一种基于摩擦自供电传感的睡眠呼吸智能监测技术

doi:10.3969/j.issn.0258-8021.2026.02.008

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Abstract Real-time monitoring of sleep apnea syndrome is crucial for early diagnosis and health management. Current clinical monitoring techniques face challenges such as complex wiring, the need for a continuous power supply, and poor portability, which hinder long-term home-based monitoring. To address these issues, this study proposed a self-powered intelligent sleep respiration monitoring system based on a triboelectric nanogenerator (TENG) for real-time monitoring and warning of sleep respiratory status. The system converted respiratory movements into electrical signals through a contact-separation mode triboelectric sensing mechanism. The sensor employed a PDMS/thermally expandable microsphere composite film and FEP as triboelectric layers, with optimized fabrication parameters (copper film size: 5 cm × 5 cm, thickness: 100 nm; PDMS-to-microsphere mass ratio: 100:1; spin-coating speed: 4000 rpm; heating conditions: 110℃ for 10 min). A dual-stream decision fusion method combining energy feature extraction and support vector machine (SVM) classification was adopted to achieve accurate recognition of respiratory status. Test results on 50 sets of sleep respiratory data from 10 volunteers showed that the system accurately extracted key parameters including respiratory rate, apnea events, and cumulative duration, with an apnea event recognition accuracy exceeding 90%, and triggered real-time alarms based on thresholds. The developed system features self-powering, high sensitivity, wearability and low cost, making it suitable for long-term respiratory monitoring in home environments and providing a feasible solution for home-based screening and health management of sleep apnea syndrome.

Key words： self-powered sleep apnea triboelectric nanogenerator real-time monitoring early warning

Received: 21 February 2025

PACS:

R318

About author:: ^#Member,Chinese Society of Biomedical Engineering

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	Articles by authors
	Cao Yixin
	Liu Bing
	Yu Yutong
	Zhu Heyao
	Yi Huansheng
	Lu Guohua
	Xu canhua
	Qi fugui

Cite this article:

Cao Yixin,Liu Bing,Yu Yutong, et al. Friction-Based Self-Powered Sensing Technology for Intelligent Monitoring of Sleep Breathing[J]. Chinese Journal of Biomedical Engineering, 2026, 45(2): 211-219.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2026.02.008 OR http://cjbme.csbme.org/EN/Y2026/V45/I2/211

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