Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
52
Recent results in monocular visual-inertial navigation (VIN) have shown that optimization-based approaches outperform filtering methods in terms of accuracy due to their capability to relinearize past states. However, the improvement comes at the cost of increased computational complexity. In this paper, we address this issue by preintegrating inertial measurements between selected keyframes. The preintegration allows us to accurately summarize hundreds of inertial measurements into a single relative motion constraint. Our first contribution is a preintegration theory that properly addresses the manifold structure of the rotation group and carefully deals with uncertainty propagation. The measurements are integrated in a local frame, which eliminates the need to repeat the integration when the linearization point changes while leaving the opportunity for belated bias corrections. The second contribution is to show that the preintegrated IMU model can be seamlessly integrated in a visual-inertial pipeline under the unifying framework of factor graphs. This enables the use of a structureless model for visual measurements, further accelerating the computation. The third contribution is an extensive evaluation of our monocular VIN pipeline: experimental results confirm that our system is very fast and demonstrates superior accuracy with respect to competitive state-of-the-art filtering and optimization algorithms, including off-the-shelf systems such as Google Tango
Posted on: December 17, 2015
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
52
Posted on: December 10, 2015
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
52
Posted on: December 10, 2015
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
52
Posted on: December 2, 2015
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
52
Objective. Decoding forelimb movements from the firing activity of cortical neurons has been interfaced with robotic and prosthetic systems to replace lost upper limb functions in humans. Despite the potential of this approach to improve locomotion and facilitate gait rehabilitation, decoding lower limb movement from the motor cortex has received comparatively little attention. Here, we performed experiments to identify the type and amount of information that can be decoded from neuronal ensemble activity in the hindlimb area of the rat motor cortex during bipedal locomotor tasks. Approach. Rats were trained to stand, step on a treadmill, walk overground and climb staircases in a bipedal posture. To impose this gait, the rats were secured in a robotic interface that provided support against the direction of gravity and in the mediolateral direction, but behaved transparently in the forward direction. After completion of training, rats were chronically implanted with a micro-wire array spanning the left hindlimb motor cortex to record single and multi-unit activity, and bipolar electrodes into 10 muscles of the right hindlimb to monitor electromyographic signals. Whole-body kinematics, muscle activity, and neural signals were simultaneously recorded during execution of the trained tasks over multiple days of testing. Hindlimb kinematics, muscle activity, gait phases, and locomotor tasks were decoded using offline classification algorithms. Main results. We found that the stance and swing phases of gait and the locomotor tasks were detected with accuracies as robust as 90% in all rats. Decoded hindlimb kinematics and muscle activity exhibited a larger variability across rats and tasks. Significance. Our study shows that the rodent motor cortex contains useful information for lower limb neuroprosthetic development. However, brain-machine interfaces estimating gait phases or locomotor behaviors, instead of continuous variables such as limb joint positions or speeds, are likely to provide more robust control strategies for the design of such neuroprostheses.
Posted on: December 2, 2015
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
52
Brain-machine interfaces (BMIs) have demonstrated how they can be used for reaching tasks with both invasive and non-invasive signal recording methods. Despite the constant improvements in this field, there still exist diverse factors to overcome before achieving a natural control. In particular, the high variability of the brain signals often leads to the incorrect decoding of the subject intentions, producing unreliable behaviours in the controlled device. A possible solution to this problem would be that of correcting this erroneous decoding using a feedback signal from the user. In this work, we evaluate the possibility of decoding neural signals associated to performance monitoring (EEG-recorded error-related potentials) during a reaching task. Compared to previous works where these error potentials were recorded under scenarios with discrete movements performed by the cursor, under real conditions the cursor is moving continuously and thus the system is required to asynchronously detect any possible error. To this end, we simulated two different erroneous events during the monitoring of a reaching task: errors at the beginning of the movement, and errors happening in the middle of the trajectory being executed. Through the analysis of the recorded EEG of three subjects, we demonstrate the existence of neural correlates for the two types of elicited error potentials, and we are able to asynchronously detect them with high accuracies.
Posted on: November 3, 2015
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
52
One of the challenges of using brain-computer interfaces (BCIs) over extended periods of time is the variation of the users’ performance across different experimental days. The goal of the current study is to propose a performance estimator for an electroencephalography-based motor imagery BCI by assessing the reliability of a command (i.e., predicting a ‘short’ or ‘long’ command delivery time, CDT). Using a short time window (< 1.5 s, shorter than the delivery time) of the mental task execution and a linear discriminant analysis classifier, we could reliably differentiate between long and short CDT (AUC around 0.8) for 9 healthy subjects. Moreover, we assessed the feasibility of providing online adaptive assistance using the performance estimator in a BCI game, comparing two conditions: (i) allowing a ‘fixed timeout’ to deliver each command or (ii) providing ‘adaptive assistance’ by giving more time if the performance estimator detects a long CDT. The results revealed that providing adaptive assistance increases the ratio of correct commands significantly (p < 0.01). Moreover, the task load index (measured via the NASA TLX questionnaire) shows a significantly higher user acceptance in case of providing adaptive assistance (p < 0.01). Furthermore, the results obtained in this study were used to simulate a robotic navigation scenario, which showed how adaptive assistance improved performance.
Posted on: October 13, 2015
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
52
A highly versatile soft gripper that can handle an unprecedented range of object types is developed based on a new design of dielectric elastomer actuators employing an interdigitated electrode geometry, simultaneously maximizing both electroadhesion and electrostatic actuation while incorporating self-sensing. The multifunctionality of the actuator leads to a highly integrated, lightweight, fast, soft gripper with simplified structure and control.
Posted on: September 29, 2015
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
52
A new method for the estimation of ego-motion (the direction and amplitude of the velocity) of a mobile device comprising optic-flow and inertial sensors (hereinafter the apparatus). The velocity is expressed in the apparatus’s reference frame, which is moving with the apparatus. The method relies on short-term inertial navigation and the direction of the translational optic- flow in order to estimate ego-motion, defined as the velocity estimate (that describes the speed amplitude and the direction of motion).A key characteristic of the invention is the use of optic- flow without the need for any kind of feature tracking. Moreover, the algorithm uses the direction of the optic-flow and does not need the amplitude, thanks to the fact that the scale of the velocity is solved by the use of inertial navigation and changes in direction of the apparatus.
Posted on: September 22, 2015
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
51
Warning: Use of undefined constant citation_author - assumed 'citation_author' (this will throw an Error in a future version of PHP) in
/home/clients/89f5f0444c120951cfdb7adc5e3aa2bf/web/dev-nccr-robotics/wp-content/themes/nccr-twentyseventeen-child/template-parts/post/content-publication.php on line
52
Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the complexity of tasks that can be performed. Here we demonstrate an alternative and complementary BMI paradigm that overcomes that limitation by decoding cognitive brain signals associated with monitoring processes relevant for achieving goals. In our approach the neuroprosthesis executes actions that the subject evaluates as erroneous or correct, and exploits the brain correlates of this assessment to learn suitable motor behaviours. Results show that, after a short user’s training period, this teaching BMI paradigm operated three different neuroprostheses and generalized across several targets. Our results further support that these error-related signals reflect a task-independent monitoring mechanism in the brain, making this teaching paradigm scalable. We anticipate this BMI approach to become a key component of any neuroprosthesis that mimics natural motor control as it enables continuous adaptation in the absence of explicit information about goals. Furthermore, our paradigm can seamlessly incorporate other cognitive signals and conventional neuroprosthetic approaches, invasive or non-invasive, to enlarge the range and complexity of tasks that can be accomplished.
Posted on: September 11, 2015
