Understanding UX Better: A New Technique to Go beyond Emotion Assessment

User experience (UX) is a quality aspect that considers the emotions evoked by the system, extending the usability concept beyond effectiveness, efficiency, and satisfaction. Practitioners and researchers are aware of the importance of evaluating UX. Thus, UX evaluation is a growing field with diverse approaches. Despite various approaches, most of them produce a general indication of the experience as a result and do not seek to capture the problem that gave rise to the bad UX. This information makes it difficult to obtain relevant results to improve the application, making it challenging to identify what caused a negative user experience. To address this gap, we developed a UX evaluation technique called UX-Tips. This paper presents UX-Tips and reports two empirical studies performed in an academic and an industrial setting to evaluate it. Our results show that UX-Tips had good performance in terms of efficiency and effectiveness, making it possible to identify the causes that led to a negative user experience, and it was easy to use. In this sense, we present a new technique suitable for use in both academic and industrial settings, allowing UX evaluation and finding the problems that may lead to a negative experience.

A Library of Old Photos Supporting Conversation of Two Generations Serving Reminiscence Therapy

In Japan, a shift in family patterns has led to a sense of social isolation among older people, which increases the risk of major neurocognitive disorder. Interventions for them using old photos to implement reminiscence therapy (RT) have been proved to be effective. A super-aged society has in turn led to a shortage of medical resources and older people prefer home care over institutional care. Therefore, there is an urgent need for volunteers to help in RT. However, the age of volunteers tends to be increasingly younger. The lack of knowledge and experience of the past for the young volunteers makes it difficult for them to select appropriate stimulated materials. To improve this situation, a library of old photos for RT was developed to support conversation between the two generations. A two-factor experiment and emotion assessment scales were designed to explore the effect of different old photo types on the fluency of conversation between the two generations and their emotion. It was found that the types of old photos have little effect on older people and that conversations were almost pleasant. However, the pleasantness of older people was enhanced when using photos that they wanted to talk about (P = 0.006). Meanwhile, pleasure in conversation of the older people increased with the attention of the young people to the topic (R = 0.304, p < 0.001). Conversely, photo type has a strong impact on young people. When photos are selected that older people do not want to talk about or photos that young people do not know the content and are not interested in, concern for the topic of young people drops dramatically. Therefore, when RT, it is important to avoid using the types of photos above that cause a drop in younger people's attention.

SAET: The Non-Verbal Measurement Tool in User Emotional Experience

In this paper, the development process and validation of a self-assessment emotion tool (SAET) is described, which establishes an emotion-assessment method to improve pictorial expression design. The tool is based on an emotion set of emotional-cognition-derived rules obtained from an OCC model proposed by Ortony, Clore, and Collins, and the emotion set and expression design are validated by numerical computation of the dimensional space pleasure–arousal–dominance (PAD) and the cognitive assessment of emotion words. The SAET consists of twenty images that display a cartoon figure expressing ten positive and ten negative emotions. The instrument can be used during interactions with visual interfaces such as websites, posters, cell phones, and vehicles, and allows participants to select interface elements that elicit specific emotions. Experimental results show the validity of this type of tool in terms of both semantic discrimination of emotions and quantitative numerical validation.

Improvement of Emotional Response to Negative Stimulations With Moderate-Intensity Physical Exercise

It is widely accepted that physical exercises (PEs) not only are good for fitness but also contribute to mental health and well-being. The positive influence of PEs on emotion has become a topic of much excitement. However, a quantitative study is required to discuss the effect of short-term moderate-intensity PE on the emotional response by using electroencephalogram (EEG) asymmetry. The experiments, including 20-min moderate-intensity cycling and EEG data acquisition with picture-induced emotion assessment protocol, were designed in this paper. The experiment procedure consists of two emotion assessment sessions, each of which contains 24 pictures. About 80 participants were randomly allocated into the exercise group and the control group. Participants in the exercise group were instructed to have a 20-min moderate-intensity cycling after the first assessment session, then rested until their heart rates recovered to baselines and their emotional states were assessed again in the second session. The control group only had a 20-min break without the cycling exercise between the two sessions. It was observed that, in the control group, the EEG asymmetry had no significant difference in these two assessment sessions for both positive and negative stimulations. However, in the exercise group, the difference of the EEG asymmetry before and after PE was significant only in response to negative stimulations. Further, the in-depth analysis of EEG asymmetry index changes of individual participants shows that the short-term moderate-intensity PE has a positive impact in response to negative stimulations. The proposed experiments show that the negative emotional experience can be reduced by the moderate-intensity PE and support the hypothesis that the moderate-intensity PE is good at improving emotional response to negative stimulations. This study provides the evidence of positive effects of PE in the domain of emotion regulation with experimental data.

An Emotion Assessment of Stroke Patients by Using Bispectrum Features of EEG Signals

Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8–13) Hz, beta (13–30) Hz and gamma (30–49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.