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A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving

Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver’s recent sitting and sleep history could help reduce safety risks. This study aimed to apply deep learning to raw accelerometry data collected during a simulated driving task to classify...

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Autores principales: Tuckwell, Georgia A., Keal, James A., Gupta, Charlotte C., Ferguson, Sally A., Kowlessar, Jarrad D., Vincent, Grace E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460180/
https://www.ncbi.nlm.nih.gov/pubmed/36081057
http://dx.doi.org/10.3390/s22176598
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author Tuckwell, Georgia A.
Keal, James A.
Gupta, Charlotte C.
Ferguson, Sally A.
Kowlessar, Jarrad D.
Vincent, Grace E.
author_facet Tuckwell, Georgia A.
Keal, James A.
Gupta, Charlotte C.
Ferguson, Sally A.
Kowlessar, Jarrad D.
Vincent, Grace E.
author_sort Tuckwell, Georgia A.
collection PubMed
description Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver’s recent sitting and sleep history could help reduce safety risks. This study aimed to apply deep learning to raw accelerometry data collected during a simulated driving task to classify recent sitting and sleep history. Participants (n = 84, Mean ± SD age = 23.5 ± 4.8, 49% Female) completed a seven-day laboratory study. Raw accelerometry data were collected from a thigh-worn accelerometer during a 20-min simulated drive (8:10 h and 17:30 h each day). Two convolutional neural networks (CNNs; ResNet-18 and DixonNet) were trained to classify accelerometry data into four classes (sitting or breaking up sitting and 9-h or 5-h sleep). Accuracy was determined using five-fold cross-validation. ResNet-18 produced higher accuracy scores: 88.6 ± 1.3% for activity (compared to 77.2 ± 2.6% from DixonNet) and 88.6 ± 1.1% for sleep history (compared to 75.2 ± 2.6% from DixonNet). Class activation mapping revealed distinct patterns of movement and postural changes between classes. Findings demonstrate the suitability of CNNs in classifying sitting and sleep history using thigh-worn accelerometer data collected during a simulated drive. This approach has implications for the identification of drivers at risk of fatigue-related impairment.
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spelling pubmed-94601802022-09-10 A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving Tuckwell, Georgia A. Keal, James A. Gupta, Charlotte C. Ferguson, Sally A. Kowlessar, Jarrad D. Vincent, Grace E. Sensors (Basel) Article Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver’s recent sitting and sleep history could help reduce safety risks. This study aimed to apply deep learning to raw accelerometry data collected during a simulated driving task to classify recent sitting and sleep history. Participants (n = 84, Mean ± SD age = 23.5 ± 4.8, 49% Female) completed a seven-day laboratory study. Raw accelerometry data were collected from a thigh-worn accelerometer during a 20-min simulated drive (8:10 h and 17:30 h each day). Two convolutional neural networks (CNNs; ResNet-18 and DixonNet) were trained to classify accelerometry data into four classes (sitting or breaking up sitting and 9-h or 5-h sleep). Accuracy was determined using five-fold cross-validation. ResNet-18 produced higher accuracy scores: 88.6 ± 1.3% for activity (compared to 77.2 ± 2.6% from DixonNet) and 88.6 ± 1.1% for sleep history (compared to 75.2 ± 2.6% from DixonNet). Class activation mapping revealed distinct patterns of movement and postural changes between classes. Findings demonstrate the suitability of CNNs in classifying sitting and sleep history using thigh-worn accelerometer data collected during a simulated drive. This approach has implications for the identification of drivers at risk of fatigue-related impairment. MDPI 2022-09-01 /pmc/articles/PMC9460180/ /pubmed/36081057 http://dx.doi.org/10.3390/s22176598 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tuckwell, Georgia A.
Keal, James A.
Gupta, Charlotte C.
Ferguson, Sally A.
Kowlessar, Jarrad D.
Vincent, Grace E.
A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving
title A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving
title_full A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving
title_fullStr A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving
title_full_unstemmed A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving
title_short A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving
title_sort deep learning approach to classify sitting and sleep history from raw accelerometry data during simulated driving
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460180/
https://www.ncbi.nlm.nih.gov/pubmed/36081057
http://dx.doi.org/10.3390/s22176598
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