dc.date.accessioned | 2023-11-08T17:10:38Z | |
dc.date.available | 2023-11-08T17:10:38Z | |
dc.date.created | 2023-06-02T15:28:50Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Baselizadeh, Adel Khaksar, Weria Uddin, Md Zia Saplacan, Diana Tørresen, Jim . Privacy-Preserving User Pose Prediction for Safe and Efficient Human-Robot Interaction. Proceedings of the 2023 19th IEEE International Conference on Automation Science and Engineering (CASE). 2023 IEEE conference proceedings | |
dc.identifier.uri | http://hdl.handle.net/10852/105699 | |
dc.description.abstract | Enhancing user privacy is crucial in improving the safety and efficiency of Human-Robot Interaction (HRI), as it is a key factor for establishing user trust in the robot. Using privacy-preserving sensors and local processing of the user's data are ways to enhance privacy in HRI. This paper presents a privacy-preserving sensing system for real-time tracking and predicting the user's movements in HRI. As privacy-preserving sensors, a thermal and a depth camera are used to monitor the user's movements and determine their current pose. In order to improve the robot's perception of the user's situation and enhance the quality of real-time user monitoring, a Deep Learning (DL) model has been developed to estimate the future poses of the user. The developed model is based on the Sequence to Sequence mechanism (Seq2Seq). Modifications have been made to Seq2Seq so it can be run locally on the robot. As a result of these modifications, the computational cost of the model has been reduced by 34%. Experimental studies have been conducted to evaluate the performance of the sensing system in tracking and predicting the user's movements in HRI. According to the test results, the proposed sensing system is able to track the movements of the user appropriately. Additionally, it is shown that the estimation of the user's movement through the proposed system with the prediction model can improve the safety and efficiency aspects of the HRI experiments by up to 24% and 17%, respectively. | |
dc.language | EN | |
dc.publisher | IEEE conference proceedings | |
dc.title | Privacy-Preserving User Pose Prediction for Safe and Efficient Human-Robot Interaction | |
dc.title.alternative | ENEngelskEnglishPrivacy-Preserving User Pose Prediction for Safe and Efficient Human-Robot Interaction | |
dc.type | Chapter | |
dc.creator.author | Baselizadeh, Adel | |
dc.creator.author | Khaksar, Weria | |
dc.creator.author | Uddin, Md Zia | |
dc.creator.author | Saplacan, Diana | |
dc.creator.author | Tørresen, Jim | |
cristin.unitcode | 185,15,5,46 | |
cristin.unitname | Forskningsgruppe for robotikk og intelligente systemer | |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
dc.identifier.cristin | 2151364 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.btitle=Proceedings of the 2023 19th IEEE International Conference on Automation Science and Engineering (CASE)&rft.spage=&rft.date=2023 | |
dc.identifier.pagecount | 0 | |
dc.identifier.doi | https://doi.org/10.1109/CASE56687.2023.10260298 | |
dc.type.document | Bokkapittel | |
dc.source.isbn | 000-0-000-00000-0 | |
dc.type.version | SubmittedVersion | |
cristin.btitle | Proceedings of the 2023 19th IEEE International Conference on Automation Science and Engineering (CASE) | |
dc.relation.project | NFR/288285 | |
dc.relation.project | NFR/247697 | |
dc.relation.project | NFR/312333 | |
dc.relation.project | NFR/262762 | |