Name
Abstract | ||
|---|---|---|
With the development and popularity of micro-electromechanical systems (MEMS) and smartphones, sensor-based human activity recognition (HAR) has been widely applied. Although various kinds of HAR systems have achieved outstanding results, there are still issues to be solved in this field, such as transition activities, which means the transitional process between two different basic activities, discussed in this paper. In this paper, we design an algorithm based on standard deviation trend analysis (STD-TA) for recognizing transition activity. Compared with other methods, which directly take them as basic activities, our method achieves a better overall performance: the accuracy is over 80% on real data. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20113117 | SENSORS |
Keywords | DocType | Volume |
human activity recognition,transition activity,smartphone,SVM,trend analysis | Journal | 20 |
Issue | ISSN | Citations |
11.0 | 1424-8220 | 2 |
PageRank | References | Authors |
0.36 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junhao Shi | 1 | 2 | 0.36 |
| De-Cheng Zuo | 2 | 86 | 18.87 |
| Zhan Zhang | 3 | 2 | 0.36 |