research-article

Information Augmentation for Human Activity Recognition and Fall Detection using Empirical Mode Decomposition on Smartphone Data

Authors:

Elif SurerAuthors Info & Claims

MOCO '19: Proceedings of the 6th International Conference on Movement and Computing

Article No.: 4, Pages 1 - 8

https://doi.org/10.1145/3347122.3347126

Published: 10 October 2019 Publication History

Abstract

In this paper, we propose a novel design to reduce the number of sensors used in activity recognition and fall detection by using empirical mode decomposition (EMD) along with gravity filtering so as to untangle the useful information gathered from a single sensor, i.e. accelerometer. We focus on reducing the number of sensors utilized by augmenting the information obtained from accelerometer only given that the accelerometer is the most common and easy to access sensor on smartphones. To do so, one gravity component and three intrinsic mode functions (IMFs) are extracted from the accelerometer signal. In order to assess how informative each component is, the raw components are directly used for classification, i.e. without hand-crafting statistical features. The extracted signal components are then individually fed into parallelized random forest (RF) classifiers. The proposed design is evaluated on the publicly available MobiAct dataset. The results show that by only using accelerometer data within the proposed scheme, it is possible to reach the performance of two sensors (accelerometer and gyroscope) used in a conventional manner. This study provides an efficient and convenient-to-use solution for the smartphone applications in human activity recognition domain.

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Digital Library

Cited By

Tay NConnie TOng TTeoh ATeh P(2023)A Review of Abnormal Behavior Detection in Activities of Daily LivingIEEE Access10.1109/ACCESS.2023.323497411(5069-5088)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3234974
Magdy OAtia A(2022)Human Activity Recognition in Maintenance Centers to Reduce Wasted Time2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)10.1109/MIUCC55081.2022.9781695(118-124)Online publication date: 8-May-2022
https://doi.org/10.1109/MIUCC55081.2022.9781695
Irfanullah Hussain TIqbal AYang BHussain A(2022)Real time violence detection in surveillance videos using Convolutional Neural NetworksMultimedia Tools and Applications10.1007/s11042-022-13169-481:26(38151-38173)Online publication date: 23-Apr-2022
https://doi.org/10.1007/s11042-022-13169-4
Show More Cited By

Index Terms

Information Augmentation for Human Activity Recognition and Fall Detection using Empirical Mode Decomposition on Smartphone Data
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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cover image ACM Other conferences

MOCO '19: Proceedings of the 6th International Conference on Movement and Computing

October 2019

23 pages

ISBN:9781450376549

DOI:10.1145/3347122

Conference Chair:
Grisha Coleman

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 10 October 2019

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MOCO '19

MOCO '19: 6th International Conference on Movement and Computing

October 10 - 12, 2019

AZ, Tempe, USA

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Cited By

Tay NConnie TOng TTeoh ATeh P(2023)A Review of Abnormal Behavior Detection in Activities of Daily LivingIEEE Access10.1109/ACCESS.2023.323497411(5069-5088)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3234974
Magdy OAtia A(2022)Human Activity Recognition in Maintenance Centers to Reduce Wasted Time2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)10.1109/MIUCC55081.2022.9781695(118-124)Online publication date: 8-May-2022
https://doi.org/10.1109/MIUCC55081.2022.9781695
Irfanullah Hussain TIqbal AYang BHussain A(2022)Real time violence detection in surveillance videos using Convolutional Neural NetworksMultimedia Tools and Applications10.1007/s11042-022-13169-481:26(38151-38173)Online publication date: 23-Apr-2022
https://doi.org/10.1007/s11042-022-13169-4
Al-Rakhami MGumaei AAltaf MHassan MAlkhamees BMuhammad KFortino G(2021)FallDeF5: A Fall Detection Framework Using 5G-Based Deep Gated Recurrent Unit NetworksIEEE Access10.1109/ACCESS.2021.30918389(94299-94308)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3091838

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