Master Project

Towards personalized gait patterns for
robot-assisted lower-limb rehabilitation

The aim of gait rehabilitation is to achieve independent ambulation after neurological injuries, such as stroke. Various technologies, including treadmill-based robots and wearable exoskeletons have been employed to address the challenges of regaining walking function. However, research on motor learning has suggested the need for a paradigm-shift towards personalized therapy, by which the robotic action is tailored and adapted to the patient’s needs.

In detail, most treadmill-based rehabilitation robots assist the user to move according to a predefined trajectory, which is played back over time. These trajectories are usually based on recorded gait data from healthy people. However, the major drawback of pre-recorded trajectories is the lack of flexibility, especially in case of rigid mobilization exercises.

In this project, to improve the personalization of gait rehabilitation exercises, you will first review available strategies to generate gait trajectories for locomotion, model physiological gait, and finally develop a synthetic trajectory-generation algorithm that creates parameterized gait profiles based on physiological measures. Finally, the algorithm is implemented (MATLAB/SIMULINK) in a treadmill-based gait rehabilitation robot (Lokomat, Hocoma AG, Switzerland). Further improvements can be introduced with online adaptation of these trajectories in terms of foot clearance, step length, step duration, walking speed, etc. according to the current status of the patient.

Aim of the project

Development and validation of a synthetic trajectory-generation algorithm for gait rehabilitation, based on physiological measures, to improve therapy personalization.

Project phases

  • Literature research: Review state-of-the-art trajectory generation algorithms for lower-limb exoskeletons and gait rehabilitation robots.

  • Development phase: Data collection of natural walking trajectories with healthy people, modeling of gait physiological patterns, and development of a synthetic algorithm to generate parameterized physiological gait trajectories.

  • Validation phase: Experiments (in simulation and possibly with the human-in-the-loop) to validate the algorithm. The algorithm is tested with a customized version of a treadmill-based gait rehabilitation robot (Lokomat, Hocoma AG, Switzerland).

  • Scientific report: The methods, results, and all research activities are documented in a scientific report.

Preferred skills

  • Basic knowledge of Robotics and Mechatronics

  • Good knowledge of Biomechanics

  • Good knowledge of MATLAB and SIMULINK

  • Interest in running human experiments