Schedule

Keynotes

Prof. Mayank Goel

Keynote Details: To be announced

Speaker Bio: Mayank Goel is an Associate Professor in the Software and Societal Systems Department (S3D) and the Human-Computer Interaction Institute (HCII) in the School of Computer Science at Carnegie Mellon University, where he leads the Smart Sensing for Humans (SMASH) Lab. His research focuses on developing practical and deployable sensing and machine-learning systems for health sensing, technologies for the developing world, and novel user interactions that reduce barriers to technology use. His work draws on human–computer interaction, mobile computing, sensing, signal processing, and machine learning, and is inherently interdisciplinary, involving close collaborations with engineers, clinicians, community health workers, patients, and caregivers worldwide. Several of his inventions are deployed in clinics and hospitals, licensed to companies, and integrated into commercial products. He received his PhD in Computer Science and Engineering from the University of Washington, an MS in Computer Science from the Georgia Institute of Technology, and a BTech in Computer Science and Engineering from GGS Indraprastha University, India.

Dr. Akshay Paruchuri

From Sensing to Understanding: Building All-Day Wearable Systems for Personal Health Management

Next-generation wearables such as smart glasses are poised to become platforms for continuous, multimodal egocentric sensing, uniquely positioning them to transform personal health management. Realizing this vision requires solving two interconnected challenges: energy-efficient operation for all-day usage and intelligent systems that transform raw sensor data into actionable health insights. Beginning with energy efficiency, smart glasses face a fundamental tension: cameras, on-device AI, and wireless transmission are power-hungry, threatening all-day usability. Smarter sensing approaches can help - for example, EgoTrigger, an audio-driven image capture approach that selectively activates cameras only when low-power audio cues indicate contextually relevant moments. EgoTrigger can significantly reduce computational requirements while maintaining performance on episodic memory tasks. With some approaches for more efficient sensing in mind, the next challenge is generating meaningful insights. Agentic systems, such as the Personal Health Insights Agent (PHIA), leverages large language models with tools such as code generation and information retrieval to analyze wearable health data, achieving over 84% accuracy on health queries. I will further discuss recent multi-agent advances including the Personal Health Agent (PHA) framework, and promising directions for incorporating egocentric visual information alongside personal context for richer health reasoning.

The convergence of these capabilities opens transformative possibilities. For general users, such glasses provide assistance benefiting from continuous egocentric context. For healthcare, passive longitudinal sensing enables previously impossible questions: How did movement patterns change before and after a fall? Even if at-risk elderly populations never adopt smart glasses, longitudinal data from healthy wearers could advance our understanding of gait deterioration and early warning signs, yielding both practical systems for vulnerable populations and fundamental scientific insights into human behavior.

Speaker Bio: Akshay Paruchuri is a Postdoctoral Scholar in the Stanford Translational AI (STAI) Lab, working with Professor Ehsan Adeli on learning from egocentric, multimodal information (e.g., data from wearables and neuroimaging) to improve healthcare outcomes for general populations and those affected by aging-related diseases. He received his PhD in Computer Science from the University of North Carolina at Chapel Hill, advised by Professor Henry Fuchs. His research spans computer vision, machine learning, and healthcare AI, with publications at Nature Communications, NeurIPS, ECCV, MICCAI, and IEEE TVCG. He has conducted research at Google AR, Google Consumer Health Research, and IDSIA USI-SUPSI, and previously developed consumer wearable devices at Nike.

Accepted Contributions

• GlucoScreen-C: Smartphone Test Strips for Health Screening User Experience Demo: Jason S. Hoffman, Kristyna Kalisova, Ananya Vaidyaraman, Qiulin Qu, Tammy Nguyen, Kate Muret, Shwetak Patel [link to paper]
• PAWS: Empowering Everyday Cannabis Use Disorder Support through a Personalized AI Digital Pet on Smartwatches: Zhihan Jiang, Mengyuan “Millie” Wu, Ruishi Zou, Shiyu Xu, Emma Macmanus, Steven Liao, Ping Zhang, Dakuo Wang, James L. David, Nabila El-Bassel, Lena Mamykina, Frances R. Levin, Ryan Sultan, Xuhai Xu [link to paper]
• From Virtual to Tangible: A Physical Emotional Avatar for Heart-Rate Biofeedback: Samuel Navas-Medrano, Jose Luis Soler-Dominguez, Patricia Pons
• From Intention and Action: An Invasive BCI-Driven Wearable Framework for Everyday Hand Assistance in SCI Patients: Yili Wen, Ceci Verbaarschot, Liang He [link to paper]
• Embodrink: Embodied MR for Physiology-Informed Beverage Recommendation: Prasanth Sasikumar, Takahiro Masuda, Soundarya Ramesh, Hyung Woon Lee, Sankha Cooray, Suranga Nanayakkara [link to paper]
• Chest Tells Who You Are: Feature Analysis of Wearable Near-Field Sensor Signals for Biometric Authentication: Shun Hinatsu, Hidetoshi Makimura [link to paper]
• HeartbeatCam: Self-Triggered Photo Elicitation of Stress Events Using Wearable Sensing: Boyang Zhou, Zara Dana [link to paper]
• OptiStrip: An Addressable Optical Strip for Multi-Bend Shape and Interaction Sensing: Jungrak Choi*, Chan-Hwa Hong
• Ordinary Days: Context-Aware Multimodal AI for Personalized Stress Intervention: Selah Key [link to paper]
• Somatic Empathy and Sensory Metaphors: Exploring Haptic Materials for Everyday Well-being: Yixuan Li, Shuai Wang, Xiuqi Tommy Zhu, Rui Zhang, YangJiao*, Liang He*
• Capturing Chewing and Swallowing with Earables: A Multimodal Dataset Across Contexts: Jun Fang*, Ka I Chan*, Xiyuxing Zhang*, Yuntao Wang†, Zihang Zhan, Zhixin Zhao, Yuanchun Shi†
• EmoDrink: Affect-Aware Beverage Recommendation via Wearable Physiological Sensing: Sankha Cooray, Soundarya Ramesh, Hyung Woon Lee, Prasanth Sasikumar, Takahiro Masuda, Suranga Nanayakkara
• Demonstrating the MoTTs: Minimalist Smartwatch-based Wearables to Support Physical Rehabilitation Activities: José Manuel Vega-Cebrián, Elena Márquez Segura, Ana Tajadura-Jiménez [link to paper]
• Supporting Dynamic Engagement in Video-Mediated Copresence through Attention-Aware Mediation: Sieun Kim, Junyi Zhu
• Mobile Health Management via Continuous Wearable Telemetry: Di Wang, Bo Huang, Zhiwei Zeng, Jun Ji, Bo Gao, Robin Chung Leung Chan, Yang Qiu, Ming Chen, Huigui Zhang, Chunyan Miao
• MDwAIstScheduler: A Low-Cost, Voice-Activated Device for Hands-Free Clinical Scheduling: Diego Mardian, Frank Liu [link to paper]
• Fatigue monitoring with multimodal sensing and machine learning: a patient participatory study: Veronica Martinez, Stephen Green, Rosalind Adam, Jonathan Cooper, Derek Hill, Yojana Lotankar, Katherine Bradbury, Daniel Powell, Lisa Duncan
• Circadian Phase Locking of Epilepsy Seizures in Wearable Data: A Single-Patient Case Study: Berenika Ewart-James, Matthew Wragg, Nawid Keshtmand, Amberly Brigden, Paul Marshall, Raul Santos-Rodriguez [link to paper]
• Influence of Sleep Physiological Data on Affect Prediction: Soundarya Ramesh, Hyung Woon Lee, Sankha Cooray, Prasanth Sasikumar, Takahiro Masuda, Suranga Nanayakkara
• EEG Headphones for Everyday Cognitive State Research: Lukas Schick, Michael T. Knierim
• BEmotion: A Dataset of Biosignals and Emotions collected using Smartwatches in the Field: Elias Mueller*, Kirsten Greiner*, Ivo Benke, Alexander Maedche
• From Everyday Wearables to Clinical Workflows: Measuring Early Engagement with Patient-Connected Devices in the EHR: Michael Sobolev
• Towards a Framework for Designing Socially Acceptable Wearables for Well-being: Sabrina Lakhdhir, Sowmya Somanath [link to paper]
• Guided Breathing Lamp: An Interactive Real-Time Breathing-Sensing and Guidance System: Jisu Yim, Rana Kamh, Yuxuan Gao, Yaxin Pang, Junyi Zhu [link to paper]