An unsupervised statistical representation learning method for human activity recognition

With the evolution of smart devices like smartphones, smartwatches, and other wearable devices, motion sensors have been integrated into these devices to collect data and analyze human activities. Consequently, sensor-based Human Activity Recognition (HAR) has emerged as a significant research area...

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Veröffentlicht in:	Signal, image and video processing Jg. 18; H. 10; S. 7041 - 7052
Hauptverfasser:	Abdi, Mohammad Foad, BabaAli, Bagher, Momeni, Saleh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.09.2024 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Computer Imaging Computer Science Computing costs Datasets Decision trees Deep learning Engineering Fourier transforms Human activity recognition Human motion Image Processing and Computer Vision Machine learning Motion sensors Multimedia Information Systems Neural networks Original Paper Pattern Recognition and Graphics Performance evaluation Regression analysis Representations Sensors Signal,Image and Speech Processing Smartwatches Support vector machines Ubiquitous computing Vision Wearable technology Feature embedding Latent Dirichlet allocation (LDA) Human activity recognition Unsupervised representation learning
ISSN:	1863-1703, 1863-1711
Online-Zugang:	Volltext
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