Dr Ekaterina Ivanova

Profile

I am a Lecturer (Assistant Professor) in Human-Computer Interaction. My main research interest is in multimodal human-robot interaction and haptic communication between agents as part of user-centred robotics. My long-term research goal is to develop robotic systems for medical applications and solutions for robot-assisted motor learning in diverse fields that are designed with humans and for humans. To achieve this goal I focus on human users by considering and integrating factors from robotics, data science, neuroscience, psychology and clinical expertise. In my research, I follow an experimental approach that is data-driven in developing new technology and quantifying user ability.

Teaching

I believe that teaching should stimulate the students’ critical thinking and motivate them to develop independent creative solutions. In my teaching, I facilitate it through interdisciplinary and hands-on approaches supported by strong theoretical foundations. I see myself as a knowledge mediator rather than a teacher and aim to create space for discussions and cooperation between students as well as provide information and practical tools that students can use for solving research and application challenges.

Semester A

• Lecturer for ECS7029P Research Methods

Semester B

• Module organiser for ECS733P Interactive System Design
• Module organiser for ECS661U User Experience Design

Research

Research Interests:

I want to develop assistive multimodal robotic systems and communication strategies with a focus on human users by considering and integrating factors from robotics, clinical rehabilitation and neuroscience. My human-centred robotics approach is based on experimental and data-driven methods to quantify user’s ability and develop new technologies. I want to develop user-friendly systems providing users optimal perception, learning and performance in various applications.

My previous projects in human-centred robotics:

Robot-assisted tele-rehabilitation for stroke survivors

Motor relearning after stroke is a lengthy process that requires an individualised, high-intensity training that should be continued after discharge from the clinic. My research in rehabilitation robotics focussed on developing systems enabling stroke patients to exercise at home autonomously or under supervision of a therapist. Within the German-founded project BeMobile, I investigated a number of aspects such as ergonomics, leaning efficiency, usability and motivation inter alia by including therapists and patients in the development and evaluation process. The human-centred design of the system was especially important for home applications to ensure efficient and comfortable use by therapists and patients.

Human-robot and human-human interaction in neurorehabilitation

A major goal of robot-based stroke rehabilitation is to achieve maximum possible recovery during high-intensity repetitive movement training through robotic assistance adapted to the patients’ abilities. During my PhD, I investigated how different control algorithms implemented in rehabilitation devices influence motor learning and perception. My hypothesis was that natural and learning-effective human-machine interaction could be achieved by programming the robotic control, so that it emulates therapists’ haptic behaviour. Therefore, I studied haptic human-human and, in particular, therapist-patient interaction and the ways to model it based on the experimentally derived data.

Haptic communication for motor learning in children

Humans can swiftly adapt to each other motions by completing a task together. It was shown that physical human interaction in a motor task increases the performance of both partners regardless of their skills level. It is explained through a sensory exchange and understanding of each other’s motion plan through a haptic channel. We call this phenomenon haptic communication. Within EU H2020 project CONBOTS I investigate whether the haptic communication has a congenital or developmental nature through studies with children. This knowledge is used to develop robot-assisted technologies for learning handwriting and playing of music instruments.

Mental load and teleoperation for space exploration

Majority of the tasks in space, which can be accomplished with robotics, require human intervention and teleoperation. Within UK EPSRC project FAIR-SPACE, I investigated how this teleoperation tasks influences operators’ mental load in a bimanual teleoperation task and performance under increased stress condition and technical difficulties such as communication delay.

Publications

Selected Publications:

Ivanova, E., Eden, J., Carboni, G., Krüger, J., & Burdet, E. (2022). Interaction with a reactive partner improves learning in contrast to passive guidance. Scientific Reports, 12(1), 15821. https://www.nature.com/articles/s41598-022-18617-7.

Ivanova, E., Eden, J., Zhu, S., Carboni, G., Yurkewich, A., & Burdet, E. (2021). Short time delay does not hinder haptic communication benefits. IEEE Transactions on Haptics, 14(2), 322-327. https://ieeexplore.ieee.org/document/9431719

Ivanova, E., Carboni, G., Eden, J., Krüger, J., & Burdet, E. (2020). For motion assistance humans prefer to rely on a robot rather than on an unpredictable human. IEEE Open Journal of Engineering in Medicine and Biology, 1, 133-139. https://ieeexplore.ieee.org/document/9069170

All publications can be found on Google Scholar: https://scholar.google.com/citations?user=7h4YXj0AAAAJ&sortby=pubdate