Semi-Supervised Adversarial Learning Using LSTM for Human Activity Recognition

The training of Human Activity Recognition (HAR) models requires a substantial amount of labeled data. Unfortunately, despite being trained on enormous datasets, most current models have poor performance rates when evaluated against anonymous data from new users. Furthermore, due to the limits and p...

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Detalles Bibliográficos
Autores principales: Yang, Sung-Hyun, Baek, Dong-Gwon, Thapa, Keshav
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269419/
https://www.ncbi.nlm.nih.gov/pubmed/35808248
http://dx.doi.org/10.3390/s22134755

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269419/
https://www.ncbi.nlm.nih.gov/pubmed/35808248
http://dx.doi.org/10.3390/s22134755

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