About the Project
• Follow a user-centred approach to co-design collaborative robot simulation scenarios within the healthcare context
• Design and prototype social navigation techniques to optimise human-robot collaboration in healthcare settings
• Develop and deploy social navigation algorithms integrating contextual information based on quantitative (e.g., sensor data) and qualitative data (e.g., user’s intentions and experiences) in healthcare scenarios
• Evaluate social navigation for human-robot interaction in healthcare context
The role of emerging technologies driving healthcare innovation to enhance healthcare efficiency is estimated to create £350 billion in technology-driven value by 2025 [Singhal, McKinsey 2020]. The smart healthcare of the future will combine data, software and algorithms to personalise healthcare delivery. Robots supporting healthcare staff would also play a role as part of a growing digital ecosystem of connected devices, which aims to improve patients' care experiences [Bo Chen, McKinsey 2020]. While robots were mostly found in manufacturing settings, we now foresee a future where humans will increasingly interact with robots in different areas of society. Such scenarios raise new questions about everyday interactions with robots under complex and uncertain environments, expectations and deployment challenges for safe and comfortable human-robot interactions.
Robots have the potential to support healthcare workers in medical environments, thus relieving clinical staff on routinely duties and hospital services, while also allowing healthcare workers to better distribute their time to patients. Some examples of such support capabilities include robots that assist surgeons through instrument handling, or robots that can conduct intra-hospital transport, as well as other repetitive tasks, which could increase the efficiency in many healthcare scenarios.
By taking a human-centred design approach, this project aims to identify human robot interaction techniques to provide contextualised social navigation strategies to improve mobile robots’ algorithms in healthcare service environments.
The overall objective is to contribute to the development of everyday robotics in healthcare, exploring what kind of task can be supported by robots and learning from a dynamic healthcare environment to enhance robots to provide appropriate social navigation.
This research will address the following research questions:
1. Which are the key aspects (social navigation strategies, cues, preferences) for human-robot interaction that shape the dynamics of collaborative tasks in healthcare environments
2. How to improve social navigation algorithms informed by qualitative measurements?
3. What kind of metrics for user experience and social acceptance emerge from Social
Individual Index(SII) and Social Group Index (SGI) in HRI?
This work will involve interdisciplinary work, conducting lab-based experiments, simulations and ethnographic studies in healthcare, human-robot interaction, robotics and social navigation.
Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas.
Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.
Application Information: If you would like to be considered for the School Funded Application, please submit your application before the 30th June 2021.
In the funding field of your application, insert “I am applying for 2021 PhD Scholarship in Computer Science and Informatics”, and specify the project title and supervisors of this project in the text box provided.
Apply online: https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/computer-science-and-informatics - Please read the "How to apply" instructions carefully prior to application.
For more information about this project, please contact Dr Carolina Fuentes Toro, FuentesToroC@cardiff.ac.uk
In the Funding field of your application, insert "I am applying for 2021 PhD Scholarship" and specify the project title and supervisor of this project in the fields provided.
This project is also open to Self-Funded students worldwide.
2. Carolina Fuentes, Martin Porcheron, Joel E. Fischer, Enrico Costanza, Obaid Malilk, and Sarvapali D. Ramchurn. "Tracking the consumption of home essentials." In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1-13. 2019.
3. Esmeralda Abarca, Solange Campos, Valeria Herskovic, and Carolina Fuentes. 2018. Perceptions on technology for volunteer respite care for bedridden elders in Chile. International Journal of Qualitative Studies on Health and Well-being.
4. Carolina Fuentes, Carlos Hernández, Lizbeth Escobedo, Valeria Herskovic, and Mónica Tentori. 2014. Promoting Self-Reflection of Social Isolation Through Persuasive Mobile Technologies: The Case of Mother Caregivers of Children With Cancer. International Journal of Human-Computer Interaction 30, 10.
5. Leandro Minku, Nervo Verdezoto, Stephan Reiff-Marganiec. 2017. From flying warehouse to robot toilets – five technologies that could shape the future. The Conversation. https://theconversation.com/from-flying-warehouses-to-robot-toilets-five-technologies-that-could-shape-the-future-81519
6. Stisen, A., Verdezoto, N., Blunck, H., Kjærgaard, M. B., & Grønbæk, K. (2016, February). Accounting for the invisible work of hospital orderlies: Designing for local and global coordination. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (pp. 980-992).
7. Hernández, J. D., Sobti, S., Sciola, A., Moll, M., & Kavraki, L. E. (2020). Increasing robot autonomy via motion planning and an augmented reality interface. IEEE Robotics and Automation Letters, 5(2), 1017–1023. https://doi.org/10.1109/LRA.2020.2967280
8. Truong, X. T., & Ngo, T. D. (2017). Toward Socially Aware Robot Navigation in Dynamic and Crowded Environments: A Proactive Social Motion Model. IEEE Transactions on Automation Science and Engineering, 14(4), 1743–1760. https://doi.org/10.1109/TASE.2017.2731371
9. Kruse, T., Pandey, A. K., Alami, R., & Kirsch, A. (2013). Human-aware robot navigation: A survey. Robotics and Autonomous Systems. https://doi.org/10.1016/j.robot.2013.05.007
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