Name
Abstract | ||
|---|---|---|
This paper explores the potential for wearable devices to identify driving activities and unsafe driving, without relying on information or sensors in the vehicle. In particular, we study how wrist-mounted inertial sensors such as those in smart watches and fitness trackers, can track steering wheel usage and inputs. Identifying steering wheel usage helps mobile device detect driving and reduce distractions. Tracking steering wheel turning angles can improve vehicle motion tracking by mobile devices and help identify unsafe driving. The approach relies on motion features that allow distinguishing steering from other confounding hand movements. Once steering wheel usage is detected, it also use wrist rotation measurements to infer steering wheel turning angles. Our preliminary experiments show that the technique is 98.9% accurate in detecting driving and can estimate turning angles with average error within two degrees. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2753509.2753518 | WearSys@MobiSys |
Field | DocType | Citations |
Fitness Trackers,Simulation,Wearable computer,Steering wheel,Mobile device,Inertial measurement unit,Engineering,Wearable technology,Smartwatch,Match moving | Conference | 16 |
PageRank | References | Authors |
0.86 | 10 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luyang Liu | 1 | 112 | 9.60 |
| Çagdas Karatas | 2 | 16 | 0.86 |
| Hongyu Li | 3 | 149 | 17.22 |
| Sheng Tan | 4 | 121 | 9.09 |
| Marco Gruteser | 5 | 4631 | 309.81 |
| Jie Yang | 6 | 1605 | 83.06 |
| Yingying Chen | 7 | 2495 | 193.14 |
| Richard P. Martin | 8 | 1777 | 165.29 |