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A dynamic gray model prediction fall detection signal analysis
- Source :
- 2017 6th International Symposium on Next Generation Electronics (ISNE).
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- This paper presented a dynamic gray model DGM(1,1) prediction implement in fall detection signal analysis. The fall detection is a popular research topic in health care fields that detected older adult fall situation real time. Tradition gray model GM(1,1) prediction methods have some disadvantages to predict signal state. The DGM(1,1) used dynamic analysis construct prediction model that tracked signal state precisely. In the experimental results, we used wearable device with BLE (Bluetooth low energy) feedback person signal, in which dynamic gray model DGM(1,1) prediction algorithms real time detected person fall state and satisfactory output response in wearable device.
- Subjects :
- Engineering
Signal processing
business.industry
Model prediction
020208 electrical & electronic engineering
Wearable computer
020206 networking & telecommunications
Pattern recognition
02 engineering and technology
Prediction algorithms
0202 electrical engineering, electronic engineering, information engineering
Wireless
Artificial intelligence
Fall detection
business
Wireless sensor network
Gray (horse)
Simulation
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2017 6th International Symposium on Next Generation Electronics (ISNE)
- Accession number :
- edsair.doi...........062afe2898677e7b104a4442eda12d2e
- Full Text :
- https://doi.org/10.1109/isne.2017.7968714