Mobile Networks and Applications

, Volume 23, Issue 4, pp 775–788 | Cite as

Collaborative Fall Detection Using Smart Phone and Kinect

  • Xue LiEmail author
  • Lanshun Nie
  • Hanchuan Xu
  • Xianzhi Wang


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%.


Fall detection Collaborative detection Kinect Smart phone 



Funded by the 2014 Microsoft Research Asia Collaborative Research Program.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Harbin Institute of TechnologyHarbinChina
  2. 2.Singapore Management UniversitySingaporeSingapore

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