Project Area C
Biomechanical modeling and condition monitoring
Project Area C explores approaches to processing, modeling and interpretation of biomechanical data. This is an integral part of EmpkinS in order to obtain meaningful movement information on the basis of the large number of available measurement data, which originate from innovative measurement principles and therefore entail completely new requirements, and thus to be able to carry out further analyzes.
In detail, musculoskeletal human models are improved in C01 by personalization using machine learning approaches. This will help to distinguish individual differences in motion characteristics from those that are caused, for example, by neurological or psychological conditions in general. In C02, musculoskeletal human models are used to clean up / filter the measurement data by relating them to the kinematic / dynamic motion possibilities of the human musculoskeletal system. The aim of C03 is then to use the (improved) models and (adjusted) measurement data to investigate a new model of postural control of walking. This model uses measurements of the empatho-kinaesthetic sensory system and a sensorimotor-enhanced musculoskeletal human model to characterize the components of dynamic balance control and to advance research into balance regulation. Finally, in C04, the analysis and prediction of biomechanical simulation models are improved by integrating empatho-kinaesthetic sensor data. This is achieved by integrating measurement data (position, orientation, speed, acceleration, force, muscle activity) into the mathematical formulation of a motion process as an optimal control problem. The selection of the model approaches is based on the requirement to research and provide the highest possible quality analysis options for the consideration of EmpkinS.
C01 Machine Learning for Personalization of Musculoskeletal Models, Movement Analysis, and Movement Predictions
The extent to which a neural network can be used to effectively personalize gait simulations using motion data is explored. We first investigate the influence of body parameters on gait simulation. An initial version of the personalization is trained with simulated motion data, since ground truth data is known for this purpose. We then explore gradient-free methods to fit the network for experimental motion data. The resulting network is validated with magnetic resonance imaging, electromyography and intra-body variables.
C02 Filtering of Multimodal Motion Capture Data through Individualized Musculoskeletal Human Models
C03 Investigation of Postural Control using Sensomotorically Extended Musculoskeletal Human Models
A novel postural control model of walking is explored to characterize the components of dynamic balance control. For this purpose, clinically annotated gait movements are used as input data and muscle actuated multi-body models are extended by a sensorimotor level. Neuromotor and control model parameters of (patho-)physiological movement are identified with the help of machine learning methods. Technical and clinical validation of the models will be performed. New EmpkinS measurement techniques are to be transferred to the developed models as soon as possible.
C04 Analysis of Degenerative Motion Impairments through Integration of Empatho-kinaesthetic Sensor Data in Biomechanical Human Models
The focus of the work programme is on the best possible integration of empathokinesthetic sensor data into biomechanical models. Specifically, degenerative movement restrictions of the hand are recorded by EmpkinS and reference sensor technology and the data are optimally integrated into the mathematical formulation of the optimal control problem depending on data type, measurement frequency and fuzziness, etc. The aim of the project is to develop a model of the degenerative hand movement. Objective biomarkers of healthy or impaired movement function are identified through movement tracking and prediction.