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...

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Detalles Bibliográficos
Autores principales: Jaén-Vargas, Milagros, Reyes Leiva, Karla Miriam, Fernandes, Francisco, Barroso Gonçalves, Sérgio, Tavares Silva, Miguel, Lopes, Daniel Simões, Serrano Olmedo, José Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Bioinformatics
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

Internet

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

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