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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A standing style transfer system, ABLE, is designed to assist a person with disabled lower limbs to travel in a standing position, to stand up from and sit down in a chair, and to go up and down steps. The ABLE system comprises three modules: a pair of telescopic Lofstrand crutches, a powered lower extremity orthosis, and a pair of mobile platforms. In this paper, the telescopic Lofstrand crutch is mainly discussed. This crutch has no actuator, and its length is switched between two levels; it assists the person when standing up and sitting down in the short length state, while it maintains the body stability in a standing position when traveling in the long length state. The experimental results related to the traveling in the standing position and standing up motion confirm the design’s effectiveness.

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Motor learning lies at the heart of how humans and animals acquire their skills. Understanding of this process enables many benefits in Robotics, physics-based Computer Animation, and other areas of science and engineering. In this thesis, we develop a computational framework for learning of agile, integrated motor skills. Our algorithm draws inspiration from the process by which humans and animals acquire their skills in nature. Specifically, all skills are learned through a process of staged, incremental learning, during which progressively more complex skills are acquired and subsequently integrated with prior abilities. Accordingly, our learning algorithm is comprised of three phases. In the first phase, a few seed motions that accomplish goals of a skill are acquired. In the second phase, additional motions are collected through active exploration. Finally, the third phase generalizes from observations made in the second phase to yield a dynamics model that is relevant to the goals of a skill. We apply our learning algorithm to a simple, planar character in a physical simulation and learn a variety of integrated skills such as hopping, ipping, rolling, stopping, getting up and continuous acrobatic maneuvers. Aspects of each skill, such as length, height and speed of the motion can be interactively controlled through a user interface. Furthermore, we show that the algorithm can be used without modification to learn all skills for a whole family of parameterized characters of similar structure. Finally, we demonstrate that our approach also scales to a more complex quadruped character.

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The sensing organs are exponentially better than any of analogous artificial ones. That is why using them in full scale is a perspective trend to the efficient (advanced) machine perception. On the other hand, limitations of sensing organs could be replaced by the perfect artificial ones with the subsequent training the nervous system on their output signals. An attempt to lay down the foundations of biosensing by natural sensors and in addition to them by the artificial trans-ducers of physical quantities, also with their expansion into space arrays and external/implantable functioning in rela-tion to the nervous system is performed. The advances in nanotechnology are opening the way to achieving direct elec-trical contact of nanoelectronic structures with electrically and electrochemically active neurocellular structures. The transmission of the sensors’ signals to a processing unit has been maintaining by an electromagnetic transis-tor/memristor (externally) and super-conducting transducer of ionic currents (implantable). The arrays of the advanced sensors give us information about the space and direction dynamics of the signals’ spreading.The measuring method and necessary performance data of the sensor for the robot’s orientation in the ambient magnetic field with living be-ing-machine interaction in order to obtain input and output signals from brain and motor nerves to the measurement system and vice versa are introduced. The range of applied sensors differs from an induction sensor to superconducting induction magnetometer. The analytical expressions for arrangements of the head sensors in differential and vector (3D) relative positions are deduced. Sensitivity of the perception method makes it possible to recognize the linear translation of 10−2 m and disposal in space of 10−3 m3. Interaction between living beings and robotic equipment is given analytical treatment.

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Numerous medical and biomechanical applications invol-ve electromyogram (EMG) signal processing in 19 real time. Amplitude analysis of the EMG often requires computation of the signal’s linear envelope. 20 For this purpose, several methods are commonly described in the literature; however, not all match 21 the speed requirement of real-time applications. We introduce an implementation which accelerates 22 the computation of EMG signals linear envelopes, based on the pipe-line commonly found in the literature 23 for this kind of operation. The algorithm improves the computa-tion’s time requirement, at the expense of 24 memory requirement, by using the result of the envelope’s computation at the previous instant. This 25 algorithm saves approximately 96% of the computation time and allows computing linear envelopes of 26 several EMG signals in real time.

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