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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A robotic orthosis has been developed for spinal cord injury patients which consists of an exoskeleton robotic suit with actuators at the hip and knee joints. In this article, a presentation is made on how to generate a gait pattern to control the robotic orthosis. First, the features needed to characterize a gait pattern are defined, and then a description is given of how to extract features from the normal gait patterns contained in experimental encoder data on the hip and knee joints of a person walking normally. Next, the physical meaning of these features in a gait pattern is discussed, and methods of varying the walking speed and step size through the adjustment of features are then considered. It is shown that the desired gait pattern can be obtained by connecting these features with spline interpolation. Finally, a crutch gait pattern is produced using these features, and this is applied to the exoskeleton robot ROBIN-P1 in order to verify the proposed method.

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 A powered exoskeleton is a powered mobile machine consisting primarily of an exoskeleton-like framework worn by a person and a power supply that supplies at least part of the activation-energy for limb movement. Powered exoskeletons are designed to assist and protect the wearer. They may be designed, for example, to assist and protect soldiers and construction workers, or to aid the survival of people in other dangerous environments. A wide medical market exists in the future of prosthetics to provide mobility assistance for aged and infirm people. Other possibilities include rescue work, such as in collapsed buildings, in which the device might allow a rescue worker to lift heavy debris, while simultaneously protecting him from falling rubble. The first exoskeleton was co-developed by General Electric and the United States military in the 1960s, named Hardiman, which made lifting 250 pounds (110 kg) feel like lifting 10 pounds (4.5 kg). It was impractical due to its 1,500 pounds (680 kg) weight. The project was not successful. Any attempt to use the full exoskeleton resulted in a violent uncontrolled motion, and as a result it was never tested with a human inside. Further research concentrated on one arm. Although it could lift its specified load of 750 pounds (340kg), it weighed three quarters of a ton, just over twice the liftable load. Without getting all the components to work together the practical uses for the Hardiman project were limited. Working examples of powered exoskeletons have been constructed but are not currently widely deployed.

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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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The EEG Processing Unit comprises a semi-rigid framework which substantially conforms to the Patient’s head and supports a set of electrodes in predetermined loci on the Patient’s head to ensure proper electrode placement. The EEG Processing Unit includes automated connectivity determination apparatus which can use pressure-sensitive electrode placement ensuring proper contact with Patient’s scalp and also automatically verifies electrode placement via measurements of electrode impedance through automated impedance checking. Voltages generated by the electrodes are amplified and filtered before being transmitted to an analysis platform, which can be a Physician’s laptop computer system, either wirelessly or via a set of tethering wires. The EEG Processing Unit includes an automatic artifacting capability which identifies when there is sufficient clean data compiled in the testing session. This process automatically eliminates muscle- or other physical-artifact-related voltages. Clean data, which represents real brain voltages as opposed to muscle- or physical-artifact-related voltages, thereby are produced.

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