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AI-Enhanced Edge Device for Real-Time Snoring Detection

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  • Jianhua Xie

Abstract

This paper presents the development of a cutting-edge, non-invasive edge device designed to monitor snoring and provide timely, moderate haptic feedback to users. Utilizing the Qualcomm Snapdragon 8cx Gen 3 processor, the device offers robust computing power and AI capabilities for real-time processing, making it a versatile tool for health monitoring applications. The system integrates a high-fidelity MEMS microphone array capable of capturing nuanced audio signals and a TDK piezoelectric haptic actuator, which delivers precise alerts through customized vibrations. The research explores the potential of this advanced hardware in detecting and managing obstructive sleep apnea (OSA), a condition often underdiagnosed due to a lack of patient awareness. By leveraging state-of-the-art digital signal processing and deep learning techniques, the device aims to enhance user awareness and intervention in sleep-related disorders, offering a promising new avenue for improving patient outcomes and quality of life.

Suggested Citation

  • Jianhua Xie, 2024. "AI-Enhanced Edge Device for Real-Time Snoring Detection," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 83-93.
    • Handle: RePEc:das:njaigs:v:6:y:2024:i:1:p:83-93:id:224
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    1. Shaojie Li & Xinqi Dong & Danqing Ma & Bo Dang & Hengyi Zang & Yulu Gong, 2024. "Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research," Papers 2403.14483, arXiv.org.
    2. Chenchen Weng & Ruizhi Yuan & Dandan Ye & Bo Huang & Jiyao Xun, 2024. "Leveraging responsible artificial intelligence to enhance salespeople well-being and performance," The Service Industries Journal, Taylor & Francis Journals, vol. 44(9-10), pages 735-765, July.
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