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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9460180 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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