Vital Sign Signal Prediction Using Human and Environmental Features
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Solution Overview
Problem
Existing vital sign measurement technologies using contact sensors like electrocardiogram monitors and millimeter-wave radar face limitations such as interference and require regular calibration, while millimeter-wave radar-based measurements suffer from accuracy issues.
Innovation Solution
A human factor intelligence-based method that obtains individual and environmental feature representation data, combined with vital sign spectrum data, to predict vital sign signals with the differential impacts removed, using a target classification model trained on these data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contact sensors such as electrocardiogram monitors are used for measurement, then measurement can be obtained, but interference occurs and regular calibration and maintenance are required
Solution Approach 1:
The patent replaces contact-based mechanical/electrical sensors with millimeter-wave radar technology, substituting a physical contact measurement system with a non-contact electromagnetic wave-based system. This eliminates the interference and calibration issues associated with contact sensors while maintaining measurement capability for vital signs such as heart rate and respiration rate.
2Ease of operation
If millimeter-wave radar-based measurement is used, then non-contact measurement is achieved, but measurement accuracy needs improvement
Solution Approach 1:
The patent introduces an attention mechanism as an intermediary component in the neural network architecture. This attention mechanism selectively weights different signal components and feature representations, allowing the system to focus on the most relevant information for vital sign extraction while filtering out noise and interference, thereby improving measurement accuracy without requiring contact.
3Measurement precision
If individual and environmental factors are considered, then measurement accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple data sources including individual characteristics, environmental factors, and radar signals into a unified neural network processing framework. By combining these diverse inputs and processing them through an integrated attention-based model, the system achieves improved accuracy while managing complexity through unified architecture rather than separate processing systems.
Data Source
AI summary
Embodiments of the present disclosure provide a human factor intelligence-based vital sign signal measurement method and apparatus, and a device. The method includes: obtaining individual feature representation data of a measured object, environmental feature representation data of an environment where the measured object is located, and vital sign spectrum data of the measured object, wherein the individual feature representation data and the environmental feature representation data have a differential impact on the vital sign spectrum data; and performing a signal value prediction based on the individual feature representation data, the environmental feature representation data, and the vital sign spectrum data to obtain a vital sign signal value with the differential impact removed.