In-vehicle Fall Prevention via Boarding Motion Analysis
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Solution Overview
Problem
Existing in-vehicle fall prevention systems face challenges in accurately estimating the fall risk of passengers, leading to either excessive safety measures that hinder vehicle operation or inadequate protection, particularly as passenger physical abilities vary and age demographics change.
Innovation Solution
An in-vehicle fall prevention device that estimates physical ability based on boarding motion analysis, determining a fall risk degree and adjusting vehicle departure control accordingly, while also learning from actual falls to improve estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traveling control is performed based on user attributes (age, gender, height), then fall prevention is improved, but service quality deteriorates due to excessively safe control
Solution Approach 1:
The system changes the parameter basis for fall prevention from static user attributes (age, gender, height) to dynamic physical ability values derived from actual boarding motion analysis. This allows the control system to adapt to each user's real-time physical state rather than applying blanket restrictions based on demographic characteristics.
Solution Approach 2:
The system performs preliminary assessment of physical ability during the boarding process itself, analyzing motion data before departure control decisions are made. This preliminary action enables accurate fall risk identification without requiring excessive post-assessment safety measures.
2Measurement precision
If physical ability estimation is performed during boarding, then fall risk determination accuracy is improved, but device complexity increases
Solution Approach 1:
The imaging unit and processing system serve multiple functions: capturing boarding motion data, analyzing physical ability, determining fall risk, and informing departure control decisions. This multi-functionality reduces the need for separate dedicated devices for each function.
Solution Approach 2:
The system replaces complex mechanical assessment devices with image processing and motion analysis algorithms. By using computational methods to analyze boarding motion captured by imaging units, the system achieves accurate physical ability estimation without requiring sophisticated mechanical measurement equipment.
Data Source
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AI summary
An in-vehicle fall prevention device (52) includes: an acquisition unit (54, 236) that acquires a captured image obtained by imaging boarding motions of each of users (M, Ma) when the users step up a stepped part (218) and board a vehicle (10, 210, 300) in which the users can get on and off; an ability estimation unit (56, 238) that estimates a physical ability value for each of the users based on the captured boarding motion; a risk degree determination unit (58, 240) that determines a fall risk degree (D) of the user based on the physical ability value; a determination unit (60) that determines whether a boarding posture corresponding to the fall risk degree is satisfied, for each of the users who boarded the vehicle; and a control unit (62) that executes departure control of the vehicle based on a determination result of the determination unit.