Assessing the user experience of older adults using a neural network trained to recognize emotions from brain signals

Abstract

The use of Ambient Assisted Living (AAL) technologies as a means to cope with problems that arise due to an increasing and aging population is becoming usual. AAL technologies are used to prevent, cure and improve the wellness and health conditions of the elderly. However, their adoption and use by older adults is still a major challenge. User Experience (UX) evaluations aim at aiding on this task, by identifying the experience that a user has while interacting with an AAL technology under particular conditions. This may help designing better products and improve user engagement and adoption of AAL solutions. However, evaluating the UX of AAL technologies is a difficult task, due to the inherent limitations of their subjects and of the evaluation methods. In this study, we validated the feasibility of assessing the UX of older adults while they use a cognitive stimulation application using a neural network trained to recognize pleasant and unpleasant emotions from electroencephalography (EEG) signals by contrasting our results with those of additional self-report and qualitative analysis UX evaluations. Our study results provide evidence about the feasibility of assessing the UX of older adults using a neural network that take as input the EEG signals; the classification accuracy of our neural network ranges from 60.87% to 82.61%. As future work we will conduct additional UX evaluation studies using the three different methods, in order to appropriately validate these results.

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

“…Moreover, different age groups ranging from infants [132,143,169], children [6,43,75,84,89,103,125,126,[128][129][130]140,183,184,[210][211][212][213], adolescents [59,65,70,135,139], and elderly [105,214] in different experiments were considered as study samples. Some studies were conducted on a single gender, such as those investigating women's emotions during motherhood and parental status [133,134,164] and a study of men [215].…”

“…Moreover, finding key emotional states to be recognized is mandatory; for example, six emotions were detected in [42,45,106,120,122,192], whereas in [108], a real-time EEG signal to classify happy and unhappy emotions was proposed, and in [113], a fear evaluation system was proposed. In our review, we found that most of the articles aim to detect unpleasant, pleasant, and neutral emotions, such as in [105,217], or positive, negative, and neutral emotions that are based on the valence-arousal dimensional emotion model, as in [159,206].…”

“…The computational methods to extract and classify emotional features from EEG are summarized in Table 5. Artificial Neural Network (ANN) 7 [105,176,190,204,216,223,227] It is noteworthy to mention that a single feature extraction technique is not optimal across all of the applications. Besides, existing signals are not enough for high accuracy feature extraction.…”

“…They were used to improve behavior, cognition, and emotion regulation. [1,40,95,105,139,194,214,227,282,283] …”

Heart Attack Detection in Colour Images Using Convolutional Neural Networks

“…This has opened the door to the detection of abnormal events in an automatic manner. Fall detection is by far the most commonly faced challenge and the top topic in health environments [15,16], but there exist other challenges like monitoring Parkinson's disease [17] or even recognising emotional states [18,19].…”