The supposed benefits of barefoot running are an often-debated topic, with many studies investigating footwear influences on the kinematics and kinetics of running. Few studies, however, analysed the effect on joint contact forces.

In this project, we investigated the influence of different footwear on the joint contact forces of the hip, knee, and ankle, while running using a 3D musculoskeletal model. We measured kinematics from volunteers that ran on a treadmill at two speeds (2.0m/s and 2.5m/s) either barefoot, wearing minimal shoes, or wearing normal shoes. We did not only analyse the joint contact forces but also stride parameters and joint angles.

The aim of this study is to determine whether the joint contact forces are higher or lower in shoes with more support and cushioning.

Project Leader: Lena Kloock

Animals display rich and coordinated motor patterns during walking and running. Previous modeling and experimental results suggest that the balance between excitation and inhibition in neural networks may be critical for generating such structured motor patterns. However, biological neural networks have an anatomical imbalance between excitatory and inhibitory neural populations.

In this study, we explore the influence of such an anatomical imbalance on the ability of a reservoir computing artificial neural network to learn human locomotor patterns for slow walking, fast walking, and running.

Preliminary findings suggest that motor pattern generation may be robust to increased inhibition but not increased excitation in neural networks.

Project Leader: Myriam de Graaf

Development of an optimizer for the improvement of human movements

Currently, the only way to optimise a movement is through trial and error. However, this method takes a lot of time and is limited by factors such as fatigue. To provide a faster solution for both competitive sports and rehabilitation, this project aims to improve a movement by simulating it. To this end, an optimiser will be developed that calculates the optimal execution of a measured movement with respect to one variable.

Project Leader: Andrea Arensmann

Sprinting

The aim of this project is to explore lower extremity joint contact forces during sprint running and use this information to improve training control and target injury prevention. Therefore, experienced sprinters were investigated with the use of most current technologies.

Project Leader: Meike Gerlach

Validation of metabolic cost calculation from kinematic data

When interested in the metabolic cost of certain activities, researchers normally have to conduct spirometric measurement. These always need to be measured over several minutes, while the person is already in steady-state, and only give the metabolic costs of the full body.  However, it is also interesting to investigate the metabolic cost of individual, for instance for sports or rehabilitation purposes. For that, models have been developed that approximate the metabolic cost of individual muscles using input parameters like muscle activation, maximum force, current force, fiber type, and more. These parameters can mostly be obtained via inverse dynamics calculations from kinematic measurements. However, those are validated for simple human models with only few muscles.

The aim of this project is, therefore, to combine the calculation methods of these various models so we can implement and validate them in the musculoskeletal model Myonardo, which includes a far greater number of muscles.

Project Leader: Lena Kloock