Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the E´ cole Polytechnique Fe´de´ rale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

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Increasing survival rates in severe illnesses and traumatic injuries have led to an increase in the number of disabled people of working age or younger. According to the World Health Organization (WHO), there are about 650 million people with disabilities world-wide. Education is one of the factors that can help them overcome their own limitations. According to the Polish Government Plenipo-tentiary for Disabled People, in 2009 only 5.9% of disabled Poles had a degree, and only 21.4% were employed, which reflects the mar-ginalization of the disabled from the rest of Polish society. Distance learning may offer hope for breakthroughs in the area of edu-cation for people with disabilities. This article aims at investigating the extent to which the available opportunities are being ex-ploited, including e-learning as a part of the concept of the disabled person’s integrated IT environment.

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In this study, a next-generation Kansei-equipment agricultural robot is proposed and focuses jn controlling the robot through body language. Kansei agri-robots are defined as agricultural robots equiped with Kansei communication and robotic Functions. Kansei communication is defined as «two-way communication between humans and robots, or other machines, that supplements or supersedes traditional one-way (human-to-machine) input and operation».

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Carbon nanotubes (CNT) are remarkable materials with a simple and inert molecular structure that gives rise to a range of potentially valuable physical and electronic properties, including high aspect ratio, high mechanical strength and excellent electrical con-ductivity. This review summarizes recent research on the application of CNT-based materials to study and control cells of the nervous system. It includes the use of CNT as cell culture substrates, to create pat-terned surfaces and to study cell–matrix interactions. It also summarizes recent investigations of CNT to-xicity, particularly as related to neural cells. The application of CNT-based materials to directing the differentiation of progenitor and stem cells toward neural lineages is also discussed. The emphasis is on how CNT surface chemistry and nanotopography can be altered, and how such changes can affect neural cell function. This knowledge can be applied to creating improved neural interfaces and devices, as well as pro-viding new approaches to neural tissue engineering and regeneration.

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This paper describes IMU (Inertial Measurement Unit) platforms and their main target applications with a special focus on the 10-degree-of-freedom (10-DOF) inertial platform iNEMO and its technical features and performances. The iNEMO module is equipped with a 3-axis MEMS accelerometer, a 3-axis MEMS gyroscope, a 3-axis MEMS magne-tometer, a pressure sensor, and a temperature sensor. Furthermore, the Microcontroller Unit (MCU) collects measurements by the sensors and computes the orientation through a customized Extended Kalman Filter (EKF) for sensor fusion.

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