Horia Maior – new paper

Telepresence Robots for Remote Participation in Higher

Junrui Hu, Gisela Reyes-Cruz, Horia Alexandru Maior, Joel Fischer

Telepresence robotics enable people to synchronously communicate
and interact at a distance. The Covid-19 pandemic caused in-person
teaching and research activities to migrate online in almost all
society sectors (including higher education). In hybrid learning
environments, telepresence robots have the potential to increase
the effectiveness of distance learning by enhancing user presence,
allowing students to remain physically embodied and socially connected. However, to be accepted, trusted, and adopted in higher
education, more work is needed to investigate the specific requirements of telepresence robotics in these settings. In this paper, we
present the results of a mixed methods study exploring how the
use of a telepresence robot in a simulated learning and research
environment affects trust in and user experience of telepresence
robots used in higher education. We aimed to understand users’
attitudes and requirements for the use of telepresence robots in academic teaching and research. Our findings suggest that the level of
trust is contingent upon the user, the performance of the robot, and
the credibility of the developing organisation. We additionally map
out the current challenges encountered by the use of telepresence
robots in these settings and provide suggestions to improve user
experience by highlighting new software and hardware capabilities.

Access the paper here.

Neelima Sailaja – new paper

User-Centred Repair: From Current Practices to Future Design

Damla, Kilic & Neelima Sailaja:

From the kitchen to the bathroom, homes are now equipped with various technological devices like smart vacuums, intelligent mirrors, digital thermostats, wearables, and voice-controlled assistants such as Amazon Alexa. This surge in ubiquitous technologies contributes to the growing concern of electronic waste, or e-waste, globally. Research focuses on developing strategies for e-waste reduction, and is considering a range of approaches on governmental, industrial and societal levels. To gain a comprehensive understanding of smart device repair, our research was structured into several distinct tasks, each supported by semi-structured interviews, each tailored to explore different facets of repair behaviours and decision-making. A total of fifteen one-on-one study sessions were conducted as part of this research. The study’s findings will be presented in three primary sections, each shedding light on distinct aspects of repair practices and decision-making. Along with the aforementioned results around current repair practices, repair decision making drivers and future expectations, our paper offers two significant contributions to human-computer interaction (HCI) research and practice. First, we place our findings in a broader context, anchoring them within the existing body of literature on HCI, repair practices, and the IoT. Second, we leverage our findings along with wider literature to conclude our paper with a set of design recommendations that align with current actual user practice around IoT repair; is inclusive of user expectations around every day reparability in future IoT; and enables user decision making around IoT repair thereby making IoT reparability an accessible and equitable process.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 14718 LNCS, Pages 52 – 712024 12th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2024, held as part of the 26th HCI International Conference, HCII 2024Washington29 June 2024through 4 July 2024: DOI10.1007/978-3-031-59988-0_4


Horia to join Max Wilson at this year’s Royal Society Summer Science Exhibition

Horizon Transitional Assistant Professor, Horia Maior will be joining Dr Max Wilson, Associate Professor of Human-Computer Interaction, School of Computer Science, University of Nottingham at the Royal Society Summer Science Exhibition in London (2-7 July) to demonstrate different types of wearable brain scanning devices.  The ‘Brain Team’ will be gathering people’s opinions on how they feel about using consumer neurotechnology.  More information about this event can be found in this University of Nottingham Press Release.


Horia Maior – new paper

Adaptive Human-Swarm Interaction based on Workload Measurement using Functional Near-Infrared Spectroscopy

One of the challenges of human-swarm interaction (HSI) is how to manage the operator’s workload. In order to do this, we propose a novel neurofeedback technique for the realtime measurement of workload using functional near-infrared spectroscopy (fNIRS). The objective is to develop a baseline
for workload measurement in human-swarm interaction using fNIRS and to develop an interface that dynamically adapts to the operator’s workload. The proposed method consists of using fNIRS device to measure brain activity, process this through a machine learning algorithm, and pass it on to the
HSI interface. By dynamically adapting the HSI interface, the swarm operator’s workload could be reduced and the performance improved.

Read more from this paper here

Horia Maior – new paper – consumer wearable technologies

The CHI’24 Workshop on the Future of Cognitive Personal Informatics

While Human-Computer Interaction (HCI) has contributed to demonstrating that physiological measures can be used to detect cognitive changes, engineering and machine learning will bring these to application in consumer wearable technology. For HCI, many open questions remain, such as: What happens when this becomes a cognitive form of personal informatics? What goals do we have for our daily cognitive activity? How should such a complex concept be conveyed to users to be useful in their everyday lives? How can we mitigate potential ethical concerns? This is different to designing BCI interactions; we are concerned with understanding how people will live with consumer neurotechnology. This workshop will directly address the future of Cognitive Personal Informatics (CPI), by bringing together design, BCI and physiological data, ethics, and personal informatics researchers to discuss and set the research agenda in this inevitable future.

Read more from this paper here


AI Safety Summit workshop

Horizon Transitional Assistant Professor Horia Maior shares his experience of facilitating a workshop on Ethics and Responsible Innovation in the application of AI within the nuclear energy sector at the AI Summit hosted by the UK Atomic Energy Authority and the Robotics and AI Collaboration (RAICo)..

“Together with Dr Virginia Portillo and Dr Pepita Barnard I had the privilege of facilitating a workshop on Ethics and Responsible Innovation in the application of AI within the nuclear energy sector.

The summit was nothing short of inspiring. It was an incredible opportunity to delve into the critical discussions surrounding AI safety and ethics, particularly in an industry as crucial as nuclear energy.

But what made the summit truly remarkable was the chance to connect with a diverse group of passionate researchers and practitioners. From academia and industry to the public sector, the breadth of knowledge, enthusiasm, and willingness to collaborate was genuinely inspiring.

I must also shout out to my University of Nottingham colleagues Jack Chaplin, and Giovanna Martinez Arellano who delivered two other enlightening workshops on AI Safety and AI Assurance, as well as colleagues from the Omnifactory® for the amazing facility tour provided on the day.

Reflecting on these amazing few days, I’m filled with gratitude for the opportunity to contribute to such vital conversations, to learn, network, and collaborate, and advance the ethical use of AI across different sectors.

Thank huge thank you to Horizon Digital Economy Research, UKRI Trustworthy Autonomous Systems (TAS) Hub and Responsible Ai UK  for supporting our work!

Thanks also to the UK Atomic Energy AuthorityRAICo, the University of NottinghamPhill Mulvana MSc FIET CMgr MCMIDavid Branson III and everyone involved for an unforgettable summit.


Upcoming TAS Hub – Living with AI Podcast

Horizon TAP Horia Maior talks about his research and involvement in the Open All Senses project and the TAS Art project in a TAS Hub Podcast session entitled ‘Living with AI: Telepresence the Human in the Robot’, release date 5th July

Also participating in the podcast, Praminda Caleb-Solly discusses the Digital Twins for Human-Assistive Robot Teams project  and Ayse Kucukyilmaz the Cognitive HumAn in the looP TEleopeRations (CHAPTER) project.

Privacy-preserving & machine-learned catchment models for national dietary surveillance via digital footprint data

Horizon’s Transitional Assistant Professor, Georgiana Nica-Avram has co-authored a paper ‘Privacy-preserving & machine-learned catchment models for national dietary surveillance via digital footprint data’ which was submitted to the 2022 IEEE International Conference on Big Data.


Big data from food retail stores is increasingly being used for population dietary surveillance, epidemiological studies of diet-related diseases, and evaluations of public health interventions. However, for retail data to be useful it is necessary to understand the spatio-temporal variation of when and where food is purchased and consumed. While some customers willingly share home location data with retailers as part of loyalty programs such data is typically too fine-grained/sensitive to be applied for research purposes. The aim of this study was to analyse differences between privacy-preserving models and actual retail catchments, and investigate if machine learning techniques could improve the accuracy of such catchment models. Based on a UK-wide sample of 4 million grocery store loyalty card holders, covering 485 million transactions over 29 months (2019-2021) and distributed across 33,000 neighbourhoods (Lower Super Output Areas, or LSOA), the study demonstrates how models trained on geolocated data perform at predicting, per store, catchment areas which contain 50, 80, and 95% of its customers’ primary location. Through comparative assessment of machine learning approaches, we find better performance from tree-based models (RF, XGB) with the best performance from an XGB model achieving an R2 of 0.72 and MAE of 1.06. To conclude, we review variable importance measures using SHAP values and discuss the relative merits of including specific features when modeling catchment areas. © 2022 IEEE.