Rotation memory neural network identify elders fall situation

This paper presented a rotation memory neural network identify elders fall situation in wearable device. The older human signal analysis have been a research topic health care fields that algorithms build in wearable device real time detect fall situation. However, the wearable device system code size is limited. Therefore, we utilize rotation memory methods to adjust weight that simplify tradition self-organizing neural network. In the experimental results, the rotation memory neural network identify human fall signal. In addition, we used wearable device combined BLE (Bluetooth low energy) feedback output response real time.