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The aim of this bachelor thesis is to introduce a product data management (PDM) system to reduce the current difficulties in the project team workflow. The main objective is to reduce or prevent problems in the project team's collaboration processes by establishing a centralized, structured data repository.
The process to achieve this goal was to first talk to the project team and monitor professors to find the most effective strategies. Early research on PDM/PLM (Product Lifecycle Management) was conducted through literature reviews, online searches, thesis reviews, and video resources.
In order to gain a comprehensive understanding of PDM systems, the thesis used a combination of group discussions and literature reviews. The emphasis was on the practical application, which included the customization of configurations.
In this paper, we propose an approach for gait phase detection for flat and inclined surfaces that can be used for an ankle-foot orthosis and the humanoid robot Sweaty. To cover different use cases, we use a rule-based algorithm. This offers the required flexibility and real-time capability. The inputs of the algorithm are inertial measurement unit and ankle joint angle signals. We show that the gait phases with the orthosis worn by a human participant and with Sweaty are reliably recognized by the algorithm under the condition of adapted transition conditions. E.g., the specificity for human gait on flat surfaces is 92 %. For the robot Sweaty, 95 % results in fully recognized gait cycles. Furthermore, the algorithm also allows the determination of the inclination angle of the ramp. The sensors of the orthosis provide 6.9 and that of the robot Sweaty 7.7 when walking onto the reference ramp with slope angle 7.9.