Name
Abstract | ||
|---|---|---|
We present a high-accuracy recognition method for various activities using smartphone sensors based on device positions. Many researchers have attempted to estimate various activities, particularly using sensors such as the built-in accelerometer of a smartphone. Considerable research has been conducted under conditions such as placing a smartphone in a trouser pocket; however, few have focused on the changing context and influence of the smartphone position. Herein, we present a method for recognising seven types of activities considering three smartphone positions, and conducted two experiments to estimate each activity and identify the actual state under continuous movement at a university campus. The results indicate that the seven states can be classified with an average accuracy of 98.53% for three different smartphone positions. We also correctly identified these activities with 91.66% accuracy. Using our method, we can create practical services such as healthcare applications with a high degree of accuracy. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1504/IJSSC.2018.094468 | INTERNATIONAL JOURNAL OF SPACE-BASED AND SITUATED COMPUTING |
Keywords | Field | DocType |
smartphone, activity recognition, device position, accelerometer, barometer, support vector machine, SVM | Activity recognition,Computer science,Accelerometer,Support vector machine,Human–computer interaction,Barometer | Journal |
Volume | Issue | ISSN |
8 | 2 | 2044-4893 |
Citations | PageRank | References |
2 | 0.39 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuki Oguri | 1 | 2 | 0.73 |
| Shogo Matsuno | 2 | 4 | 4.53 |
| Minoru Ohyama | 3 | 33 | 9.48 |