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%.
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Funded by the 2014 Microsoft Research Asia Collaborative Research Program.
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Li, X., Nie, L., Xu, H. et al. Collaborative Fall Detection Using Smart Phone and Kinect. Mobile Netw Appl 23, 775–788 (2018). https://doi.org/10.1007/s11036-018-0998-y
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DOI: https://doi.org/10.1007/s11036-018-0998-y