Energy-Efficient Activity Recognition on Smartphone
| Autor: | Wei Zheng, Naoyuki Kubota, Dalai Tang, Yuri Yoshihara, Tay Noel |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry 010401 analytical chemistry Feature extraction Decision tree 020206 networking & telecommunications 02 engineering and technology computer.software_genre 01 natural sciences 0104 chemical sciences Support vector machine Activity recognition Order (business) Human–computer interaction 0202 electrical engineering electronic engineering information engineering Global Positioning System Data mining business computer Human services Efficient energy use |
| Zdroj: | CMCSN |
| DOI: | 10.1109/cmcsn.2016.32 |
| Popis: | In recent years, with the rapid development of smart phones, smart phones have become indispensable in our life. We can monitor human activities by the built-in sensors of the smartphone, and extract useful information for human services, such as human health, life log or assistance tips. In fact, this is a very low cost and efficient method. In previous research we have classified the walking style by Decision Tree. In order to recognize the activities more precisely and comprehensively, in this paper we used Decision Tree and SVM to learn the collected data on the smartphone, meanwhile considering the energy-efficiency problem around it. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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