This paper presents FEAD, a fNIRS-EEG Affective Database that can be used for training emotion recognition models. The electrical activity and brain hemodynamic responses of 37 participants were recorded, as well as the categorical and dimensional emotion ratings they gave to 24 affective audio-visual stimuli. The relationship between the neurophysiological signals with the subjective ratings was investigated, with a significant correlation found in the prefrontal cortex region. A binary classification of affective states was performed using a subject-dependent approach, taking into account the fusion of both modalities (functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG)) and each single modality separately. In addition, we explored the temporal dynamics of the recorded data in shorter trials and found that the fusion of features from both modalities yielded significantly better results than using a single modality. This database will be made publicly available with the aim to encourage researchers to develop more advanced algorithms for affective computing and emotion recognition.

Technology integration in educational settings has led to the development of novel sensor-based tools that enable students to measure and interact with their environment. Although reports from using such tools can be positive, evaluations are often conducted under controlled conditions and short timeframes. There is a need for longitudinal data collected in realistic classroom settings. However, sustained and authentic classroom use requires technology platforms to be seen by teachers as both easy to use and of value. We describe our development of a sensor-based platform to support science teaching that followed a 14-month design process. We share insights from this design and development approach, and report findings from a six-month large-scale evaluation involving 35 schools and 1245 students. We share lessons learnt, including that technology integration is not an educational goal per se and that technology should be a transparent tool to enable students to achieve their learning goals.

Despite the increasing popularity of VR games, one factor hindering the industry’s rapid growth is motion sickness experienced by the users. Symptoms such as fatigue and nausea severely hamper the user experience. Machine Learning methods could be used to automatically detect motion sickness in VR experiences, but generating the extensive labeled dataset needed is a challenging task. It needs either very time consuming manual labeling by human experts or modification of proprietary VR application source codes for label capturing. To overcome these challenges, we developed a novel data collection tool, VRhook, which can collect data from any VR game without needing access to its source code. This is achieved by dynamic hooking, where we can inject custom code into a game’s run-time memory to record each video frame and its associated transformation matrices. Using this, we can automatically extract various useful labels such as rotation, speed, and acceleration. In addition, VRhook can blend a customized screen overlay on top of game contents to collect self-reported comfort scores. In this paper, we describe the technical development of VRhook, demonstrate its utility with an example, and describe directions for future research.

Emotional Self-Awareness (ESA) plays a vital role in physical and mental well-being. Recent advancements in artificial intelligence technologies have shown promising emotion recognition results, opening new opportunities to build systems to support ESA. However, little research has been done to understand users' perspectives on artificial-intelligence-based emotion recognition systems. We introduce Troi, an automatic emotion recognition mobile app using wearable signals. With Troi, we ran a multi-day user study with 12 users to understand user preference parameters, such as perceived accuracy, confidence, preferred emotion representations, effect of self-awareness of emotions, and real-time use cases. Further, we extend our study to evaluate the machine learning model in-the-wild to understand behaviours in-the-wild. We found that users perceived accuracy of the emotion recognition model is higher than the actual model prediction accuracy; there was no strong preference for one specific emotion representation, and users' self-awareness of emotions improved over time.