A dynamic gray model prediction fall detection signal analysis

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.