Forutseende og intuitiv robotmedhjelper (PIRC)

• van Otterdijk, Marieke; Neggers, Margot; Tørresen, Jim & Barakova, Emilia Ivanova (2021). Preferences of Seniors for Robots Delivering a Message With Congruent Approaching Behavior, Proceedings of the 2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO) Virtual Conference, July 8-10, 2021. IEEE Robotics and Automation Society. ISSN 978-1-6654-4952-6. doi: 10.1109/ARSO51874.2021.9542833.
• Gorton, Patrick & Ellefsen, Kai Olav (2021). Evaluating Predictive Deep Learning Models. I Yildirim Yayilgan, Sule; Bajwa, Imran Sarwar & Sanfilippo, Filippo (Red.), Proceeding of the 3rd International Conference on Intelligent Technologies and Applications (INTAP). Springer. ISSN 978-3-030-71711-7. s. 139–150. doi: doi.org/10.1007/978-3-030-71711-7_12. Fulltekst i vitenarkiv
• Baselizadeh, Adel; Saplacan, Diana & Tørresen, Jim (2021). Adaptive Real-time Learning-based Neuro-Fuzzy Control of Robot Manipulators. I Plapper, Peter (Red.), 9th International Conference on Control, Mechatronics and Automation (ICCMA2021). IEEE conference proceedings. ISSN 978-1-6654-10731. s. 20–26. doi: 10.1109/ICCMA54375.2021.9646224.
• Noori, Farzan Majeed; Uddin, Md Zia & Tørresen, Jim (2021). Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning. IEEE Access. ISSN 2169-3536. 9, s. 138132–138143. doi: 10.1109/ACCESS.2021.3117667. Fulltekst i vitenarkiv Vis sammendrag

  With recent advances in the field of sensing, it has become possible to build better assistive technologies. This enables the strengthening of eldercare with regard to daily routines and the provision of personalised care to users. For instance, it is possible to detect a person’s behaviour based on wearable or ambient sensors; however, it is difficult for users to wear devices 24/7, as they would have to be recharged regularly because of their energy consumption. Similarly, although cameras have been widely used as ambient sensors, they carry the risk of breaching users’ privacy. This paper presents a novel sensing approach based on deep learning for human activity recognition using a non-wearable ultra-wideband (UWB) radar sensor. UWB sensors protect privacy better than RGB cameras because they do not collect visual data. In this study, UWB sensors were mounted on a mobile robot to monitor and observe subjects from a specific distance (namely, 1.5–2.0 m). Initially, data were collected in a lab environment for five different human activities. Subsequently, the data were used to train a model using the state-of-the-art deep learning approach, namely long short-term memory (LSTM). Conventional training approaches were also tested to validate the superiority of LSTM. As a UWB sensor collects many data points in a single frame, enhanced discriminant analysis was used to reduce the dimensions of the features through application of principal component analysis to the raw dataset, followed by linear discriminant analysis. The enhanced discriminant features were fed into the LSTMs. Finally, the trained model was tested using new inputs. The proposed LSTM-based activity recognition approach performed better than conventional approaches, with an accuracy of 99.6%. We applied 5-fold cross-validation to test our approach. We also validated our approach on publically available dataset. The proposed method can be applied in many prominent fields, including human–robot interaction for various practical applications, such as mobile robots for eldercare.

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• Saplacan, Diana (2022). Participatory Design and Human-Robot Interaction - An ethical and inclusive perspective on contemporary technologies.
• Dahl, Heidi Elisabeth Iuell; Mehmandarov, Rustam Karim; Harkestad, Inge & Saplacan, Diana (2022). AI and Ethics workshop.
• Saplacan, Diana (2022). Webpage for the tutorial at RO-MAn 2022 Conference: Robots and Society: Ethical, social, legal, and technical perspectives on integrating social robots in the home and healthcare systems and services (RO-SO - Second Edition).
• Saplacan, Diana (2022). Some experiences about analysis and writing.
• Saplacan, Diana (2022). AI & Ethics Workshop. Vis sammendrag

  Workshop on AI & Ethics organized in cooperation with Tekna Big Data professional network and The Norwegian Council for Digital Ethics. The theme of the workshop: Must AI be professionalized to ensure ethical development? Role: Organizer/Contact person.

• Saplacan, Diana (2022). A talk on robots as connected devices or smart "things" in the home, part of the AI & Ethics workshop.
• Saplacan, Diana; Tørresen, Jim; Mahler, Tobias & Fosch-Villaronga, Eduard (2021). Robots and Society: Ethical, Legal, and Technical Perspectives on Integrating Robots in the Home- and Healthcare Systems and Services (RO-SO). Vis sammendrag

  The RO-SO tutorial is a continuation of the previous tutorials regarding robots, ethics, legal, and technical aspects. The tutorial is part of the 30th IEEE International Conference on Robot and Human Interactive Communication (ROMAN 2021). It focuses on providing an overview and discussing the ethical, legal, and technical perspectives, including challenges, dilemmas, and advantages and opportunities of integrating robots as part of home- and healthcare systems and services. The following two questions will guide the overall objective for this tutorial, but these will not limit the talks: How does the meaning of good care change when we integrate robots as part of the home- and healthcare systems and services? What does good care mean when we integrate assistive robots as part of the home- and healthcare services? What are some of the ethical, legal, and technical challenges and opportunities when integrating assistive care robots in the home- and healthcare services?

• Tørresen, Jim (2021). INTROMAT: INtroducing personalized TReatment Of Mental health problems using Adaptive Technology.
• Tørresen, Jim (2021). Tutorial: Ethical Considerations in User Modeling and Personalization.
• Tørresen, Jim (2021). Tutorial: Intelligent System Research – AI Ethical Challenges and Opportunities.
• Tørresen, Jim (2021). Tutorial: Ethical Considerations in Robotics and Automation.
• Tørresen, Jim (2021). Kunstig intelligens – allsidig i metoder og anvendelser.
• Tørresen, Jim; Saplacan, Diana; Lintvedt, Mona Naomi; Mahler, Tobias & Fosch-Villaronga, Eduard (2021). Tutorial: Ethical and Legal Assessments Related to Robots and Systems .
• Tørresen, Jim (2021). Tutorial: Explainability, Trust and Ethics for Robots and Autonomous Systems.
• Saplacan, Diana (2021). Ethics and Technology.
• Tørresen, Jim (2021). How to achieve ethical artificial intelligence (AI) research and development?
• Tørresen, Jim (2021). Introducing a Multimodal Elderly Care Safety Alarm Robot.
• Tørresen, Jim (2021). Tutorial: Ethical Considerations in the Development and Use of Neural Information Processing Systems.
• Ellefsen, Kai Olav (2020). Hva kan intelligente maskiner lære av biologisk liv? Biolog. ISSN 0801-0722. s. 16–19.
• Tørresen, Jim (2020). Ethical Consideration in Robotics and Intelligent Systems Research.
• Tørresen, Jim (2020). Tutorial: Ethical Challenges and Opportunities within Computational Intelligence System Development.
• Thoresen, Sindre & Ellefsen, Kai Olav (2021). Solving Long Term Planning Problems with Direct Future Prediction. Universitetet i Oslo.
• Ellefsen, Kai Olav & Bjørsvik, Vegard (2021). Solving Sparse Reward Environments Using Go-Explore with Learned Cell Representation. Universitetet i Oslo.
• Gorton, Patrick & Ellefsen, Kai Olav (2020). Backpropagating to the Future: Evaluating Predictive Deep Learning Models. Universitetet i Oslo.
• Sørensen, Scott Andreas Fiskerstrand & Ellefsen, Kai Olav (2020). Comparing Model-Free and Model-Based Reinforcement Learning for Collision Avoidance. Universitetet i Oslo.

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