Collaborative Fall Detection Using Smart Phone and Kinect
- Publisher:
- SPRINGER
- Publication Type:
- Journal Article
- Citation:
- Mobile Networks and Applications, 2018, 23, (4), pp. 775-788
- Issue Date:
- 2018-08-01
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s11036-018-0998-y.pdf | Published version | 1.51 MB |
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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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