Wearable-based human activity recognition using an IoT Approach

Diego Castro; William Coral; Camilo Rodriguez; Jose Cabra; Julian Colorado

doi:10.3390/jsan6040028

Wearable-based human activity recognition using an IoT Approach

Diego Castro
, William Coral
, Camilo Rodriguez
, Jose Cabra
, Julian Colorado

Universidad Javeriana

Research output: Contribution to journal › Article › peer-review

72 Scopus citations

Abstract

This paper presents a novel system based on the Internet of Things (IoT) to Human Activity Recognition (HAR) by monitoring vital signs remotely. We use machine learning algorithms to determine the activity done within four pre-established categories (lie, sit, walk and jog). Meanwhile, it is able to give feedback during and after the activity is performed, using a remote monitoring component with remote visualization and programmable alarms. This system was successfully implemented with a 95.83% success ratio.

Original language	English
Article number	28
Journal	Journal of Sensor and Actuator Networks
Volume	6
Issue number	4
DOIs	https://doi.org/10.3390/jsan6040028
State	Published - 24 Nov 2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bayesian classifier
C4.5
E-health
Human activity recognition (HAR)
Internet Of Things (IoT)
Rule tree classifier

Access to Document

10.3390/jsan6040028

Cite this

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title = "Wearable-based human activity recognition using an IoT Approach",

abstract = "This paper presents a novel system based on the Internet of Things (IoT) to Human Activity Recognition (HAR) by monitoring vital signs remotely. We use machine learning algorithms to determine the activity done within four pre-established categories (lie, sit, walk and jog). Meanwhile, it is able to give feedback during and after the activity is performed, using a remote monitoring component with remote visualization and programmable alarms. This system was successfully implemented with a 95.83\% success ratio.",

keywords = "Bayesian classifier, C4.5, E-health, Human activity recognition (HAR), Internet Of Things (IoT), Rule tree classifier",

author = "Diego Castro and William Coral and Camilo Rodriguez and Jose Cabra and Julian Colorado",

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AU - Coral, William

AU - Rodriguez, Camilo

AU - Cabra, Jose

AU - Colorado, Julian

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Y1 - 2017/11/24

N2 - This paper presents a novel system based on the Internet of Things (IoT) to Human Activity Recognition (HAR) by monitoring vital signs remotely. We use machine learning algorithms to determine the activity done within four pre-established categories (lie, sit, walk and jog). Meanwhile, it is able to give feedback during and after the activity is performed, using a remote monitoring component with remote visualization and programmable alarms. This system was successfully implemented with a 95.83% success ratio.

AB - This paper presents a novel system based on the Internet of Things (IoT) to Human Activity Recognition (HAR) by monitoring vital signs remotely. We use machine learning algorithms to determine the activity done within four pre-established categories (lie, sit, walk and jog). Meanwhile, it is able to give feedback during and after the activity is performed, using a remote monitoring component with remote visualization and programmable alarms. This system was successfully implemented with a 95.83% success ratio.

KW - Bayesian classifier

KW - C4.5

KW - E-health

KW - Human activity recognition (HAR)

KW - Internet Of Things (IoT)

KW - Rule tree classifier

UR - https://www.scopus.com/pages/publications/85036506434

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DO - 10.3390/jsan6040028

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JF - Journal of Sensor and Actuator Networks

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M1 - 28

ER -

Wearable-based human activity recognition using an IoT Approach

Abstract

UN SDGs

Keywords

Access to Document

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