A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
Falling is one of the causes of accidental death of elderly people over 65 years old in Taiwan. If the fall incidents are not detected in a timely manner, it could lead to serious injury or even death of those who fell. General fall detection approaches require the users to wear sensors, which could...
Gespeichert in:
| Veröffentlicht in: | Applied sciences Jg. 11; H. 1; S. 329 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Basel
MDPI AG
01.01.2021
|
| Schlagworte: | |
| ISSN: | 2076-3417, 2076-3417 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Falling is one of the causes of accidental death of elderly people over 65 years old in Taiwan. If the fall incidents are not detected in a timely manner, it could lead to serious injury or even death of those who fell. General fall detection approaches require the users to wear sensors, which could be cumbersome for the users to put on, and misalignment of sensors could lead to erroneous readings. In this paper, we propose using computer vision and applied machine-learning algorithms to detect fall without any sensors. We applied OpenPose real-time multi-person 2D pose estimation to detect movement of a subject using two datasets of 570 × 30 frames recorded in five different rooms from eight different viewing angles. The system retrieves the locations of 25 joint points of the human body and detects human movement through detecting the joint point location changes. The system is able to effectively identify the joints of the human body as well as filtering ambient environmental noise for an improved accuracy. The use of joint points instead of images improves the training time effectively as well as eliminating the effects of traditional image-based approaches such as blurriness, light, and shadows. This paper uses single-view images to reduce equipment costs. We experimented with time series recurrent neural network, long- and short-term memory, and gated recurrent unit models to learn the changes in human joint points in continuous time. The experimental results show that the fall detection accuracy of the proposed model is 98.2%, which outperforms the baseline 88.9% with 9.3% improvement. |
|---|---|
| AbstractList | Falling is one of the causes of accidental death of elderly people over 65 years old in Taiwan. If the fall incidents are not detected in a timely manner, it could lead to serious injury or even death of those who fell. General fall detection approaches require the users to wear sensors, which could be cumbersome for the users to put on, and misalignment of sensors could lead to erroneous readings. In this paper, we propose using computer vision and applied machine-learning algorithms to detect fall without any sensors. We applied OpenPose real-time multi-person 2D pose estimation to detect movement of a subject using two datasets of 570 × 30 frames recorded in five different rooms from eight different viewing angles. The system retrieves the locations of 25 joint points of the human body and detects human movement through detecting the joint point location changes. The system is able to effectively identify the joints of the human body as well as filtering ambient environmental noise for an improved accuracy. The use of joint points instead of images improves the training time effectively as well as eliminating the effects of traditional image-based approaches such as blurriness, light, and shadows. This paper uses single-view images to reduce equipment costs. We experimented with time series recurrent neural network, long- and short-term memory, and gated recurrent unit models to learn the changes in human joint points in continuous time. The experimental results show that the fall detection accuracy of the proposed model is 98.2%, which outperforms the baseline 88.9% with 9.3% improvement. |
| Author | Lin, Chuan-Bi Dong, Ziqian Kuan, Wei-Kai Huang, Yung-Fa |
| Author_xml | – sequence: 1 givenname: Chuan-Bi orcidid: 0000-0001-9090-6583 surname: Lin fullname: Lin, Chuan-Bi – sequence: 2 givenname: Ziqian orcidid: 0000-0003-3937-1311 surname: Dong fullname: Dong, Ziqian – sequence: 3 givenname: Wei-Kai surname: Kuan fullname: Kuan, Wei-Kai – sequence: 4 givenname: Yung-Fa surname: Huang fullname: Huang, Yung-Fa |
| BookMark | eNptUMtOwzAQtBBIvHriByxxRKV2Nk3iI68CUqsiCmdrY29QSoiDnQrx97gUoQqxl13Nzs6s5pDttq4lxk6kOAdQYoRdJ6WQAhK1ww4SkWdDSGW-uzXvs0EISxFLSSikOGCzCz7x-EYfzr_yynk-wabh19ST6WvX8ksMZHkc5h21Dy4QX7xSQ31EsLV8uniajW4fn_nMWWrCMdursAk0-OlH7Hly83R1N5zOb--vLqZDk4Loh6kUNrdSiQrGpoBSFEJUtsTUgFVZbjPMJUFWJQqgJImpKmxmCUyZWJtmKRyx-42udbjUna_f0H9qh7X-Bpx_0ej72jSkE5KgVEFGZln0VaVNSihIIJRV3GDUOt1odd69ryj0eulWvo3v62Q8VqlMirGIrLMNy3gXgqfq11UKvY5fb8Uf2fIP29Q9rgPtPdbNvzdfwgyGug |
| CitedBy_id | crossref_primary_10_3390_electronics13234733 crossref_primary_10_1371_journal_pone_0325253 crossref_primary_10_1109_ACCESS_2023_3299323 crossref_primary_10_3390_app12063087 crossref_primary_10_1155_2022_7835241 crossref_primary_10_3390_electronics12163513 crossref_primary_10_3390_electronics14132636 crossref_primary_10_3390_info13080363 crossref_primary_10_1007_s00500_023_09295_2 crossref_primary_10_1016_j_compbiomed_2022_105626 crossref_primary_10_1016_j_engappai_2024_108592 crossref_primary_10_3390_app12052678 crossref_primary_10_3390_s22145449 crossref_primary_10_3390_s23187896 crossref_primary_10_3390_su14105872 crossref_primary_10_1109_ACCESS_2023_3289402 crossref_primary_10_3390_s22124544 crossref_primary_10_1016_j_heliyon_2024_e39977 crossref_primary_10_1109_JSEN_2025_3593126 crossref_primary_10_3390_app122111031 crossref_primary_10_1109_JSEN_2024_3404031 crossref_primary_10_3390_s24237448 crossref_primary_10_3390_app12199671 crossref_primary_10_3390_bioengineering10080891 crossref_primary_10_3390_s21248378 crossref_primary_10_1155_2022_5827056 crossref_primary_10_1007_s11423_025_10452_7 crossref_primary_10_3390_ijerph192113762 crossref_primary_10_1038_s41598_025_98433_x crossref_primary_10_3390_electronics14142837 crossref_primary_10_1007_s11554_025_01687_x crossref_primary_10_1109_TCSVT_2023_3303258 crossref_primary_10_3390_app15010409 crossref_primary_10_1016_j_robot_2024_104755 crossref_primary_10_1109_ACCESS_2024_3443618 crossref_primary_10_1109_ACCESS_2023_3307138 crossref_primary_10_3390_ijerph19127139 crossref_primary_10_3390_s24248051 crossref_primary_10_1016_j_eswa_2022_118681 crossref_primary_10_3390_s22113991 crossref_primary_10_1016_j_knosys_2025_113038 crossref_primary_10_3390_s21196485 crossref_primary_10_1049_ipr2_12667 crossref_primary_10_1016_j_engappai_2024_109809 crossref_primary_10_1080_19393555_2025_2517597 crossref_primary_10_3390_app142411970 crossref_primary_10_1109_ACCESS_2024_3401651 crossref_primary_10_3390_electronics12010029 crossref_primary_10_1038_s41598_024_71545_6 crossref_primary_10_1155_2022_4363442 crossref_primary_10_1109_ACCESS_2022_3203174 crossref_primary_10_1007_s13369_022_06684_x crossref_primary_10_1016_j_knosys_2023_110992 crossref_primary_10_3390_electronics13081541 crossref_primary_10_1051_matecconf_202235503010 crossref_primary_10_1016_j_procs_2021_12_305 crossref_primary_10_1080_20476965_2024_2395574 crossref_primary_10_1016_j_gaitpost_2025_01_001 |
| Cites_doi | 10.1109/ICSIIT.2017.49 10.1109/AIAM48774.2019.00113 10.1109/IIH-MSP.2013.21 10.1109/INCIT.2019.8912080 10.1109/YAC.2016.7804912 10.21437/Interspeech.2010-343 10.1159/000445831 10.1109/TASLP.2016.2623559 10.3115/v1/D14-1179 10.1109/ICCMC.2019.8819830 10.1109/BigMM.2016.22 10.1109/ICCE46568.2020.9043000 10.1109/IWAIT.2018.8369778 10.1109/CVPR.2016.533 10.1016/j.cmpb.2014.09.005 10.1007/978-3-030-61746-2_10 10.1109/JBHI.2014.2312180 10.1109/ACCESS.2018.2861331 10.1109/CVPR.2017.143 10.1109/ACCESS.2020.2969453 10.1109/JSEN.2019.2898891 10.1109/ACCESS.2020.2967845 10.1109/ICASSP.2018.8462544 10.1162/neco.1997.9.8.1735 10.1109/JBHI.2019.2907498 10.1109/ACCESS.2018.2881237 10.23919/MVA.2017.7986795 10.1109/ACCESS.2019.2947518 10.1109/EMBC.2016.7591833 10.1109/MeMeA49120.2020.9137110 10.1109/WACV.2018.00135 |
| ContentType | Journal Article |
| Copyright | 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION ABUWG AFKRA AZQEC BENPR CCPQU DWQXO PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI DOA |
| DOI | 10.3390/app11010329 |
| DatabaseName | CrossRef ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest Central ProQuest One Academic Middle East (New) ProQuest One Academic UKI Edition ProQuest Central Essentials ProQuest Central Korea ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Sciences (General) |
| EISSN | 2076-3417 |
| ExternalDocumentID | oai_doaj_org_article_2e13998ec1664109bd2b38e0a3bf139a 10_3390_app11010329 |
| GroupedDBID | .4S 2XV 5VS 7XC 8CJ 8FE 8FG 8FH AADQD AAFWJ AAYXX ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS APEBS ARCSS BCNDV BENPR CCPQU CITATION CZ9 D1I D1J D1K GROUPED_DOAJ IAO IGS ITC K6- K6V KC. KQ8 L6V LK5 LK8 M7R MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PROAC TUS ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI |
| ID | FETCH-LOGICAL-c430t-410d7d190f35c83b0800fdba4c3d967d6a71e36f2933be1a498d6de3cb2dd4643 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 75 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000605819700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2076-3417 |
| IngestDate | Tue Oct 14 19:07:09 EDT 2025 Sun Nov 09 07:46:30 EST 2025 Sat Nov 29 07:19:46 EST 2025 Tue Nov 18 21:31:38 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c430t-410d7d190f35c83b0800fdba4c3d967d6a71e36f2933be1a498d6de3cb2dd4643 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-9090-6583 0000-0003-3937-1311 |
| OpenAccessLink | https://doaj.org/article/2e13998ec1664109bd2b38e0a3bf139a |
| PQID | 2559412850 |
| PQPubID | 2032433 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_2e13998ec1664109bd2b38e0a3bf139a proquest_journals_2559412850 crossref_primary_10_3390_app11010329 crossref_citationtrail_10_3390_app11010329 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-01-01 |
| PublicationDateYYYYMMDD | 2021-01-01 |
| PublicationDate_xml | – month: 01 year: 2021 text: 2021-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Applied sciences |
| PublicationYear | 2021 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Taramasco (ref_14) 2018; 6 ref_36 ref_13 ref_35 ref_34 Bahureksa (ref_6) 2017; 63 ref_33 ref_32 Clemente (ref_15) 2020; 24 Wu (ref_27) 2017; 25 Tsai (ref_21) 2019; 7 ref_18 ref_17 Hochreiter (ref_31) 1997; 9 ref_39 ref_16 ref_38 ref_37 Nho (ref_11) 2020; 8 Lotfi (ref_19) 2018; 6 Hussain (ref_12) 2019; 19 ref_25 ref_24 ref_22 ref_20 ref_1 ref_3 ref_2 ref_29 ref_28 Jun (ref_30) 2020; 8 Stone (ref_10) 2015; 19 ref_26 ref_9 ref_8 ref_5 ref_4 ref_7 Kwolek (ref_23) 2014; 117 |
| References_xml | – ident: ref_20 doi: 10.1109/ICSIIT.2017.49 – ident: ref_33 doi: 10.1109/AIAM48774.2019.00113 – ident: ref_17 doi: 10.1109/IIH-MSP.2013.21 – ident: ref_28 doi: 10.1109/INCIT.2019.8912080 – ident: ref_35 doi: 10.1109/YAC.2016.7804912 – ident: ref_29 doi: 10.21437/Interspeech.2010-343 – ident: ref_5 – ident: ref_3 – volume: 63 start-page: 67 year: 2017 ident: ref_6 article-title: The Impact of Mild Cognitive Impairment on Gait and Balance: A Systematic Review and Meta-Analysis of Studies Using Instrumented Assessment publication-title: Gerontology doi: 10.1159/000445831 – volume: 25 start-page: 102 year: 2017 ident: ref_27 article-title: A Reverberation-Time-Aware Approach to Speech Dereverberation Based on Deep Neural Networks publication-title: IEEE/Acm Trans. Audiospeechlang. Process. doi: 10.1109/TASLP.2016.2623559 – ident: ref_37 doi: 10.3115/v1/D14-1179 – ident: ref_36 doi: 10.1109/ICCMC.2019.8819830 – ident: ref_18 doi: 10.1109/BigMM.2016.22 – ident: ref_16 doi: 10.1109/ICCE46568.2020.9043000 – ident: ref_39 – ident: ref_9 doi: 10.1109/IWAIT.2018.8369778 – ident: ref_22 doi: 10.1109/CVPR.2016.533 – volume: 117 start-page: 489 year: 2014 ident: ref_23 article-title: Human Fall Detection on Embedded Platform Using Depth Maps and Wireless Accelerometer publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2014.09.005 – ident: ref_8 doi: 10.1007/978-3-030-61746-2_10 – ident: ref_1 – volume: 19 start-page: 290 year: 2015 ident: ref_10 article-title: Fall Detection in Homes of Older Adults Using the Microsoft Kinect publication-title: IEEE J. Biomed. Health Inf. doi: 10.1109/JBHI.2014.2312180 – volume: 6 start-page: 43563 year: 2018 ident: ref_14 article-title: A Novel Monitoring System for Fall Detection in Older People publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2861331 – ident: ref_25 doi: 10.1109/CVPR.2017.143 – volume: 8 start-page: 40389 year: 2020 ident: ref_11 article-title: Cluster-Analysis-Based User-Adaptive Fall Detection Using Fusion of Heart Rate Sensor and Accelerometer in a Wearable Device publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2969453 – volume: 19 start-page: 4528 year: 2019 ident: ref_12 article-title: Activity-Aware Fall Detection and Recognition Based on Wearable Sensors publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2019.2898891 – volume: 8 start-page: 19196 year: 2020 ident: ref_30 article-title: Feature Extraction Using an RNN Autoencoder for Skeleton-Based Abnormal Gait Recognition publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2967845 – ident: ref_32 doi: 10.1109/ICASSP.2018.8462544 – volume: 9 start-page: 1735 year: 1997 ident: ref_31 article-title: Long Short-Term Memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – ident: ref_4 – ident: ref_2 – volume: 24 start-page: 524 year: 2020 ident: ref_15 article-title: Smart Seismic Sensing for Indoor Fall Detection, Location, and Notification publication-title: IEEE J. Biomed. Health Inf. doi: 10.1109/JBHI.2019.2907498 – volume: 6 start-page: 70272 year: 2018 ident: ref_19 article-title: Supporting Independent Living for Older Adults; Employing A Visual Based Fall Detection Through Analysing the Motion and Shape of the Human Body publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2881237 – ident: ref_24 doi: 10.23919/MVA.2017.7986795 – volume: 7 start-page: 153049 year: 2019 ident: ref_21 article-title: Implementation of Fall Detection System Based on 3D Skeleton for Deep Learning Technique publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2947518 – ident: ref_13 – ident: ref_38 – ident: ref_26 doi: 10.1109/EMBC.2016.7591833 – ident: ref_7 doi: 10.1109/MeMeA49120.2020.9137110 – ident: ref_34 doi: 10.1109/WACV.2018.00135 |
| SSID | ssj0000913810 |
| Score | 2.5039752 |
| Snippet | Falling is one of the causes of accidental death of elderly people over 65 years old in Taiwan. If the fall incidents are not detected in a timely manner, it... |
| SourceID | doaj proquest crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 329 |
| SubjectTerms | 2D pose estimation Cameras Datasets fall detection gated recurrent units long short-term memory Neural networks openpose recurrent neural network Sensors |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Nb9NAEB1By4EegLRFhAa0hxxaJCu2196sT6iBhB7aKEobKTdrv1whLCeN0_5-ZpxN2grEhZvl3cPaszPzZnb2DUCXrgYIjI8DYxwPkkzLQMdUBKBlGipjuOPNReHL_ngs5_Ns4hNutS-r3NrExlDbhaEceY-gb4LGNA2_Lu8C6hpFp6u-hcZL2CemMtzn-4PheDLdZVmI9VJG4eZiHsf4ns6F0eMRjVz2zBU1jP1_GOTGy4ze_u_63sEbjy_Z-WZDtOCFqw7h4Anr4CG0vD7X7NSTTp8dwdU5G23rtBgCWTZSZcm-u3VTqlWxAXo7y_CBKlAmi9qx61_osRA5MlVZdnl9c9X7MZ0x6q1W1scwGw1vvl0EvtVCYBIeroMkCm3fIjgoeGok14QjC6tVYrjNRN8K1Y8cFwWCA65dpJJMWmEdNzq2NkFU8x72qkXlPgCzMjVEky8sQq3Ccp3ExqQp2gkljIhkG75s_3puPA85tcMoc4xHSET5ExG1obubvNzQb_x92oDEt5tCnNnNi8XqNvcqmMcO0W4mnYmEwO_NtI01ly5UXBc4otrQ2Uo294pc549i_fjv4RN4HVO5S5Od6cDeenXvPsEr87D-Wa8--335G66c6fQ priority: 102 providerName: ProQuest |
| Title | A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models |
| URI | https://www.proquest.com/docview/2559412850 https://doaj.org/article/2e13998ec1664109bd2b38e0a3bf139a |
| Volume | 11 |
| WOSCitedRecordID | wos000605819700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8NAEB5EPehBrA-sj7IHDyoEk2yy2T1atSq0JWgLegrZR0AsUUz19zu7SUtFwYu3PBYSJrP7fbOZ-Qbg2JYGMIyPPaUM9SIhuSdDmwQgeeznSlFDXaFwPxkO-eOjSBdafdmcsFoeuDbceWiQowhuVMBYFPhC6lBSbvycygLvOGrkJ2IhmHJrsAisdFVdkEcxrrf_gxHprHyc-AZBTqn_x0Ls0KW3CRsNLSQX9eu0YMmUW7C-IBa4Ba1mGlbkpNGKPt2GwQXpzdKrCPJP0ssnE3Jlpi7DqiRdBClN8MAmjqSvlSEPLwg0SPhIXmrSfxgNzm_ux8S2RJtUOzDuXY8ub72mQ4KnIupPPTSITjRiekFjxam09K_QMo8U1YIlmuVJYCgrENOpNEEeCa6ZNlTJUOsIycguLJevpdkDonmsrLo908iQCk1lFCoVxzi9c6ZYwNtwNjNaphr5cNvFYpJhGGEtnC1YuA3H88FvtWrG78O61vrzIVbq2l1AB8gaB8j-coA2HM6-XdbMvyqzgVKE0Bv7-__xjANYC20ui9t6OYTl6fuHOYJV9Tl9rt47sNK9Hqb3HeeCeJbeDdKnL46X3ZA |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3fb9MwED6NDgl4ADZAFDbww5AAKVoSO67zgKaNUVatrSrWSdtT8K9ME1U6mrKJf4q_kbs0KUMg3vbAWxRbkRx_vu9s390HsEWpARL3x4G1ngciNSowMQUBGJWE2lrueZUo3O8Mh-rkJB2twI8mF4bCKhubWBlqN7V0Rr5Nrq9AY5qEOxdfA1KNotvVRkJjAYtD__0Kt2zlu94-zu-rOO5-GL8_CGpVgcAKHs4DEYWu45AHc55YxQ25TLkzWljuUtlxUnciz2WOPMiNj7RIlZPOc2ti5wQSOH73FqwKAnsLVke9weh0eapDVTZVFC4SATlPQ7qHRoalsnXpb9RXKQT8QQAVq3Uf_G__4yHcr_1ntrsA_Bqs-GId7l2rqrgOa7W9Ktnruqj2m0cw2GXdJg6NoaPOunoyYft-XoWiFWwP2dwxfKAIm9G09OzoCzIyesZMF471j8aD7Y-fjhlpx03Kx3B8I4N8Aq1iWvinwJxKLMkASIeuZO64EbG1SYJ2UEsrI9WGt80sZ7aus05yH5MM91sEiewaJNqwtex8sSgv8vduewSXZReqCV69mM7OstrEZLFHbz5V3kZS4nhT42LDlQ81Nzm26DZsNEjKakNVZr9g9OzfzS_hzsF40M_6veHhc7gbU2hPdRK1Aa357JvfhNv2cn5ezl7Ua4LB55uG3U8iO0c- |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1db9MwFL0aHULjAdgAUTbAD0MCpKhJnLjOA0LbSqBaW0Vsk8ZT8FcmRJWOpjDx1_h13Jsm3RCItz3wFsVWJMfH9xzb9wNgl0IDBO6PPWMc96JES0-H5ASgZewrY7jjdaDwqD-ZyNPTJFuDn20sDLlVtjaxNtR2ZuiMvEfSN0JjGvu9onGLyAbpm_OvHlWQopvWtpzGEiKH7scFbt-q18MBzvXzMEzfHh-895oKA56JuL_wosC3fYucWPDYSK5JPhVWq8hwm4i-FaofOC4K5ESuXaCiRFphHTc6tDZCMsfv3oB1lORR2IH1bDjOPq5OeCjjpgz8ZVAg54lPd9LItpTCLvmNButqAX-QQc1w6d3_-d_cgzuNrmZ7y4WwCWuu3ILbV7ItbsFmY8cq9qJJtv3yPoz3WNr6pzEU8CxV0ykbuEXtolayfWR5y_CBPG-yWeXY0RdkalTMTJWWjY6Ox713H04Y1ZSbVg_g5FoG-RA65ax0j4BZGRsqDyAsSszCch2FxsQx2kcljAhkF161M56bJv86lQGZ5rgPI3jkV-DRhd1V5_Nl2pG_d9sn6Ky6UK7w-sVsfpY3picPHar8RDoTCIHjTbQNNZfOV1wX2KK6sNOiKm8MWJVfQurxv5ufwS3EWj4aTg63YSMkj5_6gGoHOov5N_cEbprvi8_V_GmzPBh8um7U_QJIi0_- |
| 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=A+Framework+for+Fall+Detection+Based+on+OpenPose+Skeleton+and+LSTM%2FGRU+Models&rft.jtitle=Applied+sciences&rft.au=Chuan-Bi+Lin&rft.au=Ziqian+Dong&rft.au=Wei-Kai+Kuan&rft.au=Yung-Fa+Huang&rft.date=2021-01-01&rft.pub=MDPI+AG&rft.eissn=2076-3417&rft.volume=11&rft.issue=1&rft.spage=329&rft_id=info:doi/10.3390%2Fapp11010329&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_2e13998ec1664109bd2b38e0a3bf139a |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2076-3417&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2076-3417&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2076-3417&client=summon |