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- Title
DEEP ARCHITECTURES FOR HUMAN ACTIVITY RECOGNITION USING SENSORS.
- Authors
Baloch, Zartasha; Shaikh, Faisal Karim; Unar, Mukhtiar Ali
- Abstract
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.
- Subjects
HUMAN activity recognition; BIOMETRIC identification; GLOBAL Positioning System; DEEP learning; PHYSICAL fitness
- Publication
3C Tecnologia, 2019, Issue 29-2especial, p15
- ISSN
2254-4143
- Publication type
Article
- DOI
10.17993/3ctecno.2019.specialissue2.14-35