Research in Biomechanics

Analyzing and improving human movement is interesting per se – for the treatment of medical conditions, to improve performance in sports, or to understand social cues and physical aspects in human interactions. But understanding human movement is also an important prerequisite for human-centered robotics. We perform human motion studies bringing different kinds of motion capture experiments together with the model-based optimization approaches and data-driven methods described in Research in Optimization. We are interested in the following types of motions and subjects: 

  • Activities of daily living such as walking, sitting down, standing up, lifting and carrying objects 

  • Motions in sports such as running, jumping, diving, gymnastics, slacklining, martial arts & dance, comparing movements of experts and beginners and formulating performance indicators 

  • Emotional body language and specific motion styles in humans and their effect on others  

  • Change of motions and motor capabilities over life span (from children to adults of different ages)  

  • Motions of persons with physical impairments having a direct effect on the body (e.g. cerebral palsy, stroke) and with mental illnesses and disorders such as schizophrenia or major depressive disorder that have an indirect effect on the person’s movement. 

In addition to a thorough assessment of the kinematics and dynamics of these motions, we are interested in understanding fundamental aspects of human movement, such as:

  • Inherent physical laws or behavior rules that govern whole-body movement and coordination. We follow the hypothesis that human movement (as many structures and processes in nature) is optimal, taking the underlying (neuro)musculoskeletal system into account, but that optimality criteria vary (Research in Optimization)  

  • Stability and robustness of motions in different contexts, aiming for a detailed quantitative assessment and a formulation of new criteria for stability, which will allow to detect instability, reduce the risk of falls, and develop good controllers making motions of humans and human-centred robots more stable and robust 

  • Motion learning, adaptation and training. We are interested to develop performance indicators for motion skills, quantify how movement changes during the learning process from beginner to expert, how movements are adapted to certain support or input, and how this knowledge can be exploited to develop new training concepts. 

For several of the biomedical and sports applications mentioned above, we explore the possibility of using minimal sets of wearable sensors and develop corresponding motion identification methods based on these very limited data sets.   

These studies target individual questions on human movement, but at the same time contribute to the overall picture (and model) of human movement which we are providing for human-centred robotics.