Abnormality Detection System for Elderly Driver Cognitive Decline
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Solution Overview
Problem
Current cognitive function inspections for elderly drivers are limited to static evaluations and cannot detect early signs of cognitive decline related to driving behaviors, making it difficult to identify abnormalities in cognitive function during daily driving activities.
Innovation Solution
An abnormality detection system comprising a mobility server, a medical server, and a detection and analysis device that monitors and analyzes driving data to detect unusual behaviors such as sudden braking or steering, and notifies medical institutions for further evaluation, enabling early detection of cognitive function-related symptoms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cognitive function inspections are conducted using inspection forms and electronic terminals, then the cognitive function state at the time of inspection can be confirmed, but the cognitive function state in daily life such as when actually driving cannot be confirmed
Solution Approach 1:
The system transitions from static cognitive function inspections to dynamic monitoring of driving behaviors. The detection device continuously captures real-time driving data including steering operations, braking patterns, and acceleration behaviors, enabling assessment of cognitive function in natural driving conditions rather than in controlled inspection environments.
Solution Approach 2:
The system adds a new dimension of assessment by monitoring actual driving behaviors alongside traditional inspection methods. By capturing steering angle, braking force, acceleration patterns, and other vehicular operations, the system evaluates cognitive function across multiple dimensions - both clinical test performance and real-world driving performance.
2Reliability
If abnormal driving behaviors are detected and analyzed, then early signs of cognitive decline can be identified, but the system complexity increases with multiple servers and devices
Solution Approach 1:
The system is divided into distinct functional modules: a detection device in the vehicle for capturing driving data, a mobility server for storing and managing driving data, a medical server for storing medical information, and a detection and analysis device for processing data and generating assessments. This segmentation allows each component to perform its specific function efficiently while reducing overall system complexity through clear division of labor.
Solution Approach 2:
The mobility server and medical server act as intermediary components that bridge the detection device and the analysis device. The mobility server intermediates between data collection and storage, while the medical server intermediates between driving data and medical assessment, facilitating coordinated operation without requiring direct complex interactions between all system components.
3Speed
If driving data is continuously monitored and analyzed, then cognitive function abnormalities can be detected in real-time, but the data processing and storage requirements increase
Solution Approach 1:
The system extracts only the essential driving behavior data relevant to cognitive function assessment - such as steering operations, braking patterns, and acceleration behaviors - rather than continuously processing all possible vehicle data. This selective extraction reduces data volume while maintaining detection effectiveness by focusing on behaviors most indicative of cognitive state.
Solution Approach 2:
The system implements partial monitoring by focusing on specific critical driving behaviors rather than attempting to analyze every aspect of driving. By selectively monitoring steering angle changes, braking force variations, and acceleration patterns, the system achieves effective cognitive function detection with reduced data processing requirements compared to comprehensive continuous monitoring.
Data Source
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AI summary
A mobility server that stores user data D2, and a medical server that stores vehicle data D1. Abnormal driving of the user is detected from the vehicle data D1, and medical information created based on information related to the detected abnormal driving and an opinion of a medical worker at the medical institution is notified to a user terminal.