Assessing Mental Workload Using Eye Tracking Technology and Deep Learning Models
Souvik Das, Kintada Prudhvi, Jhareswar Maiti · 2022
The current study has proposed a mental workload prediction model using neural network and Bernoulli Boltzmann machine. For measuring mental workload, eye movement metrics were considered. The eye metrics were computed from the raw eye movement data, which were recorded using eye tracking device while solving a coding problems. We have found that the Bernoulli Boltzmann machine provides better accuracy in prediction of mental workload from eye metrics. The reason of providing low accuracy by neural network model may be attributed to less data. In future, the sensitivity of the neural network model can be observed by collecting more eye movement data.