Applying Hybrid Deep Neural Network for the Recognition of Sign Language Words Used by the Deaf COVID-19 Patients
The rapid spread of the novel corona virus disease (COVID-19) has disrupted the traditional clinical services all over the world. Hospitals and healthcare centers have taken extreme care to minimize the risk of exposure to the virus by restricting the visitors and relatives of the patients. The dram...
Saved in:
| Published in | Arabian journal for science and engineering Vol. 48; no. 2; pp. 1349 - 1362 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2193-567X 1319-8025 2191-4281 2191-4281 |
| DOI | 10.1007/s13369-022-06843-0 |
Cover
| Abstract | The rapid spread of the novel corona virus disease (COVID-19) has disrupted the traditional clinical services all over the world. Hospitals and healthcare centers have taken extreme care to minimize the risk of exposure to the virus by restricting the visitors and relatives of the patients. The dramatic changes happened in the healthcare norms have made it hard for the deaf patients to communicate and receive appropriate care. This paper reports a work on automatic sign language recognition that can mitigate the communication barrier between the deaf patients and the healthcare workers in India. Since hand gestures are the most expressive components of a sign language vocabulary, a novel dataset of dynamic hand gestures for the Indian sign language (ISL) words commonly used for emergency communication by deaf COVID-19 positive patients is proposed. A hybrid model of deep convolutional long short-term memory network has been utilized for the recognition of the proposed hand gestures and achieved an average accuracy of 83.36%. The model performance has been further validated on an alternative ISL dataset as well as a benchmarking hand gesture dataset and obtained average accuracies of
97
%
and
99.34
±
0.66
%
, respectively. |
|---|---|
| AbstractList | The rapid spread of the novel corona virus disease (COVID-19) has disrupted the traditional clinical services all over the world. Hospitals and healthcare centers have taken extreme care to minimize the risk of exposure to the virus by restricting the visitors and relatives of the patients. The dramatic changes happened in the healthcare norms have made it hard for the deaf patients to communicate and receive appropriate care. This paper reports a work on automatic sign language recognition that can mitigate the communication barrier between the deaf patients and the healthcare workers in India. Since hand gestures are the most expressive components of a sign language vocabulary, a novel dataset of dynamic hand gestures for the Indian sign language (ISL) words commonly used for emergency communication by deaf COVID-19 positive patients is proposed. A hybrid model of deep convolutional long short-term memory network has been utilized for the recognition of the proposed hand gestures and achieved an average accuracy of 83.36%. The model performance has been further validated on an alternative ISL dataset as well as a benchmarking hand gesture dataset and obtained average accuracies of
and
, respectively. The rapid spread of the novel corona virus disease (COVID-19) has disrupted the traditional clinical services all over the world. Hospitals and healthcare centers have taken extreme care to minimize the risk of exposure to the virus by restricting the visitors and relatives of the patients. The dramatic changes happened in the healthcare norms have made it hard for the deaf patients to communicate and receive appropriate care. This paper reports a work on automatic sign language recognition that can mitigate the communication barrier between the deaf patients and the healthcare workers in India. Since hand gestures are the most expressive components of a sign language vocabulary, a novel dataset of dynamic hand gestures for the Indian sign language (ISL) words commonly used for emergency communication by deaf COVID-19 positive patients is proposed. A hybrid model of deep convolutional long short-term memory network has been utilized for the recognition of the proposed hand gestures and achieved an average accuracy of 83.36%. The model performance has been further validated on an alternative ISL dataset as well as a benchmarking hand gesture dataset and obtained average accuracies of 97 % and 99.34 ± 0.66 % , respectively. The rapid spread of the novel corona virus disease (COVID-19) has disrupted the traditional clinical services all over the world. Hospitals and healthcare centers have taken extreme care to minimize the risk of exposure to the virus by restricting the visitors and relatives of the patients. The dramatic changes happened in the healthcare norms have made it hard for the deaf patients to communicate and receive appropriate care. This paper reports a work on automatic sign language recognition that can mitigate the communication barrier between the deaf patients and the healthcare workers in India. Since hand gestures are the most expressive components of a sign language vocabulary, a novel dataset of dynamic hand gestures for the Indian sign language (ISL) words commonly used for emergency communication by deaf COVID-19 positive patients is proposed. A hybrid model of deep convolutional long short-term memory network has been utilized for the recognition of the proposed hand gestures and achieved an average accuracy of 83.36%. The model performance has been further validated on an alternative ISL dataset as well as a benchmarking hand gesture dataset and obtained average accuracies of $$97\%$$ 97% and $$99.34\pm 0.66\%$$ 99.34±0.66%, respectively. The rapid spread of the novel corona virus disease (COVID-19) has disrupted the traditional clinical services all over the world. Hospitals and healthcare centers have taken extreme care to minimize the risk of exposure to the virus by restricting the visitors and relatives of the patients. The dramatic changes happened in the healthcare norms have made it hard for the deaf patients to communicate and receive appropriate care. This paper reports a work on automatic sign language recognition that can mitigate the communication barrier between the deaf patients and the healthcare workers in India. Since hand gestures are the most expressive components of a sign language vocabulary, a novel dataset of dynamic hand gestures for the Indian sign language (ISL) words commonly used for emergency communication by deaf COVID-19 positive patients is proposed. A hybrid model of deep convolutional long short-term memory network has been utilized for the recognition of the proposed hand gestures and achieved an average accuracy of 83.36%. The model performance has been further validated on an alternative ISL dataset as well as a benchmarking hand gesture dataset and obtained average accuracies of 97% and 99.34±0.66%, respectively. |
| Author | Venugopalan, Adithya Reghunadhan, Rajesh |
| Author_xml | – sequence: 1 givenname: Adithya orcidid: 0000-0001-8977-1984 surname: Venugopalan fullname: Venugopalan, Adithya email: adithyaushas88@gmail.com organization: Department of Computer Science, Central University of Kerala – sequence: 2 givenname: Rajesh surname: Reghunadhan fullname: Reghunadhan, Rajesh organization: Department of Computer Science, Central University of Kerala |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35492959$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNUUtvFSEUJqbG1rZ_wIUhcT0KAwzDxqS5V22TG2tsq-4Iw8AUncIUZmzm38t9WB-LpqtDwvc633kO9nzwBoAXGL3GCPE3CRNSiQKVZYGqmpICPQEHJRa4oGWN9zZvUrCKf9sHxym5BtGaCIYxeQb2CaOiFEwcgNuTYehn5zt4OjfRtXBpzAA_mimqPo_xLsQf0IYIx2sDPxsdOu9GFzwMFl64zsOV8t2kOgO_htgmeJVMC5t5A18aZeHi_MvZssACflKjM35MR-CpVX0yx7t5CK7ev7tcnBar8w9ni5NVoSmnY1GrCreMtJQQm5NzY5GtLGKm5BZrrAQjCjGtG0O51bpqBWlqy2tORVsyxskhIFvdyQ9qvlN9L4foblScJUZy3aHcdihzh3LToUSZ9XbLGqbmxrQ6J85N3DODcvLfH--uZRd-SoFI1hBZ4NVOIIbbyaRRfg9T9HlTWXJOWYUwWdu8_NvmXv_3YTKg3AJ0DClFYx8Xvv6PpN2o1ufKUV3_MHXXVso-vjPxT-wHWL8AuE3CVQ |
| CitedBy_id | crossref_primary_10_1007_s10209_024_01162_7 crossref_primary_10_1145_3643824 crossref_primary_10_32604_cmes_2023_045731 crossref_primary_10_3390_app13095219 crossref_primary_10_3390_app15062957 crossref_primary_10_1007_s10462_024_10816_0 crossref_primary_10_1051_bioconf_20249700051 crossref_primary_10_2478_jsiot_2024_0006 crossref_primary_10_3390_electronics13071229 crossref_primary_10_1007_s00521_023_08858_6 crossref_primary_10_1109_ACCESS_2024_3421992 |
| Cites_doi | 10.1016/j.eswa.2020.113794 10.1007/s11042-019-7263-7 10.1109/LGRS.2021.3098774 10.1016/j.patcog.2019.107028 10.1109/TNNLS.2020.3007534 10.1007/s13042-017-0705-5 10.2991/ijcis.d.201215.002 10.1007/s11042-019-08266-w 10.1016/j.cviu.2015.08.004 10.1016/j.physd.2019.132306 10.1109/ACCESS.2020.3032140 10.1109/TMM.2018.2856094 10.3390/electronics8121511 10.22044/jadm.2019.7903.1929 10.1016/j.neucom.2018.11.038 10.1109/ACCESS.2020.2990699 10.1016/j.patrec.2019.12.013 10.1016/j.asej.2016.10.013 10.1016/j.patcog.2017.10.033 10.1016/j.dib.2020.106016 10.1007/s11263-015-0816-y 10.1007/s10462-020-09838-1 10.1007/s10462-020-09825-6 10.1007/s12652-020-02396-y 10.3390/s20092467 10.3390/s19245429 10.1016/j.jksuci.2019.05.002 10.1007/s11831-019-09384-2 10.1007/s10586-017-1435-x 10.1155/2020/3685614 10.1109/3DV.2019.00061 10.30693/SMJ.2020.9.1.23 10.1109/ICAEE47123.2019.9014683 10.1109/INTERCON50315.2020.9220243 10.1109/CVPR.2007.383137 10.1109/ICCAIS.2018.8570336 10.1145/3278312.3278314 10.1109/IHTC.2014.7147529 10.1109/CVPRW.2014.107 10.5244/C.22.109 10.1109/ICCC47050.2019.9064200 10.1109/LA-CCI47412.2019.9037037 10.1109/DICTA.2016.7797030 10.1109/WACV.2013.6475006 10.1109/EITCE47263.2019.9094915 10.1109/INFOSCI.2016.7845312 10.1109/STI50764.2020.9350484 10.1109/ICKII46306.2019.9042600 |
| ContentType | Journal Article |
| Copyright | King Fahd University of Petroleum & Minerals 2022 King Fahd University of Petroleum & Minerals 2022. |
| Copyright_xml | – notice: King Fahd University of Petroleum & Minerals 2022 – notice: King Fahd University of Petroleum & Minerals 2022. |
| DBID | AAYXX CITATION NPM 5PM ADTOC UNPAY |
| DOI | 10.1007/s13369-022-06843-0 |
| DatabaseName | CrossRef PubMed PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef PubMed |
| DatabaseTitleList | PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2191-4281 |
| EndPage | 1362 |
| ExternalDocumentID | 10.1007/s13369-022-06843-0 PMC9030689 35492959 10_1007_s13369_022_06843_0 |
| Genre | Journal Article |
| GroupedDBID | -EM 0R~ 203 2KG 406 AAAVM AACDK AAHNG AAIAL AAJBT AANZL AARHV AASML AATNV AATVU AAUYE AAYTO AAYZH ABAKF ABDBF ABDZT ABECU ABFTD ABFTV ABJNI ABJOX ABKCH ABMQK ABQBU ABSXP ABTEG ABTKH ABTMW ABXPI ACAOD ACBXY ACDTI ACHSB ACMDZ ACMLO ACOKC ACPIV ACUHS ACZOJ ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEJRE AEMSY AEOHA AESKC AEVLU AEXYK AFBBN AFLOW AFQWF AGAYW AGJBK AGMZJ AGQEE AGQMX AGRTI AHAVH AHBYD AHSBF AIAKS AIGIU AILAN AITGF AJBLW AJRNO AJZVZ ALFXC ALMA_UNASSIGNED_HOLDINGS AMXSW AMYLF AOCGG AXYYD BGNMA CSCUP DDRTE DNIVK DPUIP EBLON EBS EIOEI EJD ESX FERAY FIGPU FINBP FNLPD FSGXE GGCAI GQ6 GQ7 H13 HG6 I-F IKXTQ IWAJR J-C JBSCW JZLTJ L8X LLZTM M4Y MK~ NPVJJ NQJWS NU0 O9J PT4 ROL RSV SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE TSG TUS UOJIU UTJUX UZXMN VFIZW Z5O Z7R Z7V Z7X Z7Y Z7Z Z81 Z83 Z85 Z88 ZMTXR ~8M AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC AEZWR AFDZB AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION NPM 06D 0VY 23M 29~ 2KM 30V 408 5GY 96X AAJKR AARTL AAYIU AAYQN AAZMS ABTHY ACGFS ACKNC ADHHG ADHIR AEGNC AEJHL AENEX AEPYU AETCA AFWTZ AFZKB AGDGC AGWZB AGYKE AHYZX AIIXL AMKLP AMYQR ANMIH AYJHY ESBYG FFXSO FRRFC FYJPI GGRSB GJIRD GX1 HMJXF HRMNR HZ~ I0C IXD J9A KOV O93 OVT P9P R9I RLLFE S27 S3B SEG SHX T13 U2A UG4 VC2 W48 WK8 ~A9 5PM ADTOC UNPAY |
| ID | FETCH-LOGICAL-c474t-8a61d53d433f4837ef0f6f05e27f1c1a953a05ccbe47fcc6d93b8f78749d25573 |
| IEDL.DBID | UNPAY |
| ISSN | 2193-567X 1319-8025 2191-4281 |
| IngestDate | Sun Oct 26 03:56:20 EDT 2025 Tue Sep 30 15:07:44 EDT 2025 Mon Jun 30 09:09:15 EDT 2025 Thu Jan 02 22:35:03 EST 2025 Thu Apr 24 22:56:44 EDT 2025 Wed Oct 01 02:18:40 EDT 2025 Fri Feb 21 02:45:04 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | COVID-19 Deep learning Indian sign language Emergency words Hand gesture recognition |
| Language | English |
| License | King Fahd University of Petroleum & Minerals 2022. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c474t-8a61d53d433f4837ef0f6f05e27f1c1a953a05ccbe47fcc6d93b8f78749d25573 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-8977-1984 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://link.springer.com/content/pdf/10.1007/s13369-022-06843-0.pdf |
| PMID | 35492959 |
| PQID | 2774560130 |
| PQPubID | 2044268 |
| PageCount | 14 |
| ParticipantIDs | unpaywall_primary_10_1007_s13369_022_06843_0 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9030689 proquest_journals_2774560130 pubmed_primary_35492959 crossref_primary_10_1007_s13369_022_06843_0 crossref_citationtrail_10_1007_s13369_022_06843_0 springer_journals_10_1007_s13369_022_06843_0 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2023-02-01 |
| PublicationDateYYYYMMDD | 2023-02-01 |
| PublicationDate_xml | – month: 02 year: 2023 text: 2023-02-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: Germany – name: Heidelberg |
| PublicationTitle | Arabian journal for science and engineering |
| PublicationTitleAbbrev | Arab J Sci Eng |
| PublicationTitleAlternate | Arab J Sci Eng |
| PublicationYear | 2023 |
| Publisher | Springer Berlin Heidelberg Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V |
| References | Souza, Gatto, Xue, Fukui (CR49) 2020 Erhu, Xue, Cao, Duan, Lin (CR29) 2019; 8 CR39 CR36 Rao, Kishore (CR21) 2018; 9 CR31 CR30 Pisharady, Saerbeck (CR6) 2015; 141 Ji, Zhang, Jie, Ma, Wu (CR23) 2021; 32 Kurmanji, Ghaderi (CR43) 2020; 8 Jaramillo-Yánez, Benalcázar, Mena-Maldonado (CR10) 2020; 20 Li, Tang, Sun, Kong, Jiang, Jiang, Tao, Xu, Liu (CR35) 2019; 22 Hakim, Shih, Kasthuri Arachchi, Aditya, Chen (CR33) 2019; 19 Cheok, Omar, Jaward (CR7) 2019; 10 CR8 Avola, Bernardi, Cinque, Foresti, Massaroni (CR37) 2018; 21 CR9 CR48 CR47 CR46 CR44 Al-Hammadi, Muhammad, Abdu, Alsulaiman, Bencherif (CR25) 2020; 8 CR42 Kamruzzaman (CR34) 2020 Tang, Liu, Xiao, Sebe (CR52) 2019; 331 Nunez, Cabido, Pantrigo, Montemayor, Velez (CR32) 2018; 76 Athira, Sruthi, Lijiya (CR22) 2019 Elakkiya (CR3) 2020 CR19 CR18 CR17 CR16 CR14 CR13 CR11 Lim, Tan, Lee, Tan (CR28) 2019; 78 Adithya, Rajesh (CR1) 2020 Rastgoo, Kiani, Escalera (CR12) 2021 CR51 Zhang, Huang, Tian (CR41) 2020; 131 CR50 Khan, Sohail, Zahoora, Qureshi (CR4) 2020 Wadhawan, Kumar (CR2) 2017 Wang, Garg (CR53) 2020; 14 Sherstinsky (CR5) 2020 Lui (CR45) 2012; 13 Verma, Choudhary (CR15) 2020; 79 Van Houdt, Mosquera, Napoles (CR40) 2020; 53 Aly, Aly (CR26) 2020; 8 CR27 Russakovsky, Deng, Su, Krause, Satheesh (CR38) 2015; 115 CR20 Li, He, Li, Shen (CR24) 2021 MM Kamruzzaman (6843_CR34) 2020 6843_CR39 6843_CR36 NK Hakim (6843_CR33) 2019; 19 LS Souza (6843_CR49) 2020 G Li (6843_CR35) 2019; 22 Y Ji (6843_CR23) 2021; 32 H Zhang (6843_CR41) 2020; 131 GA Rao (6843_CR21) 2018; 9 X Li (6843_CR24) 2021 A Khan (6843_CR4) 2020 L Wang (6843_CR53) 2020; 14 S Aly (6843_CR26) 2020; 8 6843_CR30 YM Lui (6843_CR45) 2012; 13 6843_CR31 MJ Cheok (6843_CR7) 2019; 10 O Russakovsky (6843_CR38) 2015; 115 PK Pisharady (6843_CR6) 2015; 141 6843_CR47 6843_CR48 H Tang (6843_CR52) 2019; 331 6843_CR8 G Van Houdt (6843_CR40) 2020; 53 M Kurmanji (6843_CR43) 2020; 8 6843_CR46 D Avola (6843_CR37) 2018; 21 R Rastgoo (6843_CR12) 2021 6843_CR44 6843_CR42 6843_CR9 6843_CR18 6843_CR19 6843_CR16 6843_CR17 6843_CR14 JC Nunez (6843_CR32) 2018; 76 A Sherstinsky (6843_CR5) 2020 V Adithya (6843_CR1) 2020 6843_CR13 A Wadhawan (6843_CR2) 2017 6843_CR11 B Verma (6843_CR15) 2020; 79 6843_CR50 6843_CR51 R Elakkiya (6843_CR3) 2020 A Jaramillo-Yánez (6843_CR10) 2020; 20 6843_CR27 KM Lim (6843_CR28) 2019; 78 M Al-Hammadi (6843_CR25) 2020; 8 PK Athira (6843_CR22) 2019 Z Erhu (6843_CR29) 2019; 8 6843_CR20 |
| References_xml | – year: 2021 ident: CR12 article-title: Sign language recognition: a deep survey publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.113794 – volume: 78 start-page: 19917 year: 2019 end-page: 19944 ident: CR28 article-title: Isolated sign language recognition using convolutional neural network hand modelling and hand energy image publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-019-7263-7 – year: 2021 ident: CR24 article-title: A combined loss-based multiscale fully convolutional network for high-resolution remote sensing image change detection publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2021.3098774 – ident: CR39 – ident: CR16 – year: 2020 ident: CR49 article-title: Enhanced Grassmann discriminant analysis with randomized time warping for motion recognition publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2019.107028 – ident: CR51 – volume: 32 start-page: 2676 issue: 6 year: 2021 end-page: 2690 ident: CR23 article-title: Jonathan: CASNet: a cross-attention siamese network for video salient object detection publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2020.3007534 – volume: 10 start-page: 131 year: 2019 end-page: 153 ident: CR7 article-title: A review of hand gesture and sign language recognition techniques publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-017-0705-5 – ident: CR8 – volume: 14 start-page: 503 issue: 1 year: 2020 end-page: 527 ident: CR53 article-title: Algorithm for multiple attribute decision-making with interactive archimedean norm operations under pythagorean fuzzy uncertainty publication-title: Int. J. Comput. Intell. Syst. doi: 10.2991/ijcis.d.201215.002 – ident: CR42 – volume: 79 start-page: 2213 year: 2020 end-page: 2237 ident: CR15 article-title: Grassmann manifold based dynamic hand gesture recognition using depth data publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-019-08266-w – volume: 141 start-page: 152 year: 2015 end-page: 165 ident: CR6 article-title: Recent methods and databases in vision based hand gesture recognition: a review publication-title: Comput. Vis. Image Underst. doi: 10.1016/j.cviu.2015.08.004 – ident: CR46 – ident: CR19 – year: 2020 ident: CR5 article-title: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network publication-title: Phys. D Nonlinear Phenom. doi: 10.1016/j.physd.2019.132306 – volume: 8 start-page: 192527 year: 2020 end-page: 192542 ident: CR25 article-title: Deep learning-based approach for sign language gesture recognition with efficient hand gesture representation publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3032140 – ident: CR50 – volume: 21 start-page: 234 issue: 1 year: 2018 end-page: 245 ident: CR37 article-title: Exploiting recurrent neural networks and leap motion controller for the recognition of sign language and semaphoric hand gestures publication-title: IEEE Trans. Multimed. doi: 10.1109/TMM.2018.2856094 – ident: CR11 – ident: CR9 – volume: 8 start-page: 1511 year: 2019 ident: CR29 article-title: Fusion of 2D CNN and 3D DenseNet for dynamic gesture recognition publication-title: Electronics doi: 10.3390/electronics8121511 – ident: CR36 – volume: 8 start-page: 177 issue: 2 year: 2020 end-page: 188 ident: CR43 article-title: Hand gesture recognition from RGB-D data using 2D and 3D convolutional neural networks: a comparative study publication-title: J. AI Data Min. doi: 10.22044/jadm.2019.7903.1929 – volume: 331 start-page: 424 year: 2019 end-page: 433 ident: CR52 article-title: Fast and robust dynamic hand gesture recognition via key frames extraction and feature fusion publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.11.038 – volume: 8 start-page: 83199 year: 2020 end-page: 83212 ident: CR26 article-title: DeepArSLR: a novel signer-independent deep learning framework for isolated arabic sign language gestures recognition publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2990699 – volume: 131 start-page: 128 year: 2020 end-page: 134 ident: CR41 article-title: Facial expression recognition based on deep convolution long short-term memory networks of double-channel weighted mixture publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2019.12.013 – ident: CR18 – ident: CR47 – volume: 9 start-page: 1929 issue: 4 year: 2018 end-page: 1939 ident: CR21 article-title: Selfie Video based continuous Indian sign language recognition system publication-title: Ain Shams Eng. J. doi: 10.1016/j.asej.2016.10.013 – ident: CR14 – ident: CR30 – volume: 76 start-page: 80 year: 2018 end-page: 94 ident: CR32 article-title: Convolutional neural networks and long short-term memory for skeleton based human activity and hand gesture recognition publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.10.033 – year: 2020 ident: CR1 article-title: Hand Gestures for emergency situations: a video dataset based on words from Indian sign language publication-title: Data Brief doi: 10.1016/j.dib.2020.106016 – volume: 115 start-page: 211 issue: 3 year: 2015 end-page: 252 ident: CR38 article-title: ImageNet large scale visual recognition challenge publication-title: Int. J. Comput. Vis. (IJCV) doi: 10.1007/s11263-015-0816-y – volume: 53 start-page: 5929 year: 2020 end-page: 5955 ident: CR40 article-title: A review on the long short-term memory model publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-020-09838-1 – year: 2020 ident: CR4 article-title: A survey of the recent architectures of deep convolutional neural networks publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-020-09825-6 – ident: CR27 – year: 2020 ident: CR3 article-title: Machine learning based sign language recognition: a review and its research frontier publication-title: J. Ambient Intell. Humaniz. Comput. doi: 10.1007/s12652-020-02396-y – volume: 20 start-page: 2467 year: 2020 ident: CR10 article-title: Real-time hand gesture recognition using surface electromyography and machine learning: a systematic literature review publication-title: Sensors doi: 10.3390/s20092467 – ident: CR44 – volume: 19 start-page: 5429 issue: 24 year: 2019 ident: CR33 article-title: Dynamic hand gesture recognition using 3DCNN and LSTM with FSM context-aware model publication-title: Sensors doi: 10.3390/s19245429 – ident: CR48 – year: 2019 ident: CR22 article-title: A signer independent sign language recognition with co-articulation elimination from live videos: an Indian scenario publication-title: J. King Saud Univ. Comput. Inf. Sci. doi: 10.1016/j.jksuci.2019.05.002 – year: 2017 ident: CR2 article-title: Sign language recognition systems: a decade systematic literature review publication-title: Arch. Comput. Methods Eng. doi: 10.1007/s11831-019-09384-2 – ident: CR17 – ident: CR31 – ident: CR13 – volume: 22 start-page: 2719 year: 2019 end-page: 2729 ident: CR35 article-title: Hand gesture recognition based on convolution neural network publication-title: Cluster Comput. doi: 10.1007/s10586-017-1435-x – volume: 13 start-page: 3297 issue: 1 year: 2012 end-page: 3321 ident: CR45 article-title: Human gesture recognition on product manifolds publication-title: J. Mach. Learn. Res. – year: 2020 ident: CR34 article-title: Arabic sign language recognition and generating arabic speech using convolutional neural network publication-title: Wirel. Commun. Mobile Comput. doi: 10.1155/2020/3685614 – ident: CR20 – year: 2020 ident: 6843_CR3 publication-title: J. Ambient Intell. Humaniz. Comput. doi: 10.1007/s12652-020-02396-y – volume: 22 start-page: 2719 year: 2019 ident: 6843_CR35 publication-title: Cluster Comput. doi: 10.1007/s10586-017-1435-x – volume: 13 start-page: 3297 issue: 1 year: 2012 ident: 6843_CR45 publication-title: J. Mach. Learn. Res. – ident: 6843_CR31 doi: 10.1109/3DV.2019.00061 – volume: 14 start-page: 503 issue: 1 year: 2020 ident: 6843_CR53 publication-title: Int. J. Comput. Intell. Syst. doi: 10.2991/ijcis.d.201215.002 – ident: 6843_CR51 doi: 10.30693/SMJ.2020.9.1.23 – ident: 6843_CR19 doi: 10.1109/ICAEE47123.2019.9014683 – ident: 6843_CR16 doi: 10.1109/INTERCON50315.2020.9220243 – volume: 115 start-page: 211 issue: 3 year: 2015 ident: 6843_CR38 publication-title: Int. J. Comput. Vis. (IJCV) doi: 10.1007/s11263-015-0816-y – ident: 6843_CR42 doi: 10.1109/CVPR.2007.383137 – ident: 6843_CR48 – volume: 32 start-page: 2676 issue: 6 year: 2021 ident: 6843_CR23 publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2020.3007534 – volume: 8 start-page: 192527 year: 2020 ident: 6843_CR25 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3032140 – volume: 331 start-page: 424 year: 2019 ident: 6843_CR52 publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.11.038 – volume: 9 start-page: 1929 issue: 4 year: 2018 ident: 6843_CR21 publication-title: Ain Shams Eng. J. doi: 10.1016/j.asej.2016.10.013 – year: 2020 ident: 6843_CR49 publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2019.107028 – ident: 6843_CR18 doi: 10.1109/ICCAIS.2018.8570336 – ident: 6843_CR9 – volume: 8 start-page: 177 issue: 2 year: 2020 ident: 6843_CR43 publication-title: J. AI Data Min. doi: 10.22044/jadm.2019.7903.1929 – year: 2021 ident: 6843_CR24 publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2021.3098774 – ident: 6843_CR30 doi: 10.1145/3278312.3278314 – year: 2020 ident: 6843_CR34 publication-title: Wirel. Commun. Mobile Comput. doi: 10.1155/2020/3685614 – year: 2020 ident: 6843_CR5 publication-title: Phys. D Nonlinear Phenom. doi: 10.1016/j.physd.2019.132306 – ident: 6843_CR8 doi: 10.1109/IHTC.2014.7147529 – year: 2021 ident: 6843_CR12 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.113794 – volume: 20 start-page: 2467 year: 2020 ident: 6843_CR10 publication-title: Sensors doi: 10.3390/s20092467 – ident: 6843_CR11 – volume: 10 start-page: 131 year: 2019 ident: 6843_CR7 publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-017-0705-5 – ident: 6843_CR47 doi: 10.1109/CVPRW.2014.107 – ident: 6843_CR50 doi: 10.5244/C.22.109 – ident: 6843_CR17 doi: 10.1109/ICCC47050.2019.9064200 – volume: 21 start-page: 234 issue: 1 year: 2018 ident: 6843_CR37 publication-title: IEEE Trans. Multimed. doi: 10.1109/TMM.2018.2856094 – volume: 131 start-page: 128 year: 2020 ident: 6843_CR41 publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2019.12.013 – volume: 76 start-page: 80 year: 2018 ident: 6843_CR32 publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.10.033 – volume: 53 start-page: 5929 year: 2020 ident: 6843_CR40 publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-020-09838-1 – ident: 6843_CR14 doi: 10.1109/LA-CCI47412.2019.9037037 – year: 2019 ident: 6843_CR22 publication-title: J. King Saud Univ. Comput. Inf. Sci. doi: 10.1016/j.jksuci.2019.05.002 – ident: 6843_CR44 doi: 10.1109/DICTA.2016.7797030 – volume: 79 start-page: 2213 year: 2020 ident: 6843_CR15 publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-019-08266-w – volume: 19 start-page: 5429 issue: 24 year: 2019 ident: 6843_CR33 publication-title: Sensors doi: 10.3390/s19245429 – year: 2020 ident: 6843_CR4 publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-020-09825-6 – ident: 6843_CR46 doi: 10.1109/WACV.2013.6475006 – volume: 141 start-page: 152 year: 2015 ident: 6843_CR6 publication-title: Comput. Vis. Image Underst. doi: 10.1016/j.cviu.2015.08.004 – ident: 6843_CR13 doi: 10.1109/EITCE47263.2019.9094915 – year: 2020 ident: 6843_CR1 publication-title: Data Brief doi: 10.1016/j.dib.2020.106016 – ident: 6843_CR20 doi: 10.1109/INFOSCI.2016.7845312 – volume: 8 start-page: 83199 year: 2020 ident: 6843_CR26 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2990699 – ident: 6843_CR36 doi: 10.1109/STI50764.2020.9350484 – year: 2017 ident: 6843_CR2 publication-title: Arch. Comput. Methods Eng. doi: 10.1007/s11831-019-09384-2 – volume: 8 start-page: 1511 year: 2019 ident: 6843_CR29 publication-title: Electronics doi: 10.3390/electronics8121511 – ident: 6843_CR39 – ident: 6843_CR27 doi: 10.1109/ICKII46306.2019.9042600 – volume: 78 start-page: 19917 year: 2019 ident: 6843_CR28 publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-019-7263-7 |
| SSID | ssib048395113 ssj0001916267 ssj0061873 |
| Score | 2.3769603 |
| Snippet | The rapid spread of the novel corona virus disease (COVID-19) has disrupted the traditional clinical services all over the world. Hospitals and healthcare... |
| SourceID | unpaywall pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1349 |
| SubjectTerms | Artificial neural networks Computer Engineering and Computer Science Coronaviruses COVID-19 Datasets Engineering Health care Humanities and Social Sciences multidisciplinary Norms Patients Recognition Research Article-Computer Engineering and Computer Science Science Sign language Viral diseases Viruses |
| Title | Applying Hybrid Deep Neural Network for the Recognition of Sign Language Words Used by the Deaf COVID-19 Patients |
| URI | https://link.springer.com/article/10.1007/s13369-022-06843-0 https://www.ncbi.nlm.nih.gov/pubmed/35492959 https://www.proquest.com/docview/2774560130 https://pubmed.ncbi.nlm.nih.gov/PMC9030689 https://link.springer.com/content/pdf/10.1007/s13369-022-06843-0.pdf |
| UnpaywallVersion | publishedVersion |
| Volume | 48 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 2191-4281 dateEnd: 20241105 omitProxy: true ssIdentifier: ssj0001916267 issn: 2191-4281 databaseCode: ABDBF dateStart: 20041001 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 2191-4281 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0061873 issn: 2193-567X databaseCode: GX1 dateStart: 20020101 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVLSH databaseName: SpringerLink Journals customDbUrl: mediaType: online eissn: 2191-4281 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001916267 issn: 2191-4281 databaseCode: AFBBN dateStart: 20110101 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVAVX databaseName: SpringerLINK - Czech Republic Consortium customDbUrl: eissn: 2191-4281 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0061873 issn: 2193-567X databaseCode: AGYKE dateStart: 20110101 isFulltext: true titleUrlDefault: http://link.springer.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink Journals (ICM) customDbUrl: eissn: 2191-4281 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0061873 issn: 2193-567X databaseCode: U2A dateStart: 20110101 isFulltext: true titleUrlDefault: http://www.springerlink.com/journals/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3Nb9MwFLe29gAcNhAwNkblAzfmLq7jOD52K6UgFiqgUE5R4g-YqNJCU6Hy1-85X6UMTSAuUSQ_O3nOs_178Xs_I_SUp14oRSpI6jHlSLU1kVYIoh0a1opzU-TCXETBaOK_mvLpDhrUuTBFtHu9JVnmNDiWpiw_XWh7ukl8YyyQxEWie0HoM-J1oXgXtQMOiLyF2pNo3P_kzpUDd4QAwqblPSM8ENMqd-bPDW2vT9dA5_XYyWYD9Q66tcoWyfpHMpv9skYN95GptStDU752V3naVT9_I378X_Xvor0KxOJ-aXX30I7J7qNvDtC6pCk8Wrs0MDwwZoEd-QeIRmW0OQaIjAFy4rd13NI8w3OL311-zvDr6tcp_ggO8RJPlkbjdF2ID0xi8fmbDy8HhEo8Lrlglw_QZPj8_fmIVAc6EOULPydhElDNmfYZs47J3ljPBtbjpicsVTSRnCUeVyo1vrBKBVqyNLQwpfhSg-sj2EPUyuaZeYSwkUGiGKVGUe3DhJ36WsDsxbXDS4n1DhGtP2OsKrZzd-jGLN7wNLtOjKET46ITY6jzrKmzKLk-bpQ-rq0jrsb9Mu4BmnY-LoPig9JQmqaYI8OTXB4isWVCjYBj-t4uyS6_FIzf0nl2IdQ8qW1j88ib3vCkMci_UOjo38Qfo9s9AHxlBPsxauXfV-YJALQ87aB2f3h2FnXQ7osphWs0vuhUY_IKzZ4x6A |
| linkProvider | Unpaywall |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3Pb9MwFLZGdwAO_BAwBgP5wI25i2s7jo_TylQQlAkolFMU_4KJKi00FSp_Pc9xklKGJhC3SH52YufZ-V78vc8IPRE6yZTUkuiEmSCqbYnyUhIb0LA1Qrg6F-bVOB1N-IupmO6gYZsLU7Pd2y3JmNMQVJrK6mhh_dEm8Y2xVJHARE_SjDOS9KH4CtpNBSDyHtqdjM-OP4Zz5SAcIYCwabxmRKRy2uTO_Lmh7e_TBdB5kTvZbaBeR1dX5aJYfy9ms1--Uac3kWt7F6kpX_qrSvfNj9-EH_-3-7fQjQbE4uPodbfRjivvoK8B0IakKTxahzQwPHRugYP4B5iOI9scA0TGADnxm5a3NC_x3OO3559K_LL5dYo_QEC8xJOls1iva_OhKzw-ef3--ZBQhc-iFuzyLpqcPnt3MiLNgQ7EcMkrkhUptYJZzpgPSvbOJz71iXAD6amhhRKsSIQx2nHpjUmtYjrzsKRwZSH0kewe6pXz0t1H2Km0MIxSZ6jlsGBrbiWsXsIGvFT4ZB_R9jXmplE7D4duzPKNTnMYxBwGMa8HMYc6T7s6i6j1can1QesdeTPvl_kA0HSIcRkU70VH6ZpiQQxPCbWP5JYLdQZB6Xu7pDz_XCt-qxDZZVDzsPWNzS0ve8LDziH_okMP_s38Ibo2AMAXGewHqFd9W7lHANAq_biZfz8BETcubw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Applying+Hybrid+Deep+Neural+Network+for+the+Recognition+of+Sign+Language+Words+Used+by+the+Deaf+COVID-19+Patients&rft.jtitle=Arabian+journal+for+science+and+engineering+%282011%29&rft.au=Venugopalan%2C+Adithya&rft.au=Reghunadhan%2C+Rajesh&rft.date=2023-02-01&rft.issn=2193-567X&rft.eissn=2191-4281&rft.volume=48&rft.issue=2&rft.spage=1349&rft.epage=1362&rft_id=info:doi/10.1007%2Fs13369-022-06843-0&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s13369_022_06843_0 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2193-567X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2193-567X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2193-567X&client=summon |