Mindfulness meditation is a widely accessible and evidence-based method for supporting mental health. Despite the proliferation of mindfulness meditation apps, sustaining user engagement remains a persistent challenge. Personalizing the meditation experience is a promising strategy to improve engagement, but it often requires costly and unscalable manual effort. We present MindfulAgents, a multi-agent system powered by large language models that: (1) generates guided meditation scripts based on an expert-established mindfulness framework, (2) encourages users’ reflection on emotional states and mindfulness skills, and (3) enables real-time personalization of the mindfulness meditation experience for each user. In a formative lab study (N=13), MindfulAgents significantly improved in-session engagement (p = 0.011) and self-awareness (p = 0.014), as well as reduced momentary stress (p = 0.020). Furthermore, a four-week deployment study (N=62) demonstrated a notable increase (p = 0.002) in long-term engagement and level of mindfulness (p = 0.023). Participants reported that MindfulAgents offered more relevant meditation sessions personalized to individual needs in various contexts, supporting sustained practice. Our findings highlight the potential of LLM-driven personalization for enhancing user engagement in digital mindfulness meditation interventions.

Despite the global mental health crisis, access to screenings, professionals, and treatments remains high. In collaboration with licensed psychotherapists, we propose a C onversational AI T herapist with psychotherapeutic I nterventions (CaiTI), a platform that leverages large language models (LLMs) and smart devices to enable better mental health self-care. CaiTI can screen the day-to-day functioning using natural and psychotherapeutic conversations. CaiTI leverages reinforcement learning to provide personalized conversation flow. CaiTI can accurately understand and interpret user responses. When the user needs further attention during the conversation, CaiTI can provide conversational psychotherapeutic interventions, including cognitive behavioral therapy and motivational interviewing. Leveraging the datasets prepared by the licensed psychotherapists, we experiment and microbenchmark various LLMs’ performance in tasks along CaiTI’s conversation flow and discuss their strengths and weaknesses. With the psychotherapists, we implement CaiTI and conduct 14-day and 24-week studies. The study results, validated by therapists, demonstrate that CaiTI can converse with users naturally, accurately understand and interpret user responses, and provide psychotherapeutic interventions appropriately and effectively. We showcase the potential of CaiTI LLMs to assist the mental therapy diagnosis and treatment and improve day-to-day functioning screening and precautionary psychotherapeutic intervention systems.

Ultra-wide fisheye cameras (FoV > 180°) offer unmatched scene coverage but introduce severe geometric distortions that degrade vision system performance. Existing rectification methods struggle with such extreme FoVs due to the inherent limitations of single-perspective projections and the scarcity of ground truth data. We propose a novel split-and-combine framework that rectifies ultra-wide fisheye images into 5-face cubemaps. Our approach begins with a lightweight CNN estimating geometric parameters to guide a structured decomposition of the fisheye image into directional regions. Each region is independently corrected using a two-stage pipeline: a flow-based pre-corrector for geometric warping and a diffusion-based enhancer for detail restoration. We train on a synthetic dataset derived from real-world perspective images, enabling partial supervision. Preliminary results demonstrate strong quantitative and qualitative performance, validating our modular architecture as an effective solution for high-fidelity rectification in ultra-wide fisheye imagery.

Cardio exercise elevates both heart rate and respiration rate, resulting in distinct physiological changes that affect speech patterns, pitch, breathing sounds, and heart sounds. These variations, which occur post-exercise, are influenced by factors such as exercise intensity and individual fitness levels. A comprehensive audio dataset is critically needed to capture post-exercise physiological changes, as existing datasets focus mainly on resting speech, breathing, and heart sounds, neglecting the dynamic shifts following physical exertion. Current datasets fail to capture unique post-exercise variations like speech disfluencies, altered breathing patterns, and variable heart sound intensities, limiting model generalizability to post-exercise conditions. To address this gap, we recruited 59 subjects from diverse backgrounds to engage in cardio exercise, specifically running, reaching varied exertion levels to produce a rich dataset. Our dataset includes 250 sessions totaling 143 minutes of structured reading, 47 minutes of spontaneous speech, 71 minutes of breathing sounds, and 62.5 minutes of phonocardiogram (PCG) recordings. We designed and deployed preliminary case studies to show that speech changes post-cardio could serve as an indicator of exertion level. We envision this dataset as a foundational resource for designing models in speech and cardiorespiratory monitoring that are resilient to the physiological shifts induced by exercise. This dataset could advance natural language processing (NLP) applications, mobile health, and wearable sensing technologies by enabling resilient and accurate physiological monitoring in real-world conditions.

Over half a trillion recreational runners worldwide engage in running for psychological, health, and social benefits. Running metrics are essential for motivation, goal setting, performance improvement, health management, and injury prevention. Although wearable devices like fitness trackers and smartwatches offer various metrics, they often perform poorly on treadmills and can be uncomfortable or restrictive. In this work, we propose a non-contact, real-time, smartphone-based approach to estimate running metrics, including cadence, ground contact time (GCT), and balance, using the sound produced during treadmill running. In collaboration with a licensed running coach, we recruited over 50 subjects with varying levels of running expertise. We collected treadmill running sounds and ground-truth running metrics in different environments. We designed and developed a multi-task learning (MTL) machine learning mobile system to capture the treadmill running sounds and estimate running metrics in situ. Our proposed method shows comparable accuracy in estimating running metrics to commercial off-the-shelf (COTS) wearable devices.

In this demonstration, in collaboration with licensed therapists, we introduce an AI therapist that takes advantage of the smart-home environment to screen day-to-day functioning and infer mental wellness of an occupant. Our system can assess a user's daily functioning and mental wellness based on a combination of direct conversation with users and information obtained from smart home devices using psychological rubrics proposed in [1]. We demonstrate that our system can converse with a user in a natural way (through a smartphone or smart speaker) and analyze a user's response semantically and sentimentally. In addition, we show that our system can provide preliminary interventions to help improve the user's wellness. In particular, when abnormal behavior is detected during the conversation or by smart home devices, the system provides psychotherapeutic consolations during the conversation and will check on the occupant's condition by actuating a home robot.