A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks

Abstract 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...

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Autores principales:	Shaheen Syed, Bente Morseth, Laila A. Hopstock, Alexander Horsch
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/23d4d98bfba6484ebb75713885ef5f1e
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spelling	oai:doaj.org-article:23d4d98bfba6484ebb75713885ef5f1e2021-12-02T16:45:15ZA novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks10.1038/s41598-021-87757-z2045-2322https://doaj.org/article/23d4d98bfba6484ebb75713885ef5f1e2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87757-zhttps://doaj.org/toc/2045-2322Abstract 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 to prevent false negatives (type II errors), which limits detecting both short and longer episodes of non-wear time. In this paper, we propose a novel non-wear detection algorithm that eliminates the need for an interval. Rather than inspecting acceleration within intervals, we explore acceleration right before and right after an episode of non-wear time. We trained a deep convolutional neural network that was able to infer non-wear time by detecting when the accelerometer was removed and when it was placed back on again. We evaluate our algorithm against several baseline and existing non-wear algorithms, and our algorithm achieves a perfect precision, a recall of 0.9962, and an F1 score of 0.9981, outperforming all evaluated algorithms. Although our algorithm was developed using patterns learned from a hip-worn accelerometer, we propose algorithmic steps that can easily be applied to a wrist-worn accelerometer and a retrained classification model.Shaheen SyedBente MorsethLaila A. HopstockAlexander HorschNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shaheen Syed Bente Morseth Laila A. Hopstock Alexander Horsch A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks
description	Abstract 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 to prevent false negatives (type II errors), which limits detecting both short and longer episodes of non-wear time. In this paper, we propose a novel non-wear detection algorithm that eliminates the need for an interval. Rather than inspecting acceleration within intervals, we explore acceleration right before and right after an episode of non-wear time. We trained a deep convolutional neural network that was able to infer non-wear time by detecting when the accelerometer was removed and when it was placed back on again. We evaluate our algorithm against several baseline and existing non-wear algorithms, and our algorithm achieves a perfect precision, a recall of 0.9962, and an F1 score of 0.9981, outperforming all evaluated algorithms. Although our algorithm was developed using patterns learned from a hip-worn accelerometer, we propose algorithmic steps that can easily be applied to a wrist-worn accelerometer and a retrained classification model.
format	article
author	Shaheen Syed Bente Morseth Laila A. Hopstock Alexander Horsch
author_facet	Shaheen Syed Bente Morseth Laila A. Hopstock Alexander Horsch
author_sort	Shaheen Syed
title	A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks
title_short	A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks
title_full	A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks
title_fullStr	A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks
title_full_unstemmed	A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks
title_sort	novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/23d4d98bfba6484ebb75713885ef5f1e
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A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks

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