Bioinformation Processing Device for Accurate Person Identification
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Solution Overview
Problem
Existing biological information processing techniques face challenges in accurately associating individuals with their biological data, particularly when multiple individuals share the same bedding patterns and during sleep when face authentication is hindered by covered or moving faces.
Innovation Solution
A biological information processing device that uses a signal reception unit to receive signals from individuals, calculates arrival directions and distances to identify candidate regions, and acquires position information to accurately associate biological information with the correct individual, employing methods like LiDAR, pressure sensors, and thermal imaging to ensure accurate identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If face authentication is used to identify the person to be measured, then identification accuracy is improved, but the system fails when the face is covered or moves out of the imaging device's capture range
Solution Approach 1:
The system changes the identification parameters from facial features to body shape parameters (length, width, curvature) extracted from imaging data. This allows identification to continue even when the face is covered or not visible, as body shape parameters can be obtained from alternative regions of the body
Solution Approach 2:
The system introduces body shape parameters as an intermediary for identification when face authentication fails. These parameters serve as a backup identification method that bridges the gap when primary facial recognition cannot be performed
2Ease of operation
If bedding pattern is used to identify the person to be measured, then identification can be performed, but accuracy deteriorates when multiple persons use bedding with the same pattern
Solution Approach 1:
The system segments the identification process into multiple independent parameters: bedding pattern recognition, body position detection, and body shape parameter extraction. By combining these segmented parameters, the system can distinguish between multiple persons even when they share the same bedding pattern
Solution Approach 2:
The system adds spatial dimensionality to identification by incorporating body position and shape parameters. Instead of relying solely on the two-dimensional bedding pattern, the system uses three-dimensional body shape data (length, width, curvature) to create a more distinctive identification signature
3Productivity
If multiple persons are measured simultaneously, then productivity is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system segments the measurement space into multiple candidate regions, each potentially containing a person to be measured. By processing each region independently and then combining results, the system can handle multiple persons simultaneously without overwhelming complexity
Solution Approach 2:
The system performs measurements on all detected candidate regions rather than attempting to selectively measure only specific persons. This excessive action approach simplifies the process by treating all regions uniformly, then filtering results based on identification matching
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
This approach enables precise association of biological information with individuals, even in scenarios where traditional methods fail, such as shared bedding patterns and obstructed faces during sleep, enhancing accuracy and reliability.
Implementation Method 1
a signal reception unit configured to receive a signal related to biological information reflected from at least one person to be measured
Implementation Method 2
calculate an arrival direction of the signal and/or a distance to the at least one person to be measured from the signal received
Data Source
AI summary
A bioinformation processing device includes a signal reception unit configured to receive a signal related to bioinformation reflected from at least one person to be measured, a candidate region identification unit configured to calculate an arrival direction of the signal and/or a distance to the at least one person to be measured from the signal received and identify a candidate region of the at least one person to be measured using the arrival direction and/or the distance calculated, an information generation unit configured to generate bioinformation corresponding to the candidate region of the at least one person to be measured from the signal received, a position information acquisition unit configured to acquire position information of the at least one person to be measured, and a bioinformation association unit configured to associate the at least one person to be measured with the bioinformation generated, based on the position information acquired.