Video summary of the first data set per person.
<div><p>Accurate and reliable skeletal motion tracking is essential for rehabilitation monitoring, enabling objective assessment of patient progress and facilitating telerehabilitation applications. Traditional marker-based motion capture systems, while highly accurate, are costly and im...
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2025
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| _version_ | 1852017786478395392 |
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| author | Vytautas Abromavičius (22008573) |
| author2 | Ervinas Gisleris (22008576) Kristina Daunoravičienė (18251583) Jurgita Žižienė (22008579) Artūras Serackis (20783472) Rytis Maskeliūnas (10172664) |
| author2_role | author author author author author |
| author_facet | Vytautas Abromavičius (22008573) Ervinas Gisleris (22008576) Kristina Daunoravičienė (18251583) Jurgita Žižienė (22008579) Artūras Serackis (20783472) Rytis Maskeliūnas (10172664) |
| author_role | author |
| dc.creator.none.fl_str_mv | Vytautas Abromavičius (22008573) Ervinas Gisleris (22008576) Kristina Daunoravičienė (18251583) Jurgita Žižienė (22008579) Artūras Serackis (20783472) Rytis Maskeliūnas (10172664) |
| dc.date.none.fl_str_mv | 2025-08-07T17:35:03Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0328969.t002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Video_summary_of_the_first_data_set_per_person_/29854623 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified yet challenges remain wrist joint (< upper limb activities refine depth coordinates reducing error margins proposed system combines missing joint positions kalman filtering framework human pose estimation facilitating telerehabilitation applications experimental results demonstrate enabling objective assessment depth estimation errors deep learning approaches enhance temporal consistency scenarios involving occlusions remote patient monitoring improving tracking precision div >< p single camera setups rehabilitation monitoring patient progress integrates temporal camera systems whereas marker traditional marker time interpolation study provides study proposes spatial synchronization scalable solution rehabilitation exercises recent studies p </ occlusion conditions linear regression home rehabilitation frontal perspectives allowing real |
| dc.title.none.fl_str_mv | Video summary of the first data set per person. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Accurate and reliable skeletal motion tracking is essential for rehabilitation monitoring, enabling objective assessment of patient progress and facilitating telerehabilitation applications. Traditional marker-based motion capture systems, while highly accurate, are costly and impractical for home rehabilitation, whereas marker-less methods often suffer from depth estimation errors and occlusions. Recent studies have explored various computer vision and deep learning approaches for human pose estimation, yet challenges remain in ensuring robust depth accuracy and tracking under occlusion conditions. This study proposes a three-dimensional human skeleton tracking system for upper limb activities that integrates temporal and spatial synchronization to improve depth estimation accuracy for rehabilitation exercises. The proposed system combines a 90° secondary camera to compensate for the depth prediction inaccuracies inherent in single-camera systems, reducing error margins by up to 0.4 m. In addition, a linear regression-based depth error correction model is implemented to refine depth coordinates, further improving tracking precision. The Kalman filtering framework is employed to enhance temporal consistency, allowing real-time interpolation of missing joint positions. Experimental results demonstrate that the proposed method significantly reduces depth estimation errors of the elbow and wrist joint (<i>p</i> < 0.001) compared to single camera setups, particularly in scenarios involving occlusions and non-frontal perspectives. This study provides a cost-effective and scalable solution for remote patient monitoring and motor function evaluation.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_036743746040407af0c9a061fd29cc6a |
| identifier_str_mv | 10.1371/journal.pone.0328969.t002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29854623 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Video summary of the first data set per person.Vytautas Abromavičius (22008573)Ervinas Gisleris (22008576)Kristina Daunoravičienė (18251583)Jurgita Žižienė (22008579)Artūras Serackis (20783472)Rytis Maskeliūnas (10172664)BiotechnologySpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedyet challenges remainwrist joint (<upper limb activitiesrefine depth coordinatesreducing error marginsproposed system combinesmissing joint positionskalman filtering frameworkhuman pose estimationfacilitating telerehabilitation applicationsexperimental results demonstrateenabling objective assessmentdepth estimation errorsdeep learning approachesenhance temporal consistencyscenarios involving occlusionsremote patient monitoringimproving tracking precisiondiv >< psingle camera setupsrehabilitation monitoringpatient progressintegrates temporalcamera systemswhereas markertraditional markertime interpolationstudy providesstudy proposesspatial synchronizationscalable solutionrehabilitation exercisesrecent studiesp </occlusion conditionslinear regressionhome rehabilitationfrontal perspectivesallowing real<div><p>Accurate and reliable skeletal motion tracking is essential for rehabilitation monitoring, enabling objective assessment of patient progress and facilitating telerehabilitation applications. Traditional marker-based motion capture systems, while highly accurate, are costly and impractical for home rehabilitation, whereas marker-less methods often suffer from depth estimation errors and occlusions. Recent studies have explored various computer vision and deep learning approaches for human pose estimation, yet challenges remain in ensuring robust depth accuracy and tracking under occlusion conditions. This study proposes a three-dimensional human skeleton tracking system for upper limb activities that integrates temporal and spatial synchronization to improve depth estimation accuracy for rehabilitation exercises. The proposed system combines a 90° secondary camera to compensate for the depth prediction inaccuracies inherent in single-camera systems, reducing error margins by up to 0.4 m. In addition, a linear regression-based depth error correction model is implemented to refine depth coordinates, further improving tracking precision. The Kalman filtering framework is employed to enhance temporal consistency, allowing real-time interpolation of missing joint positions. Experimental results demonstrate that the proposed method significantly reduces depth estimation errors of the elbow and wrist joint (<i>p</i> < 0.001) compared to single camera setups, particularly in scenarios involving occlusions and non-frontal perspectives. This study provides a cost-effective and scalable solution for remote patient monitoring and motor function evaluation.</p></div>2025-08-07T17:35:03ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0328969.t002https://figshare.com/articles/dataset/Video_summary_of_the_first_data_set_per_person_/29854623CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/298546232025-08-07T17:35:03Z |
| spellingShingle | Video summary of the first data set per person. Vytautas Abromavičius (22008573) Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified yet challenges remain wrist joint (< upper limb activities refine depth coordinates reducing error margins proposed system combines missing joint positions kalman filtering framework human pose estimation facilitating telerehabilitation applications experimental results demonstrate enabling objective assessment depth estimation errors deep learning approaches enhance temporal consistency scenarios involving occlusions remote patient monitoring improving tracking precision div >< p single camera setups rehabilitation monitoring patient progress integrates temporal camera systems whereas marker traditional marker time interpolation study provides study proposes spatial synchronization scalable solution rehabilitation exercises recent studies p </ occlusion conditions linear regression home rehabilitation frontal perspectives allowing real |
| status_str | publishedVersion |
| title | Video summary of the first data set per person. |
| title_full | Video summary of the first data set per person. |
| title_fullStr | Video summary of the first data set per person. |
| title_full_unstemmed | Video summary of the first data set per person. |
| title_short | Video summary of the first data set per person. |
| title_sort | Video summary of the first data set per person. |
| topic | Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified yet challenges remain wrist joint (< upper limb activities refine depth coordinates reducing error margins proposed system combines missing joint positions kalman filtering framework human pose estimation facilitating telerehabilitation applications experimental results demonstrate enabling objective assessment depth estimation errors deep learning approaches enhance temporal consistency scenarios involving occlusions remote patient monitoring improving tracking precision div >< p single camera setups rehabilitation monitoring patient progress integrates temporal camera systems whereas marker traditional marker time interpolation study provides study proposes spatial synchronization scalable solution rehabilitation exercises recent studies p </ occlusion conditions linear regression home rehabilitation frontal perspectives allowing real |