• Robots

    To investigate human and animal locomotion, a number of legged robots were developed in our group since 2004. Read about the bipedal robot BioBiped or the research in the Locomorph project focusing on morphology and morphosis strategies in locomotion.

  • Prostheses

    To investigate models of the muscle-tendon dynamics on humans we developed the research platform PAKO. Using our insights on gait biomechanics, walking and running could be realized with the robotic Walk-Run Ankle prosthesis.

  • Facilities

    Several indoor and outdoor facilities with state-of-the-art measurement equipment helps us to perform experiments on humans, animals and robots. Details can be found here: Facilities.

  • Experiments

    Both in research projects and in teaching courses at the Sports Science Institut at TU Darmstadt experimental studies are performed. Outcomes from student research and educational projects on biomechanics can be found in the awarded Teaching Wiki of our institute.

  • Models

    Models help us to study the fundamental principles of human and animal locomotion. The derived biomechanical concepts can be applied to bipedal robots, exoskeletons or prosthesis. In the European project Balance, we are working on an active orthosis.


Pick of the Month

Stance and Swing Detection Based on the Angular Velocity of Lower Limb Segments During Walking

A new concept for stance and swing detection based on lower limb segments is introduced in a recently published paper by Grimmer et al. in Frontiers in Neurorobotics Stance and Swing Detection Based on the Angular Velocity of Lower Limb Segments During Walking.


Lower limb exoskeletons require the correct support magnitude and timing to achieve user assistance. This study evaluated whether the sign of the angular velocity of lower limb segments can be used to determine the timing of the stance and the swing phase during walking. We assumed that stance phase is characterized by a positive, swing phase by a negative angular velocity. Thus, the transitions can be used to also identify heel-strike and toe-off. Thirteen subjects without gait impairments walked on a treadmill at speeds between 0.5 and 2.1 m/s on level ground and inclinations between −10 and +10°. Kinematic and kinetic data was measured simultaneously from an optical motion capture system, force plates, and five inertial measurement units (IMUs). These recordings were used to compute the angular velocities of four lower limb segments: two biological (thigh, shank) and two virtual that were geometrical projections of the biological segments (virtual leg, virtual extended leg). We analyzed the reliability (two sign changes of the angular velocity per stride) and the accuracy (offset in timing between sign change and ground reaction force based timing) of the virtual and biological segments for detecting the gait phases stance and swing. The motion capture data revealed that virtual limb segments seem superior to the biological limb segments in the reliability of stance and swing detection. However, increased signal noise when using the IMUs required additional rule sets for reliable stance and swing detection. With IMUs, the biological shank segment had the least variability in accuracy. The IMU-based heel-strike events of the shank and both virtual segment were slightly early (3.3–4.8% of the gait cycle) compared to the ground reaction force-based timing. Toe-off event timing showed more variability (9.0% too early to 7.3% too late) between the segments and changed with walking speed. The results show that the detection of the heel-strike, and thus stance phase, based on IMU angular velocity is possible for different segments when additional rule sets are included. Further work is required to improve the timing accuracy for the toe-off detection (swing).

For further publications of the autohr please check: ResearchGate, Google Scholar, ORCID or LOOP

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