Back to Search
Start Over
Collaborative Fall Detection Using Smart Phone and Kinect.
- Source :
-
Mobile Networks & Applications . Aug2018, Vol. 23 Issue 4, p775-788. 14p. - Publication Year :
- 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%. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1383469X
- Volume :
- 23
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Mobile Networks & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 131207341
- Full Text :
- https://doi.org/10.1007/s11036-018-0998-y