Name
Abstract | ||
|---|---|---|
The physical activity is important context information to define and understand the user’s situation in real time and in detail. Therefore, we developed a context-aware function using the activity recognition and showed that it is possible to provide more appropriate support according to the user’s situation. In this study, we first constructed a model by applying machine learning to data sensed by a smartphone in order to predict the physical activity of the user. In the experiment, high accuracy of 97.6% was obtained by using the model. Next, we developed three functions using the activity recognition. These functions predict the physical activity of user in real time. In addition, user support is performed according to the predicted physical activity. In the experiment using developed functions, it is confirmed that these functions worked correctly in real-world conditions. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICAIIC.2019.8669035 | 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) |
Keywords | Field | DocType |
mobile computing,context-awareness,physical activity,activity recognition,application,sensor,GPS | Mobile computing,Activity recognition,Computer science,Context awareness,Human–computer interaction,Global Positioning System | Conference |
ISBN | Citations | PageRank |
978-1-5386-7822-0 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuki Watanabe | 1 | 0 | 0.34 |
| Reiji Suzumura | 2 | 0 | 0.68 |
| Shogo Matsuno | 3 | 4 | 4.53 |
| Minoru Ohyama | 4 | 33 | 9.48 |