A light-sensitive, externally powered microchip was surgically implanted subretinally near the macular region of volunteers blind from hereditary retinal dystrophy. The implant contains an array of 1500 active microphotodiodes (‘chip’), each with its own amplifier and local stimulation electrode. At the implant's tip, another array of 16 wire-connected electrodes allows light-independent direct stimulation and testing of the neuron–electrode interface. Visual scenes are projected naturally through the eye's lens onto the chip under the transparent retina. The chip generates a corresponding pattern of 38 × 40 pixels, each releasing light-intensity-dependent electric stimulation pulses. Subsequently, three previously blind persons could locate bright objects on a dark table, two of whom could discern grating patterns. One of these patients was able to correctly describe and name objects like a fork or knife on a table, geometric patterns, different kinds of fruit and discern shades of grey with only 15 per cent contrast. Without a training period, the regained visual functions enabled him to localize and approach persons in a room freely and to read large letters as complete words after several years of blindness. These results demonstrate for the first time that subretinal micro-electrode arrays with 1500 photodiodes can create detailed meaningful visual perception in previously blind individuals.
Purpose Restoration of letter reading and stripe pattern recognition in blind RP patients by placing subretinal implants transchoroidally near the macula, consisting of two arrays: 4x4 electrodes controlled retroauricularly via a subdermal line for direct stimulation (“DS array”) and a "chip" (3x3x0,1 mm),1500 electrodes. Methods Letters and stripe pattern were presented to 3 patients via the light sensitive chip – by patterns steadily presented at a screen. On the DS array the sensation evoked by each individual pulse consists of whitish round dot, clearly separated from its neighbor. Patterns consisting of such 4 x 4 dots correspond to letters of approximately 5 cm diameter presented at 60 cm distance. Results Pat.1 correctly (20/24) recognized the direction of the letter “U”, presented with the opening in four different directions (DS array). Pat.2 correctly (12/12) differentiated letters via DS array (e.g. COIL). With the light sensitive chip, he correctly (22/24) differentiated letters (e.g. LITZ; 8,5 cm high, 1.7 cm line width) steadily presented on a screen at 62 cm distance Pat.3 recognized (15/20 correct, 4AFC) the direction of lines or stripe patterns with the chip, as did Pat.1 (11/14, 2AFC) and Pat.2 (11/12 4AFC) up to 0.35 cycles/deg. Conclusion Active subretinal multielectrode implants with currents close to produce retinotopically correct patterns that allow for the first time recognition of individual letters and stripe patterns up to 0.35 cycles/deg clearly supporting the feasibility of light sensitive subretinal multi‐electrode devices for restoration of useful vision.
IntroductionInvestigating human motion with expensive and accurate optical marker based systems has been the state of the art since long ago. However, markerless low-cost systems have always been a desideratum in the field of biomechanics and sports science. Due to increasing computer chip power and the corresponding progress in image processing techniques the realization of such a system has become feasible. With the advent of the Microsoft Kinect sensor in 2011 a flexible low-cost tool has entered the computer game market that enables markerless tracking of human motion. At first sight the Kinect provides an amazing accuracy. The goal of the present work is to quantitatively investigate the tracking accuracy of the Kinect sensor by studying the human gait cycle on a treadmill. The Kinect results are compared with data stemming from a VICON system which is regarded as a kind of gold standard in terms of spatial resolution. Subsequently, a post processing step is applied to the Kinect data using anthropometric data as a priori information in order to enhance Kinect's tracking results. MethodsThe Kinect sensor provides a skeleton model whose measured joint positions and segment lengths only roughly match those of the VICON system. A first inspection of the measurements reveals that segment lengths determined by the Kinect system are considerably varying in time (up to +/-20% at the most). In order to freeze the length of all segments the Levenberg-Marquardt-algorithm (LMA) is applied to the Kinect data. LMA manipulates the Kinect joint positions such that the segment lengths of the Kinect skeleton to be become identical with pre-defined lengths. These segment length of the original human being can easily be determined by previous manual measurements. In our special case we use the data provided by the VICON system. ResultsThe comparison between both systems shows that the Kinect sensor is currently not able to provide anthropometrically reliable segment lengths. However, this failure of the Kinect system is healed with our above described optimization procedure. When looking at the angles relevant for the human gait cycle the Kinect sensor provides astonishing accuracy. The curves describing the time dependency of those angles are very similar when comparing the data from both systems (except for a small scaling factor). The shapes of these curves are only marginally affected by our optimization process. ConclusionOur work shows that the Kinect sensor is already capable of describing the motion of walking and running with acceptable accuracy in the frontal plane. Additionally it is proved that a posteriori optimization using a priori available knowledge can further improve the tracking of the anatomical landmarks. That does not mean the Kinect sensor is now ready for use in highly sophisticated scientific applications, but it can already be applied for basic research in simple clinical test cases. It is expected that regarding both accuracy and measurement frequency the near future will see significant improvemen...
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