Aiming at change of characteristics of acceleration and angular velocity of human body in the process of old people falling

comprehensive the existing fall monitoring methods at present

a wearable falling and real-time monitoring methods based on Kalman filtering threshold algorithm is proposed. Use embedded processor STM32 F103 ZET6 as the processing core

adopt high precision accelerometer MPU6050 to aquire acceleration and angular velocity data of human body

and then the microprocessor judge the falling based on analysis on acceleration data. If the result is falling

the buzzer will send alarm

in addition

the SIM800 C module will send a text message for help to the guardian. The model is verified on the experimental platform of wearable real-time monitoring system. By comparing different motion modes

the feasibility of the proposed method is demonstrated

and the remote alarm function for monitoring on falling of the elderly is realized.