Preliminary Study in Motion Assistance of Soft Exoskeleton Robot based on Datadriven Kinematics Model Learning Exoskeleton is widely used to enhance human mobility. Especially in recent years, the soft exoskeleton robots have developed rapidly, which could realize natural humanmachine physiological coupling. However, the motion patterns and physiological parameters are significant various between different subjects. The parameters of the soft exoskeletons change differently during motion. In this paper, we proposed kinematics model based on datadriven model learning. The proposed model learning method not only has the fast learning ability of modelbased controller, but also has the adaptability of sensorbased controller. Firstly, we use the data of the rigid model to pretrain the kinematics model network, which can make the output of the network consistent with the kinematics model. Then, we use the sensors to collect the actual motion data and send the motion data into the pretrained neural network model. By increasing the iteration times of training, the network model outputs model parameters that are consistent with the trend of the simulation model. Through the training and learning of the bionic motion platform, the speed of learning adaptation in human body can be accelerated.

Minimal Sensor Setup in Lower Limb Exoskeletons for Motion Classification based on MultiModal Sensor Data Exoskeletons are considered to be a promising technology for assisting and augmenting human performance. A number of challenges related to design, intuitive control and interfaces to the human body must be addressed. In this paper, we approach the question of a minimal sensor setup for the realization of control strategies which take into account the actions currently performed by the user. To this end, we extend our previous work on online classifications of a human wearing a lower limb exoskeleton in two directions. First, we investigate the minimal number of sensors that should be attached to the exoskeleton to achieve a certain classification accuracy by investigating different sensor setups. We compare results of motion classification of 14 different daily activities such as walking forward and going upstairs using Hidden Markov Models. Second, we analyse the influence of different window sizes, as well as the classification performance of different motion types when training on multi and singlesubjects. Our results reveal that we can reduce our sensor setup significantly while achieving about the same classification performance.