Ceiling IoT Radar Sleep Monitoring for Accurate Contactless Biosignals
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Solution Overview
Problem
Existing smart care systems for elderly individuals fail to accurately monitor health parameters without disturbing sleep and do not effectively predict potential health issues, leading to inefficiencies and increased welfare costs.
Innovation Solution
A health care monitoring and smart home convergence system utilizing a built-in ceiling IoT radar sensor that measures heart rate and respiratory rate through time of flight (ToF) of radar signals, analyzes sleep patterns, and controls the home environment based on health data, including noise removal and fall detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If contactless radar sensing is used to monitor health parameters, then user comfort and sleep quality are improved, but measurement precision deteriorates due to noise interference
Solution Approach 1:
The patent introduces multiple intermediate processing mechanisms: signal filtering algorithms to remove noise, calibration procedures using reference measurements, and environmental sensing to account for interfering factors. These intermediaries bridge the gap between contactless sensing and accurate measurement, enabling both user comfort and measurement precision to coexist
Solution Approach 2:
The patent replaces direct mechanical contact (wearable sensors) with electromagnetic field-based radar sensing. This substitution eliminates the need for physical contact while incorporating advanced signal processing to maintain measurement accuracy, thus improving user comfort without sacrificing precision
2Measurement precision
If multiple sensors and processing units are added to improve health monitoring accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent designs the radar sensor system to perform multiple functions: vital sign detection, sleep stage classification, respiratory rate monitoring, and heart rate measurement. By making the system multi-functional, additional capabilities are achieved without proportionally increasing complexity, as the same hardware platform supports diverse health monitoring applications
Solution Approach 2:
The patent combines signal processing, pattern recognition, and health analysis functions into an integrated system. Multiple processing stages (noise filtering, feature extraction, classification) are merged into a unified computational framework, reducing overall system complexity while maintaining high measurement precision
3Reliability
If continuous monitoring is performed to predict health issues early, then reliability of health prediction is improved, but energy consumption increases
Solution Approach 1:
The patent employs periodic monitoring with variable sampling rates that adapt to detected patterns. During stable sleep stages, monitoring intensity is reduced, while during transitional periods or detected anomalies, sampling frequency increases. This periodic approach maintains prediction reliability while significantly reducing average power consumption compared to continuous high-rate monitoring
Solution Approach 2:
The system incorporates automated anomaly detection and event-triggered alert generation that operate autonomously without requiring constant external intervention. The embedded processing unit continuously analyzes data streams and self-manages communication with external devices only when significant events occur, reducing energy consumption from continuous data transmission while maintaining high reliability
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and non-intrusive health monitoring, predicts potential health issues, and automatically adjusts the home environment to support user health, enhancing elderly care without disturbing sleep or daily routines.
Implementation Method 1
measure a user's heart rate or respiratory rate on the basis of time of flight (ToF) of a radar signal
Implementation Method 2
a transmitter configured to output the radar signal at set time intervals, and a receiver configured to receive a reflection signal obtained as the radar signal is reflected
Data Source
AI summary
Proposed is a health care monitoring and smart home convergence system based on a built-in ceiling IoT radar sensor. The health care monitoring and smart home convergence system includes the built-in ceiling IoT radar sensor, and a health analysis part. The built-in ceiling IoT radar sensor is installed into a ceiling of a bedroom, and configured to measure a user's heart rate or respiratory rate on the basis of time of flight (ToF) of a radar signal to generate biosignal data. The health analysis part is configured to analyze the user's sleep pattern on the basis of the biosignal data, and uses the sleep pattern to generate health prediction information on the user's health state.