Konstantin Preußer M.Sc.

This research project investigates the early prediction of human locomotion intent and transitions between different locomotion modes for wearable robotic assistance systems. The focus lies on machine learning methods and multimodal sensor data, such as EEG, IMU, and EMG signals, to identify upcoming changes in locomotion before they occur.

The aim is to investigate suitable sensor information, data representations, and learning approaches and to evaluate their influence on prediction accuracy, robustness, and prediction horizon. The project offers several opportunities for student projects and Master's theses in areas such as multimodal data analysis, signal processing, machine learning, deep learning, and wearable robotics.