Driver Distraction Recognition Using Wearable IMU Sensor Data
| العنوان: | Driver Distraction Recognition Using Wearable IMU Sensor Data |
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| المؤلفون: | Guo Mengzhu, Sun Wencai, Si Yihao, Li Shiwu |
| المصدر: | Sustainability Volume 13 Issue 3 Sustainability, Vol 13, Iss 1342, p 1342 (2021) |
| بيانات النشر: | Multidisciplinary Digital Publishing Institute, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Dynamic time warping, Computer science, media_common.quotation_subject, Geography, Planning and Development, TJ807-830, Wearable computer, Management, Monitoring, Policy and Law, TD194-195, Hidden Markov Model, Renewable energy sources, manual distraction, traffic safety, Inertial measurement unit, Distraction, Perception, 0502 economics and business, Distracted driving, GE1-350, 0501 psychology and cognitive sciences, Computer vision, Hidden Markov model, 050107 human factors, media_common, Dynamic Time Warping, 050210 logistics & transportation, Environmental effects of industries and plants, Renewable Energy, Sustainability and the Environment, business.industry, 05 social sciences, Steering wheel, Environmental sciences, wearable inertial measurement units, Gesture recognition, Artificial intelligence, business, Gesture |
| الوصف: | Distracted driving has become a major cause of road traffic accidents. There are generally four different types of distractions: manual, visual, auditory, and cognitive. Manual distractions are the most common. Previous studies have used physiological indicators, vehicle behavior parameters, or machine-visual features to support research. However, these technologies are not suitable for an in-vehicle environment. To address this need, this study examined a non-intrusive method for detecting in-transit manual distractions. Wrist kinematics data from 20 drivers were collected using wearable inertial measurement units (IMU) to detect four common gestures made while driving: dialing a hand-held cellular phone, adjusting the audio or climate controls, reaching for an object in the back seat, and maneuvering the steering wheel to stay in the lane. The study proposed a progressive classification model for gesture recognition, including two major time-based sequencing components and a Hidden Markov Model (HMM). Results show that the accuracy for detecting disturbances was 95.52%. The accuracy associated with recognizing manual distractions reached 96.63%, using the proposed model. The overall model has the advantages of being sensitive to perceptions of motion, effectively solving the problem of a fall-off in recognition performance due to excessive disturbances in motion samples. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2071-1050 |
| DOI: | 10.3390/su13031342 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33afa198e1dc1e70b9d0153e4f4f6c06 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....33afa198e1dc1e70b9d0153e4f4f6c06 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20711050 |
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| DOI: | 10.3390/su13031342 |