A fall detection, physiological sign and sleep monitoring early warning system and method
By integrating fall detection, physiological signs, and sleep monitoring through millimeter-wave radar and deep learning networks, this technology solves the problems of discomfort and poor environmental adaptability in existing technologies, achieving efficient and accurate non-contact monitoring and early warning, and improving the safety of smart healthcare and health management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- WUHAN HUAZHONG DIGITAL INNOVATION TECHNOLOGY CO LTD
- Filing Date
- 2026-01-28
- Publication Date
- 2026-05-26
AI Technical Summary
Existing technologies for fall detection and physiological sign monitoring suffer from problems such as discomfort when wearing them, poor environmental adaptability, and insufficient privacy protection. There is a lack of research on integrated systems for fall detection, physiological signs, and sleep monitoring using millimeter-wave radar, which makes it difficult to achieve efficient and accurate non-contact monitoring and early warning, especially in smart healthcare and health management.
By combining millimeter-wave radar with a deep learning network, and through phase change and point cloud data processing, physiological signs and human posture are monitored. Multi-level signal processing algorithms and real-time edge processing are used, combined with a dual verification mechanism of "posture + signs", to provide real-time early warning.
It enables all-weather, unobtrusive health monitoring, improves posture estimation accuracy and fall detection accuracy, reduces false alarm rate, ensures real-time monitoring and privacy protection, and provides reliable sleep quality assessment.
Smart Images

Figure CN121570170B_ABST