Survey on Human Activity Recognition based on Acceleration Data
| Autor: | Salwa O. Slim, Ayman Atia, Mostafa-Sami M. Mostafa, Marwa M. A. Elfattah |
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| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Computer science business.industry Deep learning 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Activity recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Classifier (UML) |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 10 |
| ISSN: | 2156-5570 2158-107X |
| Popis: | Human activity recognition is an important area of machine learning research as it has many utilization in different areas such as sports training, security, entertainment, ambient-assisted living, and health monitoring and management. Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human. Therefore, the general architecture of HAR system is presented in this paper, along with the description of its main components. The state of the art in human activity recognition based on accelerometer is surveyed. According to this survey, Most of the researches recently used deep learning for recognizing HAR, but they focused on CNN even though there are other deep learning types achieved a satisfied accuracy. The paper displays a two-level taxonomy in accordance with machine learning approach (either traditional or deep learning) and the processing mode (either online or offline). Forty eight studies are compared in terms of recognition accuracy, classifier, activities types, and used devices. Finally, the paper concludes different challenges and issues online versus offline also using deep learning versus traditional machine learning for human activity recognition based on accelerometer sensors. |
| Databáze: | OpenAIRE |
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