In recent years, gait robots have become increasingly common for gait rehabilitation in non-ambulatory stroke patients. Cardiovascular treadmill training, which has been shown to provide great benefit to stroke survivors, cannot be performed with non-ambulatory patients. We there-fore integrated cardiovascular training in robot-assisted gait therapy to combine the benefits of both training modi. We developed a model of human heart rate as a function of exercise parameters during robot-assisted gait training and applied it for automatic control purposes. This structural model of the physio-logical processes describes the change in heart rate caused by treadmill speed and the power exchanged bet-ween robot and subject. We performed physiological parameter estimation for each tested individual and de-signed a model-based feedback controller to guide heart rate to a desired time profile. Five healthy sub-jects and eight stroke patients were recorded for model parameter identification, which was successfully used for heart rate control of three healthy subjects.We showed that a model-based control approach can take into account patient-specific limitations of treadmill speed as well as individual power expenditure.

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The invention relates to an exoskeleton (1) formed by longitudinal series of segments (3, 5, 7, 9) hingedly connected to one another at the ends thereof by means of respective intermediate pieces (11a, 11b, 11c) in turn hingedly connected to respective raised mountings (S) attached by the bases (20) thereof to various predetermined points of each finger. The free end of the first (3) of the segments (3, 5, 7, 9) is attached to a mobile part (40) of the user. In addition, part of the segments (5, 7, 9) include a projecting push portion (E1, E2, E3) for pushing the respective finger at different points when the aforementioned mobile part (40) is moved, triggering the simultaneous movement of the segments (3, 5, 7, 9), such that the hand is directed towards a more closed position, in which the fingers retract towards the thumb, or vice-versa.

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Powered exoskeletons for humans have long triggered the imagination. Merely two decades ago a role was envisaged for these devices in the assistance and rehabilitation of the impaired, and the number of powered exoskeletons for the upper and lower limbs has steadily grown ever since. Gait rehabilitation exoskeletons in particular were introduced to assist in the intensive and repetitive gait training of persons with gait impairment due to spinal cord injury, stroke and other neurological disorders. In this way physiotherapists could be relieved from the strenuous effort involved in gait assistance and patients could benefit from intensive and quantifiable assistive training.

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The clinical promise of deep brain stimulation (DBS) for neuropsychiatric conditions is coupled with the potential for ethical conflicts of interest because the work is so heavily reliant upon collaborations between academia, industry and the clinic. To foster transparency and public trust, we offer ethical guidance for the management of conflicts of interest in the conduct of DBS research and practice so that this nascent field can better balance competing goods and en-gineer new and better strategies for the amelioration of human suffering. We also hope that our ethical analysis will be of relevance to those working with other related neuroprosthetic devices, such brain- omputer interfaces and neural arrays, which naturally share many of the same concerns.

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A bi-directional neural interface (NI) system was de-signed and prototyped by incorporating a novel neural recording and processing subsystem into a commercial neural stimulator architecture. The NI system prototype leverages the system infrastructure from an exi-sting neurostimulator to ensure reliable operation in a chronic implantation environment. In addition to providing predicate therapy capabilities, the device adds key elements to facilitate chronic research, such as four channels of electrocortigram/local field potential amplification and spectral analysis, a three-axis accelerometer, algorithm processing, event-based data logging, and wireless telemetry for data uploads and algorithm/configuration updates. The custom-integrated micropower sensor and interface cir-cuits facilitate extended operation in a power-limited device. The prototype underwent significant veri-fication testing to ensure reliability, and meets the requirements for a class CF instrument per IEC-60601 protocols. The ability of the device system to process and aid in classifying brain states was preclini-cally validated using an in vivo non-human primate model for brain control of a computer cursor (i.e. brain–machine interface or BMI). The primate BMI model was chosen for its ability to quantitatively mea-sure signal decoding performance from brain activity that is similar in both amplitude and spectral con-tent to other biomarkers used to detect disease states (e.g. Parkinson’s disease). A key goal of this re-search prototype is to help broaden the clinical scope and acceptance of NI techniques, particularly real-time brain state detection. These techniques have the potential to be generalized beyond motor prosthesis, and are being explored for unmet needs in other neurological conditions such as movement disorders, stroke and epilepsy.

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