Abstract
Detecting emotions is very useful in many fields, from health-care to human-computer interaction. In this paper, we propose an iterative user-centered methodology for supporting the development of an emotion detection system based on low-cost sensors. Artificial Intelligence techniques have been adopted for emotion classification. Different kind of Machine Learning classifiers have been experimentally trained on the users’ biometrics data, such as hearth rate, movement and audio. The system has been developed in two iterations and, at the end of each of them, the performance of classifiers (MLP, CNN, LSTM, Bidirectional-LSTM and Decision Tree) has been compared. After the experiment, the SAM questionnaire is proposed to evaluate the user’s affective state when using the system. In the first experiment we gathered data from 47 participants, in the second one an improved version of the system has been trained and validated by 107 people. The emotional analysis conducted at the end of each iteration suggests that reducing the device invasiveness may affect the user perceptions and also improve the classification performance.
Highlights
Many research efforts have been devoted to the recognition of human emotions
The process phases are detailed in the following of this section, where we explain the methodology we propose through a case study concerning the development of an emotion detection system based on low-cost sensors
In this paper we presented a user-centered development process of an emotion detection system based on low-cost biometric sensors and Artificial Intelligence, which analyzes the emotional perception of the user on the detection system
Summary
Many research efforts have been devoted to the recognition of human emotions. The user expressions may be affected by ethnicity and cultures These problems may be overcome by adopting emotion recognition approaches based on physiological signals, which are not visible at the human eye and immediately reflect the emotional changes. These kinds of signals may be detected by sensors. In the Russel’s Circumplex Model of Affect the emotional space is divided in four quadrants by the valence and arousal axes. Considering this classification, emotions may be grouped in four classes, each one associated to one of the four quadrants
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