Fall Detection Based on Accelerometer and Gyroscope using Back Propagation - Adlian Jefiza; Eko Pramunanto; Hanny Boedinoegroho; Mauridhy Heri Purnomo (Proceeding of the Electrical Engineering Computer Science and Informatics, 2017)
Falling is an external aspect that can lead to death for the elderly. With so many activities they can do will increase the likelihood of falling. A fall detection device  is  designed  to  minimize  post-fall risk. An MPU6050 sensor with 3 axis accelerometer and 3 gyroscope axis is used to detect the activities of the elderly. This research is expected to recognize the falling forward movement, falling aside, falling backward,   sitting,   sleeping,   squatting,   upstairs, down stairs and praying. The total data in the test is 80 data per movement of 16 participants. Backpropagation   method    is    used   for   motion recognition.  The  recognition  of  this  movement  is based on 10 input variables from the accelerometer sensor data and gyroscope sensor. The result of this study,  the  error  value  calculated  by  using  the formula  Sum  Square  Error  of  all  movements,  is 0.1818 with ROC accuracy of 98.182%.