Vehicle Occupant Pose Monitoring for Personalized Seating Guidance
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Solution Overview
Problem
Current cabin sensing solutions in vehicles lack the ability to customize recommendations for improving the seating pose of occupants, leading to potential discomfort and pain due to unfavorable seating positions, which are not easily detected until after a long ride.
Innovation Solution
A computer-implemented method and system that uses image processing and neural networks to detect and classify body points of occupants, providing personalized recommendations for adapting their pose through a sensing device, processing unit, and output unit, which can include cameras, radar, or Lidar sensors, to monitor and improve seating comfort in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general seating recommendations are provided to occupants, then basic seating guidance is available, but the recommendations cannot be customized for specific occupants and cannot be monitored over time
Solution Approach 1:
The system performs preliminary actions by capturing images of the occupant before the ride begins, identifying body points and determining pose characteristics in advance. This allows the system to provide customized seating recommendations before discomfort occurs, rather than relying on general guidelines
Solution Approach 2:
The system establishes a feedback loop by continuously monitoring the occupant's pose through image capture, analyzing body point positions, comparing against proper pose criteria, and providing recommendations. The system can track pose changes over time and adjust recommendations based on occupant responses, creating an adaptive feedback mechanism
2Reliability
If the occupant's pose is monitored continuously, then problematic positions are detected early, but the system complexity and processing requirements increase
Solution Approach 1:
The system segments the occupant's body into key body points (head, shoulders, elbows, hips, knees, etc.) that can be independently identified and tracked. This segmentation approach simplifies the monitoring task by focusing on critical anatomical landmarks rather than analyzing the entire body continuously
Solution Approach 2:
The system replaces complex mechanical pose detection devices with imaging sensors (cameras, radar, or Lidar) that capture visual data. Image processing algorithms then substitute for mechanical measurement systems, enabling non-contact, continuous monitoring with reduced mechanical complexity
3Measurement precision
If detailed body point identification is performed, then accurate pose classification is achieved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by identifying only the most critical body points (head, shoulders, elbows, hips, knees) rather than tracking every point on the body. This selective approach provides sufficient precision for pose classification while significantly reducing computational burden and processing time
Data Source
AI summary
Systems and techniques are provided for monitoring an occupant located in an interior of a vehicle. The techniques include the detection an occupant in at least one image. Body points of the occupant are identified based on the at least one image, and a pose of the occupant is classified based on the detected body points. A recommendation for adapting the occupant's pose is provided based on the classified pose.