Show simple item record

dc.contributor.authorSyed, Shaheen
dc.contributor.authorMorseth, Bente
dc.contributor.authorHopstock, Laila Arnesdatter
dc.contributor.authorHorsch, Alexander
dc.date.accessioned2020-05-28T08:34:53Z
dc.date.available2020-05-28T08:34:53Z
dc.date.issued2020-04-03
dc.description.abstractAccurate detection of accelerometer non-wear time is crucial for calculating physical activity summary statistics. In this study, we evaluated three epoch-based non-wear algorithms (Hecht, Troiano, and Choi) and one raw-based algorithm (Hees). In addition, we performed a sensitivity analysis to provide insight into the relationship between the algorithms’ hyperparameters and classification performance, as well as to generate tuned hyperparameter values to better detect episodes of wear and non-wear time. We used machine learning to construct a gold-standard dataset by combining two accelerometers and electrocardiogram recordings. The Hecht and Troiano algorithms achieved poor classification performance, while Choi exhibited moderate performance. Meanwhile, Hees outperformed all epoch-based algorithms. The sensitivity analysis and hyperparameter tuning revealed that all algorithms were able to achieve increased classification performance by employing larger intervals and windows, while more stringently defining artificial movement. These classification gains were associated with the ability to lower the false positives (type I error) and do not necessarily indicate a more accurate detection of the total non-wear time. Moreover, our results indicate that with tuned hyperparameters, epoch-based non-wear algorithms are able to perform just as well as raw-based non-wear algorithms with respect to their ability to correctly detect true wear and non-wear episodes.en_US
dc.identifier.citationSyed S, Morseth B, Hopstock LA, Horsch A. Evaluating the performance of raw and epoch non-wear algorithms using multiple accelerometers and electrocardiogram recordings. Scientific Reports. 2020;10(5866)en_US
dc.identifier.cristinIDFRIDAID 1802752
dc.identifier.doi10.1038/s41598-020-62821-2
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/10037/18395
dc.language.isoengen_US
dc.publisherNature Researchen_US
dc.relation.journalScientific Reports
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Medical disciplines: 700::Health sciences: 800en_US
dc.subjectVDP::Medisinske Fag: 700::Helsefag: 800en_US
dc.subjectVDP::Technology: 500en_US
dc.subjectVDP::Teknologi: 500en_US
dc.titleEvaluating the performance of raw and epoch non-wear algorithms using multiple accelerometers and electrocardiogram recordingsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


File(s) in this item

Thumbnail

This item appears in the following collection(s)

Show simple item record