MINDWALKER is an EC FP7 funded project which aims at researching, designing and prototyping technologies for the seamless control of a lower limbs exoskeleton, relying on BNCI (Brain Neural Computer Interfaces) technologies. It promotes a non-invasive approach minimizing the cognitive load of the user for controlling the system. This paper first introduces the MINDWALKER projects, its challenges and solutions being considered. Then the paper presents a perspective of how related technologies, which primary application field deals with the rehabilitation of spinal cord injured (SCI) subjects, may be consi-dered for applications in the space domain.

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Described herein are transducers and their fabrication. The transducers convert between mechanical and electrical energy. Some transducers of the present invention include a pre-strained polymer. The pre-strain improves the conversion between electrical and mechanical energy. The present invention provides methods for fabricating electromechanical devices including one or more electroactive polymers.

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Given that human-like robots are finding their place in different areas of our world, research is being carried out in order to improve human-robot interac-tion. Humanoid robots not only require a human appearance but also require human-like movements. Along these lines we present this project which tries to address the question of human to robot arm mapping with the objective to control in real time and to teach a robot by imitation. For the technical implementation we have worked with a SR3000 ToF camera to sense the human movements which allows us to perform a marker-less arm tracking based on depth information. The robot used is a Robotis Bioloid; still, the software has been designed to be adapted with few modifications to other robots having similar arm structures.

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This paper presents a new 3D object representation called a modified exoskeleton (mES) which is another type of skeleton that is more stable and less sensitive to rotation and noise than the conventional skeletons. The mES preserves significant characteristics about an object, that are meaningful for object recognition and reconstruction, as the skeleton does. Then a fast and reliable matching algorithm for recognition and classification system which can identify and classify an observed object is introduced. The main advantage of our matching algorithm is that it can recognize the observed 3D object regardless of its orientation and noise. The first experiment was conducted on a set of artificial 3D objects and the average recognition rate of 92.60% based on the mES is achieved. The second experiment was tested on the classification of volumetric lung tumor data sets and the obtained results are promising.

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Electrocorticography (ECoG) has emerged as a new sig-nal platform for brain–computer interface (BCI) systems. Classically, the cortical physiology that has be-en commonly investigated and utilized for device control in humans has been brain signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We de-monstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency osci-llations and enabled users to achieve final target accuracies between 68% and 91% within 15 min. Addi-tionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could pro-vide an additional cognitive and physiologic substrate for BCI operation and that these signals can be ac-quired from a cortical array that is small and minimally invasive.

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