Remote Evaluation of Parkinson's Disease Using a Conventional Webcam and Artificial Intelligence
Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam. Methods: We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded pe...
Uloženo v:
| Vydáno v: | Frontiers in neurology Ročník 12; s. 742654 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Switzerland
Frontiers Media S.A
23.12.2021
|
| Témata: | |
| ISSN: | 1664-2295, 1664-2295 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Objective:
This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam.
Methods:
We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset.
Results:
We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods.
Conclusions:
We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management. |
|---|---|
| AbstractList | Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam. Methods: We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset. Results: We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods. Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management. This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam. We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset. We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods. We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management. Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam. Methods: We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset. Results: We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods. Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management. Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam. Methods: We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset. Results: We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods. Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management.Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam. Methods: We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset. Results: We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods. Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management. |
| Author | Vera-Olmos, Javier Mestre, Tiago A. Malpica, Norberto Domínguez, Sergio Antonini, Angelo Sánchez-Ferro, Álvaro Monje, Mariana H. G. |
| AuthorAffiliation | 2 Department of Neurology, Northwestern University Feinberg School of Medicine , Chicago, IL , United States 4 Parkinson and Movement Disorders Unit, Department of Neurosciences (DNS), Padova University , Padova , Italy 1 HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales , Madrid , Spain 5 Division of Neurology, Department of Medicine, Parkinson's Disease and Movement Disorders Centre, The Ottawa Hospital Research Institute, The University of Ottawa Brain Research Institute , Ottawa, ON , Canada 6 Movement Disorders Unit, Neurology Department, Hospital Universitario 12 de Octubre , Madrid , Spain 3 LAIMBIO, Laboratorio de Análisis de Imagen Médica y Biometría, Universidad Rey Juan Carlos , Madrid , Spain |
| AuthorAffiliation_xml | – name: 4 Parkinson and Movement Disorders Unit, Department of Neurosciences (DNS), Padova University , Padova , Italy – name: 2 Department of Neurology, Northwestern University Feinberg School of Medicine , Chicago, IL , United States – name: 1 HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales , Madrid , Spain – name: 5 Division of Neurology, Department of Medicine, Parkinson's Disease and Movement Disorders Centre, The Ottawa Hospital Research Institute, The University of Ottawa Brain Research Institute , Ottawa, ON , Canada – name: 6 Movement Disorders Unit, Neurology Department, Hospital Universitario 12 de Octubre , Madrid , Spain – name: 3 LAIMBIO, Laboratorio de Análisis de Imagen Médica y Biometría, Universidad Rey Juan Carlos , Madrid , Spain |
| Author_xml | – sequence: 1 givenname: Mariana H. G. surname: Monje fullname: Monje, Mariana H. G. – sequence: 2 givenname: Sergio surname: Domínguez fullname: Domínguez, Sergio – sequence: 3 givenname: Javier surname: Vera-Olmos fullname: Vera-Olmos, Javier – sequence: 4 givenname: Angelo surname: Antonini fullname: Antonini, Angelo – sequence: 5 givenname: Tiago A. surname: Mestre fullname: Mestre, Tiago A. – sequence: 6 givenname: Norberto surname: Malpica fullname: Malpica, Norberto – sequence: 7 givenname: Álvaro surname: Sánchez-Ferro fullname: Sánchez-Ferro, Álvaro |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35002915$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kk1v1DAQhi1UREvpD-CCfIPLLv5M4gtStRRYqRIIUXE0Y2eyuCR2Gycr8e_x7paq5YAvtsbvPDP2vM_JUUwRCXnJ2VLKxrztIs7jUjDBl7USlVZPyAmvKrUQwuijB-djcpbzNStLGiMr-YwcS82YMFyfkB9fcUgT0ost9DNMIUWaOvoFxl8h5hRfZ_o-ZISM9CqHuKFAVyluMe6U0NPv6DwMFGJLz8cpdMGHEl3HCfs-bDB6fEGedtBnPLvbT8nVh4tvq0-Ly88f16vzy4VXlZ4WlZOoGy87pw1KrEGytkXPWgMGHRcaO1-x2tVcc5BoPHMgdN0BN51U0slTsj5w2wTX9mYMA4y_bYJg94E0biyUDn2PVqEE06jCAVBohKtLuVoIV1Udc6otrHcH1s3sBmx9ee4I_SPo45sYftpN2tqmllLVpgDe3AHGdDtjnuwQsi9_AhHTnK2oeKO5UoYV6auHte6L_B1REfCDwI8p5xG7ewlnducEu3eC3TnBHpxQcup_cnyY9tMt7Yb-P5l_AEHruzM |
| CitedBy_id | crossref_primary_10_1109_TNSRE_2023_3236834 crossref_primary_10_3390_healthcare12040439 crossref_primary_10_3390_s23104957 crossref_primary_10_1016_j_vrih_2024_08_001 crossref_primary_10_3390_medicina60060958 crossref_primary_10_1016_j_parkreldis_2024_107104 crossref_primary_10_1159_000547180 crossref_primary_10_12677_ijpn_2025_141001 crossref_primary_10_1109_JBHI_2024_3439492 crossref_primary_10_1186_s12984_024_01406_w crossref_primary_10_1016_j_artmed_2024_102914 crossref_primary_10_1016_j_jns_2024_123271 crossref_primary_10_1007_s40846_024_00876_6 crossref_primary_10_3917_res_243_0143 crossref_primary_10_1002_mds_30327 crossref_primary_10_12688_f1000research_138616_2 crossref_primary_10_3389_fnagi_2025_1602426 crossref_primary_10_1109_TNSRE_2024_3416446 crossref_primary_10_2196_67986 crossref_primary_10_3233_JPD_223445 crossref_primary_10_12688_f1000research_138616_1 crossref_primary_10_1016_j_eswa_2023_123077 |
| Cites_doi | 10.1002/mds.21238 10.1002/mds.26718 10.1212/WNL.0000000000002350 10.1016/j.artmed.2013.11.004 10.1016/j.jns.2020.117003 10.1002/mds.22340 10.1146/annurev-bioeng-062117-121036 10.1016/j.parkreldis.2014.02.022 10.1109/IEMBS.2011.6091086 10.1109/ICCV.2015.226 10.1145/3328925 10.3390/s17071591 10.1109/TPAMI.2019.2929257 10.1145/3369438 10.1002/mds.28297 10.1038/s41467-020-17807-z 10.3233/JPD-202402 10.1002/mds.26424 10.15585/mmwr.mm6943a3 10.1109/ICORR.2017.8009232 10.1111/j.1468-1331.2008.02148.x 10.1136/jnnp.56.3.295 10.1002/mds.28462 10.1002/mdc3.12536 10.1002/mds.28094 10.1002/mds.870130404 10.1002/mds.23893 10.1002/ana.410440703 10.1002/mds.870090114 10.3758/BF03334122 10.1002/mds.23740 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 Monje, Domínguez, Vera-Olmos, Antonini, Mestre, Malpica and Sánchez-Ferro. Copyright © 2021 Monje, Domínguez, Vera-Olmos, Antonini, Mestre, Malpica and Sánchez-Ferro. 2021 Monje, Domínguez, Vera-Olmos, Antonini, Mestre, Malpica and Sánchez-Ferro |
| Copyright_xml | – notice: Copyright © 2021 Monje, Domínguez, Vera-Olmos, Antonini, Mestre, Malpica and Sánchez-Ferro. – notice: Copyright © 2021 Monje, Domínguez, Vera-Olmos, Antonini, Mestre, Malpica and Sánchez-Ferro. 2021 Monje, Domínguez, Vera-Olmos, Antonini, Mestre, Malpica and Sánchez-Ferro |
| DBID | AAYXX CITATION NPM 7X8 5PM DOA |
| DOI | 10.3389/fneur.2021.742654 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed CrossRef MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1664-2295 |
| ExternalDocumentID | oai_doaj_org_article_4e3a9841a3aa4e92b77a3722b66f0b4d PMC8733479 35002915 10_3389_fneur_2021_742654 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: ; |
| GroupedDBID | 53G 5VS 9T4 AAFWJ AAKDD AAYXX ACGFO ACGFS ADBBV ADRAZ AENEX AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS BAWUL BCNDV CITATION DIK E3Z EMOBN F5P GROUPED_DOAJ GX1 HYE KQ8 M48 M~E O5R O5S OK1 P2P PGMZT RNS RPM ACXDI IAO IEA IHR IHW IPNFZ NPM RIG 7X8 5PM |
| ID | FETCH-LOGICAL-c465t-6b3e58c3fb59e3e7a30ddec0d9a9eb125efc607b7151a3e9c0ba257fa19f343b3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 24 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000743510100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1664-2295 |
| IngestDate | Fri Oct 03 12:49:59 EDT 2025 Tue Sep 30 16:49:21 EDT 2025 Wed Oct 01 13:33:31 EDT 2025 Thu Jan 02 22:56:39 EST 2025 Sat Nov 29 06:39:30 EST 2025 Tue Nov 18 21:38:43 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | telemedicine kinematics Parkinson's disease webcam artificial intelligence and bio-inspired algorithms |
| Language | English |
| License | Copyright © 2021 Monje, Domínguez, Vera-Olmos, Antonini, Mestre, Malpica and Sánchez-Ferro. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c465t-6b3e58c3fb59e3e7a30ddec0d9a9eb125efc607b7151a3e9c0ba257fa19f343b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: J. Lucas McKay, Emory University, United States; Norbert Brüggemann, University of Lübeck, Germany Edited by: Letizia Leocani, San Raffaele Hospital (IRCCS), Italy This article was submitted to Movement Disorders, a section of the journal Frontiers in Neurology These authors have contributed equally to this work and share first authorship These authors have contributed equally to this work and share last authorship |
| OpenAccessLink | https://doaj.org/article/4e3a9841a3aa4e92b77a3722b66f0b4d |
| PMID | 35002915 |
| PQID | 2618514490 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_4e3a9841a3aa4e92b77a3722b66f0b4d pubmedcentral_primary_oai_pubmedcentral_nih_gov_8733479 proquest_miscellaneous_2618514490 pubmed_primary_35002915 crossref_primary_10_3389_fneur_2021_742654 crossref_citationtrail_10_3389_fneur_2021_742654 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-12-23 |
| PublicationDateYYYYMMDD | 2021-12-23 |
| PublicationDate_xml | – month: 12 year: 2021 text: 2021-12-23 day: 23 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland |
| PublicationTitle | Frontiers in neurology |
| PublicationTitleAlternate | Front Neurol |
| PublicationYear | 2021 |
| Publisher | Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Media S.A |
| References | Espay (B20) 2011; 26 B24 Postuma (B1) 2015; 30 Williams (B25) 2020; 416 Heldman (B19) 2011; 26 Monje (B33) 2021; 36 Kishore (B28) 2007; 22 Mulroy (B30) 2020; 35 Bambach (B17) 2015 Khan (B11) 2014; 60 Del Din (B7) 2016; 31 Goetz (B31) 2020; 35 Goetz (B2) 2008; 23 Sibley (B10) 2021; 11 Poewe (B32) 1998; 44 Langevin (B22) 2019; 3 Coren (B15) 1993; 31 Butt (B12) 2017 Merriaux (B8) 2017; 17 Brown (B27) 1993; 56 Rizzo (B26) 2016; 86 Kidziński (B9) 2020; 11 Lin (B23) 2020; 14 Richards (B3) 1994; 9 Hoffman (B16) 2011 Heldman (B6) 2014; 20 Cao (B18) 2019 Bohnen (B21) 2008; 15 Monje (B5) 2019; 21 Hughes (B14) 1993; 60 Koonin (B29) 2020; 69 Bank (B13) 2017; 4 Stebbins (B4) 1998; 13 |
| References_xml | – volume: 22 start-page: 328 year: 2007 ident: B28 article-title: Unilateral versus bilateral tasks in early asymmetric Parkinson's disease: differential effects on bradykinesia publication-title: Mov Disord. doi: 10.1002/mds.21238 – volume: 31 start-page: 1293 year: 2016 ident: B7 article-title: Free-living monitoring of Parkinson's disease: lessons from the field publication-title: Mov Disord. doi: 10.1002/mds.26718 – volume: 86 start-page: 566 year: 2016 ident: B26 article-title: Accuracy of clinical diagnosis of Parkinson disease publication-title: Neurology. doi: 10.1212/WNL.0000000000002350 – volume: 60 start-page: 27 year: 2014 ident: B11 article-title: A computer vision framework for finger-tapping evaluation in Parkinson's disease publication-title: Artif Intell Med. doi: 10.1016/j.artmed.2013.11.004 – volume: 416 start-page: 117003 year: 2020 ident: B25 article-title: The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? publication-title: J Neurol Sci. doi: 10.1016/j.jns.2020.117003 – volume: 23 start-page: 2129 year: 2008 ident: B2 article-title: Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results publication-title: Mov Disord. doi: 10.1002/mds.22340 – volume: 21 start-page: 111 year: 2019 ident: B5 article-title: New sensor and wearable technologies to aid in the diagnosis and treatment monitoring of Parkinson's disease publication-title: Annu Rev Biomed Eng. doi: 10.1146/annurev-bioeng-062117-121036 – volume: 20 start-page: 590 year: 2014 ident: B6 article-title: Clinician versus machine: reliability and responsiveness of motor endpoints in Parkinson's disease publication-title: Park Relat Disord. doi: 10.1016/j.parkreldis.2014.02.022 – start-page: 4378 year: 2011 ident: B16 article-title: Objective measure of upper extremity motor impairment in Parkinson's disease with inertial sensors publication-title: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society doi: 10.1109/IEMBS.2011.6091086 – start-page: 1949 volume-title: 2015 IEEE International Conference on Computer Vision (ICCV) year: 2015 ident: B17 article-title: Lending A hand: detecting hands and recognizing activities in complex egocentric interactions doi: 10.1109/ICCV.2015.226 – volume: 60 start-page: 595 year: 1993 ident: B14 article-title: The clinical features of Parkinson's disease in 100 histologically proven cases publication-title: Adv Neurol. – volume: 3 start-page: 1 year: 2019 ident: B22 article-title: The PARK framework for automated analysis of Parkinson's disease characteristics publication-title: Proc ACM Interact Mobile Wear Ubiquitous Technol. doi: 10.1145/3328925 – volume: 17 start-page: 1591 year: 2017 ident: B8 article-title: A study of Vicon system positioning performance publication-title: Sensors. doi: 10.3390/s17071591 – start-page: 172 year: 2019 ident: B18 article-title: OpenPose: realtime multi-person 2D pose estimation using part affinity fields publication-title: IEEE Trans Pattern Anal Mach Intell. doi: 10.1109/TPAMI.2019.2929257 – volume: 14 start-page: 1 year: 2020 ident: B23 article-title: Bradykinesia recognition in Parkinson's disease via single RGB video publication-title: ACM Trans Knowl Discov Data. doi: 10.1145/3369438 – volume: 35 start-page: 1893 year: 2020 ident: B30 article-title: Telemedicine in movement disorders: Leçons du COVID-19 publication-title: Mov Disord. doi: 10.1002/mds.28297 – volume: 11 start-page: 4054 year: 2020 ident: B9 article-title: Deep neural networks enable quantitative movement analysis using single-camera videos publication-title: Nat Commun. doi: 10.1038/s41467-020-17807-z – volume: 11 start-page: S83 year: 2021 ident: B10 article-title: Video-based analyses of Parkinson's disease severity: a brief review publication-title: J Parkinsons Dis. doi: 10.3233/JPD-202402 – volume: 30 start-page: 1591 year: 2015 ident: B1 article-title: MDS clinical diagnostic criteria for Parkinson's disease publication-title: Mov Disord. doi: 10.1002/mds.26424 – volume: 69 start-page: 1595 year: 2020 ident: B29 article-title: Trends in the use of telehealth during the emergence of the COVID-19 pandemic — United States, January–March 2020 publication-title: MMWR Morb Mortal Wkly Rep. doi: 10.15585/mmwr.mm6943a3 – start-page: 116 volume-title: 2017 International Conference on Rehabilitation Robotics (ICORR) year: 2017 ident: B12 article-title: Leap motion evaluation for assessment of upper limb motor skills in Parkinson's disease doi: 10.1109/ICORR.2017.8009232 – volume: 15 start-page: 685 year: 2008 ident: B21 article-title: Diagnostic performance of clinical motor and non-motor tests of Parkinson disease: a matched case-control study publication-title: Eur J Neurol. doi: 10.1111/j.1468-1331.2008.02148.x – volume: 56 start-page: 295 year: 1993 ident: B27 article-title: The execution of bimanual movements in patients with Parkinson's, Huntington's and cerebellar disease publication-title: J Neurol Neurosurg Psychiatry. doi: 10.1136/jnnp.56.3.295 – volume: 36 start-page: 905 year: 2021 ident: B33 article-title: Motor onset topography and progression in Parkinson's disease: the upper limb is first publication-title: Mov Disord. doi: 10.1002/mds.28462 – volume: 4 start-page: 875 year: 2017 ident: B13 article-title: Optical hand tracking: a novel technique for the assessment of Bradykinesia in Parkinson's disease publication-title: Mov Disord Clin Pract. doi: 10.1002/mdc3.12536 – volume: 35 start-page: 911 year: 2020 ident: B31 article-title: Movement Disorder Society–Unified Parkinson's disease rating scale use in the Covid-19 era publication-title: Mov Disord. doi: 10.1002/mds.28094 – ident: B24 – volume: 13 start-page: 633 year: 1998 ident: B4 article-title: Factor structure of the Unified Parkinson's Disease Rating Scale: motor examination section publication-title: Mov Disord. doi: 10.1002/mds.870130404 – volume: 26 start-page: 2504 year: 2011 ident: B20 article-title: Differential response of speed, amplitude, and rhythm to dopaminergic medications in Parkinson's disease Alberto publication-title: Mov Disord. doi: 10.1002/mds.23893 – volume: 44 start-page: S1 year: 1998 ident: B32 article-title: The natural history of Parkinson's disease publication-title: Ann Neurol. doi: 10.1002/ana.410440703 – volume: 9 start-page: 89 year: 1994 ident: B3 article-title: Interrater reliability of the unified Parkinson's disease rating scale motor examination publication-title: Mov Disord. doi: 10.1002/mds.870090114 – volume: 31 start-page: 1 year: 1993 ident: B15 article-title: The lateral preference inventory for measurement of handedness, footedness, eyedness, and earedness: norms for young adults publication-title: Bull Psychon Soc. doi: 10.3758/BF03334122 – volume: 26 start-page: 1859 year: 2011 ident: B19 article-title: The modified bradykinesia rating scale for Parkinson's disease: reliability and comparison with kinematic measures publication-title: Mov Disord. doi: 10.1002/mds.23740 |
| SSID | ssj0000399363 |
| Score | 2.3816814 |
| Snippet | Objective:
This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard... This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam. We... Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 742654 |
| SubjectTerms | artificial intelligence and bio-inspired algorithms kinematics Neurology Parkinson's disease telemedicine webcam |
| Title | Remote Evaluation of Parkinson's Disease Using a Conventional Webcam and Artificial Intelligence |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35002915 https://www.proquest.com/docview/2618514490 https://pubmed.ncbi.nlm.nih.gov/PMC8733479 https://doaj.org/article/4e3a9841a3aa4e92b77a3722b66f0b4d |
| Volume | 12 |
| WOSCitedRecordID | wos000743510100001&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: 1664-2295 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000399363 issn: 1664-2295 databaseCode: DOA dateStart: 20100101 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: 1664-2295 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000399363 issn: 1664-2295 databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NaxUxEB9sEeml-FVdbUsEQRDWZpNssjm29RUFW0T8eLeYZLNYaPeVvleP_u1Oku1jXyn14iWH3YSEXyYzGWbmF4DXzLfUsZqVQvkWm1aWzoWuFK1zupFeqFQh9_2TOjlpplP9efTUV8wJy_TAGbg9EbjVjagst1YEzZxSlivGnJQddaKN2pcqPXKmkg6OdlfyHMZEL0zvdZEfEv1BVr1Db1DWYsUQJb7-2y6ZN3MlR8bn6CFsDrdGsp9X-wjuhf4xPDge4uJP4OeXgJAHMllyd5NZR2JFcyruejMn73MghqQUAWLJ4SjbnPyIuUrnxPZtmiKTSpCPI7bOp_DtaPL18EM5vJ1QeiHrRSkdD3XjeedqHXhAzCgqMk9bbTWqZ1aHzkuqnEKLb3nQnjqLp7ezle644I5vwXo_68NzILqN0UInnJeIXMWt1JZ59NO8sNapUAC9BtL4gVg8vm9xZtDBiNibhL2J2JuMfQFvl0MuMqvGXZ0P4u4sO0ZC7PQBxcQMYmL-JSYFvLreW4MHKEZFbB9mV3ODLiTeOoXQtIBnea-XU_E6Ri2rugC1IgUra1n905_-SiTdjeKxSPfF_1j8S9iIeMQsGsa3YX1xeRV24L7_vTidX-7Cmpo2u0n-sT3-M_kLpM0NQg |
| linkProvider | Directory of Open Access Journals |
| 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=Remote+Evaluation+of+Parkinson%27s+Disease+Using+a+Conventional+Webcam+and+Artificial+Intelligence&rft.jtitle=Frontiers+in+neurology&rft.au=Monje%2C+Mariana+H+G&rft.au=Dom%C3%ADnguez%2C+Sergio&rft.au=Vera-Olmos%2C+Javier&rft.au=Antonini%2C+Angelo&rft.date=2021-12-23&rft.issn=1664-2295&rft.eissn=1664-2295&rft.volume=12&rft.spage=742654&rft_id=info:doi/10.3389%2Ffneur.2021.742654&rft_id=info%3Apmid%2F35002915&rft.externalDocID=35002915 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1664-2295&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1664-2295&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1664-2295&client=summon |