{'fraction_non_alphanumeric': 0.01808785529715762, 'fraction_numerical': 0.002583979328165375, 'mean_word_length': 4.989690721649485, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Mental Workload Assessment in Smartphone Multitasking Users: A Feature Selection Approach using Physiological and Simulated Data

When a user of a computer system is performing more than one task at the same time, her error rate increases drastically. In any system this is a critical issue, since the goals of the tasks are not likely to be met. In that sense, the purpose of mental workload assessment is to estimate the mental demand of tasks to take action according to that, avoiding execution errors. In this paper we study two techniques of mental workload assessment, physiological signals and simulation models of mental behavior with the ACT-R cognitive architecture. The contributions of this study are in two folds: validate a positive correlation among physiological and simulated data and, to develop a supervised model of classification with a cost-sensitive feature selection algorithm using the ACT-R simulated data as an input of the model. Results show a positive, significant correlation among the two data sources, and that the model selects features of less cost and classify better than a baseline approach with 93.1% accuracy in average.