Name
Abstract | ||
|---|---|---|
Activity recognition has a growing interest in many fields like biomedical engineering, game development or for sports training. Sensors are attached to a human body to track body movement, physiological signals or environmental variables and these informations are interpreted by algorithms. The finding of characteristics of sensor data for the classification problem plays an important role and is commonly done by hand and with a lot of expertise by the researcher. In this paper, a 2-D Convolutional Neural Network (CNN) is used to extract features of images, which are fed in a Support Vector Machine (SVM) for classification. The key idea are spectrograms produced from 1-D signals of inertial sensors. To evaluate the proposed system, two datasets are used. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/NDS.2017.8070615 | 2017 10th International Workshop on Multidimensional (nD) Systems (nDS) |
Keywords | DocType | ISBN |
2D CNN,support vector machine,SVM,feature extraction,classification problem,environmental variables,physiological signals,body movement,human body,2-D convolutional neural network,inertial sensors,activity recognition | Conference | 978-1-5386-1248-4 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Wagner | 1 | 1274 | 85.03 |
| Kathrin Kalischewski | 2 | 0 | 1.01 |
| Jörg Velten | 3 | 44 | 12.37 |
| Anton Kummert | 4 | 234 | 55.14 |