KMS Chongqing Institute of Green and Intelligent Technology, CAS
| Human Activity Recognition Using Smartphone Sensor Data Via Deep Neural Networks | |
Chen, Yuwen ; Zhong, Kunhua
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| 2015 | |
| 摘要 | Human activity recognition on smartphones is a relatively new area. Smartphones are equipped with a variety of sensors. Fusing the data of these sensors could enable applications to recognize a large number of activities. Realizing this goal is challenging, however. In this paper, we captured 3-axial linear acceleration and 3-axial gyroscope data, from which 561 features are generated in both time and frequency domain. Then the deep neural networks approach is employed to recognize 6 activities, comparing with other different learning methods, ie. Decision Trees methods, support vector machine and Naive Bayes methods. Experiment results show that the classification rate of deep neural network reaches 0.98, which has the highest accuracy. |
| 语种 | 英语 |
| 会议(录)名称 | 2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING APPLICATIONS (CSEA 2015)
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| 页码 | 348-353 |
| 通讯作者 | Chen, YW (reprint author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Beijing, Peoples R China. |
| 收录类别 | ISTP |
| 会议地点 | Sanya, PEOPLES R CHINA |
| 会议日期 | DEC 26-27, 2015 |
