Abstract

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%.

Original languageEnglish
Title of host publicationProceedings - 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2017
EditorsHatib Rahmawan, Mochammad Facta, Munawar A. Riyadi, Deris Stiawan
PublisherInstitute of Advanced Engineering and Science
ISBN (Electronic)9781538605486
DOIs
Publication statusPublished - 22 Dec 2017
Event4th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2017 - Yogyakarta, Indonesia
Duration: 19 Sept 201721 Sept 2017

Publication series

NameInternational Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
Volume2017-December
ISSN (Print)2407-439X

Conference

Conference4th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2017
Country/TerritoryIndonesia
CityYogyakarta
Period19/09/1721/09/17

Keywords

  • Accelerometer
  • Backpropagation
  • Fall detection
  • Gyroscope

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