Research Projects
Emotional Drone
Emotion Appropriateness in Human-Drone Interaction
Springer Intl. Journal of Social Robotics
Motivation:
Recent works have shown promising results on the use of emotions on social drones. Yet, little is known about emotion appropriateness when presented on drones in various contexts. Appropriate refers to emotions that are "suitable, acceptable, or correct for the particular circumstances". For example, would it be acceptable for a drone accompanying children to school to express sadness when a child is late? What about a drone showing anger when people trespass? This urges the need to explore the appropriateness of emotions across various situations for drones to provide foundations to current and future research on affective drones.
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AnZoMa
Anthropomorphism and Affective Perception: Dimensions, Measurements, and Interdependencies in Aerial Robotics
IEEE Transactions on Affective Computing
Motivation:
As robots become increasingly available in everyday life, it is crucial to understand people's affective perceptions of these systems. In particular, prior research showed that ground and aerial robots are often anthropomorphized, which affects users' affective perceptions, interpretations, and expectations. Yet, there is increasing evidence that anthropomorphism is a complex multi-layered phenomenon and that its effects on user expectations might be non-linear. Specifically, we find reports of aerial robots, i.e. drones, being either anthropomorphized or zoomorphized (i.e., perceived as a person or an animal) regardless of their design.
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Above & Beyond (award paper)
A Scoping Review of Domains and Applications for Human-Drone Interaction
ACM Conference on Human Factors in Computing Systems (CHI 2022)
Motivation:
Over nearly a decade, the Human-Drone Interaction (HDI) community has been investigating new technology, artifacts, and interaction techniques allowing both remote and collocated interaction with drones. Despite the significant potential this work creates, a holistic view of how HDI research developed in the past decade is missing. We here provide a high-level perspective on current and future use cases for interacting with drones that are described, studied, and envisioned in the HDI body of work.
Drone in Love
Emotional Perception of Facial Expressions on Flying Robots
ACM Conference on Human Factors in Computing Systems (CHI 2021)
Motivation:
Recently, researchers have highlighted novel opportunities created by social drones that operate in human spaces and can support people in their daily lives. Yet, designing a social drone is not trivial, and we are only at the beginning of understanding which factors influence people's perception of drones. We address this gap in the literature by designing facial expressions to convey emotional states on a social drone.
Public Drone
Attitude Towards Drone Capabilities in Various Contexts
ACM International Conference on Mobile Human-Computer Interaction (MobileHCI 2021)
Motivation:
Despite the rich range of potential applications (e.g., pedestrian guidance, search and rescue operations) for public drones, there is a plethora of obstacles to overcome to facilitate their acceptability in public spaces. While various factors will contribute to acceptability of drones in public spaces, such as privacy, other factors may also moderate this acceptability. We here focus on the perceived severity of context - defined as the perceived severity of harm if no action is taken - which was shown to influence behavioral intention to use protective technology, such as embodied by public drones.
A Generalized User Interface Concept to Enable Retrospective System Analysis in Monitoring Systems
International Conference on Human-Computer Interaction (HCII 2020). Lecture Notes in Computer Science.
Motivation:
Nowadays, many large technical systems are controlled by semi-autonomous software systems that process large amounts of data. Yet, human operators are required to monitor the system at sporadic time points for example to analyze fault conditions that have occurred since the last observation. Due to sporadic observations, human operators face monitoring situations in which they are ''out-of-the loop''. We here create a graphical user interface concept that supports human operators in developing valid situation awareness of systems and provides an intuitive functionality for efficient retrospective system analysis.
