Social anxiety is a prevalent condition that affects interpersonal interactions and social functioning. Recent advances in artificial intelligence and social robotics offer new opportunities to examine social anxiety in the human-robot interaction context. Accurate detection of affective states and behaviours associated with social anxiety requires multimodal datasets, where each signal modality provides complementary insights into its manifestations. However, such datasets remain scarce, limiting progress in both research and applications. To address this, this paper presents a protocol for multimodal dataset collection designed to reflect social anxiety in a human-robot interaction context. The dataset will consist of synchronised audio, video, and physiological recordings acquired from at least 70 participants, grouped according to their level of social anxiety, as they engage in approximately 10-minute interactive Wizard-of-Oz role-play scenarios with the Furhat social robot under controlled experimental conditions. In addition to multimodal data, the dataset will be enriched with contextual data providing deeper insight into individual variability in social anxiety responses. This work can contribute to research on affect-adaptive human-robot interaction by providing support for robust multimodal detection of social anxiety.

Social anxiety is a prevalent mental health condition that can significantly impact overall well-being and quality of life. Despite its widespread effects, adequate support or treatment for social anxiety is often insufficient. Advances in technology, particularly in social robotics, offer promising opportunities to complement traditional mental health. As an initial step toward developing effective solutions, it is essential to understand the values that shape what is considered meaningful, acceptable, and helpful. In this study, a participatory design workshop was conducted with mental health academic researchers to elicit the underlying values that should inform the design of socially assistive robots for social anxiety support. Through creative, reflective, and envisioning activities, participants explored scenarios and design possibilities, allowing for systematic elicitation of values, expectations, needs, and preferences related to robot-supported interventions. The findings reveal rich insights into design-relevant values--including adaptivity, acceptance, and efficacy--that are core to support for individuals with social anxiety. This study highlights the significance of a research-led approach to value elicitation, emphasising user-centred and context-aware design considerations in the development of socially assistive robots.

The world can be a complex and difficult place to navigate. People with High-Functioning Autistic Spectrum Disorder as well as general social ineptitude often face navigation challenges that individuals of other demographics simply do not themselves. This can become even more pronounced with people of that specific group when they are in their teenage years and early adulthood (that being the usual age range of college students). When they are at such a vulnerable age, they can be far more susceptible to the struggles of becoming comfortable and content with social interactions as well as having strong relationships (outside their immediate family). Concerning this, the rapid emergence of artificial intelligence chatbots has led to many of them being used to benefit people of different ages and demographics with easy accessibility. With this, if there is anything that people with High-Functioning ASD and social ineptitude want when it comes to guidance towards self-improvement, surely easy accessibility would be one. What are the potential benefits and limitations of using a Mindstudio AI-powered chatbot to provide mental health support for teens and young adults with the aforementioned conditions? What could be done with a tool like this to help those individuals navigate ethical dilemmas within different social environments to reduce existing social tensions? This paper addresses these queries and offers insights to inform future discussions on the subject.

During social interactions, understanding the intricacies of the context can be vital, particularly for socially anxious individuals. While previous research has found that the presence of a social interaction can be detected from ambient audio, the nuances within social contexts, which influence how anxiety provoking interactions are, remain largely unexplored. As an alternative to traditional, burdensome methods like self-report, this study presents a novel approach that harnesses ambient audio segments to detect social threat contexts. We focus on two key dimensions: number of interaction partners (dyadic vs. group) and degree of evaluative threat (explicitly evaluative vs. not explicitly evaluative). Building on data from a Zoom-based social interaction study (N=52 college students, of whom the majority N=45 are socially anxious), we employ deep learning methods to achieve strong detection performance. Under sample-wide 5-fold Cross Validation (CV), our model distinguished dyadic from group interactions with 90\% accuracy and detected evaluative threat at 83\%. Using a leave-one-group-out CV, accuracies were 82\% and 77\%, respectively. While our data are based on virtual interactions due to pandemic constraints, our method has the potential to extend to diverse real-world settings. This research underscores the potential of passive sensing and AI to differentiate intricate social contexts, and may ultimately advance the ability of context-aware digital interventions to offer personalized mental health support.

Social anxiety represents a prevalent challenge in modern society, affecting individuals across personal and professional spheres. Left unaddressed, this condition can yield substantial negative consequences, impacting social interactions and performance. Further understanding its diverse physical and emotional symptoms becomes pivotal for comprehensive diagnosis and tailored therapeutic interventions. This study analyze prevalence and frequency of social anxiety symptoms taken from Mayo Clinic, exploring diverse human experiences from utilizing a large Reddit dataset dedicated to this issue. Leveraging these platforms, the research aims to extract insights and examine a spectrum of physical and emotional symptoms linked to social anxiety disorder. Upholding ethical considerations, the study maintains strict user anonymity within the dataset. By employing a novel approach, the research utilizes BART-based multi-label zero-shot classification to identify and measure symptom prevalence and significance in the form of probability score for each symptom under consideration. Results uncover distinctive patterns: "Trembling" emerges as a prevalent physical symptom, while emotional symptoms like "Fear of being judged negatively" exhibit high frequencies. These findings offer insights into the multifaceted nature of social anxiety, aiding clinical practices and interventions tailored to its diverse expressions.

Recent advances in remote health monitoring systems have significantly benefited patients and played a crucial role in improving their quality of life. However, while physiological health-focused solutions have demonstrated increasing success and maturity, mental health-focused applications have seen comparatively limited success in spite of the fact that stress and anxiety disorders are among the most common issues people deal with in their daily lives. In the hopes of furthering progress in this domain through the development of a more robust analytic framework for the measurement of indicators of mental health, we propose a multi-modal semi-supervised framework for tracking physiological precursors of the stress response. Our methodology enables utilizing multi-modal data of differing domains and resolutions from wearable devices and leveraging them to map short-term episodes to semantically efficient embeddings for a given task. Additionally, we leverage an inter-modality contrastive objective, with the advantages of rendering our framework both modular and scalable. The focus on optimizing both local and global aspects of our embeddings via a hierarchical structure renders transferring knowledge and compatibility with other devices easier to achieve. In our pipeline, a task-specific pooling based on an attention mechanism, which estimates the contribution of each modality on an instance level, computes the final embeddings for observations. This additionally provides a thorough diagnostic insight into the data characteristics and highlights the importance of signals in the broader view of predicting episodes annotated per mental health status. We perform training experiments using a corpus of real-world data on perceived stress, and our results demonstrate the efficacy of the proposed approach in performance improvements.

The extent to which individuals experience positive and negative affect on a daily basis is associated with mental health outcomes [1]. Higher levels of negative affect are associated with increased vulnerability to many mental disorders, including depression and anxiety disorders, two of the most common types of mental disorders in U.S. adults [2]. Mental health research typically relies on self-report questionnaires that assess negative affect at a moment in time. Repeated administration of these measures, such as in an ecological momentary assessment (EMA) framework, is resource intensive and susceptible to retrospective bias when participants are asked to recall their mood over a previous duration [3]. Ideally, negative affect would be recognized without asking participants, thereby reducing burden, improving compliance among participants, and allowing for continuous modeling of affect change.

While I personally never ever not once have I ever suffered from any sort of social anxiety, I recognize that it's a thing that people can struggle with. And especially for people with autism spectrum disorder (ASD), social situations can be very challenging. Part of the difficulty lies in insufficient or excessive agency cues (nonverbal social signals that include gaze, facial expression, and personal distance), and while there's been a substantial amount of research towards helping people with ASD improve their cognitive skills, researchers in Japan are trying a different approach through a'comfortable information environment" called the Mobile Personal Space. The Mobile Personal Space (MPS) is a physical shell that completely encloses an individual with social anxiety. Once you're in there, you can't see anybody, and nobody can see you.

There has been a much needed increase in awareness of mental health issues in recent years, and due to the rise in reported cases, there are many companies now looking at ways to deal with the numbers. According to the World Health Organisation, there are more than 300 million people globally suffering from depression, with almost 800,000 people turning to suicide each year. Unfortunately, the demand for specialist help has been met with decreased health care services. Enter, the new age of therapy. Whilst there is no getting away from the importance of the human touch when it comes to healthcare, especially mental health.

For those of us who have Asperger's or who are just really bad at reading social cues and emotions, it can be challenging to engage with others and carry on an interesting conversation. And even if you are not, we have all had those moments when you cannot tell if someone is being sincere or not when he tells some outlandish story or statement. To fix this problem, Mashable reported earlier this year that a pair of MIT researchers have developed a wearable "that could someday act as a real-time virtual social coach." The wearable comes with artificial intelligence (AI), which can analyze a person's speech patterns and vitals to determine what others are feeling. The MIT researchers used a Samsung Simband, which can run custom algorithms.