Efficient Decoding With Steady-State Kalman Filter in Neural Interface Systems

The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics.We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5[plus-over-minus sign]0.5 s (mean[plus-over-minus sign]s.d.) . The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25[plus-over-minus sign]3 single units by a factor of 7.0[plus-over-minus sign]0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.National Institutes of Health (U.S.) (Grant R01 DC009899)National Institutes of Health (U.S.) (Grant RC1 HD063931)National Institutes of Health (U.S.) (Grant N01 HD053403)National Institutes of Health (U.S.) (Grant 5K01 NS057389)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant R01-EB006385)United States. Dept. of Veterans Affairs (Office of Research and Development, Rehabilitation R&D Service)Massachusetts General Hospital (Deane Institute for Integrated Research on Atrial Fibrillation and Stroke)Doris Duke Charitable FoundationSpaulding Rehabilitation Hospita

Neural Extended Kalman Filter for State Estimation of Automated Guided Vehicle in Manufacturing Environment

To navigate autonomously in a manufacturing environment Automated Guided Vehicle (AGV) needs the ability to infer its pose. This paper presents the implementation of the Extended Kalman Filter (EKF) coupled with a feedforward neural network for the Visual Simultaneous Localization and Mapping (VSLAM). The neural extended Kalman filter (NEKF) is applied on-line to model error between real and estimated robot motion. Implementation of the NEKF is achieved by using mobile robot, an experimental environment and a simple camera. By introducing neural network into the EKF estimation procedure, the quality of performance can be improved

Nonlinear state estimation using neural-cubature Kalman filter

The cubature Kalman filter (CKF) has been widely used in solving nonlinear state estimation problems because of many advantages such as satisfactory filtering accuracy and easy implementation compared to extended Kalman filter and unscented Kalman filter. However, the performance of CKF may degrade due to the uncertainty of the nonlinear dynamic system model. To solve this problem, a neural-cubature Kalman filter (NCKF) algorithm containing a multilayer feed-forward neural network (MFNN) in CKF is proposed to further improve the estimation accuracy and enhance the robustness of CKF. In the proposed NCKF algorithm, the MFNN was used to modify the nonlinear state estimation of CKF as the measurements were processed, and the CKF was used as both a state estimator and an online training paradigm simultaneously. The experimental results show that the estimation accuracy and robustness of the proposed method are better than those of the CKF, square-root CKF and particle filter

Tree pruning/inspection robot climbing mechanism design, kinematics study and intelligent control : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronics at Massey University, Manawatu Campus, New Zealand

Forestry plays an important role in New Zealand’s economy as its third largest export earner. To achieve New Zealand Wood Council’s export target of $12 billion by 2022 in forest and improve the current situation that is the reduction of wood harvesting area, the unit value and volume of lumber must be increased. Pruning is essential and critical for obtaining high-quality timber during plantation growing. Powerful tools and robotic systems have great potential for sustainable forest management. Up to now, only a few tree-pruning robotic systems are available on the market. Unlike normal robotic manipulators or mobile robots, tree pruning robot has its unique requirements and features. The challenges include climbing pattern control, anti-free falling, and jamming on the tree trunk etc. Through the research on the available pole and tree climbing robots, this thesis presents a novel mechanism of tree climbing robotic system that could serve as a climbing platform for applications in the forest industry like tree pruning, inspection etc. that requires the installation of powerful or heavy tools. The unique features of this robotic system include the passive and active anti-falling mechanisms that prevent the robot falling to the ground under either static or dynamic situations, the capability to vertically or spirally climb up a tree trunk and the flexibility to suit different sizes of tree trunk. Furthermore, for the convenience of tree pruning and the fulfilment of robot anti-jamming feature, the robot platform while the robot climbs up should move up without tilting. An intelligent platform balance control system with real-time sensing integration was developed to overcome the climbing tilting problem. The thesis also presents the detail kinematic and dynamic study, simulation, testing and analysis. A physical testing model of this proposed robotic system was built and tested on a cylindrical rod. The mass of the prototype model is 6.8 Kg and can take 2.1 Kg load moving at the speed of 42 mm/s. The trunk diameter that the robot can climb up ranges from 120 to 160 mm. The experiment results have good matches with the simulations and analysis. This research established a basis for developing wheel-driven tree or pole climbing robots. The design and simulation method, robotic leg mechanism and the control methodologies could be easily applied for other wheeled tree/pole climbing robots. This research has produced 6 publications, two ASME journal papers and 4 IEEE international conference papers that are available on IEEE Xplore. The published content ranges from robotic mechanism design, signal processing, platform balance control, and robot climbing behavior optimization. This research also brought interesting topics for further research such as the integration with artificial intelligent module and mobile robot for remote tree/forest inspection after pruning or for pest control

The Neural Particle Filter

The robust estimation of dynamically changing features, such as the position of prey, is one of the hallmarks of perception. On an abstract, algorithmic level, nonlinear Bayesian filtering, i.e. the estimation of temporally changing signals based on the history of observations, provides a mathematical framework for dynamic perception in real time. Since the general, nonlinear filtering problem is analytically intractable, particle filters are considered among the most powerful approaches to approximating the solution numerically. Yet, these algorithms prevalently rely on importance weights, and thus it remains an unresolved question how the brain could implement such an inference strategy with a neuronal population. Here, we propose the Neural Particle Filter (NPF), a weight-less particle filter that can be interpreted as the neuronal dynamics of a recurrently connected neural network that receives feed-forward input from sensory neurons and represents the posterior probability distribution in terms of samples. Specifically, this algorithm bridges the gap between the computational task of online state estimation and an implementation that allows networks of neurons in the brain to perform nonlinear Bayesian filtering. The model captures not only the properties of temporal and multisensory integration according to Bayesian statistics, but also allows online learning with a maximum likelihood approach. With an example from multisensory integration, we demonstrate that the numerical performance of the model is adequate to account for both filtering and identification problems. Due to the weightless approach, our algorithm alleviates the 'curse of dimensionality' and thus outperforms conventional, weighted particle filters in higher dimensions for a limited number of particles

Performance Evaluation of Neural Networks for Animal Behaviors Classification: Horse Gaits Case Study

The study and monitoring of wildlife has always been a subject of great interest. Studying the behavior of wildlife animals is a very complex task due to the difficulties to track them and classify their behaviors through the collected sensory information. Novel technology allows designing low cost systems that facilitate these tasks. There are currently some commercial solutions to this problem; however, it is not possible to obtain a highly accurate classification due to the lack of gathered information. In this work, we propose an animal behavior recognition, classification and monitoring system based on a smart collar device provided with inertial sensors and a feed-forward neural network or Multi-Layer Perceptron (MLP) to classify the possible animal behavior based on the collected sensory information. Experimental results over horse gaits case study show that the recognition system achieves an accuracy of up to 95.6%.Junta de Andalucía P12-TIC-130