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Internet of Things Based Intelligent Elderly Care System

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Internet of Things: Novel Advances and Envisioned Applications

Part of the book series: Studies in Big Data ((SBD,volume 25))

Abstract

The World Health Organization (WHO) reports that most common cause of injuries to elderly people increases every year due to fall events. Human fall events are one of the most important health problem among the elderly people whos aged 65 and above, which could probably result in a significant barrier to their independent living. This chapter presents a method for fall activity detection based on Motion Projection Profile (MPP) features extracted from temporal difference image to represent a various levels of a person’s posture. Falls are detected by analyzing the projection profile features consist of the measure of motion pixel of each row, column, left diagonal and right diagonal of the temporal difference image and gives adequate information to recognize the instantaneous posture of the person. The experiments are carried out using publicly available fall detection dataset and the extracted MPP feature set are modeled by the various machine learning methods like Support Vector Machine (SVM) with polynomial kernel, SVM with Radial Basis Function (RBF) kernel, K-Nearest Neighbor (KNN) and the Decision tree (J48) algorithm are used to classify the fall activies. Experimental results show that SVM with RBF kernel is an efficient to recognize the fall activity with an overall recognition accuracy of 89.55% on the fall detection dataset, which outperforms other machine learning methods.

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Authors and Affiliations

  1. Department of CSE, Annamalai University, Chidambaram, India

    J. Arunnehru & M. Kalaiselvi Geetha

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  1. J. Arunnehru
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  2. M. Kalaiselvi Geetha
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Correspondence to J. Arunnehru .

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Editors and Affiliations

  1. School of Computer Science and Engg., VIT University School of Computer Science and Engg., Vellore, India

    D. P. Acharjya

  2. Department of Computer Science and Engineering, Annamalai University Faculty of Engineering and Technology, Chidambaram, India

    M. Kalaiselvi Geetha

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Arunnehru, J., Kalaiselvi Geetha, M. (2017). Internet of Things Based Intelligent Elderly Care System. In: Acharjya, D., Geetha, M. (eds) Internet of Things: Novel Advances and Envisioned Applications. Studies in Big Data, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-53472-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-53472-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53470-1

  • Online ISBN: 978-3-319-53472-5

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