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Driving-PASS: An Automatic Driving Performance Assessment System for Stroke Drivers Based on ANN and SVM
- Driving-PASS: An Automatic Driving Performance Assessment System for Stroke Drivers Based on ANN and SVM
- Jeon, Sanghoon; Son, Joonwoo; Park, Myoungouk; Kim, Bawul; Son, Sang Hyuk; Eun, Yongsoon
- DGIST Authors
- Son, Joonwoo; Son, Sang Hyuk; Eun, Yongsoon
- Issue Date
- 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018, 1367-1373
- Although many stroke survivors are not fully capable of driving, they drive again without any formal assessment due to an absence of valid screening tools. This leads to an elevated risk of accidents. Although an on-road test is considered a standard assessment method for items relevant to actual driving, it may be dangerous to evaluate all stroke drivers with the on-road test. For safe pre-screening of unsuitable stroke drivers, we propose an automatic Driving Performance Assessment System for Stroke drivers (Driving-PASS). Driving-PASS aims to provide not only information about problematic driving assessment items but also a decision about fitness to drive. The problematic driving items are classified by abnormal classifiers while the decision item is determined by a decision classifier in Driving-PASS. For designing the system, we firstly propose a subjective assessment method consisting of ten assessment items and one decision item. And then, we propose an automated method of the subjective assessment method with a machine learning approach (i.e., ANN and SVM) by using assessment criteria from five expert's judgments. Evaluation results demonstrate that Driving-PASS automatically assess not only the ten assessment items (total average Accuracy of 90% and F1-score of 88%) but also the decision item (Accuracy of 93% and F1-score of 92%). We expect Driving-PASS provides analytical assessment results that can be used in driving rehabilitation programs and contributes to reducing the risk of vehicle accidents by pre-screening unsuitable stroke drivers with high accuracy and reliability. © 2018 IEEE.
- Institute of Electrical and Electronics Engineers Inc.
- Related Researcher
Son, Sang Hyuk
RTCPS(Real-Time Cyber-Physical Systems) Lab
Real-time system; Wireless sensor network; Cyber-physical system; Data and event service; Information security; 실시간 임베디드 시스템
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- Companion Diagnostics and Medical Technology Research Group2. Conference Papers
Department of Information and Communication EngineeringRTCPS(Real-Time Cyber-Physical Systems) Lab2. Conference Papers
Department of Information and Communication EngineeringDSC Lab(Dynamic Systems and Control Laboratory)2. Conference Papers
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