The paper proposes the use of a smartwatch-based system for measuring the emotions of individuals in a classroom setting with respect to five mood variables: Activation, Tiredness, Pleasance, Quality of Presentation and Understanding. Internal (body) and external (environment) data such as movement, heart rate, noise, temperature and humidity were collected through the built-in sensors of the smartwatch. The system was verified by means of a longitudinal study that has been carried out in a series of workshops and lectures. Through experience-based sampling, participants were polled at periodic time intervals asking them to enter a self-assessment of the aforementioned mood states directly on the smartwatch. The goal was to demonstrate whether sensor data can be used to effectively predict the five moods. By resorting to a machine learning approach our system was able to predict the moods with an accuracy ranging between 89-95% for single-output classification, 92-99% for the chain classification task and of approximately 93% for the multi-output analysis. Our results showed also that body signals are better predictors compared to the external environmental variables. These results demonstrate and verify the potential of smartwatches in collecting and predicting human emotions, enabling dynamic feedback loops to enhance user experience.

"Emotions are the Great Captains of our Lives": Measuring Moods through the Power of Physiological and Environmental Sensing

Arano, Keith April;Orsenigo, Carlotta;Vercellis, Carlo
2020

Abstract

The paper proposes the use of a smartwatch-based system for measuring the emotions of individuals in a classroom setting with respect to five mood variables: Activation, Tiredness, Pleasance, Quality of Presentation and Understanding. Internal (body) and external (environment) data such as movement, heart rate, noise, temperature and humidity were collected through the built-in sensors of the smartwatch. The system was verified by means of a longitudinal study that has been carried out in a series of workshops and lectures. Through experience-based sampling, participants were polled at periodic time intervals asking them to enter a self-assessment of the aforementioned mood states directly on the smartwatch. The goal was to demonstrate whether sensor data can be used to effectively predict the five moods. By resorting to a machine learning approach our system was able to predict the moods with an accuracy ranging between 89-95% for single-output classification, 92-99% for the chain classification task and of approximately 93% for the multi-output analysis. Our results showed also that body signals are better predictors compared to the external environmental variables. These results demonstrate and verify the potential of smartwatches in collecting and predicting human emotions, enabling dynamic feedback loops to enhance user experience.
Modelling human emotion
Affect sensing and analysis
Machine learning
Affective computing
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1157159
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