https://www.mdu.se/

The online pupillometry-based feedback system is intended as a cognitive training and rehabilitation system developed at Mälardalen University. The purpose of the system is to engage a person in a cognitive computer task whose difficulty is adjusted in real time depending on the person's cognitive load. Previous research has uncovered a significant correlation between cognitive load and pupil dilation, suggesting that electroencephalogram usage for estimating cognitive load can be eliminated. The online pupillometry-based feedback system is measuring the pupil-diameter in real time to classify cognitive load using a neural network. The classification of cognitive load is used to modulate the difficulty level of the cognitive task, with the purpose of challenging the participant and to optimize the cognitive training. At the current state the system is fully integrated, but possesses no fault-tolerant features to produce a long-term reliable service. This paper proposes a fault-tolerant architecture for the online pupillometry-based feedback system, for which internal repairs and failure rates are modeled using continuous-time Markov chains. The results show adequacy of the extended architecture, assuming slightly optimistic failure rates. Even though the system is specific, the reliability approach presented can be applied on other medical devices and systems.