Cargando…
Improved Spatiotemporal Framework for Human Activity Recognition in Smart Environment
The rapid development of microsystems technology with the availability of various machine learning algorithms facilitates human activity recognition (HAR) and localization by low-cost and low-complexity systems in various applications related to industry 4.0, healthcare, ambient assisted living as w...
Autores principales: | Salem, Ziad, Weiss, Andreas Peter |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824688/ https://www.ncbi.nlm.nih.gov/pubmed/36616729 http://dx.doi.org/10.3390/s23010132 |
Ejemplares similares
-
Human activity recognition and behaviour analysis: for cyber-physical systems in smart environments
por: Chen, Liming, et al.
Publicado: (2019) -
Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments
por: Irvine, Naomi, et al.
Publicado: (2019) -
QLearn: Towards a framework for smart learning environments
por: Şerban, Camelia, et al.
Publicado: (2020) -
The Lifespan of Human Activity Recognition Systems for Smart Homes
por: Hiremath, Shruthi K., et al.
Publicado: (2023) -
A Unified Framework for Activity Recognition-Based Behavior Analysis and Action Prediction in Smart Homes
por: Fatima, Iram, et al.
Publicado: (2013)