Depth-Enhanced Video Analysis for Home Health Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing home health care monitoring systems are expensive and inefficient, particularly for elderly patients, as they require numerous healthcare workers and complex algorithms for accurate 3D motion detection, which can be computationally intensive and prone to errors, especially in environments where depth data is incomplete or inaccurate.
Innovation Solution
A home health care monitoring system that performs depth-enhanced video content analysis using a network of cameras capturing two-dimensional and depth data, allowing for accurate detection of patient activities such as sitting, standing, or lying down, by integrating depth data with traditional image analysis to create a more reliable and efficient monitoring system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full 3D motion detection is performed by analyzing three-dimensional data for all parts of a video scene, then measurement precision is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the video scene into multiple depth planes, analyzing only relevant regions at each depth level rather than processing all pixels in the entire scene. This divides the computationally intensive 3D analysis into manageable segments, reducing processing requirements while maintaining detection accuracy for patient movements.
Solution Approach 2:
The system applies different processing depths and analysis methods to different regions of the video scene based on their importance. Critical areas where patient movements occur receive more detailed 3D analysis, while less important background regions receive simplified processing, optimizing the balance between precision and computational load.
2Measurement precision
If depth sensor systems are used to generate three-dimensional data, then object detection accuracy is improved, but manufacturing precision and data completeness deteriorate due to lower resolution and errors in determining three-dimensional coordinates
Solution Approach 1:
The patent merges depth sensor data with traditional 2D video analysis by integrating results from both methods. The system combines the three-dimensional spatial information from depth sensors with the high-resolution visual details from 2D cameras, creating a complementary analysis that compensates for the weaknesses of each individual approach.
Solution Approach 2:
The system uses an intermediary processing layer that reconciles depth sensor data with 2D video data. This intermediary analysis compares and validates findings from both sources, correcting errors in depth coordinate determination using 2D visual cues while enhancing 2D detection with 3D spatial context.
3Reliability
If a large number of health care workers are used to monitor patients, then reliability of monitoring is improved, but loss of energy and operational cost increase
Solution Approach 1:
The monitoring system performs self-service by automatically detecting patient conditions, movements, and potential emergencies using video analysis algorithms. The system autonomously monitors patients without requiring constant human intervention, only triggering alerts to health care workers when actual events occur, thereby reducing their workload and operational costs while maintaining reliable monitoring.
Solution Approach 2:
The patent replaces the mechanical system of continuous human monitoring with an automated video content analysis system. Computer-based algorithms continuously analyze video data to detect patient events, substituting human labor with automated computational processes that reduce energy consumption and operational costs while maintaining or improving monitoring reliability.
4Device complexity
If traditional two-dimensional monitoring schemes are used, then device complexity is reduced, but measurement precision and ability to detect certain patient conditions deteriorate
Solution Approach 1:
The patent incorporates depth information as an additional dimension to traditional 2D video monitoring. By adding the depth dimension to create a more comprehensive three-dimensional understanding of patient movements and positions, the system improves detection accuracy for conditions like falls and posture changes while only moderately increasing complexity compared to pure 2D systems.
Data Source
AI summary
A monitoring method and system are disclosed. In one embodiment, a method includes monitoring the health of a person in a home. The method includes capturing a video sequence from a first camera disposed within the home, including capturing two-dimensional image data for the video sequence; receiving depth data corresponding to the two-dimensional data, and associating the depth data with the video sequence as metadata; setting a plurality of events to monitor associated with the person, the events defined to include actions captured from the first camera, at least a first event including the person's body being in a particular bodily position and performing video content analysis on the video sequence to determine whether the events have occurred. The video content analysis includes automatically detecting a potential human object from the video sequence based on the two-dimensional image data; using the depth data to determine a size and bodily position of the potential human object; and based on the size of the potential object, confirming that the potential human object is an actual human, thereby confirming the potential human object as a target. The method further includes determining that the first event has occurred based on the determined bodily position of the target.