Her primary scientific interests are in Artificial intelligence, Human–robot interaction, Motion, Robot and Pattern recognition. Her study in Artificial intelligence is interdisciplinary in nature, drawing from both Wearable computer and Computer vision. Her Human–robot interaction research incorporates elements of Semantic differential, Social psychology, Simulation and Human–computer interaction.
Her studies in Motion integrate themes in fields like Motion estimation, Tree structure and Humanoid robot. Dana Kulic combines subjects such as Applied psychology and Animacy with her study of Robot. Dana Kulic studied Pattern recognition and Cluster analysis that intersect with Tree.
Her main research concerns Artificial intelligence, Robot, Computer vision, Motion and Human–computer interaction. Her Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition. Her research in the fields of Human–robot interaction, Robotics and Motion planning overlaps with other disciplines such as Preference learning.
The study incorporates disciplines such as Robot control and Articulated robot in addition to Human–robot interaction. Many of her research projects under Computer vision are closely connected to Accelerometer with Accelerometer, tying the diverse disciplines of science together. The concepts of her Motion study are interwoven with issues in Humanoid robot, Tree structure, Representation and Cluster analysis.
Her scientific interests lie mostly in Human–computer interaction, Robot, Artificial intelligence, Human–robot interaction and Reinforcement learning. Her study on Behavior-based robotics, Motion planning and Social robot is often connected to Preference learning as part of broader study in Robot. Her studies examine the connections between Motion planning and genetics, as well as such issues in Motion, with regards to Mobile robot, Hidden Markov model and Affect.
As a part of the same scientific study, Dana Kulic usually deals with the Artificial intelligence, concentrating on Pattern recognition and frequently concerns with Object detection. Human–robot interaction combines with fields such as Context and Field in her work. Her study looks at the relationship between Robotic arm and fields such as Maximum principle, as well as how they intersect with chemical problems.
Dana Kulic focuses on Robot, Human–computer interaction, Motion planning, Artificial intelligence and Behavior-based robotics. Her Robot research includes elements of Structure and User expectations. Her Human–computer interaction study integrates concerns from other disciplines, such as Wearable technology, Salient and Human–robot interaction.
Her work carried out in the field of Human–robot interaction brings together such families of science as Domain and Table. Her Convolutional neural network study, which is part of a larger body of work in Artificial intelligence, is frequently linked to High resolution, bridging the gap between disciplines. Her Set research includes themes of Motion and Robotics.
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Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots
Christoph Bartneck;Dana Kulic;Elizabeth A. Croft;Susana Zoghbi.
International Journal of Social Robotics (2009)
Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains
Dana Kulić;Wataru Takano;Yoshihiko Nakamura.
The International Journal of Robotics Research (2008)
Incremental learning of full body motion primitives and their sequencing through human motion observation
Dana Kulić;Christian Ott;Dongheui Lee;Junichi Ishikawa.
The International Journal of Robotics Research (2012)
Affective State Estimation for Human–Robot Interaction
Dana Kulic;Elizabeth A. Croft.
IEEE Transactions on Robotics (2007)
Pre-collision safety strategies for human-robot interaction
Dana Kulić;Elizabeth Croft.
Autonomous Robots (2007)
Body Movements for Affective Expression: A Survey of Automatic Recognition and Generation
Michelle Karg;Ali-Akbar Samadani;Rob Gorbet;Kolja Kuhnlenz.
IEEE Transactions on Affective Computing (2013)
Safe planning for human-robot interaction
Dana Kulić;Elizabeth A. Croft.
Journal of Robotic Systems (2005)
Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks
Terry T. Um;Franz M. J. Pfister;Daniel Pichler;Satoshi Endo.
international conference on multimodal interfaces (2017)
Measuring the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots
Christoph Bartneck;Dana Kulic;Elizabeth Croft.
human-robot interaction (2008)
Real-time safety for human–robot interaction☆
Dana Kulić;Elizabeth A. Croft.
Robotics and Autonomous Systems (2006)
Profile was last updated on December 6th, 2021.
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