Pilot Outcomes

Award: McKnight’s Tech Awards 2025 Golds in Emerging Technology and in Falls Prevention, Management or Detection Categories

Emerging Technology Category: The Emergency Technology category acknowledges innovators who have developed technology-driven tools showing great potential for improving care and/or the bottom line, even though they are not yet in the broad marketplace. Neursantys won top honors in this category for their pilot study with The Forum at Rancho San Antonio and LCS for their entry titled “NEURVESTA Vestibular Stimulation Therapy for Restoring Balance.” The study, involving 35 residents, showed that the protocol can enhance balance and lower the risk of falls, resulting in residents feeling more confident to participate in group outings and return to their exercise classes, and delaying the potential need for a higher level of care. Falls Prevention, Management or Detection: The Falls Prevention, Management or Detection category recognizes the use of technology that helps providers reduce the risk of…

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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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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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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…

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