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In this paper we propose to use Recurrence Plots (RP) to generate 2D representations of physiological activity which should be less subject dependent and better suited for non-stationary signals such as EDA. The performance of spectrograms and RPs are compared on two publicly available datasets: AMIGOS and DEAP. Transfer learning is employed by using a pre-trained ResNet-50 model to recognize emotional states (high vs low arousal and high vs low valence) from the two types of representations. Results show that RPs reach a similar performance to spectrograms on periodic signals such as ECG and plethysmography (F1 of 0.76 for valence and 0.74 for arousal on the AMIGOS dataset) while they outperform spectrograms on EDA (F1 of 0.74 for valence and 0.75 for arousal). By combining the two sources of information we were able to reach a F1 of 0.76 for valence and 0.75 for arousal.

Emotion recognition is usually achieved by collecting features (physiological signals, events, facial expressions, etc.) to predict an emotional ground truth. This ground truth is arguably unreliable due to its subjective nature. In this paper, we introduce a new approach to measure the magnitude of an emotion in the latent space of a Neural Network without the need for a subjective ground truth. Our data consists of physiological measurements during video gameplay, game events, and subjective rankings of game events for the validation of our model. Our model encodes physiological features into a latent variable which is then decoded into video game events. We show that the events are ranked in the latent space similarly to the participants' subjective ranks. For instance, our model's ranking is correlated (Kendall $\tau$ of 0.91) with the predictability rankings.

We analyze the users’ intimacy to investigate the differences in smartphone usage, considering the user’s location and number and kind of people physically around the user. With a first user study we (1) validate the intimacy concept, (2) evaluate its correlation to smartphone usage features and (3) we computationally model it. Shorter, more frequent, and less engaging interactions take place when intimacy is lower, while longer, less frequent, and engaging interactions when intimacy is higher. With a second user study, we investigate the intimacy predictability in practice. Location–time features are predictive for the intimacy, and other smartphone-based features can improve the intimacy prediction accuracy.

Widespread acceptance and use of personalized mobile devices facilitates the provision of the ubiquitous mobile communications and computing applications that enable the human Quality of Life (QoL) improvement. However, there are multiple human factors influencing these applications, stemming from their users' needs, expectations, and ways of maximizing their experience and the impact on their QoL. For a successful adoption of these applications, our mobile technologies lab, mQoL, employs an iterative, user-centric, Living Lab approach for the applications' design and evaluation. This paper introduces the concept of mobile technologies for QoL specifically aiming to first understand the individual's QoL from information available in mobile devices, i.e., assess physical, psychological, social or environmental aspects of a daily life of an individual, and, based on this understanding, provide services to improve this individual's QoL. This paper also explains the methodological aspects of our research, including transdisciplinarity aspects, and delineates the research challenges for a Living Lab approach at large.