Occupant Convulsion Detection Using Skeleton Points in Vehicles
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Solution Overview
Problem
Conventional methods struggle to detect physical condition abnormalities in occupants that do not involve a large change in posture, such as posture collapse, leading to potential undetection of conditions like seizures.
Innovation Solution
A physical condition abnormality determination device that utilizes a posture collapse detecting unit to analyze head position and a skeleton point detecting unit to identify convulsions, determining an abnormality based on the absence of posture collapse and presence of convulsions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional posture collapse detection methods are used, then detection simplicity is maintained, but detection coverage is insufficient for abnormalities without large posture changes
Solution Approach 1:
The detection system is divided into two independent modules: posture collapse detection (head position analysis) and convulsion detection (skeleton coordinate analysis). Each module processes specific types of abnormalities independently, allowing the system to expand detection coverage without requiring complete redesign of the entire system.
Solution Approach 2:
The imaging device and processing system serve multiple functions: they detect both posture collapse (through head position analysis) and convulsions (through skeleton coordinate analysis). This multi-functionality allows a single system to address various types of physical abnormalities without requiring separate specialized devices for each condition.
2Measurement precision
If only head position analysis is used, then processing speed is maintained, but detection accuracy for all abnormality types is reduced
Solution Approach 1:
The system dynamically selects which detection method to apply based on the situation: it can use quick head position analysis for potential posture collapse cases, and more detailed skeleton coordinate analysis for detecting convulsions. This dynamic approach ensures high detection accuracy while maintaining processing efficiency by not always applying the most computationally intensive method.
Solution Approach 2:
The skeleton coordinate information serves as an intermediary that bridges head position data and convulsion detection. Rather than directly analyzing raw image data for convulsions, the system first extracts skeleton coordinates as intermediate features, which then facilitate accurate convulsion detection while reducing overall processing complexity.
Data Source
AI summary
Included are: a posture collapse detecting unit that detects posture collapse of an occupant in a mobile object on the basis of a head position of the occupant in an imaged image obtained by imaging at least a face of the occupant, the head position being detected on the basis of the imaged image; a skeleton point detecting unit that detects a skeleton coordinate point indicating a body part of the occupant on the imaged image on the basis of the imaged image; a convulsion detecting unit that detects a convulsion of the occupant on the basis of skeleton coordinate point information regarding the skeleton coordinate point detected by the skeleton point detecting unit; and a determination unit that determines that the occupant has a physical condition abnormality when posture collapse of the occupant is not detected and convulsion of the occupant is detected.