GESTURE RECOGNITION FOR TOUCH-FREE PC CONTROL USING A NEURAL NETWORK APPROACH Article Swipe
N. Sharma
,
Vaishali Nirgude
,
T.P. Shah
,
Chandani bhagat
,
Amar Gupta
,
Yash Gupta
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21917/ijdsml.2024.0142
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21917/ijdsml.2024.0142
In the pursuit of advancing the field of touch-free human-computer interaction, this paper is focused on developing a gesture enabled PC control system that aims for enhancing user engagement and providing intuitive and flexible control methods, across various applications, particularly those benefiting individuals with mobility impairments. This system has expanding potential use in virtual and augmented reality environments. This study describes a unique method for temporal gesture identification that employs gesture kinematics for feature extraction and classification. Real-time hand tracking and key point identification were performed using MediaPipe. The Euclidean distances between the key points was normalised and input into a Multilayer perceptron model, which classified the gestures and mapped them to specific commands for controlling PC functions. This approach performed well over a large dataset, improving accuracy and usability. The gesture recognition system achieved an average accuracy of 97%, with precision, recall, and F1 score of 0.924, 0.924, and 0.926, respectively, across the five gestures. This system provides the ability of customization to users which allows them to create and map their own gestures to specific commands, in addition to using predefined ones. This level of personalization and flexibility is a significant advancement over existing systems, which typically offer fixed gesture-command mappings.
Related Topics
Concepts
Gesture
Computer science
Gesture recognition
Usability
Flexibility (engineering)
Artificial intelligence
Identification (biology)
Personalization
Key (lock)
Human–computer interaction
Convolutional neural network
Feature (linguistics)
Computer vision
Biology
Linguistics
Botany
Philosophy
Statistics
Computer security
Mathematics
World Wide Web
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.21917/ijdsml.2024.0142
- OA Status
- hybrid
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411499353
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4411499353Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21917/ijdsml.2024.0142Digital Object Identifier
- Title
-
GESTURE RECOGNITION FOR TOUCH-FREE PC CONTROL USING A NEURAL NETWORK APPROACHWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-01Full publication date if available
- Authors
-
N. Sharma, Vaishali Nirgude, T.P. Shah, Chandani bhagat, Amar Gupta, Yash GuptaList of authors in order
- Landing page
-
https://doi.org/10.21917/ijdsml.2024.0142Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.21917/ijdsml.2024.0142Direct OA link when available
- Concepts
-
Gesture, Computer science, Gesture recognition, Usability, Flexibility (engineering), Artificial intelligence, Identification (biology), Personalization, Key (lock), Human–computer interaction, Convolutional neural network, Feature (linguistics), Computer vision, Biology, Linguistics, Botany, Philosophy, Statistics, Computer security, Mathematics, World Wide WebTop 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/W4411499353 |
|---|---|
| doi | https://doi.org/10.21917/ijdsml.2024.0142 |
| ids.doi | https://doi.org/10.21917/ijdsml.2024.0142 |
| ids.openalex | https://openalex.org/W4411499353 |
| fwci | 0.0 |
| type | article |
| title | GESTURE RECOGNITION FOR TOUCH-FREE PC CONTROL USING A NEURAL NETWORK APPROACH |
| biblio.issue | 4 |
| biblio.volume | 5 |
| biblio.last_page | 697 |
| biblio.first_page | 690 |
| 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/T10914 |
| topics[1].field.id | https://openalex.org/fields/28 |
| topics[1].field.display_name | Neuroscience |
| topics[1].score | 0.9945999979972839 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2805 |
| topics[1].subfield.display_name | Cognitive Neuroscience |
| topics[1].display_name | Tactile and Sensory Interactions |
| topics[2].id | https://openalex.org/T10510 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9674999713897705 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2742 |
| topics[2].subfield.display_name | Rehabilitation |
| topics[2].display_name | Stroke Rehabilitation and Recovery |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C207347870 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8864741921424866 |
| 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 | 0.7861976623535156 |
| 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 | 0.6597638130187988 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1519524 |
| concepts[2].display_name | Gesture recognition |
| concepts[3].id | https://openalex.org/C170130773 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5501148104667664 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q216378 |
| concepts[3].display_name | Usability |
| concepts[4].id | https://openalex.org/C2780598303 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5264509320259094 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q65921492 |
| concepts[4].display_name | Flexibility (engineering) |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4987778663635254 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C116834253 |
| concepts[6].level | 2 |
| concepts[6].score | 0.49595269560813904 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2039217 |
| concepts[6].display_name | Identification (biology) |
| concepts[7].id | https://openalex.org/C183003079 |
| concepts[7].level | 2 |
| concepts[7].score | 0.47069859504699707 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1000371 |
| concepts[7].display_name | Personalization |
| concepts[8].id | https://openalex.org/C26517878 |
| concepts[8].level | 2 |
| concepts[8].score | 0.46174269914627075 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q228039 |
| concepts[8].display_name | Key (lock) |
| concepts[9].id | https://openalex.org/C107457646 |
| concepts[9].level | 1 |
| concepts[9].score | 0.46167680621147156 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q207434 |
| concepts[9].display_name | Human–computer interaction |
| concepts[10].id | https://openalex.org/C81363708 |
| concepts[10].level | 2 |
| concepts[10].score | 0.45336347818374634 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[10].display_name | Convolutional neural network |
| concepts[11].id | https://openalex.org/C2776401178 |
| concepts[11].level | 2 |
| concepts[11].score | 0.4251285493373871 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[11].display_name | Feature (linguistics) |
| concepts[12].id | https://openalex.org/C31972630 |
| concepts[12].level | 1 |
| concepts[12].score | 0.40548574924468994 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[12].display_name | Computer vision |
| concepts[13].id | https://openalex.org/C86803240 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[13].display_name | Biology |
| concepts[14].id | https://openalex.org/C41895202 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[14].display_name | Linguistics |
| concepts[15].id | https://openalex.org/C59822182 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q441 |
| concepts[15].display_name | Botany |
| concepts[16].id | https://openalex.org/C138885662 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[16].display_name | Philosophy |
| concepts[17].id | https://openalex.org/C105795698 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[17].display_name | Statistics |
| concepts[18].id | https://openalex.org/C38652104 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[18].display_name | Computer security |
| concepts[19].id | https://openalex.org/C33923547 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[19].display_name | Mathematics |
| concepts[20].id | https://openalex.org/C136764020 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q466 |
| concepts[20].display_name | World Wide Web |
| keywords[0].id | https://openalex.org/keywords/gesture |
| keywords[0].score | 0.8864741921424866 |
| keywords[0].display_name | Gesture |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.7861976623535156 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/gesture-recognition |
| keywords[2].score | 0.6597638130187988 |
| keywords[2].display_name | Gesture recognition |
| keywords[3].id | https://openalex.org/keywords/usability |
| keywords[3].score | 0.5501148104667664 |
| keywords[3].display_name | Usability |
| keywords[4].id | https://openalex.org/keywords/flexibility |
| keywords[4].score | 0.5264509320259094 |
| keywords[4].display_name | Flexibility (engineering) |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.4987778663635254 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/identification |
| keywords[6].score | 0.49595269560813904 |
| keywords[6].display_name | Identification (biology) |
| keywords[7].id | https://openalex.org/keywords/personalization |
| keywords[7].score | 0.47069859504699707 |
| keywords[7].display_name | Personalization |
| keywords[8].id | https://openalex.org/keywords/key |
| keywords[8].score | 0.46174269914627075 |
| keywords[8].display_name | Key (lock) |
| keywords[9].id | https://openalex.org/keywords/human–computer-interaction |
| keywords[9].score | 0.46167680621147156 |
| keywords[9].display_name | Human–computer interaction |
| keywords[10].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[10].score | 0.45336347818374634 |
| keywords[10].display_name | Convolutional neural network |
| keywords[11].id | https://openalex.org/keywords/feature |
| keywords[11].score | 0.4251285493373871 |
| keywords[11].display_name | Feature (linguistics) |
| keywords[12].id | https://openalex.org/keywords/computer-vision |
| keywords[12].score | 0.40548574924468994 |
| keywords[12].display_name | Computer vision |
| language | en |
| locations[0].id | doi:10.21917/ijdsml.2024.0142 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S5407010506 |
| locations[0].source.issn | 2583-9292, 3049-0197 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 2583-9292 |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | ICTACT Journal on Data Science and Machine Learning |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | cc-by-nc-sa |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-sa |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | ICTACT Journal on Data Science and Machine Learning |
| locations[0].landing_page_url | https://doi.org/10.21917/ijdsml.2024.0142 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5016378457 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8046-1752 |
| authorships[0].author.display_name | N. Sharma |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Naina Sharma |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5033559172 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Vaishali Nirgude |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Vaishali Nirgude |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5030036559 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | T.P. Shah |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Tanya Shah |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5069764104 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Chandani bhagat |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Chirag Bhagat |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5089511560 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-9306-1256 |
| authorships[4].author.display_name | Amar Gupta |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Amithesh Gupta |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5110502129 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Yash Gupta |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Yash Gupta |
| 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://doi.org/10.21917/ijdsml.2024.0142 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-06-21T00:00:00 |
| display_name | GESTURE RECOGNITION FOR TOUCH-FREE PC CONTROL USING A NEURAL NETWORK APPROACH |
| has_fulltext | False |
| 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/W2537963312, https://openalex.org/W2066003895, https://openalex.org/W2902873204, https://openalex.org/W2185750513, https://openalex.org/W2010878661, https://openalex.org/W3147379364, https://openalex.org/W2026258298, https://openalex.org/W3204639664, https://openalex.org/W2970836791, https://openalex.org/W2989699735 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.21917/ijdsml.2024.0142 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S5407010506 |
| best_oa_location.source.issn | 2583-9292, 3049-0197 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 2583-9292 |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | ICTACT Journal on Data Science and Machine Learning |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | cc-by-nc-sa |
| 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-nc-sa |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | ICTACT Journal on Data Science and Machine Learning |
| best_oa_location.landing_page_url | https://doi.org/10.21917/ijdsml.2024.0142 |
| primary_location.id | doi:10.21917/ijdsml.2024.0142 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S5407010506 |
| primary_location.source.issn | 2583-9292, 3049-0197 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2583-9292 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | ICTACT Journal on Data Science and Machine Learning |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | cc-by-nc-sa |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-sa |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | ICTACT Journal on Data Science and Machine Learning |
| primary_location.landing_page_url | https://doi.org/10.21917/ijdsml.2024.0142 |
| publication_date | 2024-09-01 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 17, 61, 100, 123, 191 |
| abstract_inverted_index.F1 | 144 |
| abstract_inverted_index.In | 0 |
| abstract_inverted_index.PC | 20, 116 |
| abstract_inverted_index.an | 135 |
| abstract_inverted_index.in | 52, 178 |
| abstract_inverted_index.is | 13, 190 |
| abstract_inverted_index.of | 3, 7, 138, 146, 161, 186 |
| abstract_inverted_index.on | 15 |
| abstract_inverted_index.to | 111, 163, 168, 175, 180 |
| abstract_inverted_index.The | 88, 130 |
| abstract_inverted_index.and | 29, 32, 54, 75, 80, 97, 108, 128, 143, 149, 170, 188 |
| abstract_inverted_index.for | 25, 64, 72, 114 |
| abstract_inverted_index.has | 48 |
| abstract_inverted_index.key | 81, 93 |
| abstract_inverted_index.map | 171 |
| abstract_inverted_index.own | 173 |
| abstract_inverted_index.the | 1, 5, 92, 106, 153, 159 |
| abstract_inverted_index.use | 51 |
| abstract_inverted_index.was | 95 |
| abstract_inverted_index.97%, | 139 |
| abstract_inverted_index.This | 46, 58, 118, 156, 184 |
| abstract_inverted_index.aims | 24 |
| abstract_inverted_index.five | 154 |
| abstract_inverted_index.hand | 78 |
| abstract_inverted_index.into | 99 |
| abstract_inverted_index.over | 122, 194 |
| abstract_inverted_index.that | 23, 68 |
| abstract_inverted_index.them | 110, 167 |
| abstract_inverted_index.this | 11 |
| abstract_inverted_index.user | 27 |
| abstract_inverted_index.well | 121 |
| abstract_inverted_index.were | 84 |
| abstract_inverted_index.with | 43, 140 |
| abstract_inverted_index.field | 6 |
| abstract_inverted_index.fixed | 200 |
| abstract_inverted_index.input | 98 |
| abstract_inverted_index.large | 124 |
| abstract_inverted_index.level | 185 |
| abstract_inverted_index.offer | 199 |
| abstract_inverted_index.ones. | 183 |
| abstract_inverted_index.paper | 12 |
| abstract_inverted_index.point | 82 |
| abstract_inverted_index.score | 145 |
| abstract_inverted_index.study | 59 |
| abstract_inverted_index.their | 172 |
| abstract_inverted_index.those | 40 |
| abstract_inverted_index.users | 164 |
| abstract_inverted_index.using | 86, 181 |
| abstract_inverted_index.which | 104, 165, 197 |
| abstract_inverted_index.0.924, | 147, 148 |
| abstract_inverted_index.0.926, | 150 |
| abstract_inverted_index.across | 36, 152 |
| abstract_inverted_index.allows | 166 |
| abstract_inverted_index.create | 169 |
| abstract_inverted_index.mapped | 109 |
| abstract_inverted_index.method | 63 |
| abstract_inverted_index.model, | 103 |
| abstract_inverted_index.points | 94 |
| abstract_inverted_index.system | 22, 47, 133, 157 |
| abstract_inverted_index.unique | 62 |
| abstract_inverted_index.ability | 160 |
| abstract_inverted_index.average | 136 |
| abstract_inverted_index.between | 91 |
| abstract_inverted_index.control | 21, 34 |
| abstract_inverted_index.employs | 69 |
| abstract_inverted_index.enabled | 19 |
| abstract_inverted_index.feature | 73 |
| abstract_inverted_index.focused | 14 |
| abstract_inverted_index.gesture | 18, 66, 70, 131 |
| abstract_inverted_index.pursuit | 2 |
| abstract_inverted_index.reality | 56 |
| abstract_inverted_index.recall, | 142 |
| abstract_inverted_index.various | 37 |
| abstract_inverted_index.virtual | 53 |
| abstract_inverted_index.accuracy | 127, 137 |
| abstract_inverted_index.achieved | 134 |
| abstract_inverted_index.addition | 179 |
| abstract_inverted_index.approach | 119 |
| abstract_inverted_index.commands | 113 |
| abstract_inverted_index.dataset, | 125 |
| abstract_inverted_index.existing | 195 |
| abstract_inverted_index.flexible | 33 |
| abstract_inverted_index.gestures | 107, 174 |
| abstract_inverted_index.methods, | 35 |
| abstract_inverted_index.mobility | 44 |
| abstract_inverted_index.provides | 158 |
| abstract_inverted_index.specific | 112, 176 |
| abstract_inverted_index.systems, | 196 |
| abstract_inverted_index.temporal | 65 |
| abstract_inverted_index.tracking | 79 |
| abstract_inverted_index.Euclidean | 89 |
| abstract_inverted_index.Real-time | 77 |
| abstract_inverted_index.advancing | 4 |
| abstract_inverted_index.augmented | 55 |
| abstract_inverted_index.commands, | 177 |
| abstract_inverted_index.describes | 60 |
| abstract_inverted_index.distances | 90 |
| abstract_inverted_index.enhancing | 26 |
| abstract_inverted_index.expanding | 49 |
| abstract_inverted_index.gestures. | 155 |
| abstract_inverted_index.improving | 126 |
| abstract_inverted_index.intuitive | 31 |
| abstract_inverted_index.mappings. | 202 |
| abstract_inverted_index.performed | 85, 120 |
| abstract_inverted_index.potential | 50 |
| abstract_inverted_index.providing | 30 |
| abstract_inverted_index.typically | 198 |
| abstract_inverted_index.MediaPipe. | 87 |
| abstract_inverted_index.Multilayer | 101 |
| abstract_inverted_index.benefiting | 41 |
| abstract_inverted_index.classified | 105 |
| abstract_inverted_index.developing | 16 |
| abstract_inverted_index.engagement | 28 |
| abstract_inverted_index.extraction | 74 |
| abstract_inverted_index.functions. | 117 |
| abstract_inverted_index.kinematics | 71 |
| abstract_inverted_index.normalised | 96 |
| abstract_inverted_index.perceptron | 102 |
| abstract_inverted_index.precision, | 141 |
| abstract_inverted_index.predefined | 182 |
| abstract_inverted_index.touch-free | 8 |
| abstract_inverted_index.usability. | 129 |
| abstract_inverted_index.advancement | 193 |
| abstract_inverted_index.controlling | 115 |
| abstract_inverted_index.flexibility | 189 |
| abstract_inverted_index.individuals | 42 |
| abstract_inverted_index.recognition | 132 |
| abstract_inverted_index.significant | 192 |
| abstract_inverted_index.impairments. | 45 |
| abstract_inverted_index.interaction, | 10 |
| abstract_inverted_index.particularly | 39 |
| abstract_inverted_index.applications, | 38 |
| abstract_inverted_index.customization | 162 |
| abstract_inverted_index.environments. | 57 |
| abstract_inverted_index.respectively, | 151 |
| abstract_inverted_index.human-computer | 9 |
| abstract_inverted_index.identification | 67, 83 |
| abstract_inverted_index.classification. | 76 |
| abstract_inverted_index.gesture-command | 201 |
| abstract_inverted_index.personalization | 187 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.36204087 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |