Movano Evie Ring heart rate study finds AI improves in-motion accuracy via deep learning
Movano Health has unveiled promising results from a recent engineering accuracy study it conducted to see what impacts artificial intelligence (AI) with deep learning techniques has on the accuracy of heart rate in motion as monitored on a wearable device. In the study, the Evie Ring, a wearable device specifically designed for women, was used.
Deep learning is a subset of machine learning, which is an approach to realize artificial intelligence. Hence, deep learning is a technique for implementing machine learning to achieve more capable artificial intelligence.
Called Enhanced Heart Rate in Motion Accuracy with the Evie Ring Using Advanced Deep Learning Algorithms, the study demonstrated significant improvements in heart rate accuracy during physical activity.
Traditionally, heart rate monitors have faced challenges in maintaining accuracy when users are in motion, due to the effects of movement. Movano Health’s latest research addressed this issue by leveraging deep learning algorithms to better interpret the data collected by the Evie Ring. This study shows that users will receive more accurate heart rate measurements, whether they are walking, running or engaged in other physical activities, when wearing a fitness wearable powered by deep learning.
Michael Leabman, CTO of Movano Health, stated, “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.”
He added, “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 Evie Ring, apart from heart rate monitoring, offers a comprehensive suite of health metrics including blood oxygen saturation (SpO2), respiration rate and skin temperature variability. It also features period and ovulation tracking, menstrual symptom tracking, activity profile (including steps, active minutes and calories burned), sleep stages and duration and mood tracking.
This development is particularly relevant as the wearable technology market continues to grow, with consumers increasingly seeking devices that provide accurate health data across various activities.
The deep learning model used in the Evie Ring represents a significant step forward in ensuring that wearable devices can deliver reliable and actionable health insights in real-time.
Photo by Jenny Hill on Unsplash
