Depth Camera Movement Indication for Selective Breathing Detection
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Solution Overview
Problem
Existing technologies lack effective methods for non-contact, discrete longitudinal monitoring of sleep quality, particularly breathing patterns, which are crucial for understanding and improving care for sleep disorders.
Innovation Solution
A system using a depth camera to capture depth data from a scene, selectively processing a non-contiguous subset of pixels to generate movement data, including breathing and pulse patterns, through frequency domain filtering and threshold analysis, reducing data storage and processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth data from all pixels is processed continuously to monitor breathing patterns, then measurement precision is improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The patent divides the video image into multiple blocks and selectively processes only certain blocks containing pixels that show depth changes over time. This segmentation approach processes a subset of pixels rather than all pixels, reducing data storage requirements while maintaining detection accuracy for breathing patterns.
Solution Approach 2:
The system performs partial processing by selecting and processing only those pixel blocks that exhibit depth variations, rather than processing all pixels uniformly. This partial action reduces the quantity of data stored and processed while still capturing sufficient information to accurately detect breathing patterns through frequency domain filtering.
2Measurement precision
If depth data from all pixels is processed continuously to monitor breathing patterns, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the image into blocks and applies selective processing only to blocks with depth changes. This reduces processing complexity by avoiding unnecessary computation on static regions while maintaining precision in detecting breathing movements through targeted frequency analysis.
Solution Approach 2:
The system applies partial processing by focusing computational resources only on pixel blocks showing depth variations. This partial action reduces device complexity by eliminating redundant processing steps while preserving the ability to accurately detect breathing patterns through selective frequency domain filtering.
3Quantity of substance
If a selective subset of pixels is processed to reduce data storage, then data storage requirements are reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent applies local quality by differentiating processing intensity across different image regions. Blocks with depth changes receive full processing attention to maintain measurement precision, while blocks without changes are excluded from processing, reducing data storage requirements without compromising breathing detection accuracy.
Solution Approach 2:
The system changes the parameter of pixel selection dynamically based on depth variation detection. By adjusting which pixels are processed based on their temporal depth characteristics, the system reduces data storage requirements while maintaining measurement precision through adaptive parameter selection that focuses on relevant breathing-related movements.
4Measurement precision
If frequency domain filtering is applied to identify breathing rates, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent applies partial frequency domain filtering only to selected pixel blocks showing depth changes, rather than performing filtering on all pixels. This partial action reduces processing time while maintaining breathing rate identification accuracy by focusing computational effort only on relevant data regions.
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 effectively monitors sleep quality and breathing patterns with reduced data storage and processing, providing accurate movement indications for improved sleep disorder diagnosis and care.
Implementation Method 1
receiving depth data for a first plurality of pixels of a video image of a scene
Implementation Method 2
The means for processing the depth data may perform frequency domain filtering. The frequency domain filtering may identify movements with frequencies in a normal range of breathing rates.
Data Source
AI summary
An apparatus, method and computer program is described comprising: receiving depth data for a first plurality of pixels of a video image of a scene; determining depth data over time for each of a second plurality of pixels of the video image, wherein the second plurality of pixels comprises at least some of the first plurality of pixels; and processing the determined depth data of successive instances of the second plurality of pixels to generate movement data, wherein each instance of the second plurality of pixels comprises depth data of the second plurality of pixels.