TY - CHAP A1 - Fuhrmann, Thomas T1 - Semi-Structured Lab Projects in Communication Engineering Education T2 - 2023 IEEE Global Engineering Education Conference (EDUCON), 01-04 May 2023, Kuwait N2 - It is generally known that project-based learning is a very important part of engineering education to connect theoretical knowledge with practical work. Students learn to apply their knowledge to real-world challenges as it is the case in their later professional life. If students are not used to project work or the scientific topic is new and relatively complex, they may be overwhelmed. The consequence is that students achieve poor results, are frustrated, and therefore learning success is low. Semi-structured projects are introduced that combine the advantages of structured experiments with projects. The project work is structured into several parts with detailed descriptions of the tasks. In the end, students get similar results to doing a free project, but the success rate is higher due to higher guidance. Therefore, these semi-structured projects are seen to be an appropriate method to guide students to learn how to do project work. The feedback from most students is very positive. Some students with no previous lab experience complained about the project work and wished for more guidance to become familiar with lab work. In sum, the student feedback is encouraging to develop semi-structured projects further. KW - Knowledge engineering KW - Task analysis KW - Communication engineering education Y1 - 2023 U6 - https://doi.org/10.1109/EDUCON54358.2023.10125244 SP - 1 EP - 5 PB - IEEE ER - TY - CHAP A1 - Pohlt, Clemens A1 - Haubner, Franz A1 - Lang, Jonas A1 - Rochholz, Sandra A1 - Schlegl, Thomas A1 - Wachsmuth, Sven T1 - Effects on User Experience During Human-Robot Collaboration in Industrial Scenarios T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 7-10 Oct. 2018, Miyazaki, Japan N2 - In smart manufacturing environments robots collaborate with human operators as peers. They even share the same working space and time. An intuitive interaction with different input modalities is decisive to reduce workload and training periods for collaboration. We introduce our interaction system that is able to recognize gestures, actions and objects in a typical smart working scenario. As key aspect, this article considers an empirical investigation of input modalities (touch, gesture), individual differences (performance, recognition rate, previous knowledge) and boundary conditions (level of automation) on user experience. Therefore, answers from 31 participants within two experiments are collected. We show that the arrangement of the human-robot collaboration (input modalities, boundary conditions) has a significant effect on user experience in real-world environments. This effect and the individual differences between participants can be measured utilizing recognition rates and standardized usability questionnaires. KW - Collaboration KW - Gesture recognition KW - Robots KW - Task analysis KW - Three-dimensional displays KW - Training KW - Tutorials Y1 - 2018 U6 - https://doi.org/10.1109/SMC.2018.00150 SP - 837 EP - 842 PB - IEEE ER - TY - CHAP A1 - Pohlt, Clemens A1 - Schlegl, Thomas A1 - Wachsmuth, Sven T1 - Human Work Activity Recognition for Working Cells in Industrial Production Contexts T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 6-9 Oct. 2019, Bari, Italy N2 - Collaboration between robots and humans requires communicative skills on both sides. The robot has to understand the conscious and unconscious activities of human workers. Many state-of-the-art activity recognition algorithms with high performance rates on existing benchmark datasets are available for this task. This paper re-evaluates appropriate architectures in light of human work activity recognition for working cells in industrial production contexts. The specific constraints of such a domain is elaborated and used as prior knowledge. We utilize state-of-the-art algorithms as spatiotemporal feature encoders and search for appropriate classification and fusion strategies. Furthermore, we combine keypoint-based with appearance-based approaches to a multi-stream recognition system. Due to data protection rules and the high effort of data annotation within industrial domains only small datasets are available that reflect production aspects. Therefore, we use transfer learning approaches to reduce the dependency on data volume and variance in the target domain. The resulting recognition system achieves high performance for both singular person action and human-object interaction. KW - Activity recognition KW - Pose estimation KW - Robots KW - Task analysis KW - Three-dimensional displays KW - Two dimensional displays Y1 - 2019 U6 - https://doi.org/10.1109/SMC.2019.8913873 SP - 4225 EP - 4230 ER -