Name
Abstract | ||
|---|---|---|
ABSTRACT Head movement for traffic visual searching, is one of the important factors in traffic safety. In this paper, we present the design, implementation, and preliminary evaluation of the HeadSense, a helmet device that detects the head movement of micro-mobility rider. HeadSense is capable of generating data streams using the embedded 9-axis inertial measurement unit (IMU) sensor. After the process of segmentation and classification algorithm, HeadSense can automatically detect an individual’s head movement sequence and visual search episodes, across the rider’s entire riding journey. Experiments with 5 participants show that our system achieves 94.7% for per-second level detection and 80.59% F1-score for per-episode level detection. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3460418.3479282 | Ubiquitous Computing |
Keywords | DocType | Citations |
Head Movement Detection, Human Activity recognition, Wearable | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zengyi Han | 1 | 0 | 1.01 |
| Xuefu Dong | 2 | 0 | 1.01 |
| Yuuki Nishiyama | 3 | 4 | 8.52 |
| Kaoru Sezaki | 4 | 292 | 39.15 |