research-article

IoT system for Human Activity Recognition using BioHarness 3 and Smartphone

Authors:

Camilo Rodriguez,

Diego M. Castro,

Nicolas Velasquez,

Julian Colorado,

Luis C. TrujilloAuthors Info & Claims

ICFNDS '17: Proceedings of the International Conference on Future Networks and Distributed Systems

Article No.: 49, Pages 1 - 7

https://doi.org/10.1145/3102304.3105828

Published: 19 July 2017 Publication History

Abstract

This paper presents an Internet of Things (IoT) approach to Human Activity Recognition (HAR) using remote monitoring of vital signs in the context of a healthcare system for self-managed chronic heart patients. Our goal is to create a HAR-IoT system using learning algorithms to infer the activity done within 4 categories (lie, sit, walk and run) as well as the time consumed performing these activities and, finally giving feedback during and after the activity. Alike in this work, we provide a comprehensive insight on the cloud-based system implemented and the conclusions after implementing two different learning algorithms and the results of the overall system for larger implementations.

References

[1]

Maria B.M. Alkmim, Milena S. Marcolino, Renato M. Figueira, Lidiane Sousa, Mariana S. Nunes, Clareci S. Cardoso, and Antonio L. Ribeiro. 2015. Factors Associated with the Use of a Teleconsultation System in Brazilian Primary Care. Telemedicine and e-Health 21, 6 (2015), 473--483.

[2]

Aintzane Armentia, Unai Gangoiti, Rafael Priego, Elisabet Est??vez, and Marga Marcos. 2015. Flexibility support for homecare applications based on models and multi-agent technology. Sensors (Switzerland) 15, 12 (2015), 31939--31964.

[3]

Girija Chetty and Matthew White. 2016. Body sensor networks for human activity recognition. 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN) (2016), 660--665.

[4]

Fitbit. Heart Rate tracker: Fitbit Charge 2ffl. (????). https://www.fitbit.com/charge2

[5]

Zhenyu He and Lianwen Jin. 2009. Activity recognition from acceleration data based on discrete consine transform and SVM. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 5041--5044.

[6]

Hui Huang, Student Member, Xian Li, Student Member, and Ye Sun. 2016. A Triboelectric Motion Sensor in Wearable Body Sensor Network for Human Activity Recognition. 906 (2016), 4889--4892.

[7]

Gerwyn Hughes Tim Watson Andrew C. S. Mitchell James A.Johnstone, Paul A. Ford and Andrew T. Garrett. 2012. Field Based Reliability and Validity of the Bioharnessffl Multivariable Monitoring Device. -. (2012). (Accessed on 01/09/2017).

[8]

Heli Koskim. 2016. Adaptive Model Fusion for Wearable Sensors Based Human Activity Recognition. (2016).

[9]

Oscar D. Lara and Miguel a. Labrador. 2013. A Survey on Human Activity Recognition using Wearable Sensors. IEEE Communications Surveys & Tutorials 15, 3 (2013), 1192--1209.

[10]

Misfit. Misfit: Fitness Trackers & Wearable Technology - Misfit.com. (????). https://misfit.com/

[11]

L. Mo, F. Li, Y. Zhu, and A. Huang. 2016. Human physical activity recognition based on computer vision with deep learning model. Conference Record - IEEE Instrumentation and Measurement Technology Conference 2016-July (2016).

[12]

Alfredo Nazabal, Pablo Garcia-Moreno, Antonio Artes-Rodriguez, and Zoubin Ghahramani. 2015. Human Activity Recognition by Combining a Small Number of Classifiers. IEEE Journal of Biomedical and Health Informatics 2194, c (2015), 1--1.

[13]

R. Poppe. 2010. A survey on vision-based human action recognition. Image and vision computing 28, 6 (2010), 976--990.

Digital Library

[14]

Marie Postma-Nilsenová, Eric Postma, and Kiek Tates. 2015. Automatic detection of confusion in elderly users of a web-based health instruction video. Telemedicine journal and e-health: the official journal of the American Telemedicine Association 21, 6 (2015), 514--9.

[15]

Leopoldo Marchiori Rodrigues. 2016. Classification Methods based on Bayes and Neural Networks for Human Activity Recognition. (2016), 1141--1146.

[16]

Jayashri Sankaranarayanan, Lori J Murante, and Lisa M Moffett. 2014. A retrospective evaluation of remote pharmacist interventions in a telepharmacy service model using a conceptual framework. Telemedicine journal and e-health: the official journal of the American Telemedicine Association 20, 10 (2014), 893--901.

[17]

Delaram Yazdansepas, Anzah H Niazi, Jennifer L Gay, Frederick W Maier, Lakshmish Ramaswamy, Khaled Rasheed, and Matthew P Buman. 2016. A Multi-Featured Approach for Wearable Sensor-based Human Activity Recognition. (2016).

Cited By

Thakur DSaini J(2023)The Significance of IoT and Deep Learning in Activity RecognitionIoT, Big Data and AI for Improving Quality of Everyday Life: Present and Future Challenges10.1007/978-3-031-35783-1_18(311-329)Online publication date: 24-Aug-2023
https://doi.org/10.1007/978-3-031-35783-1_18
Kristoffersson ALindén M(2022)A Systematic Review of Wearable Sensors for Monitoring Physical ActivitySensors10.3390/s2202057322:2(573)Online publication date: 12-Jan-2022
https://doi.org/10.3390/s22020573
Mohamad Jawad HBin Hassan ZZaidan BMohammed Jawad FMohamed Jawad DAlredany W(2022)A Systematic Literature Review of Enabling IoT in Healthcare: Motivations, Challenges, and RecommendationsElectronics10.3390/electronics1119322311:19(3223)Online publication date: 8-Oct-2022
https://doi.org/10.3390/electronics11193223
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IoT system for Human Activity Recognition using BioHarness 3 and Smartphone

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cover image ACM Other conferences

ICFNDS '17: Proceedings of the International Conference on Future Networks and Distributed Systems

July 2017

325 pages

ISBN:9781450348447

DOI:10.1145/3102304

Copyright © 2017 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 ACM 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: 19 July 2017

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ICFNDS '17

ICFNDS '17: International Conference on Future Networks and Distributed Systems

July 19 - 20, 2017

Cambridge, United Kingdom

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Cited By

Thakur DSaini J(2023)The Significance of IoT and Deep Learning in Activity RecognitionIoT, Big Data and AI for Improving Quality of Everyday Life: Present and Future Challenges10.1007/978-3-031-35783-1_18(311-329)Online publication date: 24-Aug-2023
https://doi.org/10.1007/978-3-031-35783-1_18
Kristoffersson ALindén M(2022)A Systematic Review of Wearable Sensors for Monitoring Physical ActivitySensors10.3390/s2202057322:2(573)Online publication date: 12-Jan-2022
https://doi.org/10.3390/s22020573
Mohamad Jawad HBin Hassan ZZaidan BMohammed Jawad FMohamed Jawad DAlredany W(2022)A Systematic Literature Review of Enabling IoT in Healthcare: Motivations, Challenges, and RecommendationsElectronics10.3390/electronics1119322311:19(3223)Online publication date: 8-Oct-2022
https://doi.org/10.3390/electronics11193223
Babangida LPerumal TMustapha NYaakob R(2022)Internet of Things (IoT) Based Activity Recognition Strategies in Smart Homes: A ReviewIEEE Sensors Journal10.1109/JSEN.2022.316179722:9(8327-8336)Online publication date: 1-May-2022
https://doi.org/10.1109/JSEN.2022.3161797
Chiang TKuo PShiu HTseng Y(2022)A Framework for Fusing Video and Wearable Sensing Data by Deep Learning2022 8th International Conference on Applied System Innovation (ICASI)10.1109/ICASI55125.2022.9774477(134-139)Online publication date: 22-Apr-2022
https://doi.org/10.1109/ICASI55125.2022.9774477
Gupta RKumar J(2022)Smartphone Applications for Monitoring Physical ActivitiesInformation and Communication Technology (ICT) Frameworks in Telehealth10.1007/978-3-031-05049-7_12(191-207)Online publication date: 23-Aug-2022
https://doi.org/10.1007/978-3-031-05049-7_12
Bocus MChetty KPiechocki R(2021)UWB and WiFi Systems as Passive Opportunistic Activity Sensing Radars2021 IEEE Radar Conference (RadarConf21)10.1109/RadarConf2147009.2021.9455175(1-6)Online publication date: 7-May-2021
https://doi.org/10.1109/RadarConf2147009.2021.9455175
Yatbaz HEver EYazici A(2021)Activity Recognition and Anomaly Detection in E-Health Applications Using Color-Coded Representation and Lightweight CNN ArchitecturesIEEE Sensors Journal10.1109/JSEN.2021.306145821:13(14191-14202)Online publication date: 1-Jul-2021
https://doi.org/10.1109/JSEN.2021.3061458
Bocus MLi WPaulavicius JMcConville RSantos-Rodriguez RChetty KPiechocki R(2021)Translation Resilient Opportunistic WiFi Sensing2020 25th International Conference on Pattern Recognition (ICPR)10.1109/ICPR48806.2021.9412263(5627-5633)Online publication date: 10-Jan-2021
https://doi.org/10.1109/ICPR48806.2021.9412263
Tyagi A(2020)Healthcare-Internet of Things and Its ComponentsOptimizing Health Monitoring Systems With Wireless Technology10.4018/978-1-5225-6067-8.ch018(258-277)Online publication date: 11-Dec-2020
https://doi.org/10.4018/978-1-5225-6067-8.ch018
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