find out the temporal relationship indicating shifts in physiological arousal due to mental workload before driving performance is impaired.

Studies have shown that cardiovascular measures such as heart rate and blood pressure increase with the increasing of cognitive demand. Skin conductance is another measure that also increases with the increasing cognitive demand. Various studies have suggested that these physiological measures may be useful indices of mental workload, however, the overall pattern of cognitive load and driving performance is unclear.

The goal of the thesis work is to analyse physiological signals i.e., heart rate variability, skin conductance, and respiration rate for cognitive load detection. It also aims to investigate machine-learning classification for automatically identification of cognitive workload for different task scenarios. The work requires literature study, signal analysis and feature extraction, and implementation of an intelligent system using machine-learning algorithms for classification of cognitive load and evaluation of a prototype system.
Start date: 2018-01-01
End date: 2018-06-30
Prerequisites: Knowledge in Matlab and machine learning is advantageous
IDT supervisors: Shaibal Barua
Examiner: Shahina Begum
Comments: The thesis is within the VDM project and data is protected by the PUL and data sharing contract. Same PUL and data sharing contract will be applied for students who will work on this thesis.
Company contact: Volvo Car