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.…

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

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…

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

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…

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

Grant Funding: Novel orthostatic vital signs measured by an earlobe wearable device (1R21AG088945)

Principal investigator, Amar Basu, professor of electrical and computer engineering at Wayne State University and CEO of TRACE Biometrics LLC, has received a National Institute on Aging R21 award to further validate the TRACE sensor against gold standard clinical orthostatic measures, which will build upon the pilot award's work towards securing FDA clearance. Project Summary: Orthostatic disorders, including orthostatic hypotension (OH), disproportionately affect older adults, presenting in 30% of older adults and up to 70% of nursing home residents. As OH is a major risk factor for syncope, falls, and cognitive decline, medical agencies stress the public health need for monitoring orthostatic vital signs (OVS) in at-risk individuals. This proposal investigates an NIA award-winning wearable device called TRACE, which addresses fundamental limitations of the current clinical standard, the blood pressure (BP) cuff: 1) The BP…

Continue ReadingGrant Funding: Novel orthostatic vital signs measured by an earlobe wearable device (1R21AG088945)

Publication: Developing an Equitable Machine Learning-Based Music Intervention for Older Adults At Risk for Alzheimer Disease: Protocol for Algorithm Development and Validation

Authors: Chelsea S Brown, Luna Dziewietin, Virginia Partridge, Jennifer Rae Myers Abstract Background: Given the high prevalence and cost of Alzheimer disease (AD), it is crucial to develop equitable interventions to address lifestyle factors associated with AD incidence (eg, depression). While lifestyle interventions show promise for reducing cognitive decline, culturally sensitive interventions are needed to ensure acceptability and engagement. Given the increased risk for AD and health care barriers among rural-residing older adults, tailoring interventions to align with rural culture and distinct needs is important to improve accessibility and adherence. Objective: This protocol aims to develop an intelligent recommendation system capable of identifying the optimal therapeutic music components to elicit engagement and resonate with diverse rural-residing older adults at risk for AD. Aim 1 is to develop culturally inclusive user personas for rural-residing older adults to understand…

Continue ReadingPublication: Developing an Equitable Machine Learning-Based Music Intervention for Older Adults At Risk for Alzheimer Disease: Protocol for Algorithm Development and Validation