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Human Activity Recognition Using Smartphones Sensors for Ambient Assisted Living

CG Igiri1 , OE Taylor2 , Orji Friday3

Section:Research Paper, Product Type: Journal Paper
Volume-9 , Issue-2 , Page no. 5-11, Feb-2021

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v9i2.511

Online published on Feb 28, 2021

Copyright © CG Igiri, OE Taylor, Orji Friday . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: CG Igiri, OE Taylor, Orji Friday, “Human Activity Recognition Using Smartphones Sensors for Ambient Assisted Living,” International Journal of Computer Sciences and Engineering, Vol.9, Issue.2, pp.5-11, 2021.

MLA Style Citation: CG Igiri, OE Taylor, Orji Friday "Human Activity Recognition Using Smartphones Sensors for Ambient Assisted Living." International Journal of Computer Sciences and Engineering 9.2 (2021): 5-11.

APA Style Citation: CG Igiri, OE Taylor, Orji Friday, (2021). Human Activity Recognition Using Smartphones Sensors for Ambient Assisted Living. International Journal of Computer Sciences and Engineering, 9(2), 5-11.

BibTex Style Citation:
@article{Igiri_2021,
author = {CG Igiri, OE Taylor, Orji Friday},
title = {Human Activity Recognition Using Smartphones Sensors for Ambient Assisted Living},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2021},
volume = {9},
Issue = {2},
month = {2},
year = {2021},
issn = {2347-2693},
pages = {5-11},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5298},
doi = {https://doi.org/10.26438/ijcse/v9i2.511}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v9i2.511}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5298
TI - Human Activity Recognition Using Smartphones Sensors for Ambient Assisted Living
T2 - International Journal of Computer Sciences and Engineering
AU - CG Igiri, OE Taylor, Orji Friday
PY - 2021
DA - 2021/02/28
PB - IJCSE, Indore, INDIA
SP - 5-11
IS - 2
VL - 9
SN - 2347-2693
ER -

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Abstract

With the rapid growth in the elderly population, conventional health care system is no longer sufficient to provide personalized healthcare services for the elderly and healthcare givers are looking for a technological based solution. Ambient Assisted Living(AAL) is such a solution and at the heart of AAL is human activity recognition. Modern smartphone embedded with a lot of sensors has become an integral part of our life and is a vital option for collecting data for activity recognition. In this paper we looked at the use of smartphone accelerometer with supervised machine learning algorithm in WEKA framework for monitoring Activity of Daily Living (ADL): standing, walking, lying, walking upstairs and walking down stairs. Sitting, for the elderly in their environment of choice. We examined two common classification algorithms: Random Forest (RF), instance-based learning (KNN), RF gave us the highest accuracy of 94.4% which is considered adequate for activity recognition.

Key-Words / Index Term

Human Activity Recognition, Ambient Assisted Living Smartphone, ReliefF, Sequential Forward Floating Selection.

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