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A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks
To date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value. A major drawback of such intervals is that they need to be long enough to prevent false positives (type I errors), while short enough t...
Autores principales: | Syed, Shaheen, Morseth, Bente, Hopstock, Laila A., Horsch, Alexander |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8065130/ https://www.ncbi.nlm.nih.gov/pubmed/33893345 http://dx.doi.org/10.1038/s41598-021-87757-z |
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