Supervised learning for improving the accuracy of robot-mounted 3D camera applied to human gait analysis Article Swipe
Diego Guffanti
,
Alberto Brunete
,
Miguel Hernando
,
David Álvarez
,
Javier Rueda
,
Enrique Navarro
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.01002
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.01002
The use of 3D cameras for gait analysis has been highly questioned due to the low accuracy they have demonstrated in the past. The objective of the study presented in this paper is to improve the accuracy of the estimations made by robot-mounted 3D cameras in human gait analysis by applying a supervised learning stage. The 3D camera was mounted in a mobile robot to obtain a longer walking distance. This study shows an improvement in detection of kinematic gait signals and gait descriptors by post-processing the raw estimations of the camera using artificial neural networks trained with the data obtained from a certified Vicon system. To achieve this, 37 healthy participants were recruited and data of 207 gait sequences were collected using an Orbbec Astra 3D camera. There are two basic possible approaches for training: using kinematic gait signals and using gait descriptors. The former seeks to improve the waveforms of kinematic gait signals by reducing the error and increasing the correlation with respect to the Vicon system. The second is a more direct approach, focusing on training the artificial neural networks using gait descriptors directly. The accuracy of the 3D camera was measured before and after training. In both training approaches, an improvement was observed. Kinematic gait signals showed lower errors and higher correlations with respect to the ground truth. The accuracy of the system to detect gait descriptors also showed a substantial improvement, mostly for kinematic descriptors rather than spatio-temporal. When comparing both training approaches, it was not possible to define which was the absolute best. Therefore, we believe that the selection of the training approach will depend on the purpose of the study to be conducted. This study reveals the great potential of 3D cameras and encourages the research community to continue exploring their use in gait analysis.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2207.01002
- https://arxiv.org/pdf/2207.01002
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283825326
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4283825326Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2207.01002Digital Object Identifier
- Title
-
Supervised learning for improving the accuracy of robot-mounted 3D camera applied to human gait analysisWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-03Full publication date if available
- Authors
-
Diego Guffanti, Alberto Brunete, Miguel Hernando, David Álvarez, Javier Rueda, Enrique NavarroList of authors in order
- Landing page
-
https://arxiv.org/abs/2207.01002Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2207.01002Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2207.01002Direct OA link when available
- Concepts
-
Gait, Kinematics, Artificial intelligence, Computer science, Computer vision, Gait analysis, Ground reaction force, Artificial neural network, Ground truth, Physical medicine and rehabilitation, Classical mechanics, Physics, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4283825326 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2207.01002 |
| ids.doi | https://doi.org/10.48550/arxiv.2207.01002 |
| ids.openalex | https://openalex.org/W4283825326 |
| fwci | |
| type | preprint |
| title | Supervised learning for improving the accuracy of robot-mounted 3D camera applied to human gait analysis |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11227 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9980000257492065 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2712 |
| topics[0].subfield.display_name | Endocrinology, Diabetes and Metabolism |
| topics[0].display_name | Diabetic Foot Ulcer Assessment and Management |
| topics[1].id | https://openalex.org/T12740 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9958000183105469 |
| 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 | Gait Recognition and Analysis |
| topics[2].id | https://openalex.org/T11196 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9771999716758728 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2204 |
| topics[2].subfield.display_name | Biomedical Engineering |
| topics[2].display_name | Non-Invasive Vital Sign Monitoring |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C151800584 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7817858457565308 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q2370000 |
| concepts[0].display_name | Gait |
| concepts[1].id | https://openalex.org/C39920418 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7795315384864807 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11476 |
| concepts[1].display_name | Kinematics |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.7549675703048706 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.6136472225189209 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C31972630 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5959654450416565 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[4].display_name | Computer vision |
| concepts[5].id | https://openalex.org/C173906292 |
| concepts[5].level | 3 |
| concepts[5].score | 0.5782739520072937 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1493441 |
| concepts[5].display_name | Gait analysis |
| concepts[6].id | https://openalex.org/C96332660 |
| concepts[6].level | 3 |
| concepts[6].score | 0.5072348117828369 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q5610971 |
| concepts[6].display_name | Ground reaction force |
| concepts[7].id | https://openalex.org/C50644808 |
| concepts[7].level | 2 |
| concepts[7].score | 0.46213996410369873 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[7].display_name | Artificial neural network |
| concepts[8].id | https://openalex.org/C146849305 |
| concepts[8].level | 2 |
| concepts[8].score | 0.42588409781455994 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q370766 |
| concepts[8].display_name | Ground truth |
| concepts[9].id | https://openalex.org/C99508421 |
| concepts[9].level | 1 |
| concepts[9].score | 0.13449224829673767 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2678675 |
| concepts[9].display_name | Physical medicine and rehabilitation |
| concepts[10].id | https://openalex.org/C74650414 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11397 |
| concepts[10].display_name | Classical mechanics |
| concepts[11].id | https://openalex.org/C121332964 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[11].display_name | Physics |
| concepts[12].id | https://openalex.org/C71924100 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[12].display_name | Medicine |
| keywords[0].id | https://openalex.org/keywords/gait |
| keywords[0].score | 0.7817858457565308 |
| keywords[0].display_name | Gait |
| keywords[1].id | https://openalex.org/keywords/kinematics |
| keywords[1].score | 0.7795315384864807 |
| keywords[1].display_name | Kinematics |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.7549675703048706 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.6136472225189209 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/computer-vision |
| keywords[4].score | 0.5959654450416565 |
| keywords[4].display_name | Computer vision |
| keywords[5].id | https://openalex.org/keywords/gait-analysis |
| keywords[5].score | 0.5782739520072937 |
| keywords[5].display_name | Gait analysis |
| keywords[6].id | https://openalex.org/keywords/ground-reaction-force |
| keywords[6].score | 0.5072348117828369 |
| keywords[6].display_name | Ground reaction force |
| keywords[7].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[7].score | 0.46213996410369873 |
| keywords[7].display_name | Artificial neural network |
| keywords[8].id | https://openalex.org/keywords/ground-truth |
| keywords[8].score | 0.42588409781455994 |
| keywords[8].display_name | Ground truth |
| keywords[9].id | https://openalex.org/keywords/physical-medicine-and-rehabilitation |
| keywords[9].score | 0.13449224829673767 |
| keywords[9].display_name | Physical medicine and rehabilitation |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2207.01002 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2207.01002 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2207.01002 |
| locations[1].id | pmh:oai:oa.upm.es:80435 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306402421 |
| 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 | Archivo Digital UPM (Universidad Politécnica de Madrid) |
| locations[1].source.host_organization | https://openalex.org/I88060688 |
| locations[1].source.host_organization_name | Universidad Politécnica de Madrid |
| locations[1].source.host_organization_lineage | https://openalex.org/I88060688 |
| locations[1].license | cc-by-nc-nd |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | info:eu-repo/semantics/article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Heliyon, ISSN 2405-8440, 2024-02-12, Vol. 10, No. 4 |
| locations[1].landing_page_url | https://oa.upm.es/80435/ |
| locations[2].id | doi:10.48550/arxiv.2207.01002 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400194 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | arXiv (Cornell University) |
| locations[2].source.host_organization | https://openalex.org/I205783295 |
| locations[2].source.host_organization_name | Cornell University |
| locations[2].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://doi.org/10.48550/arxiv.2207.01002 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5023242045 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1244-291X |
| authorships[0].author.display_name | Diego Guffanti |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Guffanti, Diego |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5050231912 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-9873-232X |
| authorships[1].author.display_name | Alberto Brunete |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Brunete, Alberto |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5091481016 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-9997-0266 |
| authorships[2].author.display_name | Miguel Hernando |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Hernando, Miguel |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5101485872 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-3350-4144 |
| authorships[3].author.display_name | David Álvarez |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Álvarez, David |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5073517400 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-6593-0055 |
| authorships[4].author.display_name | Javier Rueda |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Rueda, Javier |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5100599497 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-3964-4431 |
| authorships[5].author.display_name | Enrique Navarro |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Navarro, Enrique |
| authorships[5].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2207.01002 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2022-07-07T00:00:00 |
| display_name | Supervised learning for improving the accuracy of robot-mounted 3D camera applied to human gait analysis |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11227 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9980000257492065 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2712 |
| primary_topic.subfield.display_name | Endocrinology, Diabetes and Metabolism |
| primary_topic.display_name | Diabetic Foot Ulcer Assessment and Management |
| related_works | https://openalex.org/W2133973503, https://openalex.org/W2295597290, https://openalex.org/W2471060339, https://openalex.org/W1590989708, https://openalex.org/W2148547327, https://openalex.org/W2390094925, https://openalex.org/W4226236273, https://openalex.org/W2125892956, https://openalex.org/W2790560349, https://openalex.org/W2130975749 |
| cited_by_count | 0 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:arXiv.org:2207.01002 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2207.01002 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2207.01002 |
| primary_location.id | pmh:oai:arXiv.org:2207.01002 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2207.01002 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2207.01002 |
| publication_date | 2022-07-03 |
| publication_year | 2022 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 51, 61, 66, 102, 172, 233 |
| abstract_inverted_index.37 | 109 |
| abstract_inverted_index.3D | 3, 43, 56, 126, 191, 287 |
| abstract_inverted_index.In | 199 |
| abstract_inverted_index.To | 106 |
| abstract_inverted_index.an | 73, 123, 203 |
| abstract_inverted_index.be | 278 |
| abstract_inverted_index.by | 41, 49, 84, 155 |
| abstract_inverted_index.in | 20, 29, 45, 60, 75, 299 |
| abstract_inverted_index.is | 32, 171 |
| abstract_inverted_index.it | 248 |
| abstract_inverted_index.of | 2, 25, 37, 77, 89, 116, 151, 189, 224, 265, 274, 286 |
| abstract_inverted_index.on | 177, 271 |
| abstract_inverted_index.to | 13, 33, 64, 147, 165, 218, 227, 252, 277, 294 |
| abstract_inverted_index.we | 260 |
| abstract_inverted_index.207 | 117 |
| abstract_inverted_index.The | 0, 23, 55, 144, 169, 187, 222 |
| abstract_inverted_index.and | 81, 114, 140, 159, 196, 213, 289 |
| abstract_inverted_index.are | 129 |
| abstract_inverted_index.due | 12 |
| abstract_inverted_index.for | 5, 134, 237 |
| abstract_inverted_index.has | 8 |
| abstract_inverted_index.low | 15 |
| abstract_inverted_index.not | 250 |
| abstract_inverted_index.raw | 87 |
| abstract_inverted_index.the | 14, 21, 26, 35, 38, 86, 90, 98, 149, 157, 161, 166, 179, 190, 219, 225, 256, 263, 266, 272, 275, 283, 291 |
| abstract_inverted_index.two | 130 |
| abstract_inverted_index.use | 1, 298 |
| abstract_inverted_index.was | 58, 193, 205, 249, 255 |
| abstract_inverted_index.This | 70, 280 |
| abstract_inverted_index.When | 243 |
| abstract_inverted_index.also | 231 |
| abstract_inverted_index.been | 9 |
| abstract_inverted_index.both | 200, 245 |
| abstract_inverted_index.data | 99, 115 |
| abstract_inverted_index.from | 101 |
| abstract_inverted_index.gait | 6, 47, 79, 82, 118, 138, 142, 153, 184, 208, 229, 300 |
| abstract_inverted_index.have | 18 |
| abstract_inverted_index.made | 40 |
| abstract_inverted_index.more | 173 |
| abstract_inverted_index.than | 241 |
| abstract_inverted_index.that | 262 |
| abstract_inverted_index.they | 17 |
| abstract_inverted_index.this | 30 |
| abstract_inverted_index.were | 112, 120 |
| abstract_inverted_index.will | 269 |
| abstract_inverted_index.with | 97, 163, 216 |
| abstract_inverted_index.Astra | 125 |
| abstract_inverted_index.There | 128 |
| abstract_inverted_index.Vicon | 104, 167 |
| abstract_inverted_index.after | 197 |
| abstract_inverted_index.basic | 131 |
| abstract_inverted_index.best. | 258 |
| abstract_inverted_index.error | 158 |
| abstract_inverted_index.great | 284 |
| abstract_inverted_index.human | 46 |
| abstract_inverted_index.lower | 211 |
| abstract_inverted_index.paper | 31 |
| abstract_inverted_index.past. | 22 |
| abstract_inverted_index.robot | 63 |
| abstract_inverted_index.seeks | 146 |
| abstract_inverted_index.shows | 72 |
| abstract_inverted_index.study | 27, 71, 276, 281 |
| abstract_inverted_index.their | 297 |
| abstract_inverted_index.this, | 108 |
| abstract_inverted_index.using | 92, 122, 136, 141, 183 |
| abstract_inverted_index.which | 254 |
| abstract_inverted_index.Orbbec | 124 |
| abstract_inverted_index.before | 195 |
| abstract_inverted_index.camera | 57, 91, 192 |
| abstract_inverted_index.define | 253 |
| abstract_inverted_index.depend | 270 |
| abstract_inverted_index.detect | 228 |
| abstract_inverted_index.direct | 174 |
| abstract_inverted_index.errors | 212 |
| abstract_inverted_index.former | 145 |
| abstract_inverted_index.ground | 220 |
| abstract_inverted_index.higher | 214 |
| abstract_inverted_index.highly | 10 |
| abstract_inverted_index.longer | 67 |
| abstract_inverted_index.mobile | 62 |
| abstract_inverted_index.mostly | 236 |
| abstract_inverted_index.neural | 94, 181 |
| abstract_inverted_index.obtain | 65 |
| abstract_inverted_index.rather | 240 |
| abstract_inverted_index.second | 170 |
| abstract_inverted_index.showed | 210, 232 |
| abstract_inverted_index.stage. | 54 |
| abstract_inverted_index.system | 226 |
| abstract_inverted_index.truth. | 221 |
| abstract_inverted_index.achieve | 107 |
| abstract_inverted_index.believe | 261 |
| abstract_inverted_index.camera. | 127 |
| abstract_inverted_index.cameras | 4, 44, 288 |
| abstract_inverted_index.healthy | 110 |
| abstract_inverted_index.improve | 34, 148 |
| abstract_inverted_index.mounted | 59 |
| abstract_inverted_index.purpose | 273 |
| abstract_inverted_index.respect | 164, 217 |
| abstract_inverted_index.reveals | 282 |
| abstract_inverted_index.signals | 80, 139, 154, 209 |
| abstract_inverted_index.system. | 105, 168 |
| abstract_inverted_index.trained | 96 |
| abstract_inverted_index.walking | 68 |
| abstract_inverted_index.absolute | 257 |
| abstract_inverted_index.accuracy | 16, 36, 188, 223 |
| abstract_inverted_index.analysis | 7, 48 |
| abstract_inverted_index.applying | 50 |
| abstract_inverted_index.approach | 268 |
| abstract_inverted_index.continue | 295 |
| abstract_inverted_index.focusing | 176 |
| abstract_inverted_index.learning | 53 |
| abstract_inverted_index.measured | 194 |
| abstract_inverted_index.networks | 95, 182 |
| abstract_inverted_index.obtained | 100 |
| abstract_inverted_index.possible | 132, 251 |
| abstract_inverted_index.reducing | 156 |
| abstract_inverted_index.research | 292 |
| abstract_inverted_index.training | 178, 201, 246, 267 |
| abstract_inverted_index.Kinematic | 207 |
| abstract_inverted_index.analysis. | 301 |
| abstract_inverted_index.approach, | 175 |
| abstract_inverted_index.certified | 103 |
| abstract_inverted_index.collected | 121 |
| abstract_inverted_index.community | 293 |
| abstract_inverted_index.comparing | 244 |
| abstract_inverted_index.detection | 76 |
| abstract_inverted_index.directly. | 186 |
| abstract_inverted_index.distance. | 69 |
| abstract_inverted_index.exploring | 296 |
| abstract_inverted_index.kinematic | 78, 137, 152, 238 |
| abstract_inverted_index.objective | 24 |
| abstract_inverted_index.observed. | 206 |
| abstract_inverted_index.potential | 285 |
| abstract_inverted_index.presented | 28 |
| abstract_inverted_index.recruited | 113 |
| abstract_inverted_index.selection | 264 |
| abstract_inverted_index.sequences | 119 |
| abstract_inverted_index.training. | 198 |
| abstract_inverted_index.training: | 135 |
| abstract_inverted_index.waveforms | 150 |
| abstract_inverted_index.Therefore, | 259 |
| abstract_inverted_index.approaches | 133 |
| abstract_inverted_index.artificial | 93, 180 |
| abstract_inverted_index.conducted. | 279 |
| abstract_inverted_index.encourages | 290 |
| abstract_inverted_index.increasing | 160 |
| abstract_inverted_index.questioned | 11 |
| abstract_inverted_index.supervised | 52 |
| abstract_inverted_index.approaches, | 202, 247 |
| abstract_inverted_index.correlation | 162 |
| abstract_inverted_index.descriptors | 83, 185, 230, 239 |
| abstract_inverted_index.estimations | 39, 88 |
| abstract_inverted_index.improvement | 74, 204 |
| abstract_inverted_index.substantial | 234 |
| abstract_inverted_index.correlations | 215 |
| abstract_inverted_index.demonstrated | 19 |
| abstract_inverted_index.descriptors. | 143 |
| abstract_inverted_index.improvement, | 235 |
| abstract_inverted_index.participants | 111 |
| abstract_inverted_index.robot-mounted | 42 |
| abstract_inverted_index.post-processing | 85 |
| abstract_inverted_index.spatio-temporal. | 242 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |