UWB Radar Point Cloud Classification for Elderly Activity Monitoring
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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 fail to distinguish between activities like walking, standing, and sitting, posing challenges for accurate monitoring of Activities of Daily Living (ADLs) and fall detection in long-term care settings.
Innovation Solution
A system utilizing ultra-wideband radar and a geometric and AI model to detect positions and movements of elderly individuals, transforming data into point clouds that vary by movement, correlating with known states, and using a neural network classifier to determine physical states like walking, standing, sitting, or falling, with audio feedback and alerts for caregivers.
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 continuous monitoring is enabled, but measurement precision deteriorates and the system cannot distinguish between different physical states
Solution Approach 1:
The patent transforms the radar data from simple presence detection to detailed physical state classification by changing the analysis parameters from basic motion detection to multi-dimensional point cloud analysis including position, velocity, acceleration, and spatial distribution patterns. This enables distinction between walking, standing, sitting, and falling states while maintaining continuous non-invasive monitoring
Solution Approach 2:
The patent adds dimensional analysis by creating point clouds that represent three-dimensional spatial positions of body parts over time. This dimensional transformation from simple radar signals to multi-dimensional point cloud data structures enables precise discrimination of physical states that cannot be distinguished by traditional radar health monitors
2Measurement precision
If video cameras are used for monitoring, then measurement precision improves for activity detection, but privacy requirements are violated and the system cannot be used permanently
Solution Approach 1:
The patent substitutes the optical/mechanical video camera system with an electromagnetic radar-based point cloud system. This substitution maintains measurement precision for activity detection while eliminating privacy violations, enabling permanent continuous monitoring in long-term care settings
3Ease of operation
If existing radar systems are used, then non-invasive monitoring is achieved, but the system cannot distinguish between walking, standing, and sitting states
Solution Approach 1:
The patent segments the radar return signal into multiple point clouds representing different body parts (head, torso, limbs) and analyzes their relative positions and movements. This segmentation enables distinction between physical states by tracking how different body segments move and position themselves in space during walking, standing, sitting, or falling
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 comprehensive, non-invasive, and accurate monitoring of elderly individuals' activities and vital signs, detecting falls and deviations from customary routines, providing timely alerts and improving safety in long-term care facilities.
Implementation Method 1
The tracking device may include at least one wide band radar
Data Source
AI summary
Determining a physical state of a person includes detecting positions of different portions of the person, transforming detected positions of the person 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, and choosing a particular physical state by matching the data from the point cloud with the particular physical state. Positions of different portions of the person may be detected using a tracking device. The tracking device may be a non-contact tracking device. The tracking device may include at least one wide band radar. The tracking devices may communicate wirelessly with at least one server in a cloud computing system. The states may include walking, standing, sitting, laying down, turning in bed, falling, and/or departed.