An argumentation enabled decision making approach for Fall Activity Recognition in Social IoT based Ambient Assisted Living systems

With the advancement in Information and Communication Technologies (ICTs), smart devices are becoming even more smart and intelligent with every passing day. Further, the evolution of speaking and hearing enabled devices in an IoT network is transforming the face of research in the Social IoT domain. However, the integration of argumentation enabled devices in Social IoT network has not been fully explored by researchers in the past. Therefore, this research work focuses on development of argument enabled Social IoT networks. In this paper, a fuzzy argument based classification scheme termed as Classification Enhanced with Fuzzy Argumentation (CleFAR) is proposed. The proposed scheme is deployed for classification of fall activities in fall prevention applications. A novel framework for fall prevention system using Fall Activity Recognition (FAR) is presented. The proposed system is designed for the purpose of fall activity recognition in smart home Ambient Assisted Living (AAL) systems. To experimentally evaluate the system's performance, a smart home AAL environment is simulated and the inhabitant's routine activity dataset is generated. The fall activities are simulated using wearable fall detection systems. The proposed scheme is trained and tested on generated datasets and its performance is compared with traditional classification algorithms such as Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), Decision Tree (DT) and Artificial Neural Networks (ANN) as well as existing argumentation based game theoretic Weighted Voting Scheme (WVS). Experimental results indicate that the proposed scheme outperforms the traditional classification schemes and WVS approach with prediction accuracy up to 91%. It turns out that the proposed approach achieves significant improvement over the existing schemes. (C) 2021 Elsevier B.V. All rights reserved.