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Smartphone acoustics enable non-invasive respiratory phase detection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel framework for detecting respiratory phases using acoustic signals from standard smartphones. This approach employs a hybrid CNN-Transformer architecture to analyze spectral features and temporal patterns of breathing. The system achieved significant accuracy in detecting breathing cycles and exhalation phases, demonstrating its potential for real-time, privacy-preserving respiratory monitoring on mobile devices. AI

IMPACT Enables privacy-preserving, real-time respiratory monitoring on commodity mobile devices.

RANK_REASON Academic paper detailing a new AI model and framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Smartphone acoustics enable non-invasive respiratory phase detection

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17 / 100
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Academic paper detailing a new AI model and framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Julian Szymanski, Tomasz Sankowski, Piotr Sulewski, Iwo Czartowski, Javier Medina Quero ·

    Acoustic Respiratory Phase Detection on Mobile Devices

    arXiv:2611.16710v1 Announce Type: new Abstract: Breathing monitoring is important for well-being, stress regulation, and guided breathing exercises, yet existing approaches often rely on contact-based sensors or controlled environments. This paper presents a non-invasive framewor…

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