Affiliations: [a] Faculty of Electronics and Communication Engineering, University College of Engineering Panruti, Tamilnadu, India | [b] Faculty of Electronics and Communication Engineering, University College of Engineering Tindivanam, Tamilandu, India
Correspondence:
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Corresponding author: G. Nivetha, Faculty of Electronics and Communication Engineering, University College of Engineering Panruti, Panruti 607106, Tamilnadu, India. Tel.: +91 9486456400; E-mail: [email protected].
Abstract: With the accessibility of reasonably valued sensors and sensor systems, sensor-based Human Activity Recognition (HAR) has attracted much consideration nowadays. The use of smart mobile phones for HAR has been a continuous zone of research in which the improvement of fast and efficient machine learning approaches is essential. In the current years, wireless sensor networks had been positioned in the real world to collect measures of information. However, the major task is to extract high-level knowledge from such raw data. In the utilizations of sensor systems, outlier detection has paid more concentration in recent years. Outlier detection is used to expel noisy data, to discover faulty nodes and also to distinguish interesting events. Conventional outlier detection methods are not directly applicable to sensor networks because of the dynamic way of sensor information and confines of the wireless sensor networks. In this paper, a hybrid outlier detection and removal method is proposed to detect abnormal human activities based on the mobile sensor data. Exploratory investigation is done on datasets gathered in various conditions. The outcomes demonstrate that the proposed method in combination with standard classifiers performs superior to other anomaly detection methods as far as different quality measurements.
Keywords: Data mining, human activity recognition, outlier detection, wireless sensor networks
