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Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models
Deep learning (DL) models are very useful for human activity recognition (HAR); these methods present better accuracy for HAR when compared to traditional, among other advantages. DL learns from unlabeled data and extracts features from raw data, as for the case of time-series acceleration. Sliding...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
PeerJ Inc.
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455026/ https://www.ncbi.nlm.nih.gov/pubmed/36091986 http://dx.doi.org/10.7717/peerj-cs.1052 |