Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2018

Abstract

Humanfall detection has attracted broad attentions as sensors and mobile devices are increasingly adopted in real-life scenarios such as smart homes. The complexity of activities in home environments pose severe challenges to the fall detection research with respect to the detection accuracy. We propose a collaborative detection platform that combines two subsystems: a threshold-based fall detection subsystem using mobile phones and a support vector machine (SVM)-based fall detection subsystem using Kinects. Both subsystems have their respective confidence models and the platform detects falls by fusing the data of both subsystems using two methods: the logical rules-based and D-S evidence fusion theory-based methods. We have validated the two confidence models based on mobile phone and Kinect, which achieve the accuracy of 84.17% and 97.08%, respectively. Our collaborative fall detection approach achieves the best accuracy of 100%.

Keywords

Fall detection, Collaborative detection, Kinect, Smart phone

Discipline

Digital Communications and Networking | OS and Networks | Software Engineering

Publication

Mobile Networks and Applications

Volume

23

Issue

4

First Page

775

Last Page

788

ISSN

1383-469X

Identifier

10.1007/s11036-018-0998-y

Publisher

Springer

Additional URL

https://doi.org/10.1007/s11036-018-0998-y

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