Deep Architectures for Human Activity Recognition using Sensors

Deep Architectures for Human Activity Recognition using Sensors

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Publicado en 3C Tecnología. Special Issue – May 2019





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.


Palabras clave


Deep Learning models, Sensors, Human activity recognition.

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