Personalized Activity Recognition with Deep Triplet Embeddings

A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data generated by individual users, resulting in very poor performance for some subjects. We present an approach to personalized activity recognition based on deep feature repres...

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Detalles Bibliográficos
Autores principales: Burns, David, Boyer, Philip, Arrowsmith, Colin, Whyne, Cari
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324610/
https://www.ncbi.nlm.nih.gov/pubmed/35890902
http://dx.doi.org/10.3390/s22145222

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324610/
https://www.ncbi.nlm.nih.gov/pubmed/35890902
http://dx.doi.org/10.3390/s22145222

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