Neurophysiological Approach for Psychological Safety: Enhancing Mental Health in Human–Robot Collaboration in Smart Manufacturing Setups Using Neuroimaging Article Swipe
Arshia Arif
,
Zohreh Zakeri
,
Ahmet Omurtag
,
Philip Breedon
,
Azfar Khalid
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/info15100640
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/info15100640
Human–robot collaboration (HRC) has become increasingly prevalent due to innovative advancements in the automation industry, especially in manufacturing setups. Although HRC increases productivity and efficacy, it exposes human workers to psychological stress while interfacing with collaborative robotic systems as robots may not provide visual or auditory cues. It is crucial to comprehend how HRC impacts mental stress in order to enhance occupational safety and well-being. Though academics and industrial interest in HRC is expanding, safety and mental stress problems are still not adequately studied. In particular, human coworkers’ cognitive strain during HRC has not been explored well, although being fundamental to sustaining a secure and constructive workplace environment. This study, therefore, aims to monitor the mental stress of factory workers during HRC using behavioural, physiological and subjective measures. Physiological measures, being objective and more authentic, have the potential to replace conventional measures i.e., behavioural and subjective measures, if they demonstrate a good correlation with traditional measures. Two neuroimaging modalities including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used as physiological measures to track neuronal and hemodynamic activity of the brain, respectively. Here, the correlation between physiological data and behavioural and subjective measurements has been ascertained through the implementation of seven different machine learning algorithms. The results imply that the EEG and fNIRS features combined produced the best results for most of the targets. For subjective measures being the target, linear regression has outperformed all other models, whereas tree and ensemble performed the best for predicting the behavioural measures. The outcomes indicate that physiological measures have the potential to be more informative and often substitute other skewed metrics.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/info15100640
- OA Status
- gold
- Cited By
- 2
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403585234
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4403585234Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/info15100640Digital Object Identifier
- Title
-
Neurophysiological Approach for Psychological Safety: Enhancing Mental Health in Human–Robot Collaboration in Smart Manufacturing Setups Using NeuroimagingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-15Full publication date if available
- Authors
-
Arshia Arif, Zohreh Zakeri, Ahmet Omurtag, Philip Breedon, Azfar KhalidList of authors in order
- Landing page
-
https://doi.org/10.3390/info15100640Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/info15100640Direct OA link when available
- Concepts
-
Neuroimaging, Neurophysiology, Mental health, Psychology, Robot, Human–robot interaction, Computer science, Artificial intelligence, Neuroscience, PsychotherapistTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
-
61Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4403585234 |
|---|---|
| doi | https://doi.org/10.3390/info15100640 |
| ids.doi | https://doi.org/10.3390/info15100640 |
| ids.openalex | https://openalex.org/W4403585234 |
| fwci | 1.40560113 |
| type | article |
| title | Neurophysiological Approach for Psychological Safety: Enhancing Mental Health in Human–Robot Collaboration in Smart Manufacturing Setups Using Neuroimaging |
| biblio.issue | 10 |
| biblio.volume | 15 |
| biblio.last_page | 640 |
| biblio.first_page | 640 |
| topics[0].id | https://openalex.org/T10429 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 0.974399983882904 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2805 |
| topics[0].subfield.display_name | Cognitive Neuroscience |
| topics[0].display_name | EEG and Brain-Computer Interfaces |
| is_xpac | False |
| apc_list.value | 1400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 1515 |
| apc_paid.value | 1400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 1515 |
| concepts[0].id | https://openalex.org/C58693492 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7277812957763672 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q551875 |
| concepts[0].display_name | Neuroimaging |
| concepts[1].id | https://openalex.org/C152478114 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5947004556655884 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q660910 |
| concepts[1].display_name | Neurophysiology |
| concepts[2].id | https://openalex.org/C134362201 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5788167119026184 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q317309 |
| concepts[2].display_name | Mental health |
| concepts[3].id | https://openalex.org/C15744967 |
| concepts[3].level | 0 |
| concepts[3].score | 0.4883091449737549 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[3].display_name | Psychology |
| concepts[4].id | https://openalex.org/C90509273 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4200526177883148 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11012 |
| concepts[4].display_name | Robot |
| concepts[5].id | https://openalex.org/C145460709 |
| concepts[5].level | 3 |
| concepts[5].score | 0.41537076234817505 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q859951 |
| concepts[5].display_name | Human–robot interaction |
| concepts[6].id | https://openalex.org/C41008148 |
| concepts[6].level | 0 |
| concepts[6].score | 0.3788854479789734 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[6].display_name | Computer science |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.2883811593055725 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C169760540 |
| concepts[8].level | 1 |
| concepts[8].score | 0.246830552816391 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[8].display_name | Neuroscience |
| concepts[9].id | https://openalex.org/C542102704 |
| concepts[9].level | 1 |
| concepts[9].score | 0.17124491930007935 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q183257 |
| concepts[9].display_name | Psychotherapist |
| keywords[0].id | https://openalex.org/keywords/neuroimaging |
| keywords[0].score | 0.7277812957763672 |
| keywords[0].display_name | Neuroimaging |
| keywords[1].id | https://openalex.org/keywords/neurophysiology |
| keywords[1].score | 0.5947004556655884 |
| keywords[1].display_name | Neurophysiology |
| keywords[2].id | https://openalex.org/keywords/mental-health |
| keywords[2].score | 0.5788167119026184 |
| keywords[2].display_name | Mental health |
| keywords[3].id | https://openalex.org/keywords/psychology |
| keywords[3].score | 0.4883091449737549 |
| keywords[3].display_name | Psychology |
| keywords[4].id | https://openalex.org/keywords/robot |
| keywords[4].score | 0.4200526177883148 |
| keywords[4].display_name | Robot |
| keywords[5].id | https://openalex.org/keywords/human–robot-interaction |
| keywords[5].score | 0.41537076234817505 |
| keywords[5].display_name | Human–robot interaction |
| keywords[6].id | https://openalex.org/keywords/computer-science |
| keywords[6].score | 0.3788854479789734 |
| keywords[6].display_name | Computer science |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.2883811593055725 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/neuroscience |
| keywords[8].score | 0.246830552816391 |
| keywords[8].display_name | Neuroscience |
| keywords[9].id | https://openalex.org/keywords/psychotherapist |
| keywords[9].score | 0.17124491930007935 |
| keywords[9].display_name | Psychotherapist |
| language | en |
| locations[0].id | doi:10.3390/info15100640 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210219776 |
| locations[0].source.issn | 2078-2489 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2078-2489 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Information |
| 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].license | cc-by |
| locations[0].pdf_url | |
| 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 | Information |
| locations[0].landing_page_url | https://doi.org/10.3390/info15100640 |
| locations[1].id | pmh:oai:irep.ntu.ac.uk:52432 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306400559 |
| 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 | Nottingham Trent University's Institutional Repository (Nottingham Trent Repository) |
| locations[1].source.host_organization | https://openalex.org/I52590639 |
| locations[1].source.host_organization_name | Nottingham Trent University |
| locations[1].source.host_organization_lineage | https://openalex.org/I52590639 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | acceptedVersion |
| locations[1].raw_type | Article |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | |
| locations[2].id | pmh:oai:doaj.org/article:9140d3e9212543a8b60d1c0155c6878c |
| 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].source.host_organization_lineage | |
| 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 | Information, Vol 15, Iss 10, p 640 (2024) |
| locations[2].landing_page_url | https://doaj.org/article/9140d3e9212543a8b60d1c0155c6878c |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5036182173 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Arshia Arif |
| authorships[0].countries | GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I52590639 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Engineering, School of Science & Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK |
| authorships[0].institutions[0].id | https://openalex.org/I52590639 |
| authorships[0].institutions[0].ror | https://ror.org/04xyxjd90 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I52590639 |
| authorships[0].institutions[0].country_code | GB |
| authorships[0].institutions[0].display_name | Nottingham Trent University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Arshia Arif |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Engineering, School of Science & Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK |
| authorships[1].author.id | https://openalex.org/A5055380968 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Zohreh Zakeri |
| authorships[1].countries | GB |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I52590639 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Engineering, School of Science & Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK |
| authorships[1].institutions[0].id | https://openalex.org/I52590639 |
| authorships[1].institutions[0].ror | https://ror.org/04xyxjd90 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I52590639 |
| authorships[1].institutions[0].country_code | GB |
| authorships[1].institutions[0].display_name | Nottingham Trent University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zohreh Zakeri |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Engineering, School of Science & Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK |
| authorships[2].author.id | https://openalex.org/A5106025406 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3773-8506 |
| authorships[2].author.display_name | Ahmet Omurtag |
| authorships[2].countries | GB |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I52590639 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Engineering, School of Science & Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK |
| authorships[2].institutions[0].id | https://openalex.org/I52590639 |
| authorships[2].institutions[0].ror | https://ror.org/04xyxjd90 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I52590639 |
| authorships[2].institutions[0].country_code | GB |
| authorships[2].institutions[0].display_name | Nottingham Trent University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Ahmet Omurtag |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Engineering, School of Science & Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK |
| authorships[3].author.id | https://openalex.org/A5013284706 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-1006-0942 |
| authorships[3].author.display_name | Philip Breedon |
| authorships[3].countries | GB |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I52590639 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Engineering, School of Science & Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK |
| authorships[3].institutions[0].id | https://openalex.org/I52590639 |
| authorships[3].institutions[0].ror | https://ror.org/04xyxjd90 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I52590639 |
| authorships[3].institutions[0].country_code | GB |
| authorships[3].institutions[0].display_name | Nottingham Trent University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Philip Breedon |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Engineering, School of Science & Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK |
| authorships[4].author.id | https://openalex.org/A5042598146 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-5270-6599 |
| authorships[4].author.display_name | Azfar Khalid |
| authorships[4].countries | GB |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I52590639 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Engineering, School of Science & Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK |
| authorships[4].institutions[0].id | https://openalex.org/I52590639 |
| authorships[4].institutions[0].ror | https://ror.org/04xyxjd90 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I52590639 |
| authorships[4].institutions[0].country_code | GB |
| authorships[4].institutions[0].display_name | Nottingham Trent University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Azfar Khalid |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Engineering, School of Science & Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.3390/info15100640 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2024-10-21T00:00:00 |
| display_name | Neurophysiological Approach for Psychological Safety: Enhancing Mental Health in Human–Robot Collaboration in Smart Manufacturing Setups Using Neuroimaging |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10429 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 0.974399983882904 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2805 |
| primary_topic.subfield.display_name | Cognitive Neuroscience |
| primary_topic.display_name | EEG and Brain-Computer Interfaces |
| related_works | https://openalex.org/W2327340211, https://openalex.org/W2915014248, https://openalex.org/W2027542625, https://openalex.org/W4292199793, https://openalex.org/W2282195379, https://openalex.org/W4287179229, https://openalex.org/W3205513966, https://openalex.org/W3120459843, https://openalex.org/W4366547574, https://openalex.org/W3200191727 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/info15100640 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210219776 |
| best_oa_location.source.issn | 2078-2489 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2078-2489 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Information |
| 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.license | cc-by |
| best_oa_location.pdf_url | |
| 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 | Information |
| best_oa_location.landing_page_url | https://doi.org/10.3390/info15100640 |
| primary_location.id | doi:10.3390/info15100640 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210219776 |
| primary_location.source.issn | 2078-2489 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2078-2489 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Information |
| 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.license | cc-by |
| primary_location.pdf_url | |
| 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 | Information |
| primary_location.landing_page_url | https://doi.org/10.3390/info15100640 |
| publication_date | 2024-10-15 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4386766746, https://openalex.org/W3036435246, https://openalex.org/W2811256013, https://openalex.org/W2765218414, https://openalex.org/W4283824349, https://openalex.org/W4381149319, https://openalex.org/W4304142273, https://openalex.org/W3103051828, https://openalex.org/W4386251972, https://openalex.org/W4226503007, https://openalex.org/W4380884472, https://openalex.org/W3092672306, https://openalex.org/W4387345628, https://openalex.org/W2950522868, https://openalex.org/W3160935864, https://openalex.org/W2523950919, https://openalex.org/W3202081564, https://openalex.org/W1996299251, https://openalex.org/W3003596721, https://openalex.org/W2129760369, https://openalex.org/W4366411596, https://openalex.org/W2481365383, https://openalex.org/W2114186931, https://openalex.org/W2032568497, https://openalex.org/W2969633172, https://openalex.org/W4389478005, https://openalex.org/W3132530498, https://openalex.org/W3041183752, https://openalex.org/W3199395343, https://openalex.org/W3120791867, https://openalex.org/W3126434523, https://openalex.org/W2890128731, https://openalex.org/W3126247264, https://openalex.org/W3210819471, https://openalex.org/W4289868886, https://openalex.org/W3186292377, https://openalex.org/W3210832174, https://openalex.org/W4313681026, https://openalex.org/W2556333702, https://openalex.org/W2890929258, https://openalex.org/W4286306113, https://openalex.org/W4387695561, https://openalex.org/W2121145464, https://openalex.org/W4285310696, https://openalex.org/W3202291450, https://openalex.org/W3080089411, https://openalex.org/W4245990317, https://openalex.org/W2057559934, https://openalex.org/W2151313489, https://openalex.org/W2107364464, https://openalex.org/W1947251450, https://openalex.org/W2897502782, https://openalex.org/W2007333865, https://openalex.org/W2402494095, https://openalex.org/W2025564775, https://openalex.org/W2739160562, https://openalex.org/W4281732118, https://openalex.org/W3026124437, https://openalex.org/W3151640016, https://openalex.org/W4389938215, https://openalex.org/W3142672147 |
| referenced_works_count | 61 |
| abstract_inverted_index.a | 102, 150 |
| abstract_inverted_index.In | 84 |
| abstract_inverted_index.It | 47 |
| abstract_inverted_index.as | 38, 170 |
| abstract_inverted_index.be | 260 |
| abstract_inverted_index.if | 147 |
| abstract_inverted_index.in | 11, 16, 57, 70 |
| abstract_inverted_index.is | 48, 72 |
| abstract_inverted_index.it | 25 |
| abstract_inverted_index.of | 117, 179, 200, 222 |
| abstract_inverted_index.or | 44 |
| abstract_inverted_index.to | 8, 29, 50, 59, 100, 112, 138, 173, 259 |
| abstract_inverted_index.EEG | 211 |
| abstract_inverted_index.For | 225 |
| abstract_inverted_index.HRC | 20, 53, 71, 91, 121 |
| abstract_inverted_index.The | 206, 250 |
| abstract_inverted_index.Two | 156 |
| abstract_inverted_index.all | 235 |
| abstract_inverted_index.and | 23, 63, 67, 75, 104, 125, 132, 144, 162, 176, 189, 191, 212, 240, 263 |
| abstract_inverted_index.are | 79 |
| abstract_inverted_index.due | 7 |
| abstract_inverted_index.for | 220, 245 |
| abstract_inverted_index.has | 3, 92, 194, 233 |
| abstract_inverted_index.how | 52 |
| abstract_inverted_index.may | 40 |
| abstract_inverted_index.not | 41, 81, 93 |
| abstract_inverted_index.the | 12, 114, 136, 180, 184, 198, 210, 217, 223, 229, 243, 247, 257 |
| abstract_inverted_index.This | 108 |
| abstract_inverted_index.aims | 111 |
| abstract_inverted_index.been | 94, 168, 195 |
| abstract_inverted_index.best | 218, 244 |
| abstract_inverted_index.data | 188 |
| abstract_inverted_index.good | 151 |
| abstract_inverted_index.have | 135, 167, 256 |
| abstract_inverted_index.more | 133, 261 |
| abstract_inverted_index.most | 221 |
| abstract_inverted_index.that | 209, 253 |
| abstract_inverted_index.they | 148 |
| abstract_inverted_index.tree | 239 |
| abstract_inverted_index.used | 169 |
| abstract_inverted_index.with | 34, 153 |
| abstract_inverted_index.(EEG) | 161 |
| abstract_inverted_index.(HRC) | 2 |
| abstract_inverted_index.Here, | 183 |
| abstract_inverted_index.being | 98, 130, 228 |
| abstract_inverted_index.cues. | 46 |
| abstract_inverted_index.fNIRS | 213 |
| abstract_inverted_index.human | 27, 86 |
| abstract_inverted_index.i.e., | 142 |
| abstract_inverted_index.imply | 208 |
| abstract_inverted_index.often | 264 |
| abstract_inverted_index.order | 58 |
| abstract_inverted_index.other | 236, 266 |
| abstract_inverted_index.seven | 201 |
| abstract_inverted_index.still | 80 |
| abstract_inverted_index.track | 174 |
| abstract_inverted_index.using | 122 |
| abstract_inverted_index.well, | 96 |
| abstract_inverted_index.while | 32 |
| abstract_inverted_index.Though | 65 |
| abstract_inverted_index.become | 4 |
| abstract_inverted_index.brain, | 181 |
| abstract_inverted_index.during | 90, 120 |
| abstract_inverted_index.linear | 231 |
| abstract_inverted_index.mental | 55, 76, 115 |
| abstract_inverted_index.robots | 39 |
| abstract_inverted_index.safety | 62, 74 |
| abstract_inverted_index.secure | 103 |
| abstract_inverted_index.skewed | 267 |
| abstract_inverted_index.strain | 89 |
| abstract_inverted_index.stress | 31, 56, 77, 116 |
| abstract_inverted_index.study, | 109 |
| abstract_inverted_index.visual | 43 |
| abstract_inverted_index.(fNIRS) | 166 |
| abstract_inverted_index.between | 186 |
| abstract_inverted_index.crucial | 49 |
| abstract_inverted_index.enhance | 60 |
| abstract_inverted_index.exposes | 26 |
| abstract_inverted_index.factory | 118 |
| abstract_inverted_index.impacts | 54 |
| abstract_inverted_index.machine | 203 |
| abstract_inverted_index.models, | 237 |
| abstract_inverted_index.monitor | 113 |
| abstract_inverted_index.provide | 42 |
| abstract_inverted_index.replace | 139 |
| abstract_inverted_index.results | 207, 219 |
| abstract_inverted_index.robotic | 36 |
| abstract_inverted_index.setups. | 18 |
| abstract_inverted_index.systems | 37 |
| abstract_inverted_index.target, | 230 |
| abstract_inverted_index.through | 197 |
| abstract_inverted_index.whereas | 238 |
| abstract_inverted_index.workers | 28, 119 |
| abstract_inverted_index.Although | 19 |
| abstract_inverted_index.activity | 178 |
| abstract_inverted_index.although | 97 |
| abstract_inverted_index.auditory | 45 |
| abstract_inverted_index.combined | 215 |
| abstract_inverted_index.ensemble | 241 |
| abstract_inverted_index.explored | 95 |
| abstract_inverted_index.features | 214 |
| abstract_inverted_index.indicate | 252 |
| abstract_inverted_index.interest | 69 |
| abstract_inverted_index.learning | 204 |
| abstract_inverted_index.measures | 141, 172, 227, 255 |
| abstract_inverted_index.metrics. | 268 |
| abstract_inverted_index.neuronal | 175 |
| abstract_inverted_index.outcomes | 251 |
| abstract_inverted_index.problems | 78 |
| abstract_inverted_index.produced | 216 |
| abstract_inverted_index.studied. | 83 |
| abstract_inverted_index.targets. | 224 |
| abstract_inverted_index.academics | 66 |
| abstract_inverted_index.cognitive | 88 |
| abstract_inverted_index.different | 202 |
| abstract_inverted_index.efficacy, | 24 |
| abstract_inverted_index.including | 159 |
| abstract_inverted_index.increases | 21 |
| abstract_inverted_index.industry, | 14 |
| abstract_inverted_index.measures, | 129, 146 |
| abstract_inverted_index.measures. | 127, 155, 249 |
| abstract_inverted_index.objective | 131 |
| abstract_inverted_index.performed | 242 |
| abstract_inverted_index.potential | 137, 258 |
| abstract_inverted_index.prevalent | 6 |
| abstract_inverted_index.workplace | 106 |
| abstract_inverted_index.adequately | 82 |
| abstract_inverted_index.authentic, | 134 |
| abstract_inverted_index.automation | 13 |
| abstract_inverted_index.comprehend | 51 |
| abstract_inverted_index.especially | 15 |
| abstract_inverted_index.expanding, | 73 |
| abstract_inverted_index.functional | 163 |
| abstract_inverted_index.industrial | 68 |
| abstract_inverted_index.innovative | 9 |
| abstract_inverted_index.modalities | 158 |
| abstract_inverted_index.predicting | 246 |
| abstract_inverted_index.regression | 232 |
| abstract_inverted_index.subjective | 126, 145, 192, 226 |
| abstract_inverted_index.substitute | 265 |
| abstract_inverted_index.sustaining | 101 |
| abstract_inverted_index.therefore, | 110 |
| abstract_inverted_index.algorithms. | 205 |
| abstract_inverted_index.ascertained | 196 |
| abstract_inverted_index.behavioural | 143, 190, 248 |
| abstract_inverted_index.correlation | 152, 185 |
| abstract_inverted_index.demonstrate | 149 |
| abstract_inverted_index.fundamental | 99 |
| abstract_inverted_index.hemodynamic | 177 |
| abstract_inverted_index.informative | 262 |
| abstract_inverted_index.interfacing | 33 |
| abstract_inverted_index.particular, | 85 |
| abstract_inverted_index.traditional | 154 |
| abstract_inverted_index.well-being. | 64 |
| abstract_inverted_index.advancements | 10 |
| abstract_inverted_index.behavioural, | 123 |
| abstract_inverted_index.constructive | 105 |
| abstract_inverted_index.conventional | 140 |
| abstract_inverted_index.coworkers’ | 87 |
| abstract_inverted_index.environment. | 107 |
| abstract_inverted_index.increasingly | 5 |
| abstract_inverted_index.measurements | 193 |
| abstract_inverted_index.neuroimaging | 157 |
| abstract_inverted_index.occupational | 61 |
| abstract_inverted_index.outperformed | 234 |
| abstract_inverted_index.productivity | 22 |
| abstract_inverted_index.spectroscopy | 165 |
| abstract_inverted_index.Human–robot | 0 |
| abstract_inverted_index.Physiological | 128 |
| abstract_inverted_index.collaboration | 1 |
| abstract_inverted_index.collaborative | 35 |
| abstract_inverted_index.manufacturing | 17 |
| abstract_inverted_index.near-infrared | 164 |
| abstract_inverted_index.physiological | 124, 171, 187, 254 |
| abstract_inverted_index.psychological | 30 |
| abstract_inverted_index.respectively. | 182 |
| abstract_inverted_index.implementation | 199 |
| abstract_inverted_index.electroencephalography | 160 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.75884266 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |