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J. Sens. Actuator Netw. 2017, 6(4), 28;

Wearable-Based Human Activity Recognition Using an IoT Approach

Department of Electronics, School of Engineering, Pontificia Universidad Javeriana, Cr. 7 No. 40-62 Bldg. Jose Gabriel Maldonado, Bogota 110111, Colombia
Author to whom correspondence should be addressed.
Received: 30 September 2017 / Revised: 15 November 2017 / Accepted: 17 November 2017 / Published: 24 November 2017
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
PDF [3970 KB, uploaded 27 November 2017]


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. View Full-Text
Keywords: e-health; human activity recognition (HAR); Internet of Things (IoT); rule tree classifier; C4.5; Bayesian classifier e-health; human activity recognition (HAR); Internet of Things (IoT); rule tree classifier; C4.5; Bayesian classifier

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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 (CC BY 4.0).
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Castro, D.; Coral, W.; Rodriguez, C.; Cabra, J.; Colorado, J. Wearable-Based Human Activity Recognition Using an IoT Approach. J. Sens. Actuator Netw. 2017, 6, 28.

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