I am a research assistant at the Bielefeld University of Applied Sciences and my research interest is in the fields of computer vision, machine learning and human-robot interaction.
During my doctoral studies at the Otto-von-Guericke-University Magdeburg, I developed new interaction methods to allow non-expert users the interactive restriction of their mobile robots’ workspaces in human-centered environments. These restriction areas cannot be directly recognized by the robot’s on-board sensors and/or require explicit knowledge of a human. Therefore, an interaction process between human and robot is necessary to incorporate humans’ preferences in the mobile robot’s navigation framework. For example, carpets should be circumvented to avoid navigation errors or certain rooms should not be entered due to privacy concerns. My research focused on questions of (1) how can certain user interfaces be employed to restrict a mobile robot’s workspace in a traditional home environment, (2) how can a smart home environment improve the interaction process and (3) how can a combination of robot and smart environment (network robot system) learn from user interactions and apply the knowledge in future interaction processes.
Previously, I worked in a research project on fall and activity recognition in a smart home environment. It is based on a small wearable device that is attached to the user’s waist. My activities involved the development of the hardware and the implementation of machine learning algorithms on the resource-limited hardware. The activity recognition system was successfully integrated into an intelligent environment to react on the current user’s activity.
- Researchgate: https://www.researchgate.net/profile/Dennis_Sprute
- Google Scholar: https://scholar.google.de/citations?user=DIZDEzUAAAAJ
- LinkedIn: https://www.linkedin.com/in/dennissprute