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Personality Assessment Using Biosignals and Human Computer Interaction applied to Medical Decision Making
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Clinical decision-making for patients with multiple acute or chronic diseases (i.e. multimorbidity)
is complex. There is often no ’right’ or optimal treatment due to the potentially
harmful effects of multiple interactions between drugs and diseases. This makes
it necessary to establish trade-offs between the benefits and risks of different treatment
strategies. This means also that there may be high levels of risk and uncertainty when
making decisions. One factor that can influence how decisions are made under conditions
of risk and uncertainty is the decision maker’s personality. The studies of this dissertation
used biosignals and eye-tracking methods and developed pointer tracking techniques to
monitor human computer interaction to assess, using machine learning techniques, the
individual personality of decision makers.
Data acquisition systems were designed and prepared to collect and synchronize: 1)
physiological data - electrocardiogram, blood volume pulse and electrodermal activity;
2) human-computer interaction data - pointer movements, eye tracking and pupil diameter;
3) decision-making task data; and 4) personality questionnaire’ results. A set
of processing tools was developed to ensure the correct extraction of psychophysiologyrelated
features that could manifest personality. These features were combined by several
machine learning algorithms to predict the Big-Five personality traits: Openness, Conscientiousness,
Extraversion, Agreeableness and Conscientiousness.
The five personality traits were well modelled by, at least, one of the sets of features
extracted. With a sample of 88 students, features from the pointer movements in online
surveys predicted four personality traits with a mean squared error (MSE)<0.46. The
blood volume pulse responses in a decision-making task trained in a distinct sample of
79 students predicted four personality traits with a MSE<0.49. The application of the
personality models based on the pointer movements in the personality questionnaire in
a sample of 12 medical doctors achieved a MSE<0.40 for three personality traits. These
were the best results achieved in each context of this thesis.
The outcomes of this work demonstrate the huge potential of broader models that
predict personality through human behaviour, with possible application in a wide variety
of fields, such as human resources, medical research studies or machine learning
approaches.
Descrição
Palavras-chave
Biosignals Human-Computer Interaction Knowledge Extraction Psychophysiology Personality Machine Learning Algorithms