31 августа 2011г. в издании Дайджест финансы прошла информация о проекте EXOMEN.

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The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking acti-vity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were succes-sfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical micro-electrode array can provide repeatable, accurate point-and-click control of a computer interface to an individual with tetraplegia 1000 days after implantation of this sensor.

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Background: Current guidelines for rehabilitation of arm and hand function after stroke recommend that motor training focus on re-alistic tasks that require reaching and manipulation and engage the patient intensively, actively, and adaptively. Here, we inves-tigated the feasibility of a novel robotic task-practice system, ADAPT, designed in accordance with such guidelines. At each tri-al, ADAPT selects a functional task according to a training schedule and with difficulty based on previous performance. Once the task is selected, the robot picks up and presents the corresponding tool, simulates the dynamics of the tasks, and the patient in-teracts with the tool to perform the task. Methods: Five participants with chronic stroke with mild to moderate impairments (> 9 months post-stroke; Fugl-Meyer arm score 49.2 ± 5.6) practiced four functional tasks (selected out of six in a pre-test) with ADAPT for about one and half hour and 144 trials in a pseudo-random schedule of 3-trial blocks per task. Results: No adverse events occurred and ADAPT successfully presented the six functional tasks without human intervention for a total of 900 trials. Qualita-tive analysis of trajectories showed that ADAPT simulated the desired task dynamics adequately, and participants reported good, al-though not excellent, task fidelity. During training, the adaptive difficulty algorithm progressively increased task difficulty lea-ding towards an optimal challenge point based on performance; difficulty was then continuously adjusted to keep performance around the challenge point. Furthermore, the time to complete all trained tasks decreased significantly from pretest to one-hour post-test. Finally, post-training questionnaires demonstrated positive patient acceptance of ADAPT. Conclusions: ADAPT successfully provided adaptive progressive training for multiple functional tasks based on participant’s performance. Our encouraging results es-tablish the feasibility of ADAPT; its efficacy will next be tested in a clinical trial.

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The portability, ease of use and improved accuracy of miniature inertial sensors brought by current microelectromechanical system (MEMS) technology has inspired researchers to develop human movement monitoring system with body-fixed sensors. Although a large number of studies have attempted to explore the use of miniature inertial sensors in estimating walking speed for the past two decades, there still remain some questions regarding applying inertial sensors in estimating walking speed under dierent walking conditions and for dierent subject populations. In this thesis, I focus on evalua-ting and improving the performance of a shank-mounted mounted inertial measurement unit (IMU) based walking speed estimation method. My research can be divided into four parts. The first part was a systematic review regarding the state of the art of current development of the inertial sensor based walking speed estimation method. A total of 16 articles were fully reviewed in terms of sensor specifica-tion, sensor attachment location, experimental design and spatial parameter estimation algorithm. In the second part, a comprehensive performance evaluation was conducted, which included the treadmill and overground walking experiments with constraint on the walking speed, stride length and stride frequency. A systematic error was observed in the error analysis of this study, which was adjusted by subtracting the bias by linear regression. In the third part, a post-stroke subject overground walking experiment was carried out with an improved walking speed estimation method that reduced the systematic error caused by previous false initial speed assumption. In addition to walking speed estimation, the gait asymmetry for post-stroke hemiparetic gait was also evaluated with the proposed method. The last part was the sensor error model analysis. We elaborately analyzed and discussed the estimation errors involved in this method in order to completely understand the sensor error compensation in walking speed estimation algorithm design. Two existing sensor error models and one newly developed sensor error model were compared with the treadmill walking experiment, which demonstrated the eect of each sensor error component on the estimation result and the importance of the sensor error model selection.

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In this paper, we explore the idea of designing nonanthropomorphic, multi-fingered robotic hands for tasks that replicate the motion of the human hand. Taking as input data rigid-body trajectories for the five fingertips, we develop a method to perform dimensional synthesis for a kinematic chain with a tree structure, with three common joints and five branches. We state the forward kinematics equations of relative displacements for each serial chain expressed as dual quaternions, and solve for up to five chains simultaneously to reach a number of poses along the hand trajectory using a hybrid global numerical solver that integrates a genetic algorithm and a Levenberg- Marquardt local optimizer. Although the number of candidate solutions in this problem is very high, the use of the genetic algorithm lets us to perform an exhaustive exploration of the solution space and retain a subset of them. We then can choose some of the solutions based on the specific task to perform. Note that these designs could match the task exactly while having a finger design radically different from that of the human hand.

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