| Everyone has experienced muscle fatigue during daily life, but it is more common during physical training or after an illness. In our research project, the muscle fatigue estimator is used as a control unit that regulates the amount of support provided by an exoskeletal system. To be more precise, the estimator is intended to predict muscle fatigue during dynamic arm contraction. For this purpose, muscle activity is measured by Electromyography (EMG), and arm movements are recorded by additional inertial measurement units (IMU). This project is divided into three parts. First, the task environment must be developed, preferable as a small game (like Jump and Run), which is controlled by arm movements. Second, the data acquisition, here up to 8 participants have to perform the previously developed task. Third, the data analysis, includes state-of-the-art feature extraction and estimator development. Depending on individual interest and time-scale the different parts can be adapted. Due to the current corona situation, most of the work should be done from home, with communication via WebEx, etc. However, the final data acquisition must be done in the laboratory under controlled hygienic conditions. |
| Planned steps with estimated time: Literature review (30 days)Task development (30 days)Data acquisition (15 days)Data analysis(60 days)Writing Task: Master Thesis (45 days) |
| Prerequisites: Interest, motivation and knowledge in mathematics and informatics. Programming skills in Matlab® or Python, Unity, Java Proficient use of scientific work |
| Begin and duration: Immediately, 3-6 months |
| Co-supervisors: Marie Schmidt, M. Sc. marie.schmidt@hs-ruhrwest.de Supervisors: Prof. Dr. Ioannis Iossifidis, iossifidis@hs-ruhrwest.de |