machine and deep learning approaches for human activity recognition | ||
| International Journal of Intelligent Computing and Information Sciences | ||
| Article 13, Volume 21, Issue 3, November 2021, Pages 44-52 PDF (559.07 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijicis.2021.82008.1106 | ||
| Authors | ||
| maha alhumayani* 1; Mahmoud Monir2; rasha ismail3 | ||
| 1information systems, faculty of computing and information technology , ain shams university , cairo , egypt | ||
| 2Faculty of Computer and Information Sciences, Ain Shams University | ||
| 3information Systems Department Faculty of Computer and Information Science , Ainshams University | ||
| Abstract | ||
| Human Activity Recognition (HAR) is a domain that has shown great interest in the past years and tills now. The main cause for this is that it can be used in various applications. There exist several devices and sensors that can capture and record activities. In this paper, a survey about the machine learning and deep learning methodologies in HAR is provided with information about the data, filtering methods, feature extraction methods, classification, and different performance measurements. The main aim is to target the old and the recent papers published in HAR and to determine whether the machine learning or deep learning methods is better in performance. In addition to this, the survey will cover the types of actions or activities that are predicted. Then, a discussion about the main points obtained from the survey. Finally, the conclusions, limitations, and challenges in HAR are presented clearly. Human activity recognition (HAR) can be known with various types of definitions. HAR is preserved to be a field of studying and identifying the movements of the individuals or the action of the human based on sensor data . These movements can be different activities such as walking, talking, standing, and sitting. They are also called indoor activities. | ||
| Keywords | ||
| Human activity recognition; Machine learning; Deep learning; smartphone; sensors | ||
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