Gesture recognition of sign language alphabet with a convolutional neural network using a magnetic positioning system Article Swipe
Emanuele Buchicchio
,
Francesco Santoni
,
Alessio De Angelis
,
Antonio Moschitta
,
Paolo Carbone
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21014/acta_imeko.v10i4.1185
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21014/acta_imeko.v10i4.1185
Gesture recognition is a fundamental step to enable efficient communication for the deaf through the automated translation of sign language. This work proposes the usage of a high-precision magnetic positioning system for 3D positioning and orientation tracking of the fingers and hands palm. The gesture is reconstructed by the MagIK (magnetic and inverse kinematics) method and then processed by a deep learning gesture classification model trained to recognize the gestures associated with the sign language alphabet. Results confirm the limits of vision-based systems and show that the proposed method based on hand skeleton reconstruction has good generalization properties. The proposed system, which combines sensor-based gesture acquisition and deep learning techniques for gesture recognition, provides a 100% classification accuracy, signer independent, after a few hours of training using transfer learning technique on well-known ResNet CNN architecture. The proposed classification model training method can be applied to other sensor-based gesture tracking systems and other applications, regardless of the specific data acquisition technology.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.21014/acta_imeko.v10i4.1185
- https://acta.imeko.org/index.php/acta-imeko/article/download/IMEKO-ACTA-10%20%282021%29-04-17/pdf
- OA Status
- diamond
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4205881966
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4205881966Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21014/acta_imeko.v10i4.1185Digital Object Identifier
- Title
-
Gesture recognition of sign language alphabet with a convolutional neural network using a magnetic positioning systemWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-30Full publication date if available
- Authors
-
Emanuele Buchicchio, Francesco Santoni, Alessio De Angelis, Antonio Moschitta, Paolo CarboneList of authors in order
- Landing page
-
https://doi.org/10.21014/acta_imeko.v10i4.1185Publisher landing page
- PDF URL
-
https://acta.imeko.org/index.php/acta-imeko/article/download/IMEKO-ACTA-10%20%282021%29-04-17/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://acta.imeko.org/index.php/acta-imeko/article/download/IMEKO-ACTA-10%20%282021%29-04-17/pdfDirect OA link when available
- Concepts
-
Gesture, Computer science, Gesture recognition, Sign language, Artificial intelligence, Convolutional neural network, Computer vision, Speech recognition, Deep learning, Pattern recognition (psychology), Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4205881966 |
|---|---|
| doi | https://doi.org/10.21014/acta_imeko.v10i4.1185 |
| ids.doi | https://doi.org/10.21014/acta_imeko.v10i4.1185 |
| ids.openalex | https://openalex.org/W4205881966 |
| fwci | 0.0 |
| type | article |
| title | Gesture recognition of sign language alphabet with a convolutional neural network using a magnetic positioning system |
| biblio.issue | 4 |
| biblio.volume | 10 |
| biblio.last_page | 97 |
| biblio.first_page | 97 |
| topics[0].id | https://openalex.org/T11398 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9995999932289124 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1709 |
| topics[0].subfield.display_name | Human-Computer Interaction |
| topics[0].display_name | Hand Gesture Recognition Systems |
| topics[1].id | https://openalex.org/T11707 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9322999715805054 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1709 |
| topics[1].subfield.display_name | Human-Computer Interaction |
| topics[1].display_name | Gaze Tracking and Assistive Technology |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C207347870 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7987754344940186 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q371174 |
| concepts[0].display_name | Gesture |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.7533949017524719 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C159437735 |
| concepts[2].level | 3 |
| concepts[2].score | 0.6675006151199341 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1519524 |
| concepts[2].display_name | Gesture recognition |
| concepts[3].id | https://openalex.org/C522192633 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6342661380767822 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q34228 |
| concepts[3].display_name | Sign language |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.6196295022964478 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C81363708 |
| concepts[5].level | 2 |
| concepts[5].score | 0.560392439365387 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[5].display_name | Convolutional neural network |
| concepts[6].id | https://openalex.org/C31972630 |
| concepts[6].level | 1 |
| concepts[6].score | 0.5344855785369873 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[6].display_name | Computer vision |
| concepts[7].id | https://openalex.org/C28490314 |
| concepts[7].level | 1 |
| concepts[7].score | 0.495402455329895 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q189436 |
| concepts[7].display_name | Speech recognition |
| concepts[8].id | https://openalex.org/C108583219 |
| concepts[8].level | 2 |
| concepts[8].score | 0.449457049369812 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[8].display_name | Deep learning |
| concepts[9].id | https://openalex.org/C153180895 |
| concepts[9].level | 2 |
| concepts[9].score | 0.3485717177391052 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[9].display_name | Pattern recognition (psychology) |
| concepts[10].id | https://openalex.org/C138885662 |
| concepts[10].level | 0 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[10].display_name | Philosophy |
| concepts[11].id | https://openalex.org/C41895202 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[11].display_name | Linguistics |
| keywords[0].id | https://openalex.org/keywords/gesture |
| keywords[0].score | 0.7987754344940186 |
| keywords[0].display_name | Gesture |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.7533949017524719 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/gesture-recognition |
| keywords[2].score | 0.6675006151199341 |
| keywords[2].display_name | Gesture recognition |
| keywords[3].id | https://openalex.org/keywords/sign-language |
| keywords[3].score | 0.6342661380767822 |
| keywords[3].display_name | Sign language |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.6196295022964478 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[5].score | 0.560392439365387 |
| keywords[5].display_name | Convolutional neural network |
| keywords[6].id | https://openalex.org/keywords/computer-vision |
| keywords[6].score | 0.5344855785369873 |
| keywords[6].display_name | Computer vision |
| keywords[7].id | https://openalex.org/keywords/speech-recognition |
| keywords[7].score | 0.495402455329895 |
| keywords[7].display_name | Speech recognition |
| keywords[8].id | https://openalex.org/keywords/deep-learning |
| keywords[8].score | 0.449457049369812 |
| keywords[8].display_name | Deep learning |
| keywords[9].id | https://openalex.org/keywords/pattern-recognition |
| keywords[9].score | 0.3485717177391052 |
| keywords[9].display_name | Pattern recognition (psychology) |
| language | en |
| locations[0].id | doi:10.21014/acta_imeko.v10i4.1185 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210188141 |
| locations[0].source.issn | 0237-028X, 2221-870X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 0237-028X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | ACTA IMEKO |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://acta.imeko.org/index.php/acta-imeko/article/download/IMEKO-ACTA-10%20%282021%29-04-17/pdf |
| 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 | ACTA IMEKO |
| locations[0].landing_page_url | https://doi.org/10.21014/acta_imeko.v10i4.1185 |
| locations[1].id | pmh:oai:zenodo.org:115911 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400562 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| locations[1].source.host_organization | https://openalex.org/I67311998 |
| locations[1].source.host_organization_name | European Organization for Nuclear Research |
| locations[1].source.host_organization_lineage | https://openalex.org/I67311998 |
| locations[1].license | other-oa |
| 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/other-oa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://www.openaccessrepository.it/record/115911 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5060808647 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-2786-5931 |
| authorships[0].author.display_name | Emanuele Buchicchio |
| authorships[0].countries | IT |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I27483092 |
| authorships[0].affiliations[0].raw_affiliation_string | University of Perugia |
| authorships[0].institutions[0].id | https://openalex.org/I27483092 |
| authorships[0].institutions[0].ror | https://ror.org/00x27da85 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I27483092 |
| authorships[0].institutions[0].country_code | IT |
| authorships[0].institutions[0].display_name | University of Perugia |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Emanuele Buchicchio |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | University of Perugia |
| authorships[1].author.id | https://openalex.org/A5063612109 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-4964-8556 |
| authorships[1].author.display_name | Francesco Santoni |
| authorships[1].countries | IT |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I27483092 |
| authorships[1].affiliations[0].raw_affiliation_string | University of Perugia |
| authorships[1].institutions[0].id | https://openalex.org/I27483092 |
| authorships[1].institutions[0].ror | https://ror.org/00x27da85 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I27483092 |
| authorships[1].institutions[0].country_code | IT |
| authorships[1].institutions[0].display_name | University of Perugia |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Francesco Santoni |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | University of Perugia |
| authorships[2].author.id | https://openalex.org/A5083411180 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-7953-4272 |
| authorships[2].author.display_name | Alessio De Angelis |
| authorships[2].countries | IT |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I27483092 |
| authorships[2].affiliations[0].raw_affiliation_string | University of Perugia |
| authorships[2].institutions[0].id | https://openalex.org/I27483092 |
| authorships[2].institutions[0].ror | https://ror.org/00x27da85 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I27483092 |
| authorships[2].institutions[0].country_code | IT |
| authorships[2].institutions[0].display_name | University of Perugia |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Alessio De Angelis |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | University of Perugia |
| authorships[3].author.id | https://openalex.org/A5047539322 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-9807-5948 |
| authorships[3].author.display_name | Antonio Moschitta |
| authorships[3].countries | IT |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I27483092 |
| authorships[3].affiliations[0].raw_affiliation_string | University of Perugia |
| authorships[3].institutions[0].id | https://openalex.org/I27483092 |
| authorships[3].institutions[0].ror | https://ror.org/00x27da85 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I27483092 |
| authorships[3].institutions[0].country_code | IT |
| authorships[3].institutions[0].display_name | University of Perugia |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Antonio Moschitta |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | University of Perugia |
| authorships[4].author.id | https://openalex.org/A5090244413 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-0540-1287 |
| authorships[4].author.display_name | Paolo Carbone |
| authorships[4].countries | IT |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I27483092 |
| authorships[4].affiliations[0].raw_affiliation_string | University of Perugia |
| authorships[4].institutions[0].id | https://openalex.org/I27483092 |
| authorships[4].institutions[0].ror | https://ror.org/00x27da85 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I27483092 |
| authorships[4].institutions[0].country_code | IT |
| authorships[4].institutions[0].display_name | University of Perugia |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Paolo Carbone |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | University of Perugia |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://acta.imeko.org/index.php/acta-imeko/article/download/IMEKO-ACTA-10%20%282021%29-04-17/pdf |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Gesture recognition of sign language alphabet with a convolutional neural network using a magnetic positioning system |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11398 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9995999932289124 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1709 |
| primary_topic.subfield.display_name | Human-Computer Interaction |
| primary_topic.display_name | Hand Gesture Recognition Systems |
| related_works | https://openalex.org/W4308478915, https://openalex.org/W2893683540, https://openalex.org/W1986488374, https://openalex.org/W3040456104, https://openalex.org/W2902873204, https://openalex.org/W2185750513, https://openalex.org/W4312416068, https://openalex.org/W4226493464, https://openalex.org/W4312417841, https://openalex.org/W3147379364 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.21014/acta_imeko.v10i4.1185 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210188141 |
| best_oa_location.source.issn | 0237-028X, 2221-870X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 0237-028X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | ACTA IMEKO |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://acta.imeko.org/index.php/acta-imeko/article/download/IMEKO-ACTA-10%20%282021%29-04-17/pdf |
| 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 | ACTA IMEKO |
| best_oa_location.landing_page_url | https://doi.org/10.21014/acta_imeko.v10i4.1185 |
| primary_location.id | doi:10.21014/acta_imeko.v10i4.1185 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210188141 |
| primary_location.source.issn | 0237-028X, 2221-870X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 0237-028X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | ACTA IMEKO |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://acta.imeko.org/index.php/acta-imeko/article/download/IMEKO-ACTA-10%20%282021%29-04-17/pdf |
| 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 | ACTA IMEKO |
| primary_location.landing_page_url | https://doi.org/10.21014/acta_imeko.v10i4.1185 |
| publication_date | 2021-12-30 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2096976709, https://openalex.org/W2995336103, https://openalex.org/W2743239999, https://openalex.org/W3082666011, https://openalex.org/W3046952127, https://openalex.org/W3152567630, https://openalex.org/W3173996913, https://openalex.org/W2922662340, https://openalex.org/W2980188646, https://openalex.org/W2941491269, https://openalex.org/W2910430509, https://openalex.org/W2053411694, https://openalex.org/W2903106404, https://openalex.org/W2911479971, https://openalex.org/W2992492204, https://openalex.org/W6679942958, https://openalex.org/W3096186689, https://openalex.org/W3136484707, https://openalex.org/W2194775991, https://openalex.org/W2911132314, https://openalex.org/W2964054038, https://openalex.org/W3098350627 |
| referenced_works_count | 22 |
| abstract_inverted_index.a | 5, 28, 61, 116, 123 |
| abstract_inverted_index.3D | 34 |
| abstract_inverted_index.be | 144 |
| abstract_inverted_index.by | 49, 60 |
| abstract_inverted_index.is | 4, 47 |
| abstract_inverted_index.of | 19, 27, 39, 82, 126, 156 |
| abstract_inverted_index.on | 92, 132 |
| abstract_inverted_index.to | 8, 68, 146 |
| abstract_inverted_index.CNN | 135 |
| abstract_inverted_index.The | 45, 100, 137 |
| abstract_inverted_index.and | 36, 42, 53, 57, 85, 108, 152 |
| abstract_inverted_index.can | 143 |
| abstract_inverted_index.few | 124 |
| abstract_inverted_index.for | 12, 33, 112 |
| abstract_inverted_index.has | 96 |
| abstract_inverted_index.the | 13, 16, 25, 40, 50, 70, 74, 80, 88, 157 |
| abstract_inverted_index.100% | 117 |
| abstract_inverted_index.This | 22 |
| abstract_inverted_index.data | 159 |
| abstract_inverted_index.deaf | 14 |
| abstract_inverted_index.deep | 62, 109 |
| abstract_inverted_index.good | 97 |
| abstract_inverted_index.hand | 93 |
| abstract_inverted_index.show | 86 |
| abstract_inverted_index.sign | 20, 75 |
| abstract_inverted_index.step | 7 |
| abstract_inverted_index.that | 87 |
| abstract_inverted_index.then | 58 |
| abstract_inverted_index.with | 73 |
| abstract_inverted_index.work | 23 |
| abstract_inverted_index.<p | 0 |
| abstract_inverted_index.MagIK | 51 |
| abstract_inverted_index.after | 122 |
| abstract_inverted_index.based | 91 |
| abstract_inverted_index.hands | 43 |
| abstract_inverted_index.hours | 125 |
| abstract_inverted_index.model | 66, 140 |
| abstract_inverted_index.other | 147, 153 |
| abstract_inverted_index.palm. | 44 |
| abstract_inverted_index.usage | 26 |
| abstract_inverted_index.using | 128 |
| abstract_inverted_index.which | 103 |
| abstract_inverted_index.ResNet | 134 |
| abstract_inverted_index.enable | 9 |
| abstract_inverted_index.limits | 81 |
| abstract_inverted_index.method | 56, 90, 142 |
| abstract_inverted_index.signer | 120 |
| abstract_inverted_index.system | 32 |
| abstract_inverted_index.Results | 78 |
| abstract_inverted_index.applied | 145 |
| abstract_inverted_index.confirm | 79 |
| abstract_inverted_index.fingers | 41 |
| abstract_inverted_index.gesture | 46, 64, 106, 113, 149 |
| abstract_inverted_index.inverse | 54 |
| abstract_inverted_index.system, | 102 |
| abstract_inverted_index.systems | 84, 151 |
| abstract_inverted_index.through | 15 |
| abstract_inverted_index.trained | 67 |
| abstract_inverted_index.combines | 104 |
| abstract_inverted_index.gestures | 71 |
| abstract_inverted_index.language | 76 |
| abstract_inverted_index.learning | 63, 110, 130 |
| abstract_inverted_index.magnetic | 30 |
| abstract_inverted_index.proposed | 89, 101, 138 |
| abstract_inverted_index.proposes | 24 |
| abstract_inverted_index.provides | 115 |
| abstract_inverted_index.skeleton | 94 |
| abstract_inverted_index.specific | 158 |
| abstract_inverted_index.tracking | 38, 150 |
| abstract_inverted_index.training | 127, 141 |
| abstract_inverted_index.transfer | 129 |
| abstract_inverted_index.(magnetic | 52 |
| abstract_inverted_index.accuracy, | 119 |
| abstract_inverted_index.alphabet. | 77 |
| abstract_inverted_index.automated | 17 |
| abstract_inverted_index.efficient | 10 |
| abstract_inverted_index.language. | 21 |
| abstract_inverted_index.processed | 59 |
| abstract_inverted_index.recognize | 69 |
| abstract_inverted_index.technique | 131 |
| abstract_inverted_index.associated | 72 |
| abstract_inverted_index.regardless | 155 |
| abstract_inverted_index.techniques | 111 |
| abstract_inverted_index.well-known | 133 |
| abstract_inverted_index.acquisition | 107, 160 |
| abstract_inverted_index.fundamental | 6 |
| abstract_inverted_index.kinematics) | 55 |
| abstract_inverted_index.orientation | 37 |
| abstract_inverted_index.positioning | 31, 35 |
| abstract_inverted_index.properties. | 99 |
| abstract_inverted_index.recognition | 3 |
| abstract_inverted_index.translation | 18 |
| abstract_inverted_index.independent, | 121 |
| abstract_inverted_index.recognition, | 114 |
| abstract_inverted_index.sensor-based | 105, 148 |
| abstract_inverted_index.vision-based | 83 |
| abstract_inverted_index.applications, | 154 |
| abstract_inverted_index.architecture. | 136 |
| abstract_inverted_index.communication | 11 |
| abstract_inverted_index.reconstructed | 48 |
| abstract_inverted_index.classification | 65, 118, 139 |
| abstract_inverted_index.generalization | 98 |
| abstract_inverted_index.high-precision | 29 |
| abstract_inverted_index.reconstruction | 95 |
| abstract_inverted_index.lang="EN-US">Gesture | 2 |
| abstract_inverted_index.class="Abstract"><span | 1 |
| abstract_inverted_index.technology.</span></p> | 161 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.18185223 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |