Studio dell'interazione tra Sistema Muscoloscheletrico Umano e Dispositivi di Assistenza Robotici

In the latest years, robotic technologies have been increasingly introduced in rehabilitation with the main purpose of reducing the costs and speeding up the recovery process of patients. However, most of the commercial devices impose a pre-programmed trajectory to the limbs of the patients, who therefore behave in a passive way. Another current major limitation is the inability to accurately evaluate the dynamics of the interaction between the patient and the robotic device. This interaction plays a central role in the mutual modulation of human and robot system behavior with respect of their standalone behavior. In particular, the prediction of the interaction can provide useful information to better design the exoskeleton as well as the rehabilitation treatment. This thesis presents my proposed solution for the development of a simulator able to dynamically simulate at the same time the actuated robot device, the human body, and their emerging physical interaction during the movement cooperation. The main idea behind this solution is to decompose the main system in different levels. I called the proposed solution Multi-Level modeling approach, which is the main topic of this thesis. I proposed the following decomposition: Human, Robot, and Boundary Level. The levels are integrated into a whole system in which each of them addresses specific challenges. The Human Level represents the subject who is wearing, for example, an exoskeleton for the lower limbs. To reach a symbiotic collaboration between the subject and the exoskeleton, the proposed approach has to include the subject's intentions and efforts. Moreover, user's internal transformations provide important information about the internal dynamic parameters modulation due to the external device. The Robot Level consists of the wearable robot system which supports the movements. Our proposed approach includes models of both device mechanics and control strategies. This allows to test different control strategies and find the one that better fits each specific patient's needs and characteristics. The last level is the Boundary Level, which has the main objective to model the human-robot mechanical power transfer, including also the non-idealities (such as dissipative forces), in order to accurately estimate interactions. Challenges emerged during the development of the simulator system were faced, investigating different solutions, and selecting and validating the most promising one. First, I selected a common software platform, able to simultaneously reproduce the dynamic behavior of the three levels. The common software platform allows to build a quite flexible system where different solution could be evaluated simply modifying model parameters. Among different available software, OpenSim was selected because it is well known and used for the dynamic study of human movement. Although OpenSim was well tested in biomechanics, it required a further evaluation as simulator for Robot and Boudary Levels. Performed tests and their motivations are reported in this work. Human internal dynamics parameters are modulated by the influence of the external device. I proposed to monitor this variation, taking into consideration the neural drive sent to the muscles. This can be done by measuring the muscles' electromyographic (EMG) signals, which are the electrical potential generated by muscle cells when they are activated, prior to muscle contraction. These signals can be used as input for a physiologically accurate human musculoskeletal model, to calculate the subject contribution to the movement. As the relation between EMGs and the generated muscle forces and joint moments is not linear, the neuromusculoskeletal model is indeed needed to replicate step-by-step all the internal transformations which occur from the excitation of the muscle to the joint movement. Estimation of the emerging interaction, during the human-robot cooperation, can be performed through an interaction model which is basically a set of contact models. Due to the specific rehabilitation purpose of our work, this contact model needs special attention. I introduced and validated a procedure to calibrate the contact models to improve the accuracy of the estimated interaction forces. One of the problems of using EMG signals is that, in order to acquire them in a non-invasive way, surface electrodes must be used; however, this means that the collected data quality is quite susceptible to the electrodes placements and decay, and to electric and magnetic interferences. In many contexts, such as home rehabilitation, this could be a limitation. An alternative solution to avoid the direct EMG measurement is presented in this work. The idea is that for some repetitive tasks, which are most interesting for rehabilitation, it is possible to substitute the direct data collection with a subject specific model of EMGs. The objective of this work is to provide an effective approach to estimate the emerging interaction during the human-robot movement cooperation. The Multi-Level Modeling approach, presented in this thesis, decomposes this complex problem allowing to find all the required components to realize a whole system able to reach this objective