Name
Abstract | ||
|---|---|---|
This paper proposes an accurate estimation method of walking speed using deep learning for smartphone-based Pedestrian Dead Reckoning (PDR).PDR requires to estimate speed and direction of pedestrians accurately using accelerometer and gyroscope.To improve the accuracy of PDR, existing works focused to improve the key factors of speed estimation (i.e., stride and/or step estimation) by adapting deep learning.On the contrary, our research proposes to adapt deep learning more directly to estimate walking speed from sensor data of smartphone. We evaluate the accuracy of proposed method by comparing with conventional PDR method. As a result, we confirmed that proposed method can estimate the speed more accurately.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3307334.3328667 | Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services |
Keywords | DocType | ISBN |
deep learning, location estimation, pdr | Conference | 978-1-4503-6661-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshida, T. | 1 | 0 | 1.69 |
| Junto Nozaki | 2 | 3 | 1.81 |
| Kenta Urano | 3 | 8 | 4.17 |
| Kei Hiroi | 4 | 19 | 12.00 |
| Takuro Yonezawa | 5 | 84 | 22.34 |
| Nobuo Kawaguchi | 6 | 313 | 64.23 |