Hybrid deep learning approaches for smartphone sensor-based human activity recognition

Human Activity Recognition (HAR) has become one of the most important research fields to achieve real-time monitoring of human activities for timely decision making in various applications like fall detection, elderly care etc. Now-a-days, most people use smartphones which come with various embedded...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Multimedia tools and applications Jg. 80; H. 28-29; S. 35585 - 35604
Hauptverfasser:	Ghate, Vasundhara, C, Sweetlin Hemalatha
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2021 Springer Nature B.V
Schlagworte:	1166: Advances of machine learning in data analytics and visual information processing Acceleration Accelerometers Activities of daily living Angular velocity Artificial neural networks Computer Communication Networks Computer Science Data mining Data Structures and Information Theory Datasets Decision making Decision trees Deep learning Fall detection Human activity recognition Inertial sensing devices Machine learning Moving object recognition Multimedia Information Systems Neural networks Sensors Smartphones Special Purpose and Application-Based Systems CNN Inertial sensors LSTM HAR ADL GRU DeepCNN-RF
ISSN:	1380-7501, 1573-7721
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!