Comparative study of muscles effort during gait phases for multi-muscle humanoids

The comparative study of the muscles effort is presented. The normal gait with bare foot is studied. The effort of the main groups of human leg muscles is investigated using the muscular model of the human body and the Opensim simulator. The data are processed by the classifying artificial neural networks. The classification results are matching the gait phases. In the next stage the recorded and preprocessed EMG data are applied to the classifying artificial neural network. In this case the strong coincidence between the gait phases and the obtained classes was observed as well. The joint trajectories were also analyzed. The general aim of this study is to provide the information and tool supporting the development of multi-muscle humanoids. The information about muscle effort is needed when selecting the artificial muscles for the humanoid legs. A muscle type actuators generates force depends on the activation, therefore for robotic prosthesis with EMG control the study of EMG signals is relevant. Moreover it was proved that the classifying artificial neural network makes a good tool for recognizing the gait phases.