Movano Health Applies Advanced AI through Deep Learning to Deliver Improved Accuracy of Heart Rate in Motion

Enhancements address significant clinical enterprise need in wearables for a variety of healthcare applications

PLEASANTON, Calif., June 13, 2024 /PRNewswire/ -- Movano Health (Nasdaq: MOVE) announced major advances in the accuracy of its heart rate in motion algorithm following the implementation of deep learning into the processing. The Company recently released an engineering accuracy study directed by Movano Health Founder and CTO Michael Leabman, Enhanced Heart Rate in Motion Accuracy with the Evie Ring Using Advanced Deep Learning Algorithms, which demonstrates the value of deep learning integration into heart rate (HR) algorithms for improved accuracy.

"Utilizing deep learning is significantly better than standard techniques as it is the optimal solution for removing the effects of motion, eliminating the noise and motion artifacts in the optical signal," said Leabman. "We believe that this is a first of its kind implementation and an innovation that has the potential to enhance the reliability of wearable health monitors, providing users with more accurate and consistent heart rate measurements."

The study was conducted with 65 subjects, completing 7-10 sessions of various activities including sleeping, resting, walking, running, climbing stairs, working out at the gym and swimming. Data was collected with the Evie Ring and a Polar H7 chest strap used as a control device. The results demonstrated a high correlation with the Polar H7 chest strap outputs across a diverse data set, confirming the reliable reporting of heart rate by Evie's HR algorithm across all activities.

To overcome the challenges of measuring heart rate from PPG signals in wearables, Evie's HR solution combines the best from the signal processing world as well as recent advances in AI-based Deep Learning.

• Optimally filtering out motion artifacts and more accurately tracking heart rate through development of AI algorithms in a specific, novel Deep Learning solution.
• Removing motion artifacts from the PPG signal by leveraging both PPG and 3D accelerometer data.
• Enhancing the signal-to-noise ratio (SNR) through Deep Learning.

The Company plans to convert all Evie Ring algorithms including sleep, respiration, heart rate variability (HRV), and blood oxygen saturation (SpO2) through this same process.

About Movano Health
Founded in 2018, Movano Inc. (Nasdaq: MOVE) dba Movano Health is developing a suite of purpose-driven healthcare solutions to bring medical-grade data to the forefront of wearables. Featuring modern and flexible form factors, Movano Health's devices offer an innovative approach to delivering trusted data to both customers and enterprises, capturing a comprehensive picture of an individual's health data and uniquely translating it into personalized and intelligent insights.

Movano Health's proprietary technologies and wearable medical device solutions will soon enable the use of data as a tool to proactively monitor and manage health outcomes across a number of patient populations that exist in healthcare. For more information on Movano Health, visit https://movanohealth.com/.

Forward Looking Statements
This press release contains forward-looking statements concerning our expectations, anticipations, intentions, beliefs, or strategies regarding the future. These forward-looking statements are based on assumptions that we have made as of the date hereof and are subject to known and unknown risks and uncertainties that could cause actual results, conditions, and events to differ materially from those anticipated. Therefore, you should not place undue reliance on forward-looking statements. Examples of forward-looking statements include, among others, statements we make regarding plans with respect to the commercial launches of the Evie Ring and Evie Med; planned cost-cutting initiatives; anticipated FDA clearance decisions with respect to our products; expected future operating results; product development and features, product releases, clinical trials and regulatory initiatives; our strategies, positioning and expectations for future events or performance. Important factors that could cause actual results to differ materially from those in the forward-looking statements are set forth in our most recent Annual Report on Form 10-K and any subsequent Quarterly Reports on Form 10-Q, and in our other reports filed with the Securities and Exchange Commission, including under the caption "Risk Factors."  Any forward-looking statement in this release speaks only as of the date of this release. We undertake no obligation to publicly update any forward-looking statement, whether written or oral, that may be made from time to time, whether as a result of new information, future developments or otherwise.

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