Gabriele Canova
Machine Learning and data fusion of physiological signals for assessing a subject’s stress level and cognitive load.
Rel. Danilo Demarchi, Irene Buraioli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2024
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Abstract
The increase in artificial intelligence integration across various industries underlines its significant impact on promoting secure working spaces, especially in preventing accidents, and mitigating risks. With human-machine interaction (HMI), operators are often tasked with performing complex duties, raising mental workload and stress levels, potentially compromising their performance, and elevating the risk of accidents. Stress refers to the psychological and physiological responses elicited by perceived demands exceeding an individual's coping capacity, while cognitive workload denotes the amount of mental effort and resources required to perform tasks effectively. Existing literature shows a correlation between these cognitive states and the physiological signals of the human body.
To investigate this relationship further, the eLions group at Politecnico di Torino conducted a study involving 61 subjects
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