Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology Article Swipe
Adonay S. Nunes
,
Matthew Patterson
,
Dawid Gerstel
,
Sheraz Khan
,
Christine C. Guo
,
Ali Neishabouri
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24247982
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24247982
Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that this new model outperformed existing sleep algorithms in classifying sleep–wake and estimating sleep outcomes based on wrist-worn accelerometry. This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.1% (sensitivity 84% and specificity 58%). Compared to commonly used sleep algorithms, this model resulted in the smallest error in wake after sleep onset (MAE of 48.7, Cole–Kripke of 86.2, Sadeh of 108.2, z-angle of 57.5) and sleep efficiency (MAE of 11.8, Cole–Kripke of 18.4, Sadeh of 23.3, z-angle of 9.3) outcomes. Despite being around for many years, accelerometer-alone devices continue to be useful due to their low cost, long battery life, and ease of use. Improving the accuracy and generalizability of sleep algorithms for accelerometer wrist devices is of utmost importance. We here demonstrated that domain adversarial convolutional neural networks can improve the overall accuracy, especially the specificity, of sleep–wake classification using wrist-worn accelerometer data, substantiating its use as a scalable and valid approach for sleep outcome assessment in real life.
Related Topics
Concepts
Generalizability theory
Accelerometer
Convolutional neural network
Sleep (system call)
Computer science
Wearable computer
Artificial intelligence
Machine learning
Actigraphy
Wearable technology
Scalability
Deep learning
Sensitivity (control systems)
Artificial neural network
Medicine
Engineering
Embedded system
Statistics
Mathematics
Circadian rhythm
Operating system
Electronic engineering
Endocrinology
Database
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s24247982
- https://www.mdpi.com/1424-8220/24/24/7982/pdf?version=1734153285
- OA Status
- gold
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405476003
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4405476003Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s24247982Digital Object Identifier
- Title
-
Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment TechnologyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-14Full publication date if available
- Authors
-
Adonay S. Nunes, Matthew Patterson, Dawid Gerstel, Sheraz Khan, Christine C. Guo, Ali NeishabouriList of authors in order
- Landing page
-
https://doi.org/10.3390/s24247982Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/24/24/7982/pdf?version=1734153285Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/24/24/7982/pdf?version=1734153285Direct OA link when available
- Concepts
-
Generalizability theory, Accelerometer, Convolutional neural network, Sleep (system call), Computer science, Wearable computer, Artificial intelligence, Machine learning, Actigraphy, Wearable technology, Scalability, Deep learning, Sensitivity (control systems), Artificial neural network, Medicine, Engineering, Embedded system, Statistics, Mathematics, Circadian rhythm, Operating system, Electronic engineering, Endocrinology, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4405476003 |
|---|---|
| doi | https://doi.org/10.3390/s24247982 |
| ids.doi | https://doi.org/10.3390/s24247982 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39771718 |
| ids.openalex | https://openalex.org/W4405476003 |
| fwci | 0.0 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D006801 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Humans |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D000076251 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Wearable Electronic Devices |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D016571 |
| mesh[2].is_major_topic | True |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Neural Networks, Computer |
| mesh[3].qualifier_ui | Q000502 |
| mesh[3].descriptor_ui | D012890 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | physiology |
| mesh[3].descriptor_name | Sleep |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D000465 |
| mesh[4].is_major_topic | True |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Algorithms |
| mesh[5].qualifier_ui | Q000295 |
| mesh[5].descriptor_ui | D061725 |
| mesh[5].is_major_topic | True |
| mesh[5].qualifier_name | instrumentation |
| mesh[5].descriptor_name | Accelerometry |
| mesh[6].qualifier_ui | Q000379 |
| mesh[6].descriptor_ui | D061725 |
| mesh[6].is_major_topic | True |
| mesh[6].qualifier_name | methods |
| mesh[6].descriptor_name | Accelerometry |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D008297 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Male |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D005260 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Female |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D000328 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Adult |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D008875 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Middle Aged |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D000077321 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Deep Learning |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D055815 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Young Adult |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D006801 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Humans |
| mesh[14].qualifier_ui | |
| mesh[14].descriptor_ui | D000076251 |
| mesh[14].is_major_topic | True |
| mesh[14].qualifier_name | |
| mesh[14].descriptor_name | Wearable Electronic Devices |
| mesh[15].qualifier_ui | |
| mesh[15].descriptor_ui | D016571 |
| mesh[15].is_major_topic | True |
| mesh[15].qualifier_name | |
| mesh[15].descriptor_name | Neural Networks, Computer |
| mesh[16].qualifier_ui | Q000502 |
| mesh[16].descriptor_ui | D012890 |
| mesh[16].is_major_topic | True |
| mesh[16].qualifier_name | physiology |
| mesh[16].descriptor_name | Sleep |
| mesh[17].qualifier_ui | |
| mesh[17].descriptor_ui | D000465 |
| mesh[17].is_major_topic | True |
| mesh[17].qualifier_name | |
| mesh[17].descriptor_name | Algorithms |
| mesh[18].qualifier_ui | Q000295 |
| mesh[18].descriptor_ui | D061725 |
| mesh[18].is_major_topic | True |
| mesh[18].qualifier_name | instrumentation |
| mesh[18].descriptor_name | Accelerometry |
| mesh[19].qualifier_ui | Q000379 |
| mesh[19].descriptor_ui | D061725 |
| mesh[19].is_major_topic | True |
| mesh[19].qualifier_name | methods |
| mesh[19].descriptor_name | Accelerometry |
| mesh[20].qualifier_ui | |
| mesh[20].descriptor_ui | D008297 |
| mesh[20].is_major_topic | False |
| mesh[20].qualifier_name | |
| mesh[20].descriptor_name | Male |
| mesh[21].qualifier_ui | |
| mesh[21].descriptor_ui | D005260 |
| mesh[21].is_major_topic | False |
| mesh[21].qualifier_name | |
| mesh[21].descriptor_name | Female |
| mesh[22].qualifier_ui | |
| mesh[22].descriptor_ui | D000328 |
| mesh[22].is_major_topic | False |
| mesh[22].qualifier_name | |
| mesh[22].descriptor_name | Adult |
| mesh[23].qualifier_ui | |
| mesh[23].descriptor_ui | D008875 |
| mesh[23].is_major_topic | False |
| mesh[23].qualifier_name | |
| mesh[23].descriptor_name | Middle Aged |
| mesh[24].qualifier_ui | |
| mesh[24].descriptor_ui | D000077321 |
| mesh[24].is_major_topic | False |
| mesh[24].qualifier_name | |
| mesh[24].descriptor_name | Deep Learning |
| mesh[25].qualifier_ui | |
| mesh[25].descriptor_ui | D055815 |
| mesh[25].is_major_topic | False |
| mesh[25].qualifier_name | |
| mesh[25].descriptor_name | Young Adult |
| type | article |
| title | Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology |
| biblio.issue | 24 |
| biblio.volume | 24 |
| biblio.last_page | 7982 |
| biblio.first_page | 7982 |
| topics[0].id | https://openalex.org/T10234 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9994999766349792 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2737 |
| topics[0].subfield.display_name | Physiology |
| topics[0].display_name | Obstructive Sleep Apnea Research |
| topics[1].id | https://openalex.org/T11196 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9980999827384949 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2204 |
| topics[1].subfield.display_name | Biomedical Engineering |
| topics[1].display_name | Non-Invasive Vital Sign Monitoring |
| topics[2].id | https://openalex.org/T10444 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.996999979019165 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Context-Aware Activity Recognition Systems |
| is_xpac | False |
| apc_list.value | 2400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2598 |
| apc_paid.value | 2400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2598 |
| concepts[0].id | https://openalex.org/C27158222 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9027066230773926 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q5532422 |
| concepts[0].display_name | Generalizability theory |
| concepts[1].id | https://openalex.org/C89805583 |
| concepts[1].level | 2 |
| concepts[1].score | 0.8020673990249634 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q192940 |
| concepts[1].display_name | Accelerometer |
| concepts[2].id | https://openalex.org/C81363708 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7889583110809326 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[2].display_name | Convolutional neural network |
| concepts[3].id | https://openalex.org/C2775841894 |
| concepts[3].level | 2 |
| concepts[3].score | 0.7136489748954773 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q4683692 |
| concepts[3].display_name | Sleep (system call) |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.6735788583755493 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C150594956 |
| concepts[5].level | 2 |
| concepts[5].score | 0.6408402323722839 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1334829 |
| concepts[5].display_name | Wearable computer |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.6293693780899048 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C119857082 |
| concepts[7].level | 1 |
| concepts[7].score | 0.6100399494171143 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[7].display_name | Machine learning |
| concepts[8].id | https://openalex.org/C2778996325 |
| concepts[8].level | 3 |
| concepts[8].score | 0.5634140968322754 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q422201 |
| concepts[8].display_name | Actigraphy |
| concepts[9].id | https://openalex.org/C54290928 |
| concepts[9].level | 3 |
| concepts[9].score | 0.5365585684776306 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q4845080 |
| concepts[9].display_name | Wearable technology |
| concepts[10].id | https://openalex.org/C48044578 |
| concepts[10].level | 2 |
| concepts[10].score | 0.5234251022338867 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q727490 |
| concepts[10].display_name | Scalability |
| concepts[11].id | https://openalex.org/C108583219 |
| concepts[11].level | 2 |
| concepts[11].score | 0.5024714469909668 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[11].display_name | Deep learning |
| concepts[12].id | https://openalex.org/C21200559 |
| concepts[12].level | 2 |
| concepts[12].score | 0.47682255506515503 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7451068 |
| concepts[12].display_name | Sensitivity (control systems) |
| concepts[13].id | https://openalex.org/C50644808 |
| concepts[13].level | 2 |
| concepts[13].score | 0.47063586115837097 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[13].display_name | Artificial neural network |
| concepts[14].id | https://openalex.org/C71924100 |
| concepts[14].level | 0 |
| concepts[14].score | 0.21509623527526855 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[14].display_name | Medicine |
| concepts[15].id | https://openalex.org/C127413603 |
| concepts[15].level | 0 |
| concepts[15].score | 0.1204107403755188 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[15].display_name | Engineering |
| concepts[16].id | https://openalex.org/C149635348 |
| concepts[16].level | 1 |
| concepts[16].score | 0.1140533983707428 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q193040 |
| concepts[16].display_name | Embedded system |
| concepts[17].id | https://openalex.org/C105795698 |
| concepts[17].level | 1 |
| concepts[17].score | 0.10608682036399841 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[17].display_name | Statistics |
| concepts[18].id | https://openalex.org/C33923547 |
| concepts[18].level | 0 |
| concepts[18].score | 0.0883297324180603 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[18].display_name | Mathematics |
| concepts[19].id | https://openalex.org/C121446783 |
| concepts[19].level | 2 |
| concepts[19].score | 0.07291412353515625 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q208353 |
| concepts[19].display_name | Circadian rhythm |
| concepts[20].id | https://openalex.org/C111919701 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[20].display_name | Operating system |
| concepts[21].id | https://openalex.org/C24326235 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q126095 |
| concepts[21].display_name | Electronic engineering |
| concepts[22].id | https://openalex.org/C134018914 |
| concepts[22].level | 1 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q162606 |
| concepts[22].display_name | Endocrinology |
| concepts[23].id | https://openalex.org/C77088390 |
| concepts[23].level | 1 |
| concepts[23].score | 0.0 |
| concepts[23].wikidata | https://www.wikidata.org/wiki/Q8513 |
| concepts[23].display_name | Database |
| keywords[0].id | https://openalex.org/keywords/generalizability-theory |
| keywords[0].score | 0.9027066230773926 |
| keywords[0].display_name | Generalizability theory |
| keywords[1].id | https://openalex.org/keywords/accelerometer |
| keywords[1].score | 0.8020673990249634 |
| keywords[1].display_name | Accelerometer |
| keywords[2].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[2].score | 0.7889583110809326 |
| keywords[2].display_name | Convolutional neural network |
| keywords[3].id | https://openalex.org/keywords/sleep |
| keywords[3].score | 0.7136489748954773 |
| keywords[3].display_name | Sleep (system call) |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.6735788583755493 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/wearable-computer |
| keywords[5].score | 0.6408402323722839 |
| keywords[5].display_name | Wearable computer |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.6293693780899048 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/machine-learning |
| keywords[7].score | 0.6100399494171143 |
| keywords[7].display_name | Machine learning |
| keywords[8].id | https://openalex.org/keywords/actigraphy |
| keywords[8].score | 0.5634140968322754 |
| keywords[8].display_name | Actigraphy |
| keywords[9].id | https://openalex.org/keywords/wearable-technology |
| keywords[9].score | 0.5365585684776306 |
| keywords[9].display_name | Wearable technology |
| keywords[10].id | https://openalex.org/keywords/scalability |
| keywords[10].score | 0.5234251022338867 |
| keywords[10].display_name | Scalability |
| keywords[11].id | https://openalex.org/keywords/deep-learning |
| keywords[11].score | 0.5024714469909668 |
| keywords[11].display_name | Deep learning |
| keywords[12].id | https://openalex.org/keywords/sensitivity |
| keywords[12].score | 0.47682255506515503 |
| keywords[12].display_name | Sensitivity (control systems) |
| keywords[13].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[13].score | 0.47063586115837097 |
| keywords[13].display_name | Artificial neural network |
| keywords[14].id | https://openalex.org/keywords/medicine |
| keywords[14].score | 0.21509623527526855 |
| keywords[14].display_name | Medicine |
| keywords[15].id | https://openalex.org/keywords/engineering |
| keywords[15].score | 0.1204107403755188 |
| keywords[15].display_name | Engineering |
| keywords[16].id | https://openalex.org/keywords/embedded-system |
| keywords[16].score | 0.1140533983707428 |
| keywords[16].display_name | Embedded system |
| keywords[17].id | https://openalex.org/keywords/statistics |
| keywords[17].score | 0.10608682036399841 |
| keywords[17].display_name | Statistics |
| keywords[18].id | https://openalex.org/keywords/mathematics |
| keywords[18].score | 0.0883297324180603 |
| keywords[18].display_name | Mathematics |
| keywords[19].id | https://openalex.org/keywords/circadian-rhythm |
| keywords[19].score | 0.07291412353515625 |
| keywords[19].display_name | Circadian rhythm |
| language | en |
| locations[0].id | doi:10.3390/s24247982 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S101949793 |
| locations[0].source.issn | 1424-8220 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1424-8220 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Sensors |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1424-8220/24/24/7982/pdf?version=1734153285 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Sensors |
| locations[0].landing_page_url | https://doi.org/10.3390/s24247982 |
| locations[1].id | pmid:39771718 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Sensors (Basel, Switzerland) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39771718 |
| locations[2].id | pmh:oai:doaj.org/article:49cb7aa7d4124352a8fc1de48886eb76 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Sensors, Vol 24, Iss 24, p 7982 (2024) |
| locations[2].landing_page_url | https://doaj.org/article/49cb7aa7d4124352a8fc1de48886eb76 |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:11679400 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Sensors (Basel) |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11679400 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5062263326 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-8755-2240 |
| authorships[0].author.display_name | Adonay S. Nunes |
| authorships[0].affiliations[0].raw_affiliation_string | ActiGraph LLC, Pensacola, FL 32502, USA |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Adonay S. Nunes |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | ActiGraph LLC, Pensacola, FL 32502, USA |
| authorships[1].author.id | https://openalex.org/A5008740902 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-9774-4094 |
| authorships[1].author.display_name | Matthew Patterson |
| authorships[1].affiliations[0].raw_affiliation_string | ActiGraph LLC, Pensacola, FL 32502, USA |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Matthew R. Patterson |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | ActiGraph LLC, Pensacola, FL 32502, USA |
| authorships[2].author.id | https://openalex.org/A5107913143 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Dawid Gerstel |
| authorships[2].affiliations[0].raw_affiliation_string | ActiGraph LLC, Pensacola, FL 32502, USA |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Dawid Gerstel |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | ActiGraph LLC, Pensacola, FL 32502, USA |
| authorships[3].author.id | https://openalex.org/A5047904405 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6792-3577 |
| authorships[3].author.display_name | Sheraz Khan |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I136199984, https://openalex.org/I4210087915, https://openalex.org/I4210127055, https://openalex.org/I63966007 |
| authorships[3].affiliations[0].raw_affiliation_string | Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA 02129, USA |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I144112489, https://openalex.org/I63966007 |
| authorships[3].affiliations[1].raw_affiliation_string | McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA |
| authorships[3].affiliations[2].institution_ids | https://openalex.org/I136199984, https://openalex.org/I4210087915 |
| authorships[3].affiliations[2].raw_affiliation_string | Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA |
| authorships[3].institutions[0].id | https://openalex.org/I4210127055 |
| authorships[3].institutions[0].ror | https://ror.org/032q5ym94 |
| authorships[3].institutions[0].type | facility |
| authorships[3].institutions[0].lineage | https://openalex.org/I136199984, https://openalex.org/I4210087915, https://openalex.org/I4210127055, https://openalex.org/I48633490, https://openalex.org/I63966007 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Athinoula A. Martinos Center for Biomedical Imaging |
| authorships[3].institutions[1].id | https://openalex.org/I136199984 |
| authorships[3].institutions[1].ror | https://ror.org/03vek6s52 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I136199984 |
| authorships[3].institutions[1].country_code | US |
| authorships[3].institutions[1].display_name | Harvard University |
| authorships[3].institutions[2].id | https://openalex.org/I4210087915 |
| authorships[3].institutions[2].ror | https://ror.org/002pd6e78 |
| authorships[3].institutions[2].type | healthcare |
| authorships[3].institutions[2].lineage | https://openalex.org/I4210087915, https://openalex.org/I48633490 |
| authorships[3].institutions[2].country_code | US |
| authorships[3].institutions[2].display_name | Massachusetts General Hospital |
| authorships[3].institutions[3].id | https://openalex.org/I63966007 |
| authorships[3].institutions[3].ror | https://ror.org/042nb2s44 |
| authorships[3].institutions[3].type | education |
| authorships[3].institutions[3].lineage | https://openalex.org/I63966007 |
| authorships[3].institutions[3].country_code | US |
| authorships[3].institutions[3].display_name | Massachusetts Institute of Technology |
| authorships[3].institutions[4].id | https://openalex.org/I144112489 |
| authorships[3].institutions[4].ror | https://ror.org/05ymca674 |
| authorships[3].institutions[4].type | facility |
| authorships[3].institutions[4].lineage | https://openalex.org/I144112489, https://openalex.org/I63966007 |
| authorships[3].institutions[4].country_code | US |
| authorships[3].institutions[4].display_name | McGovern Institute for Brain Research |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Sheraz Khan |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA 02129, USA, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA |
| authorships[4].author.id | https://openalex.org/A5081230016 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-1530-0172 |
| authorships[4].author.display_name | Christine C. Guo |
| authorships[4].affiliations[0].raw_affiliation_string | ActiGraph LLC, Pensacola, FL 32502, USA |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Christine C. Guo |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | ActiGraph LLC, Pensacola, FL 32502, USA |
| authorships[5].author.id | https://openalex.org/A5003765514 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-0259-5645 |
| authorships[5].author.display_name | Ali Neishabouri |
| authorships[5].affiliations[0].raw_affiliation_string | ActiGraph LLC, Pensacola, FL 32502, USA |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Ali Neishabouri |
| authorships[5].is_corresponding | True |
| authorships[5].raw_affiliation_strings | ActiGraph LLC, Pensacola, FL 32502, USA |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1424-8220/24/24/7982/pdf?version=1734153285 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10234 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9994999766349792 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2737 |
| primary_topic.subfield.display_name | Physiology |
| primary_topic.display_name | Obstructive Sleep Apnea Research |
| related_works | https://openalex.org/W3090300519, https://openalex.org/W2514492205, https://openalex.org/W4250401876, https://openalex.org/W2943851981, https://openalex.org/W2566526749, https://openalex.org/W3047461507, https://openalex.org/W3126390843, https://openalex.org/W4245880644, https://openalex.org/W4312415459, https://openalex.org/W2528680939 |
| cited_by_count | 0 |
| locations_count | 4 |
| best_oa_location.id | doi:10.3390/s24247982 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S101949793 |
| best_oa_location.source.issn | 1424-8220 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1424-8220 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Sensors |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1424-8220/24/24/7982/pdf?version=1734153285 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Sensors |
| best_oa_location.landing_page_url | https://doi.org/10.3390/s24247982 |
| primary_location.id | doi:10.3390/s24247982 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S101949793 |
| primary_location.source.issn | 1424-8220 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1424-8220 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Sensors |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1424-8220/24/24/7982/pdf?version=1734153285 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Sensors |
| primary_location.landing_page_url | https://doi.org/10.3390/s24247982 |
| publication_date | 2024-12-14 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2569242619, https://openalex.org/W6769812455, https://openalex.org/W2266982813, https://openalex.org/W72748393, https://openalex.org/W4296695189, https://openalex.org/W4285393688, https://openalex.org/W117545607, https://openalex.org/W2948054370, https://openalex.org/W1507001215, https://openalex.org/W3042780256, https://openalex.org/W3151219741, https://openalex.org/W2180429985, https://openalex.org/W2998284821, https://openalex.org/W2980365524, https://openalex.org/W4360826160, https://openalex.org/W6640601759, https://openalex.org/W2805075551, https://openalex.org/W2962734882, https://openalex.org/W3126219352, https://openalex.org/W2900928098, https://openalex.org/W147630397, https://openalex.org/W2952406719, https://openalex.org/W2034447161, https://openalex.org/W2945073425, https://openalex.org/W2109784184, https://openalex.org/W3006544884, https://openalex.org/W2065940963, https://openalex.org/W2963447013, https://openalex.org/W2808857154, https://openalex.org/W3099969680, https://openalex.org/W3104370314, https://openalex.org/W4254751698, https://openalex.org/W1937674079 |
| referenced_works_count | 33 |
| abstract_inverted_index.a | 28, 201 |
| abstract_inverted_index.In | 23 |
| abstract_inverted_index.We | 173 |
| abstract_inverted_index.an | 6, 78 |
| abstract_inverted_index.as | 5, 200 |
| abstract_inverted_index.be | 143 |
| abstract_inverted_index.in | 17, 51, 96, 100, 210 |
| abstract_inverted_index.is | 169 |
| abstract_inverted_index.of | 80, 106, 109, 112, 115, 121, 124, 127, 130, 155, 162, 170, 190 |
| abstract_inverted_index.on | 59, 70 |
| abstract_inverted_index.to | 38, 66, 88, 142, 146 |
| abstract_inverted_index.we | 26 |
| abstract_inverted_index.84% | 83 |
| abstract_inverted_index.and | 9, 21, 41, 54, 74, 84, 117, 153, 160, 203 |
| abstract_inverted_index.are | 2 |
| abstract_inverted_index.can | 182 |
| abstract_inverted_index.due | 145 |
| abstract_inverted_index.for | 12, 136, 165, 206 |
| abstract_inverted_index.its | 198 |
| abstract_inverted_index.low | 148 |
| abstract_inverted_index.new | 45 |
| abstract_inverted_index.the | 97, 158, 184, 188 |
| abstract_inverted_index.use | 199 |
| abstract_inverted_index.(MAE | 105, 120 |
| abstract_inverted_index.9.3) | 131 |
| abstract_inverted_index.This | 62 |
| abstract_inverted_index.both | 18 |
| abstract_inverted_index.deep | 29 |
| abstract_inverted_index.ease | 154 |
| abstract_inverted_index.here | 174 |
| abstract_inverted_index.long | 150 |
| abstract_inverted_index.many | 137 |
| abstract_inverted_index.real | 211 |
| abstract_inverted_index.task | 40 |
| abstract_inverted_index.that | 43, 176 |
| abstract_inverted_index.this | 24, 39, 44, 93 |
| abstract_inverted_index.use. | 156 |
| abstract_inverted_index.used | 4, 90 |
| abstract_inverted_index.wake | 101 |
| abstract_inverted_index.well | 65 |
| abstract_inverted_index.11.8, | 122 |
| abstract_inverted_index.18.4, | 125 |
| abstract_inverted_index.23.3, | 128 |
| abstract_inverted_index.48.7, | 107 |
| abstract_inverted_index.57.5) | 116 |
| abstract_inverted_index.58%). | 86 |
| abstract_inverted_index.80.1% | 81 |
| abstract_inverted_index.86.2, | 110 |
| abstract_inverted_index.Sadeh | 111, 126 |
| abstract_inverted_index.after | 102 |
| abstract_inverted_index.based | 58, 69 |
| abstract_inverted_index.being | 134 |
| abstract_inverted_index.care. | 22 |
| abstract_inverted_index.cost, | 149 |
| abstract_inverted_index.data, | 196 |
| abstract_inverted_index.error | 99 |
| abstract_inverted_index.life, | 152 |
| abstract_inverted_index.life. | 212 |
| abstract_inverted_index.model | 37, 46, 63, 94 |
| abstract_inverted_index.onset | 104 |
| abstract_inverted_index.sleep | 15, 49, 56, 91, 103, 118, 163, 207 |
| abstract_inverted_index.their | 147 |
| abstract_inverted_index.using | 193 |
| abstract_inverted_index.valid | 8, 204 |
| abstract_inverted_index.wrist | 167 |
| abstract_inverted_index.108.2, | 113 |
| abstract_inverted_index.around | 135 |
| abstract_inverted_index.domain | 31, 177 |
| abstract_inverted_index.neural | 34, 180 |
| abstract_inverted_index.study, | 25 |
| abstract_inverted_index.useful | 144 |
| abstract_inverted_index.utmost | 171 |
| abstract_inverted_index.widely | 3 |
| abstract_inverted_index.years, | 138 |
| abstract_inverted_index.(DACNN) | 36 |
| abstract_inverted_index.Despite | 133 |
| abstract_inverted_index.another | 67 |
| abstract_inverted_index.applied | 27 |
| abstract_inverted_index.at-home | 14 |
| abstract_inverted_index.battery | 151 |
| abstract_inverted_index.counts, | 76 |
| abstract_inverted_index.dataset | 68 |
| abstract_inverted_index.devices | 73, 140, 168 |
| abstract_inverted_index.improve | 183 |
| abstract_inverted_index.network | 35 |
| abstract_inverted_index.outcome | 208 |
| abstract_inverted_index.overall | 185 |
| abstract_inverted_index.z-angle | 114, 129 |
| abstract_inverted_index.Compared | 87 |
| abstract_inverted_index.Wearable | 0 |
| abstract_inverted_index.accuracy | 79, 159 |
| abstract_inverted_index.activity | 75 |
| abstract_inverted_index.approach | 205 |
| abstract_inverted_index.clinical | 19 |
| abstract_inverted_index.commonly | 89 |
| abstract_inverted_index.continue | 141 |
| abstract_inverted_index.existing | 48 |
| abstract_inverted_index.learning | 30 |
| abstract_inverted_index.networks | 181 |
| abstract_inverted_index.outcomes | 57 |
| abstract_inverted_index.research | 20 |
| abstract_inverted_index.resulted | 95 |
| abstract_inverted_index.scalable | 10, 202 |
| abstract_inverted_index.smallest | 98 |
| abstract_inverted_index.solution | 11 |
| abstract_inverted_index.wearable | 72 |
| abstract_inverted_index.Improving | 157 |
| abstract_inverted_index.accuracy, | 186 |
| abstract_inverted_index.achieving | 77 |
| abstract_inverted_index.different | 71 |
| abstract_inverted_index.long-term | 13 |
| abstract_inverted_index.outcomes. | 132 |
| abstract_inverted_index.algorithms | 50, 164 |
| abstract_inverted_index.assessment | 209 |
| abstract_inverted_index.efficiency | 119 |
| abstract_inverted_index.especially | 187 |
| abstract_inverted_index.estimating | 55 |
| abstract_inverted_index.monitoring | 16 |
| abstract_inverted_index.wrist-worn | 60, 194 |
| abstract_inverted_index.adversarial | 32, 178 |
| abstract_inverted_index.algorithms, | 92 |
| abstract_inverted_index.classifying | 52 |
| abstract_inverted_index.generalized | 64 |
| abstract_inverted_index.importance. | 172 |
| abstract_inverted_index.specificity | 85 |
| abstract_inverted_index.(sensitivity | 82 |
| abstract_inverted_index.demonstrated | 42, 175 |
| abstract_inverted_index.ecologically | 7 |
| abstract_inverted_index.outperformed | 47 |
| abstract_inverted_index.sleep–wake | 53, 191 |
| abstract_inverted_index.specificity, | 189 |
| abstract_inverted_index.Cole–Kripke | 108, 123 |
| abstract_inverted_index.accelerometer | 166, 195 |
| abstract_inverted_index.convolutional | 33, 179 |
| abstract_inverted_index.accelerometers | 1 |
| abstract_inverted_index.accelerometry. | 61 |
| abstract_inverted_index.classification | 192 |
| abstract_inverted_index.substantiating | 197 |
| abstract_inverted_index.generalizability | 161 |
| abstract_inverted_index.accelerometer-alone | 139 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5003765514 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.37296676 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |