Publication
Exploring Human Action Recognition for Rehabilitation Game Application
| dc.contributor.author | Lopes, Júlio Castro | |
| dc.contributor.author | Van-Deste, Isaac | |
| dc.contributor.author | Lopes, Rui Pedro | |
| dc.date.accessioned | 2024-07-01T10:07:28Z | |
| dc.date.available | 2024-07-01T10:07:28Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Through computer vision algorithms motion can be computed, which can be a crucial element to be integrated with serious game environments. To evaluate the efficacy of motion detection algorithms, the information of these algorithms can be used to perform Human Action Recognition (HAR). There are several algorithms to perform HAR, although skeleton approaches can be seen as the best way to isolate human motion. To extract the human skeleton representation, the work described in this paper evaluates three distinct methods: OpenPose (2D), YOLO-Pose (2D) and BlazePose (3D). The information translated by the skeleton representations is normalized by lightweight normalization algorithms (for further real-time application). To classify the video sequence and further action identification, a Long Short Term Memory network (LSTM), was used. Using the N-UCLA dataset, the highest F1 score of 0.745 was achieved using OpenPose skeleton extraction (2D), followed by the computation of the angles in each joint, demonstrating that the OpenPose skeleton representation can be the most viable solution for computing human motion in serious games. | pt_PT |
| dc.description.sponsorship | The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDP/05757/2020) and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020). | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Lopes, Júlio Castro; Van-Deste, Isaac; Lopes, Rui Pedro (2024). Exploring Human Action Recognition for Rehabilitation Game Application. In 2024 IEEE 12th International Conference on Serious Games and Applications for Health (SeGAH). IEEE, p. 1-8. ISBN 979-8-3503-8438-3 | pt_PT |
| dc.identifier.doi | 10.1109/SeGAH61285.2024.10639556 | pt_PT |
| dc.identifier.isbn | 979-8-3503-8438-3 | |
| dc.identifier.uri | http://hdl.handle.net/10198/29974 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | IEEE | pt_PT |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.relation | Associate Laboratory for Sustainability and Tecnology in Mountain Regions | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Human Action Recognition | pt_PT |
| dc.subject | Skeleton extraction | pt_PT |
| dc.subject | OpenPose | pt_PT |
| dc.subject | YOLO-Pose | pt_PT |
| dc.subject | BlazePose | pt_PT |
| dc.subject | Moving Joints Descriptor | pt_PT |
| dc.title | Exploring Human Action Recognition for Rehabilitation Game Application | pt_PT |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardTitle | Associate Laboratory for Sustainability and Tecnology in Mountain Regions | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT | |
| oaire.citation.endPage | 8 | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| oaire.citation.title | 2024 IEEE 12th International Conference on Serious Games and Applications for Health (SeGAH) | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Van-Deste | |
| person.familyName | Lopes | |
| person.givenName | Isaac | |
| person.givenName | Rui Pedro | |
| person.identifier.ciencia-id | 871B-13B1-BE1E | |
| person.identifier.ciencia-id | 8E14-54E4-4DB5 | |
| person.identifier.orcid | 0000-0003-0651-8567 | |
| person.identifier.orcid | 0000-0002-9170-5078 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | restrictedAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
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