Nowadays, touch remains essential for emotional conveyance and interpersonal communication as more interactions are mediated remotely. While many studies have discussed the effectiveness of using haptics to communicate emotions, incorporating affect into haptic design still faces challenges due to individual user tactile acuity and preferences. We assessed the conveying of emotions using a two-channel haptic display, emphasizing individual differences. First, 24 participants generated 187 haptic messages reflecting their immediate sentiments after watching 8 emotionally charged film clips. Afterwards, 19 participants were asked to identify emotions from haptic messages designed by themselves and others, yielding 593 samples. Our findings suggest potential links between haptic message decoding ability and emotional traits, particularly Emotional Competence (EC) and Affect Intensity Measure (AIM). Additionally, qualitative analysis revealed three strategies participants used to create touch messages: perceptive, empathetic, and metaphorical expression.

Introspectus AI is a generative AI-based system designed to enhance self-reflection and support positive behavior change. By leveraging multimodal information from users' daily life recordings, it provides personalized and detailed feedback, aiming to deepen self-awareness and facilitate positive behavioral adjustments. This study explores the short-term and long-term impacts of interacting with Introspectus AI, focusing on its potential to enhance reflective practices and improve the acceptance of generative AI tools. Following the user experience was defined through an initial round of workshops with four experts. The resulting system was evaluated through a long-term study involving 64 participants. The results demonstrate that AI-supported interventions significantly improved engagement in self-reflection, the need for reflection, and insight, while also increasing user acceptance of generative AI over time. These findings underscore the potential of generative AI as a practical tool for self-improvement, offering insights into its broader applicability in promoting well-being and personal growth.

We present ThermoQuest, a self-contained wearable head-mounted display system for enhancing Virtual Reality experiences with temperature feedback. It’s constructed with commodity hardware elements, featuring 6 Peltier elements on the rim of the headset touching the users face. We explain the design and implementation of an affordable wearable thermal VR prototype build with commodity hardware. In a user study with 15 participants, we show evidence for a significant difference in reported presence in VR between thermal VR and control conditions (p > 0.017) in a counterbalanced experimental setup. We end with a discussion and use cases of the presented VR prototype and similar systems.