News & Events

Past Webinar – No One Left Behind: Building Low-Cost Wearables for Low-Income Communities, Longfei Shangguan

https://youtu.be/_Xja_2HiQ9k?si=Y95jpPKXsb25agHp Abstract: Wearable devices such as Apple Watch and Fitbit wristband allow users to track their health statistics around the clock. They have become increasingly popular over the past few years. However, in the context of low-income areas of United States, these wearable devices are still pricey and thus constitute a critical bottleneck in their adoption. In this talk, I will present our past and ongoing works on repurposing electronic wastes, particularly everyday earphones into health trackers - from heart rate monitoring, heart sound recovery, all the way down to pulse wave velocity estimation in home settings. I will also discuss the potential of these technologies for filling the gap of remote health care. I believe this research creates a holistic approach toward recycling and repurposing electronic waste while fostering a sustainable and equitable future.…

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Grant Funding: NSF SBIR Phase II – AI-based Accessible Visual-Assessment App for Active Healthy Aging of Older Adults

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project will result from providing remote, accessible fall risk assessments and exercise programs. This project empowers older adults to maintain independence and improve their quality of life. Falls are a major health risk for older adults, with significant physical, psychological, and economic impacts, costing the U.S. $50 billion annually. Current fall prevention methods are costly, inconsistent, or difficult to access, particularly in rural communities. This project introduces an AI-based video assessment app for routine fall risk assessments and personalized exercises for older adults using common smartphones or tablets. This innovation aims to improve the quality of life for older adults. Beyond improving individual health outcomes, this project has the potential to significantly lower healthcare costs by reducing fall-related hospitalizations, rehabilitation expenses, and long-term…

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Publication: Just-in-Time Adaptive Interventions: Where Are We Now and What Is Next?

Authors: Inbal Nahum-Shani, Susan A Murphy Abstract The past decade has seen a surge in developing just-in-time adaptive interventions (JITAIs)-an intervention approach that leverages advancements in digital technologies to address the rapidly changing needs of individuals in daily life. This article provides an overview of the state of science on JITAI development and highlights important directions for future research. We explain what a JITAI is (and what it is not) and review the scientific and practical rationales underlying this approach. We also call attention to three key challenges relating to the development of JITAIs. The first challenge is that individuals may not be able to engage with (i.e., invest energy in) an intervention when they need it most in daily life. The second concerns the generally suboptimal engagement of individuals in interventions that leverage digital…

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New Product Launch: BIDSleep iPhone and Apple Watch App

BIDSleep helps you access wellness data from your Apple Watch, including heart rate, motion, blood oxygen, and sleep stages, all securely stored on your device. App Purpose: BIDSleep is a wellness app that helps users collect heart rate, blood oxygen (SpO₂), motion, and optional sleep stage data during rest or overnight sessions. It is designed solely for personal data logging and self-monitoring purposes. Source: https://apps.apple.com/us/app/bidsleep/id6747012248?platform=iphone

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Open Source AI-model Released: SLAMSS-IFS

To the study team's knowledge, this is the first open-source four-class sleep staging model developed from a multi-night Apple Watch sleepstudy. SLAMSS-IFS, an advanced version of our previous SLAMSS model, for four-class sleep staging using IHR and accelerometry signals fromthese wearable devices. Key innovations in the model, including an intra-epoch learning LSTM, frequency information incorporation, andskip connections, contribute to substantial performance improvements over other SLAMSS variants and other state-of-the-art models. Ourresults show that SLAMSS-IFS outperforms competing models in overall accuracy, sensitivity, specificity, precision, weighted F1 score, weighted MCC, and most clinical sleep metrics. SLAMSS-IFS: The SLAMSS-IFS model builds on the original SLAMSS model with three additional components: an intraepoch learning sequence-to-sequence long short-term memory (LSTM (“I”), a frequency variable (“F”), and a skip connection (“S”). The intra-epoch learning LSTM processes temporal dependencies within individual epochs.…

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Past Webinar – Intelligent Mobile Systems for an Aging World, Justin Chan (September 23, 2025 @4pm ET)

https://www.youtube.com/watch?v=EUH-9jR48Fo Abstract: By 2050, older adults will make up about 22% of the global population, driving an urgent need for accessible and reliable health technologies. In this talk, I will present our work on intelligent mobile systems designed for older adults. The first enables low-cost health screening using everyday earphones and wireless earbuds. The second is an ambient sensing system that uses smart devices to detect emergent, life-threatening events such as cardiac arrest. The third leverages compact AI-enabled radios for cardiovascular monitoring, including blood pressure. Through these examples, I will show how computational and sensing techniques that generalize across hardware and operate in real-world environments can address pressing societal challenges. Biography: Justin Chan, PhD, Assistant Professor at Carnegie Mellon University Justin is an assistant professor in CS and ECE at Carnegie Mellon University, where he…

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Data Set and Open Source Software Released: Materials used in “Can You Walk Me Through It? Explainable SMS-Phishing Detection Using LLM-Based Agents”

Full article: https://www.usenix.org/system/files/soups2025-wang.pdf Dataset and Software: https://github.com/yizhu-joy/SmishX/tree/main

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