Smartphone Based Data Mining for Fall Detection: Analysis and Design

Abdul Hakim, M. Saiful Huq, Shahnoor Shanta, B. S.K.K. Ibrahim

Research output: Contribution to journalConference articlepeer-review

73 Citations (Scopus)
296 Downloads (Pure)


Falls can be devastating to the affected individual, yet a common event and hence one of the major causes of injury or disability within the aged population in Malaysia and worldwide. This paper aims to detect human fall utilizing the built inertial measurement unit (IMU) sensors of a smartphone attached to the subject's body with the signals wirelessly transmitted to remote PC for processing. Matlab's mobile and the Smartphone Sensor Support is used to acquire the data from the smartphone which is then analysed to design an algorithm for the detection of fall. Falls in human are usually characterized by large acceleration. However, focusing only on a large value of the acceleration can result in many false positives from fall-like activities such as sitting down quickly and jumping. Thus, in this work, a threshold based fall detection algorithm is implemented while a supervised machine learning algorithm is used to classify activity daily living (ADL). This combination has been found effective in increasing the accuracy of the fall detection. The aim is to develop and verify the high precision detection algorithm using Matlab Simulink, followed by a few real time testing.

Original languageEnglish
Pages (from-to)46-51
Number of pages6
JournalProcedia Computer Science
Publication statusPublished - 1 Jan 2017
Externally publishedYes
EventIEEE International Symposium on Robotics and Intelligent Sensors, IRIS 2016 - Tokyo, Japan
Duration: 17 Dec 201620 Dec 2016

Bibliographical note

© 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (


  • cellular phone
  • fall detection
  • machine learning
  • smartphone
  • supervised learning

ASJC Scopus subject areas

  • Computer Science(all)


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