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Better Physical Activity Classification using Smartphone Acceleration Sensor

Authors:

S. Iqbal AhamedAuthors Info & Claims

Journal of Medical Systems, Volume 38, Issue 9

Pages 1 - 10

https://doi.org/10.1007/s10916-014-0095-0

Published: 01 September 2014 Publication History

Abstract

Obesity is becoming one of the serious problems for the health of worldwide population. Social interactions on mobile phones and computers via internet through social e-networks are one of the major causes of lack of physical activities. For the health specialist, it is important to track the record of physical activities of the obese or overweight patients to supervise weight loss control. In this study, acceleration sensor present in the smartphone is used to monitor the physical activity of the user. Physical activities including Walking, Jogging, Sitting, Standing, Walking upstairs and Walking downstairs are classified. Time domain features are extracted from the acceleration data recorded by smartphone during different physical activities. Time and space complexity of the whole framework is done by optimal feature subset selection and pruning of instances. Classification results of six physical activities are reported in this paper. Using simple time domain features, 99 % classification accuracy is achieved. Furthermore, attributes subset selection is used to remove the redundant features and to minimize the time complexity of the algorithm. A subset of 30 features produced more than 98 % classification accuracy for the six physical activities.

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Cited By

Teng SD'Alfonso SKostakos V(2024)A Tool for Capturing Smartphone Screen TextProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642347(1-24)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642347
Hussein DBhat G(2024)SensorGAN: A Novel Data Recovery Approach for Wearable Human Activity RecognitionACM Transactions on Embedded Computing Systems10.1145/360942523:3(1-28)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3609425
Zou SSun HXu GWang CZhang XQuan R(2023)A Robust Continuous Authentication System Using Smartphone Sensors and Wasserstein Generative Adversarial NetworksSecurity and Communication Networks10.1155/2023/36731132023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/3673113
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Better Physical Activity Classification using Smartphone Acceleration Sensor
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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Published In

cover image Journal of Medical Systems

Journal of Medical Systems Volume 38, Issue 9

September 2014

309 pages

ISSN:0148-5598

Issue’s Table of Contents

Copyright © Copyright © 2014 Springer Science+Business Media New York.

Publisher

Plenum Press

United States

Publication History

Published: 01 September 2014

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Cited By

Teng SD'Alfonso SKostakos V(2024)A Tool for Capturing Smartphone Screen TextProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642347(1-24)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642347
Hussein DBhat G(2024)SensorGAN: A Novel Data Recovery Approach for Wearable Human Activity RecognitionACM Transactions on Embedded Computing Systems10.1145/360942523:3(1-28)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3609425
Zou SSun HXu GWang CZhang XQuan R(2023)A Robust Continuous Authentication System Using Smartphone Sensors and Wasserstein Generative Adversarial NetworksSecurity and Communication Networks10.1155/2023/36731132023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/3673113
Hussein DBhat G(2023)CIM: A Novel Clustering-based Energy-Efficient Data Imputation Method for Human Activity RecognitionACM Transactions on Embedded Computing Systems10.1145/360911122:5s(1-26)Online publication date: 31-Oct-2023
https://dl.acm.org/doi/10.1145/3609111
Ishwarya KAlice Nithya A(2021)Performance-enhanced real-time lifestyle tracking model based on human activity recognition (PERT-HAR) model through smartphonesThe Journal of Supercomputing10.1007/s11227-021-04065-z78:4(5241-5268)Online publication date: 20-Sep-2021
https://dl.acm.org/doi/10.1007/s11227-021-04065-z
Bhat GTuncel YAn SLee HOgras U(2019)An Ultra-Low Energy Human Activity Recognition Accelerator for Wearable Health ApplicationsACM Transactions on Embedded Computing Systems10.1145/335817518:5s(1-22)Online publication date: 7-Oct-2019
https://dl.acm.org/doi/10.1145/3358175
Bhat GDeb RChaurasia VShill HOgras U(2018)Online Human Activity Recognition using Low-Power Wearable Devices2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)10.1145/3240765.3240833(1-8)Online publication date: 5-Nov-2018
https://dl.acm.org/doi/10.1145/3240765.3240833
Sun XByrns SCheng IZheng BBasu A(2017)Smart Sensor-Based Motion Detection System for Hand Movement Training in Open SurgeryJournal of Medical Systems10.1007/s10916-016-0665-441:2(1-13)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s10916-016-0665-4
Guo JZhou XSun YPing GZhao GLi Z(2016)Smartphone-Based Patients' Activity Recognition by Using a Self-Learning Scheme for Medical MonitoringJournal of Medical Systems10.1007/s10916-016-0497-240:6(1-14)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/s10916-016-0497-2
Simpao ALingappan AAhumada LRehman MGálvez J(2015)Perioperative Smartphone Apps and Devices for Patient-Centered CareJournal of Medical Systems10.1007/s10916-015-0310-739:9(1-5)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1007/s10916-015-0310-7
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