Activity Recognition Based on Deep Learning and Android Software

被引：0
|
作者：
Wang, Chao [1 ,2 ]
Lin, Chuang [1 ]
Yang, Mo [3 ]
机构：
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Res Ctr Neural Engn, Inst Biomed & Hlth Engn, Shenzhen 518055, Peoples R China
[2] Northeastern Univ, Sino Dutch Biomed & Informat Engn Sch, Biomed Engn, Shenyang 110819, Liaoning, Peoples R China
[3] Univ Colorado, Dept Elect Engn, Boulder, CO 80309 USA
来源：
2018 IEEE INTERNATIONAL CONFERENCE ON CYBORG AND BIONIC SYSTEMS (CBS) | 2018年
关键词：
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This project transplants the deep learning framework to the android platform. It combines Deep learning based on Tensor Flow (TF) with Android software to recognize people's activities. Provided by android sensors, dataset is in the form of float arrays. And then we used them to train TF model with the data that was tagged by the different actions. Then we transplant the model into android to achieve activity recognition in mobile phone. In this project, six activities are recognized, including downstairs, upstairs, jogging, walking, sitting and standing. Finally, the probability of each activity will be seen in an android mobile phone. According to the results of experiments, almost all of six types of activities can be recognized successfully.
引用
收藏
页码：31 / 35
页数：5
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