| 1. |
- Demir Kanik, Sumeyra Ummuhan, PhD, et al.
(författare)
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Improving EEG-based Motor Execution Classification for Robot Control
- 2022
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Ingår i: Proceedings 14th International Conference, SCSM 2022, Held as Part of the 24th HCI International Conference, HCII 2022. - Cham : Springer Nature. ; , s. 65-82
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Konferensbidrag (refereegranskat)abstract
- Brain Computer Interface (BCI) systems have the potential to provide a communication tool using non-invasive signals which can be applied to various fields including neuro-rehabilitation and entertainment. Interpreting multi-class movement intentions in a real time setting to control external devices such as robotic arms remains to be one of the main challenges in the BCI field. We propose a learning framework to decode upper limb movement intentions before and during the movement execution (ME) with the inclusion of motor imagery (MI) trials. The design of the framework allows the system to evaluate the uncertainty of the classification output and respond accordingly. The EEG signals collected during MI and ME trials are fed into a hybrid architecture consisting of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) with limited pre-processing. Outcome of the proposed approach shows the potential to anticipate the intended movement direction before the onset of the movement, while waiting to reach a certainty level by potentially observing more EEG data from the beginning of the actual movement before sending control commands to the robot to avoid undesired outcomes. Presented results indicate that both the accuracy and the confidence level of the model improves with the introduction of MI trials right before the movement execution. Our results confirm the possibility of the proposed model to contribute to real-time and continuous decoding of movement directions for robotic applications.
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| 2. |
- Güneysu Özgür, Arzu, et al.
(författare)
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Designing Online Multiplayer Games with Haptically and Virtually Linked Tangible Robots to Enhance Social Interaction in Therapy
- 2022
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Ingår i: RO-MAN 2022 - 31st IEEE International Conference on Robot and Human Interactive Communication: Social, Asocial, and Antisocial Robots. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 358-364
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Konferensbidrag (refereegranskat)abstract
- The social aspects of therapy and training are important for patients to avoid social isolation and must be considered when designing a platform, especially for home-based rehabilitation. We proposed an online version of the previously proposed tangible Pacman game for upper limb training with haptic-enabled tangible Cellulo robots. Our main objective is to enhance motivation and engagement through social integration and also to form a gamified multiplayer rehabilitation at a distance. Thus, allowing relatives, children, and friends to connect and play with their loved ones while also helping them with their training from anywhere in the world. As well as connecting therapists to their patients through haptically linking capabilities. This is especially relevant when there are social distancing measures which might isolate the elderly population, a majority of all rehabilitation patients.
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| 3. |
- Güneysu Özgür, Arzu, et al.
(författare)
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Designing Tangible Robot Mediated Co-located Games to Enhance Social Inclusion for Neurodivergent Children
- 2022
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Ingår i: IDC '22. - New York : Association for Computing Machinery. - 9781450391979 ; , s. 536-543, s. 536-543
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Konferensbidrag (refereegranskat)abstract
- Neurodivergent children with cognitive and communicative difficulties often experience a lower level of social integration in comparison to neurotypical children. Therefore it is crucial to understand social inclusion challenges and address exclusion. Since previous work shows that gamified robotic activities have a high potential to enable inclusive and collaborative environments we propose using robot-mediated games for enhancing social inclusion. In this work, we present the design of a multiplayer tangible Pacman game with three different inter-player interaction modalities: semi-dependent collaborative, dependent collaborative, and competitive. The initial usability evaluation and the observations of the experiments show the benefits of the game for creating collaborative and cooperative practices for the players and thus also potential for social interaction and social inclusion. Importantly, we observe that inter-player interaction design affects the communication between the players and their physical interaction with the game.
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| 4. |
- Güneysu Özgür, Arzu, et al.
(författare)
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Lessons Learned from in the Wild Child-Robot Interaction in Multiple Ecosystems of Care and Education
- 2023
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Ingår i: HAI 2023 - Proceedings of the 11th Conference on Human-Agent Interaction. - : Association for Computing Machinery (ACM). ; , s. 142-151
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Konferensbidrag (refereegranskat)abstract
- We present here some lessons learned from observations and applications of child-robot interaction research in diverse ecosystems such as schools, therapy centers, and hospitals, where the interaction was facilitated in real-world circumstances rather than lab settings. Specifically, we use observational results from our reflections on multiple child-robot interaction practices in the wild conducted over a 9-year research period. Using these exploratory studies, we outline some general design considerations and adaptation guidelines for improving the design and implementation of robotic systems in healthcare and education that might lead to more practical, feasible, and ethically sustainable results.
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| 5. |
- Güneysu Özgür, Arzu, et al.
(författare)
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The effect of gamified robot-enhanced training on motor performance in chronic stroke survivors
- 2022
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Ingår i: Heliyon. - : Elsevier BV. - 2405-8440. ; 8:11, s. e11764-
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Tidskriftsartikel (refereegranskat)abstract
- Task-specific training constitutes a core element for evidence-based rehabilitation strategies targeted at improving upper extremity activity after stroke. Its combination with additional treatment strategies and neurotechnologybased solutions could further improve patients' outcomes. Here, we studied the effect of gamified robot-assisted upper limb motor training on motor performance, skill learning, and transfer with respect to a non-gamified control condition with a group of chronic stroke survivors. The results suggest that a gamified training strategy results in more controlled motor performance during the training phase, which is characterized by a higher accuracy (lower deviance), higher smoothness (lower jerk), but slower speed. The responder analyses indicated that mildly impaired patients benefited most from the gamification approach. In conclusion, gamified robot-assisted motor training, which is personalized to the individual capabilities of a patient, constitutes a promising investigational strategy for further improving motor performance after a stroke.
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| 6. |
- Olsen, J. K., et al.
(författare)
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Leveraging eye tracking to understand children's attention during game-based, tangible robotics activities
- 2022
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Ingår i: International Journal of Child-Computer Interaction. - : Elsevier BV. - 2212-8689 .- 2212-8697. ; 31
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Tidskriftsartikel (refereegranskat)abstract
- The difficulty in maintaining attention can interfere with the acquisition of critical academic skills. Recently, researchers have used embodied and game-based learning to support skill acquisition for children with learning difficulties. In this context, robots can be an interesting asset to foster engagement and investigate game dynamics. However, it is still an open question of how to develop adaptive learning environments for children with learning difficulties. Before one can provide effective adaptation, a first step is needed to understand the differentiating behaviors during the activity for children with attention difficulties. Three such differentiating behaviors are how a child divides his or her attention during the learning activity, the child's level of cognitive load, and the child's physiological fatigue, which are the focus of our study Using a robot assisted, gamified activity, we conducted a user study with 18 children having difficulty in maintaining attention. Using process mining techniques and eye-tracking data, we found the importance of integrating the autonomous robots into the attention patterns to successfully complete a game and the influence their behaviors can have on the participant's attention. This importance was supported by the cognitive load of participants decreasing the more they focused on the autonomous robots in successful games. This work contributes to the understanding of children's behaviors during tangible game-based activities and can be used to build effective adaptation for children with attention difficulties.
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| 7. |
- Sylla, C., et al.
(författare)
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Smart Toys, Smart Tangibles, Robots and other Smart Things for Children
- 2022
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Ingår i: International Journal of Child-Computer Interaction. - : Elsevier BV. - 2212-8689 .- 2212-8697. ; 33, s. 100489-
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Tidskriftsartikel (refereegranskat)abstract
- Smart toys are regarded as able to offer possibilities to develop social, cognitive, and behavioural skills (among others); however, while appealing, such claims are not yet substantiated by rigorous and sufficient scientific evidence. The first edition of the workshop on Smart Toys, Smart Tangibles, Robots and other Smart Things for Children took place at the 19th Interaction Design and Children Conference (IDC'20), bringing together experts from different fields working on smart technologies for children. Following the workshop, participants were invited to submit their work to this Special Issue in the International Journal of Child Computer Interaction. Together, these contributions address relevant issues in the emerging areas of smart toys and interactive technologies for children, offering different approaches and perspectives, such us as guidelines for the prototyping of innovative Toy Interfaces; guidelines to support the design of inclusive technologies for children with special needs, design considerations about the social aspects of the experience with the technology, concerns of data protection and children's understanding of data. This editorial introduction draws attention to the great potential and need for furthering research on the field to unleash the potential that the new generation of smart toys and related devices may bring.
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| 8. |
- Tozadore, Daniel C., et al.
(författare)
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Robots for Learning 7 (R4L) : A Look from Stakeholders' Perspective
- 2023
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Ingår i: HRI 2023. - New York, NY, USA : Association for Computing Machinery (ACM). ; , s. 935-937
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Konferensbidrag (refereegranskat)abstract
- This year's conference theme "HRI for all" not just raises the importance of reflecting on how to promote inclusion for every type of user but also calls for careful consideration of the different layers of people potentially impacted by such systems. In educational setups, for instance, the users to be considered first and foremost are the learners. However, teachers, school directors, therapists and parents also form a more secondary layer of users in this ecosystem. The 7th edition of R4L focuses on the issues that HRI experiments in educational environments may cause to stakeholders and how we could improve on bringing the stakeholders' point of view into the loop. This goal is expected to be achieved in a very practical and dynamic way by the means of: (i) lightening talks from the participants; (ii) two discussion panels with special guests: One with active researchers from academia and industry about their experience and point of view regarding the inclusion of stakeholders; another panel with teachers, school directors, and parents that are/were involved in HRI experiments and will share their viewpoint; (iii) semi-structured group discussions and hands-on activities with participants and panellists to evaluate and propose guidelines for good practices regarding how to promote the inclusion of stakeholders, especially teachers, in educational HRI activities. By acquiring the viewpoint from the experimenters and stakeholders and analysing them in the same workshop, we expect to identify current gaps, propose practical solutions to bridge these gaps, and capitalise on existing synergies with the collective intelligence of the two communities.
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| 9. |
- Tozadore, Daniel C., et al.
(författare)
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Teacher's Perception on Social Robots to Promote the Integration of Children with Migration Background
- 2023
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Ingår i: HAI '23: Proceedings of the 11th International Conference on Human-Agent Interaction. - : Association for Computing Machinery (ACM).
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Konferensbidrag (refereegranskat)abstract
- This paper describes the first steps of an ongoing participatory design with teachers from Switzerland to co-create Human-Robot Interaction setups for integrating children with migration history. The herein presented phase had two main goals: (i) initially mapping the current issues and the teachers’ strategy when integrating these children, and (ii) understanding teachers’ perceptions regarding social robots for this goal. Results show that teachers we interviewed are already using technology to communicate with immigrant children, not necessarily for inclusion or promote socialisation with their peers, but simply to understand them. Findings also point to a well-defined application of social robots in inclusion activities, even when never seeing or using them, which contradicts previous results in the literature and which gives potential ways to unfold the next steps of the participatory design.
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| 10. |
- Yuceturk, Nihal Ezgi, et al.
(författare)
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Predictive Analysis of Errors During Robot-Mediated Gamified Training
- 2022
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Ingår i: 2022 International Conference On Rehabilitation Robotics (ICORR). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- This paper presents our approach to predicting future error-related events in a robot-mediated gamified physical training activity for stroke patients. The ability to predict future error under such conditions suggests the existence of distinguishable features and separated class characteristics between the casual gameplay state and error prune state in the data. Identifying such features provides valuable insight to creating individually tailored, adaptive games as well as possible ways to increase rehabilitation success by patients. Considering the time-series nature of sensory data created by motor actions of patients we employed a predictive analysis strategy on carefully engineered features of sequenced data. We split the data into fixed time windows and explored logistic regression models, decision trees, and recurrent neural networks to predict the likelihood of a patient making an error based on the features from the time window before the error. We achieved an 84.4% F1-score with a 0.76 ROC value in our best model for predicting motion accuracy related errors. Moreover, we computed the permutation importance of the features to explain which ones are more indicative of future errors.
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