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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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MDPI
2022
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| Materias: | |
| 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 |