Although electromechanical-assisted gait training after stroke seems to be effective, in the absence of a direct comparison between electromechanical devices it is not clear which device may be the most effective for recovery of walking. The aim of this study was therefore to compare the effects of different devices used in gait training after stroke.

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Many biological and articial systems are made up from similar, relatively simple elements that interact directly with their nearest neighbors. Despite the simplicity of the individual building blocks, systems of this type, network systems, often display complex behavior — an observation which has inspired disciplines such as artificial neural networks and modular robotics. Network systems have several attractive properties, including distributed functionality, which enables robustness, and the possibility to use the same elements in dierent configurations. The uniformity of the elements should also facilitate development of eficient methods for system design, or even self-reconfiguration. These properties make it interesting to investigate the idea of constructing mechatronic systems based on networks of simple elements. This thesis concerns modeling and optimal design of a class of active mechanical network systems referred to as Neuro-Mechanical Networks (NMNs). To make matters concrete, a mathematical model that describes an actuated truss with an artificial recurrent neural network superimposed onto it is developed and used. A typical NMN is likely to consist of a substantial number of elements, making design of NMNs for various tasks a complex undertaking. For this reason, the use of numerical optimization methods in the design process is advocated. Application of such methods is exemplified in four appended papers that describe optimal design of NMNs which should take on static configurations or follow time-varying trajectories given certain input stimuli. The considered optimization problems are nonlinear, nonconvex, and potentially large-scale, but numerical results indicate that useful designs can be obtained in practice. The last paper in the thesis deals with a solution method for optimization problems with matrix inequality constraints. The method described was developed primarily for solving optimization problems stated in some of the other appended papers, but is also applicable to other problems in control theory and structural optimization.

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This paper presents a study conducted to evaluate and optimize the interaction experience between a human and a 9 DOF arm-exoskeleton by the integration of predictions based on electroencephalographic signals (EEG). Due to an ergonomic kinematic architecture and the presence of three contact points, which enable the reflection of complex force patterns, the developed exoskeleton takes full advantage of the human arm mobility, allowing the operator to tele-control complex robotic systems in an intuitive way via an immersive simulation environment. Taking into account the operator’s percept and a set of constraints on the exoskeleton control system, it is illustrated how to quantitatively enhance the comfort and the performance of this sophisticated human–machine interface. Our approach of integrating EEG signals into the control of the exoskeleton guarantees the safety of the operator in any working modality, while reducing effort and ensuring functionality and comfort even in case of possible misclassification of the EEG instances. Tests on different subjects with simulated movement prediction values were performed in order to prove that the integration of EEG signals into the control architecture can significantly smooth the transition between the control states of the exoske-leton, as revealed by a significant decrease in the interaction force.

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This paper presents a unique and innovative approach for Soft-Robotics that introduces the “One-to-Many” design concept. This concept allows a single artificial actuator to store energy in the form of potential energy and drive multiple degrees of freedom. Utilized in an ExoMusculature, this approach has many advantages over existing technologies for assistive and augmentative applications. The basic elements have been designed and prototyped along with physical simulations. A biologically inspired control system has been developed and the results of the simulation were compared with the results of a human arm neuro-motor control experiment. Performance improves dramatically with increased numbers of artificial muscle motor units.

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Neuroprostheses are electronic devices using electrophysiological signals to stimulate muscles, electronic/mechanical devices such as substitutes for limbs or parts of limbs, or computers. The development of neuroprostheses was possible thanks to advances in understanding of the physiology of the human brain and in the capabilities of hardware and software. Recent progress in the area of neuroprosthetics may offer important breakthroughs in therapy and rehabilitation. New dedicated solutions for disabled people can lead to their increased participation in social, educational and profes-sional areas. It is worth focussing particular attention on new solutions for people with paralysis, people with communication disorders and amputees. This article aims at investigating the extent to which the available opportunities are being exploited, including current and potential future applications of braincomputer interfaces.

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