TY - GEN
T1 - MagTrack
T2 - 17th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2019
AU - Huang, Hua
AU - Chen, Hongkai
AU - Lin, Shan
N1 - Publisher Copyright: © 2019 Association for Computing Machinery.
PY - 2019/6/12
Y1 - 2019/6/12
N2 - "Hands on the wheel, eyes on the road" is the central guideline of safe vehicle driving practices. Many advanced driver assistance systems can effectively detect abnormal vehicle motions. However, these systems often leave insufficient time for drivers to respond to complex road situations, especially when the drivers are distracted. To reduce accidents, it is essential to detect whether a driver complies with safe driving guidelines in real time and provide warnings early before any dangerous maneuvers occur. There are vision-based driver distraction monitoring systems which rely on cameras in high-end vehicles, but their performances are heavily constrained by visibility requirements. In this paper, we present MagTrack, a driver monitoring system that is based on tracking magnetic tags worn by the user. With a single smartwatch and two low-cost magnetic accessories: a hand magnetic ring and a head magnetic eyeglasses clip, our system tracks and classifies a driver’s bimanual and head movements simultaneously using both analytical and approximation sensing models. Our approach is robust to driver’s postures, vehicles, and environmental changes. We demonstrate that a wide range of activities can be detected by our system, including bimanual steering, visual and manual distractions, and lane changes and turns. In extensive road tests with 500+ instances of driving activities and 500+ minutes of road driving with 10 subjects, MagTrack achieves 87% of precision and 90% of recall rate on the detection of unsafe driving activities.
AB - "Hands on the wheel, eyes on the road" is the central guideline of safe vehicle driving practices. Many advanced driver assistance systems can effectively detect abnormal vehicle motions. However, these systems often leave insufficient time for drivers to respond to complex road situations, especially when the drivers are distracted. To reduce accidents, it is essential to detect whether a driver complies with safe driving guidelines in real time and provide warnings early before any dangerous maneuvers occur. There are vision-based driver distraction monitoring systems which rely on cameras in high-end vehicles, but their performances are heavily constrained by visibility requirements. In this paper, we present MagTrack, a driver monitoring system that is based on tracking magnetic tags worn by the user. With a single smartwatch and two low-cost magnetic accessories: a hand magnetic ring and a head magnetic eyeglasses clip, our system tracks and classifies a driver’s bimanual and head movements simultaneously using both analytical and approximation sensing models. Our approach is robust to driver’s postures, vehicles, and environmental changes. We demonstrate that a wide range of activities can be detected by our system, including bimanual steering, visual and manual distractions, and lane changes and turns. In extensive road tests with 500+ instances of driving activities and 500+ minutes of road driving with 10 subjects, MagTrack achieves 87% of precision and 90% of recall rate on the detection of unsafe driving activities.
KW - Driver Assistance System
KW - Smartwatch Sensing
KW - Wearable Magnetics
UR - https://www.scopus.com/pages/publications/85069171153
U2 - 10.1145/3307334.3326107
DO - 10.1145/3307334.3326107
M3 - Conference contribution
T3 - MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
SP - 326
EP - 339
BT - MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
PB - Association for Computing Machinery
Y2 - 17 June 2019 through 21 June 2019
ER -