UWB Radar Point Cloud Vital Sign Detection
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Solution Overview
Problem
Current non-invasive tracking technologies for elderly individuals, such as Wi-Fi motion technology and Radar Health Monitor, are imprecise and unable to distinguish between different activities like walking, standing, and sitting, posing challenges for accurate monitoring of Activities of Daily Living (ADLs) and early detection of health issues like falls and chronic diseases.
Innovation Solution
A system utilizing ultra-wideband radar and a geometric and AI model, specifically a long short-term memory recurrent neural network, to detect positions and vital signs of elderly individuals, differentiate between various states, and alert caregivers to potential dangers by correlating movement and position data with known physical states, enabling non-contact monitoring of activities and health conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Wi-Fi motion technology or Radar Health Monitor is used for non-invasive tracking, then privacy requirements are satisfied and non-contact monitoring is achieved, but measurement precision and ability to distinguish different activities deteriorate
Solution Approach 1:
The patent changes the operating parameters of the radar system by using ultra-wideband frequency range with multiple sub-bands, adjusting signal frequency and pulse duration to capture different types of movements. This enables differentiation between various activities (walking, standing, sitting, falling) while maintaining non-contact monitoring and privacy compliance
Solution Approach 2:
The patent segments the monitoring function into multiple independent components: point cloud generation from radar signals, point cloud processing to extract movement features, activity classification algorithms, and vital sign detection. This segmentation allows each component to be optimized for its specific function, improving overall measurement precision while maintaining the non-invasive nature of the system
2Reliability
If existing radar systems are used to monitor vital signs, then non-contact monitoring is achieved, but the system cannot distinguish between static states (standing, sitting, laying) deteriorating measurement precision
Solution Approach 1:
The patent applies dynamics by continuously analyzing changes in point cloud characteristics over time, including position, velocity, and acceleration of detected points. By examining temporal variations in these parameters, the system can distinguish between different static states (standing, sitting, laying) even when the subject appears motionless, while maintaining non-contact monitoring
Solution Approach 2:
The patent transitions from traditional 2D radar imaging to 3D point cloud representation, adding spatial depth information. This dimensional enhancement allows the system to differentiate between various static postures by analyzing the spatial distribution and orientation of points in three-dimensional space, improving measurement precision while maintaining non-contact capability
3Measurement precision
If comprehensive monitoring of all activities is implemented, then detection accuracy for health issues improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-processing radar signals into point cloud representations and pre-training machine learning models with labeled activity data. This preparation work is done in advance, allowing the system to quickly classify activities and detect health issues in real-time without requiring complex real-time computation, thus improving detection accuracy while managing device complexity
Solution Approach 2:
The patent introduces point cloud data structures as an intermediary between raw radar signals and activity classification algorithms. This intermediate representation simplifies the data format and extracts relevant features automatically, making the subsequent classification and detection processes more efficient and reducing overall system complexity while maintaining high detection accuracy
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
The system provides comprehensive, non-invasive monitoring of elderly individuals, accurately detecting states like walking, standing, and falling, and capturing vital signs to alert caregivers of dangerous situations, improving the detection of health issues and ensuring the safety of elderly individuals in long-term care facilities.
Implementation Method 1
detecting positions of different portions of the person... Positions of different portions of the person may be detected using a non-contact detector. The non-contact detector may be provided by reflections of a radar signal from a wide-band radar.
Implementation Method 2
The non-contact detector may be provided by reflections of a radar signal from a wide-band radar.
Implementation Method 3
Vital signs may be obtained by detecting pulsations in the point cloud representing breathing and heartbeats.
Data Source
AI summary
Determining a physical state of a person includes detecting positions of different portions of the person, transforming detected positions into a point cloud having a density that varies according to movement of each of the portions, correlating movement and position data from the point cloud with known physical state positions and transitions between different states, choosing a particular physical state by matching the data from the point cloud with the particular physical state, and obtaining vital signs of the person during an optimal period of time for automatic capturing of vital signs by detecting when the person is in a particular state. The particular state may be a static state. The static state may be standing, sitting, or laying down. The vital signs may include measuring a breathing rate and measuring a heartbeat rate. Vital signs may be obtained by detecting pulsations in the point cloud representing breathing and heartbeats.