ABSTRACT
Emotion, mood, and stress recognition (EMSR) has been studied in laboratory settings for decades. In particular, physiological signals are widely used to detect and classify affective states in lab conditions. However, physiological reactions to emotional stimuli have been found to differ in laboratory and natural settings. Thanks to recent technological progress (e.g., in wearables) the creation of EMSR systems for a large number of consumers during their everyday activities is increasingly possible. Therefore, datasets created in the wild are needed to insure the validity and the exploitability of EMSR models for real-life applications. In this paper, we initially present common techniques used in laboratory settings to induce emotions for the purpose of physiological dataset creation. Next, advantages and challenges of data collection in the wild are discussed. To assess the applicability of existing datasets to real-life applications, we propose a set of categories to guide and compare at a glance different methodologies used by researchers to collect such data. For this purpose, we also introduce a visual tool called Graphical Assessment of Real-life Application-Focused Emotional Dataset (GARAFED). In the last part of the paper, we apply the proposed tool to compare existing physiological datasets for EMSR in the wild and to show possible improvements and future directions of research. We wish for this paper and GARAFED to be used as guidelines for researchers and developers who aim at collecting affect-related data for real-life EMSR-based applications.
ABSTRACT
Eye-tracking (ET) is one of the most intuitive solutions for enabling people with severe motor impairments to control devices. Nevertheless, even such an effective assistive solution can detrimentally affect user experience during demanding tasks because of, for instance, the user's mental workload - using gaze-based controls for an extensive period of time can generate fatigue and cause frustration. Thus, it is necessary to design novel solutions for ET contexts able to improve the user experience, with particular attention to its aspects related to workload. In this paper, a pilot study evaluates the effects of a relaxation biofeedback system on the user experience in the context of a gaze-controlled task that is mentally and temporally demanding: ET-based gaming. Different aspects of the subjects' experience were investigated under two conditions of a gaze-controlled game. In the Biofeedback group (BF), the user triggered a command by means of voluntary relaxation, monitored through Galvanic Skin Response (GSR) and represented by visual feedback. In the No Biofeedback group (NBF), the same feedback was timed according to the average frequency of commands in BF. After the experiment, each subject filled out a user experience questionnaire. The results showed a general appreciation for BF, with a significant between-group difference in the perceived session time duration, with the latter being shorter for subjects in BF than for the ones in NBF. This result implies a lower mental workload for BF than for NBF subjects. Other results point toward a potential role of user's engagement in the improvement of user experience in BF. Such an effect highlights the value of relaxation biofeedback for improving the user experience in a demanding gaze-controlled task.
