Seed round raised from existing investors (True Ventures, BKR Capital, Centre for Aging and Brain Health Innovation, Health2047 and others) - $4.5M to date. Securing of MassAITC pilot funding helped in due diligence with investors for their seed round, and thus was critical to closing their funding including with Health2047, venture arm of the American Medical Association and with other early stage investors. Source: businesswire
"We are excited to partner with Moneta to make personalized, evidence-based cognitive rehabilitation the standard of care for our patients and families. Montana is vast and has the sixth oldest population in the nation. Moneta’s proven program is an accessible approach to proactive brain health for our aging demographic." Dr. Greg Tierney, President of System Clinical Operations at Benefis Health System. LAS VEGAS--(BUSINESS WIRE)--Moneta Health, a brain health company pioneering cognitive rehabilitation therapy through AI-powered delivery, today announced a $4.5 million funding round and a new partnership with Benefis Health System to expand access to cognitive care in neurology deserts. The company has signed a multi-year partnership with Benefis Health System in Montana and secured investment from venture capital firms including True Ventures and Health2047, the American Medical Association’s venture studio, to support its mission…
Authors: Tzu-An Song, Yubo Zhang, Ziyuan Zhou, Luke Hou, Masoud Malekzadeh, Aida Behzad, Joyita Dutta Abstract Polysomnography, the gold standard for sleep evaluations, involves complex setup and data acquisition protocols and requires manual scoring of sleep data. Smartwatches and other multi-sensor consumer wearable devices with automated sleep staging capabilities offer a promising and scalable alternative for routine and long-term sleep evaluations in individuals. We conducted a multi-night study using a smartwatch for sleep assessment and created an AI-driven automated sleep staging framework based on instantaneous heart rate (IHR) and accelerometry data using sleep stage labels based on electroencephalography (EEG) as the reference. 47 healthy adults were recruited to record their sleep for up to seven consecutive nights using an Apple Watch Series 6 and a Dreem 2 Headband. Our sleep staging framework relies on a…
Zoom Registration: https://umass-amherst.zoom.us/meeting/register/4y4Tc9JsS6WTXxrNzTMgkA 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…
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
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.…
Zoom Registration: https://umass-amherst.zoom.us/meeting/register/Hw27HSCgThCh41eN466wiQ 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,…