Name
Abstract | ||
|---|---|---|
In order to improve the accuracy of human activity recognition based on smart device, we proposed a recognition method based on convolutional neural network. We preprocess the raw acceleration data and input the processed data directly into the convolution neural network to do local feature analysis. After processing, we get the characteristic output result, which can be directly inputted into the SoftMax classifier, which can recognize five activity, such as walking, running, going downstairs, going upstairs and standing. By comparing the experimental results, the recognition rate of different experimenters is 84.8%, which proves that the method is effective. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-27535-8_6 | INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2019, PT III |
Keywords | DocType | Volume |
Human activity recognition, Deep learning, Convolutional neural network | Conference | 11742 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haibin Zhang | 1 | 118 | 18.58 |
| Naoyuki Kubota | 2 | 740 | 144.39 |