Home Community Google AI Introduces Audioplethysmography (APG): An Artificial Intelligence-Powered Novel Cardiac Monitoring Modality for Lively Noise Cancellation (ANC) Headphones

Google AI Introduces Audioplethysmography (APG): An Artificial Intelligence-Powered Novel Cardiac Monitoring Modality for Lively Noise Cancellation (ANC) Headphones

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Google AI Introduces Audioplethysmography (APG): An Artificial Intelligence-Powered Novel Cardiac Monitoring Modality for Lively Noise Cancellation (ANC) Headphones

In the sector of consumer electronics and health technology, the incorporation of health monitoring features in energetic noise cancelling (ANC) wearables has develop into a outstanding area of interest. The standard methods, nevertheless, often require the mixing of supplementary sensors, resulting in intricate hardware configurations and compromised battery life. In response to those challenges, the research team at Google has introduced a groundbreaking technique often called Audioplethysmography (APG), enabling ANC wearables to conduct robust and precise cardiac monitoring without additional hardware components. This pioneering approach has the potential to redefine the landscape of consumer health sensing, offering a promising and accessible solution for heart rate and heart rate variability monitoring.

Before the arrival of APG, integrating various sensors and microcontrollers for health monitoring in ANC wearables posed significant challenges, particularly in design complexity and price. The research team proposed a novel approach using APG, which involves the transmission of a low-intensity ultrasound signal through the headphones’ speakers, followed by the capturing of modulated echoes through the feedback microphones. This progressive technique allows for the detection and evaluation of subtle changes within the ear canal, providing helpful insights into the user’s cardiac activities without compromising the general design or battery lifetime of the device.

APG leverages a cylindrical resonance model, enabling the extraction of a pulse-like waveform that closely mirrors the user’s heartbeat. Using channel diversity and coherent detection enhances APG’s resilience to motion artefacts, ensuring improved signal quality and accurate monitoring during various physical activities. The research team has successfully demonstrated the effectiveness of APG in measuring heart rate and heart rate variability, even when users are engaged in diverse biological activities, making it a promising and reliable method for low-cost health monitoring through consumer-grade ANC headphones.

The implementation of APG represents a big step forward in consumer health sensing, because it overcomes the constraints related to existing methods without compromising device performance or design complexity. By harnessing the ability of ultrasound technology, the research team has developed a method that continues to be robust and accurate even during users’ dynamic physical activities or diverse physical attributes. This breakthrough has the potential to pave the way in which for the widespread adoption of health-sensing technologies in consumer-grade ANC headphones, thereby making health monitoring more accessible and convenient for a broader population.

Moreover, the unique benefits of APG extend beyond its technical capabilities. Unlike traditional methods, which frequently encounter challenges in accommodating various skin tones and ear canal sizes, APG showcases remarkable resilience to such variations. This inclusivity enhances the accessibility and applicability of APG for a various user base, ensuring its advantages might be experienced by a big selection of people.

In conclusion, introducing APG signifies a critical milestone in hearable health sensing. Its ability to accurately monitor cardiac activities without additional sensors or complex hardware setups underscores its potential to revolutionize consumer health monitoring. By addressing the challenges posed by existing methods and showcasing remarkable resilience to diverse user characteristics, APG opens recent pathways for low-cost and effective health monitoring, making it a promising and accessible technology for a big selection of users.


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Madhur Garg is a consulting intern at MarktechPost. He’s currently pursuing his B.Tech in Civil and Environmental Engineering from the Indian Institute of Technology (IIT), Patna. He shares a powerful passion for Machine Learning and enjoys exploring the newest advancements in technologies and their practical applications. With a keen interest in artificial intelligence and its diverse applications, Madhur is decided to contribute to the sector of Data Science and leverage its potential impact in various industries.


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