Automatic facial expression recognition systems can provide information about our emotions and how they change over time. However, based on different statistical methods the results of automatic systems have not yet been compared. In the current paper we evaluate the emotion detection between three different commercial systems (i.e. Affectiva, Kairos and Microsoft) when detecting dynamic and spontaneous facial expressions. Even if the study was performed on a limited sample of videos, the results show significant differences between the systems for the same video and per system across comparable facial expressions. Finally, we reflect on the implications according the generalization of the results provided by automatic emotion detection.

Previous research has found that stigma can be a barrier to service use but there has been little work examining actual service encounters involving members of stigmatized groups. One such group are those with problematic or unmanageable debts. Providing advice to members of this group is likely to be particularly difficult due to the stigma associated with being in debt. Using conversation analysis and discursive psychology, this study examines 12 telephone advice conversations between debt advisors and individuals in debt. Both clients and advisors oriented to the negative moral implications of indebtedness and typically worked collaboratively to manage these issues. Clients often claimed a moral disposition as a way to disclaim any unwanted associations with debt, but could find it difficult to reconcile this with an insolvency agreement. Moreover, the institutional requirements of the interaction could disrupt the collaborative management of stigma and advisors could manage the subsequent resistance from clients in either client‐centred or institution‐centred ways. The findings suggest that the products offered by debt advice agencies, as well as the manner in which they are offered to clients, can either help or hinder debtors negotiate the stigma‐related barriers to service engagement.

A growing literature has found a link between performance-related pay (PRP) and poor health, but the causal direction of the relationship is not known. To address this gap, the current paper utilises a crossover experimental design to randomly allocate subjects into a work task paid either by performance or a fixed payment. Stress is measured through self-reporting and salivary cortisol. The study finds that PRP subjects had significantly higher cortisol levels and self-rated stress than those receiving fixed pay, ceteris paribus. By circumventing issues of self-report and self-selection, these results provide novel evidence for the detrimental effect PRP may have on health.

Despite the popularity of physiological wearable sensors in sport activities to provide feedback on athletes' performance, understanding the factors influencing changes in athletes' physiological rhythms remains a challenge. Changes in physiological rhythms such as heart rate, breathing rate or galvanic skin response can be due to both physical exertion and psychoemotional states. Separating the influence of physical exertion and psychoemotional states in activities that involves both is complicated. As a result, the influence of psycho-emotional states is usually underestimated. In order to identify the specific influence of psycho-emotional states in physiological rhythm changes, 28 participants were asked to participate in a zipline activity, which involve little or no physical exertion while stimulating psycho-emotional states. By using nonlinear analyses, results show that specific changes in phys-This research was supported and led by Sensum Ltd. in collaboration with Queen's University Belfast.

Background: It is known that there is an association between debt and poor mental health. However, much of the literature is observational and focuses on how debt may lead to poor mental health. Here, we are interested in how poor mental health may be associated with debt advice adherence. Aims: The aim of the study was to investigate the relationship between mental health and debt advice adherence in individuals applying for a formal debt resolution mechanism (an Individual Voluntary Arrangement, IVA). Method: Eighty-six participants completed a survey measuring mental health (MHI-5), memory for information discussed during the appointment, attitudes towards IVAs, and trust in the advisor shortly after having a debt advice appointment. Adherence to the advice (whether participants completed the IVA application) was measured 10 weeks later. Results: The study found that the sample demonstrated poor levels of mental health overall but that non-adherent participants had significantly poorer mental health than those who adhered to the advice. Conclusion: These results suggest that (a) mental health needs to be considered when advising people with problem debt and (b) future research might examine if mental health support should coincide with important decision points in the debtor’s journey out of debt.

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Sharing personal information is an important way of communicating on social media. Among the information possibly shared, new sensors and tools allow people to share emotion information via facial emotion recognition. This paper questions whether people are prepared to share personal information such as their own emotion on social media. In the current study we examined how factors such as felt emotion, motivation for sharing on social media as well as personality affected participants’ willingness to share self-reported emotion or facial expression online. By carrying out a GLMM analysis, this study found that participants’ willingness to share self-reported emotion and facial expressions was influenced by their personality traits and the motivation for sharing their emotion information that they were given. From our results we can conclude that the estimated level of privacy for certain emotional information, such as facial expression, is influenced by the motivation for sharing the information online.

The analysis of facial expressions is currently a favoured method of inferring experienced emotion, and consequently significant efforts are currently being made to develop improved facial expression recognition techniques. Among these new techniques, those which allow the automatic recognition of facial expression appear to be most promising. This paper presents a new method of facial expression analysis with a focus on the continuous evolution of emotions using Generalized Additive Mixed Models (GAMM) and Significant Zero Crossing of the Derivatives (SiZer). The time-series analysis of the emotions experienced by participants watching a series of three different online videos suggests that analysis of facial expressions at the overall level may lead to misinterpretation of the emotional experience whereas non-linear analysis allows the significant expressive sequences to be identified.