Designing and evaluating a wearable device for affective state level classification using machine learning techniques

The emotional or affective state has a direct impact not only on personal life, but also in the field of work, sports, rehabilitation processes, among other fields. In the evolving understanding of emotional theory, it has been theorized that an emotion can be classified according to a two-dimensional model composed of an Arousal value and a Valence value, as well as empirically demonstrating the impact of emotions on physiological variables. This work presents the development of a wearable device for capturing physiological signals, the collection of a dataset (after approval by the ethics committee) in which participants' emotional states are induced, and the development of an automatic classifier of the emotional state based on neural networks. According to this last point, a 4-phase optimization process is presented in which the physiological sensors are evaluated independently and with multiple variations of the hyperparameters of the neural networks, keeping those that provide the most information, combinations are made between them and the robustness of the final system obtained is evaluated. The results exceed 92% accuracy in all cases, which, compared with previous work, significantly improves the classifiers developed in recent years. The key contributions of this study are detailed as follows: (a) a wearable device designed to collect physiological signals from the user in a non-invasive way is presented, proving that it works properly in a controlled environment; (b) a data-collection protocol is designed to induce emotional states in test subjects using small video clips, demonstrating that the user evokes the feelings that are induced; and (c) a machine learning-based system is developed and optimized to classify the emotional state based on the two-dimensional model of emotion, demonstrating its efficiency and accuracy.

Automated summary

This work presents the development of a wearable device for capturing physiological signals, the collection of a dataset to induce emotional states, and the development of an automatic classifier based on neural networks. The optimization process results in over 92% accuracy in all cases, significantly improving previous classifiers. The key contributions include the design of a wearable device for non-invasive collection of physiological signals, a data-collection protocol to induce emotional states, and the development and optimization of a machine learning-based system for emotional state classification based on a two-dimensional model.