Validation results on the Tromsø Study accelerometer data subsample.

<p>(A) Accuracies for the same criteria as for the ActiGraph/Oura/Polar dataset. (B) Predicted times in bed plotted against the annotated TiB. The selected model reached an accuracy of over 90% on the additional, unseen data from the Tromsø Study and up to 95.6% when removing unreasonably long...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Marc Weitz (8131560) (author)
مؤلفون آخرون:	Shaheen Syed (6987770) (author), Laila A. Hopstock (8072348) (author), Bente Morseth (3454490) (author), André Henriksen (20783199) (author), Alexander Horsch (405483) (author)
منشور في:	2025
الموضوعات:	Medicine Biotechnology Space Science Biological Sciences not elsewhere classified troms &# 248 related research questions neural network model monitor movement patterns g ., predicted 24 &# 8201 worn acceleration signals usually either worn xlink "> accelerometers wearable data obtained two additional datasets already collected data additionally assess sleep sedentary time needs placement like time trained model fundamentally large epidemiological studies demographically different population assess physical activity point even sleep sedentary time analyses wrongly worn accelerometer study </ p worn accelerometers physical activity unseen data training data starting point longitudinal analyses additional noise underestimated time automatic time widely neglected task due sophisticated methods several days recently got random croping particular days overall good new method living conditions introduced new improve training generalization performance frequently used comparable results analysis options algorithm achieved
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Validation results on the Tromsø Study accelerometer data subsample.

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