Transfer of Human Motion Primitives for Digital Human Model Control in the Scope of Ergonomic Assessment
To assess ergonomic aspects of a (future) workplace already in the design phase where no physical prototypes exist, the use of digital human models (DHMs) becomes essential. Thereby, the prediction of human motions is a key aspect when simulating human work tasks. For ergonomic assessment e.g. the resulting postures, joint angles, the duration of the motion and muscle loads are important quantities. From a physical point of view, there is an infinite number of possible ways for a human to fulfill a given goal (trajectories, velocities...), which makes human motions and behavior hard to predict. A common approach used in state of the art commercial DHMs is the manual definition of joint angles by the user, which requires expert knowledge and is limited to postural assessments. Another way is to make use of pre-recorded motions from a real human that operates on a physical prototype, which limits assessments to scenarios which have been measured before. Both approaches need further post processing and inverse dynamics calculations with other software tools to get information about inner loads and muscle data, which leads to further uncertainties concerning validity of the simulated data. In this thesis work a DHM control and validation framework is developed, which allows to investigate in how far the implemented human like actuation and control principles directly lead to human like motions and muscle actuations. From experiments performed in the motion laboratory, motion data is captured and muscle activations are measured using surface electromyography measurements (EMG). From the EMG data, time invariant muscle synergies are extracted by the use of a non-negative Matrix Factorization algorithm (NMF). Muscle synergies are one hypothesis from neuroscience to explain how the human central nervous system might reduce control complexity: instead of activating each muscle separately, muscles are grouped into functional units, whereas each muscle is present in each unit with a fixed amplitude. The measured experiment is then simulated in an optimal control framework. The used framework allows to build up DHMs as multibody system (MBS): bones are modeled as rigid bodies connected via joints, actuated by joint torques or by Hill type muscle models (1D string elements transferring fundamental characteristics of muscle force generation in humans). The OC code calculates the actuation signals for the modeled DHM in a way that a certain goal is fulfilled (e.g. reach for an object) while minimizing some cost function (e.g. minimizing time) and considering the side constraints that the equations of motion of the MBS are fulfilled. Therefore, three different Actuation Modes (AM) can be used (joint torques (AM-T), direct muscle actuation (AM-M) and muscle synergy actuation (AM-S), using the before extracted synergies as control parameters)). Simulation results are then compared with measured data, to investigate the influence of the different Actuation Modes and the solved OC cost function. The approach is applied to three different experiments, the basic reaching test, the weight lift test and a box lifting task, where a human arm model actuated by 29 Hill muscles is used for simulation. It is shown that, in contrast to a joint torque actuation (AM-T), using muscles as actuators (AM-M & AM-S) leads to very human like motion trajectories. Muscle synergies as control parameters, resulted in smoother velocity profiles, which were closer to those measured and appeared to be more robust, concerning the underlying muscle activation signals (compared to AM-M). In combination with a developed biomechanical cost function (a mix of different OC cost functions), the approach showed promising results, concerning the simulation of valid, human like motions, in a predictive manner.
Kaiserslautern, TU, Diss., 2020