Camera-Based Health Status Detection With Automatic Face Capture
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Solution Overview
Problem
Existing health status detection methods require manual user intervention and are cumbersome, making it difficult for busy individuals to monitor their health status efficiently.
Innovation Solution
An electronic device with a first and second camera, utilizing a neural network to automatically detect health status data by collecting environmental images and face images under specific conditions, including luminance and face position, without manual user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual health status detection methods are used (requiring users to manually start applications and photograph), then users can obtain health status information, but the detection process is long and cumbersome
Solution Approach 1:
The system performs health status detection automatically without requiring user initiation. The electronic device autonomously captures images, processes them through the neural network model, and generates health status data, allowing the system to serve itself rather than requiring manual user operation.
Solution Approach 2:
The neural network model is pre-established and trained beforehand to perform health status analysis. The system prepares the detection framework in advance, so when image data is available, the analysis can be performed immediately without requiring users to manually start detection processes or wait for complex setup procedures.
2Productivity
If automatic health status detection is implemented, then health monitoring efficiency is improved, but device complexity increases due to multiple cameras and neural network processing
Solution Approach 1:
The electronic device utilizes existing multi-functional components (display and camera) that serve both their primary functions and health status detection functions. The camera captures images for both general photography and health analysis, while the display shows both regular content and health status results, reducing the need for dedicated separate components.
Solution Approach 2:
The neural network model acts as an intermediary that processes raw image data and transforms it into meaningful health status information. This intermediary layer simplifies the overall system architecture by handling the complex analysis tasks in a standardized manner, making the integration of multiple cameras and processing steps more manageable.
Data Source
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AI summary
A health status detection method and device, and a computer storage medium are provided. A first camera (112) is controlled to collect an environmental image, and detection parameters are obtained from the environmental image. If it is determined that the detection parameters meet detection conditions corresponding to the detection parameters, a second camera (113) or the first camera (112) is controlled to collect a face image, and health status data is generated based on the face image. The first camera (112) collects the environmental image to determine the detection conditions corresponding to the detection parameters, and the second camera (113) or the first camera (112) is controlled to collect the face image to generate the health status data. A user does not need to manually perform health status detection, but health status detection can be automatically performed, to implement senseless health status detection.