Reflecting on personal challenges can be difficult. Without encouragement, the reflection process often remains superficial, thus inhibiting deeper understanding and learning from past experiences. To allow people to immerse themselves in and deeply reflect on past challenges, we developed SelVReflect, a VR experience which offers active voice-based guidance and a space to freely express oneself. SelVReflect was developed in an iterative design process (N=5) and evaluated in a user study with N=20 participants. We found that SelVReflect enabled participants to approach their challenge and its (emotional) components from different perspectives and to discover new relationships between these components. By making use of the spatial possibilities in VR, participants developed a better understanding of the situation and of themselves. We contribute empirical evidence of how a guided VR experience can support reflection. We discuss opportunities and design requirements for guided VR experiences that aim to foster deeper reflection.

The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit provides a one-stop pipeline consisting of (i) a sensing and data acquisition layer that can be configured in a modular manner per research needs, and (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study with 16 participants, PhysioKit shows strong agreement with research-grade sensors on measuring heart rate and heart rate variability metrics data. Furthermore, we report usability survey results from 10 small-project teams (44 individual members in total) who used PhysioKit for 4–6 weeks, providing insights into its use cases and research benefits. Lastly, we discuss the extensibility and potential impact of the toolkit on the research community.

With the rising prevalence of autism diagnoses, it is essential for research to understand how to leverage technology to support the diverse nature of autistic traits. While traditional interventions focused on technology for medical cure and rehabilitation, recent research aims to understand how technology can accommodate each unique situation in an efficient and engaging way. Extended reality (XR) technology has been shown to be effective in improving attention in autistic users given that it is more engaging and motivating than other traditional mediums. Here, we conducted a systematic review of 59 research articles that explored the role of attention in XR interventions for autistic users. We systematically analyzed demographics, study design and findings, including autism screening and attention measurement methods. Furthermore, given methodological inconsistencies in the literature, we systematically synthesize methods and protocols including screening tools, physiological and behavioral cues of autism and XR tasks. While there is substantial evidence for the effectiveness of using XR in attention-based interventions for autism to support autistic traits, we have identified three principal research gaps that provide promising research directions to examine how autistic populations interact with XR. First, our findings highlight the disproportionate geographic locations of autism studies and underrepresentation of autistic adults, evidence of gender disparity, and presence of individuals diagnosed with co-occurring conditions across studies. Second, many studies used an assortment of standardized and novel tasks and self-report assessments with limited tested reliability. Lastly, the research lacks evidence of performance maintenance and transferability. Based on these challenges, this paper discusses inclusive future research directions considering greater diversification of participant recruitment, robust objective evaluations using physiological measurements (e.g., eye-tracking), and follow-up maintenance sessions that promote transferrable skills. Pursuing these opportunities would lead to more effective therapy solutions, improved accessible interfaces, and engaging interactions.

The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit consists of (i) a sensor and hardware layer that can be configured in a modular manner per research needs, (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study for photo-plethysmography (PPG) sensor with 15 participants, PhysioKit shows strong agreement with research-grade PPG sensors. Furthermore, we report usability survey results from 10 micro-project teams (44 individual members in total) who use PhysioKit for 4-6 weeks, providing insights into its use cases and research benefits. We conclude by discussing extensibility and potential impact of the toolkit on the research community.

Examining remote virtual audiences is a vital part of understanding social experiences in modern human–computer interaction contexts. Doing so raises intriguing questions about how these mediated connections relate to emotional and physiological states. The vagus nerve is central to socioemotional processing and physiological well-being, with its activation represented as vagally mediated heart rate variability (vmHRV). We examine how participants’ social connectedness to virtual partners relates to their experience of socioemotional competences and psychophysiological states while observing streamed gameplay. In this experimental study with 48 participants, we compared self-reported empathy, empathic concern, and continuously measured vmHRV (from a PPG sensor) during different types of gameplay. The results revealed that viewers who felt greater social connectedness to remote partners also felt more empathic concern (quantitative detail) and had significantly heightened vmHRV (quant detail) across all conditions compared to those who felt lower connectedness. These findings reveal that stronger feelings of connectedness to remote partners are associated with enhanced socioemotional competences and physiological well-being. This research highlights the intertwined nature of social connectedness, empathy, and physiological health, providing valuable insights for designing virtual platforms that foster deeper interpersonal connections and promote well-being.