Deep Architectures for Human Activity Recognition using Sensors

Human activity recognition (HAR) is a renowned research field in recent years due to its applications such as physical fitness monitoring, assisted living, elderly–care, biometric authentication and many more. The ubiquitous nature of sensors makes them a good choice to use for activity recognition....

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Bibliographic Details
Published in:3C tecnología 2019-05, Vol.8 (1), p.14-35
Main Authors: Baloch, Zartasha, Shaikh, Faisal Karim, Unar, Mukhtiar Ali
Format: Article
Language:English
Subjects:
Deep Learning models
Human activity recognition
Sensors
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Summary:Human activity recognition (HAR) is a renowned research field in recent years due to its applications such as physical fitness monitoring, assisted living, elderly–care, biometric authentication and many more. The ubiquitous nature of sensors makes them a good choice to use for activity recognition. The latest smart gadgets are equipped with most of the wearable sensors i.e. accelerometer, gyroscope, GPS, compass, camera, microphone etc. These sensors measure various aspects of an object, and are easy to use with less cost. The use of sensors in the field of HAR opens new avenues for machine learning (ML) researchers to accurately recognize human activities. Deep learning (DL) is becoming popular among HAR researchers due to its outstanding performance over conventional ML techniques. In this paper, we have reviewed recent research studies on deep models for sensor–based human activity recognition. The aim of this article is to identify recent trends and challenges in HAR.
ISSN:2254-4143
2254-4143
DOI:10.17993/3ctecno.2019.specialissue2.14-35