Human Arm Motion Tracking by Orientation-Based Fusion of Inertial Sensors and Kinect Using Unscented Kalman Filter.

Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the inertial sensors drift problem in high dynamic motions and also joints occlusion in Kinect. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 50% compared to cases when either inertial sensor or Kinect measurements were utilized.

Optimal Magnetic Field for Crossing Super-Para-Magnetic Nanoparticles through the Brain Blood Barrier: A Computational Approach.

This paper scrutinizes the magnetic field effect to deliver the superparamagnetic nanoparticles (SPMNs) through the Blood Brain Barrier (BBB). Herein we study the interaction between the nanoparticle (NP) and BBB membrane using Molecular Dynamic (MD) techniques. The MD model is used to enhance our understanding of the dynamic behavior of SPMNs crossing the endothelial cells in the presence of a gradient magnetic field. Actuation of NPs under weak magnetic field offers the great advantage of a non-invasive drug delivery without the risk of causing injury to the brain. Furthermore, a weak magnetic portable stimulator can be developed using low complexity prototyping techniques. Based on MD simulation results in this paper, SPMNs can cross the cell membrane while experiencing very weak mechanical forces in the range of pN. This study also derives guidelines for the design of the SPMNs dedicated to crossing the BBB using external magnetic fields.

Toward Epileptic Brain Region Detection Based on Magnetic Nanoparticle Patterning.

Resection of the epilepsy foci is the best treatment for more than 15% of epileptic patients or 50% of patients who are refractory to all forms of medical treatment. Accurate mapping of the locations of epileptic neuronal networks can result in the complete resection of epileptic foci. Even though currently electroencephalography is the best technique for mapping the epileptic focus, it cannot define the boundary of epilepsy that accurately. Herein we put forward a new accurate brain mapping technique using superparamagnetic nanoparticles (SPMNs). The main hypothesis in this new approach is the creation of super-paramagnetic aggregates in the epileptic foci due to high electrical and magnetic activities. These aggregates may improve tissue contrast of magnetic resonance imaging (MRI) that results in improving the resection of epileptic foci. In this paper, we present the mathematical models before discussing the simulation results. Furthermore, we mimic the aggregation of SPMNs in a weak magnetic field using a low-cost microfabricated device. Based on these results, the SPMNs may play a crucial role in diagnostic epilepsy and the subsequent treatment of this disease.

MRI-guided epilepsy detection.

One of the most common neurological brain disorder is epilepsy that happen as an abrupt seizure. Around 30% of patients with epilepsy resist to all forms of medical treatments and, therefore, the removal of epileptic brain tissue is the only solution to get these patients free from chronical seizures. Discovering the epileptic region is a first key into the treatment. In this paper, we introduced a method for epilepsy detection. In this method superparamagnetic nanoparticle, (SPMN) is used as a sensing material in order to investigate the epileptic area. Based on the magnetic field, first they are crossed through the Blood Brain Barrier (BBB). They can cross the blood-brain barrier into the brain by means of magnetic forces. In this study, the optimal force for crossing to the brain and nanoparticles aggregation by means of MRI magnetic field for crossing and weak magnetic field inside the brain have been considered. Nanoparticles aggregation can be used as a marker to increase the contrast of MRI images in the epileptic brain area.

Linear optimal control of continuous time chaotic systems.

In this research study, chaos control of continuous time systems has been performed by using dynamic programming technique. In the first step by crossing the response orbits with a selected Poincare section and subsequently applying linear regression method, the continuous time system is converted to a discrete type. Then, by solving the Riccati equation a sub-optimal algorithm has been devised for the obtained discrete chaotic systems. In the next step, by implementing the acquired algorithm on the quantized continuous time system, the chaos has been suppressed in the Rossler and AFM systems as some case studies.

Modeling and simulation of crossing magnetic nanoparticles through blood brain barrier (BBB).

Crossing the Blood Brain Barrier (BBB), despite its tight junctions, is of the great importance in a plethora of medical applications. As a result, this work is dedicated to molecular dynamics (MD) simulation of crossing through the BBB particularly under the effect of magnetic force. For this purpose, two cases of a coated gold nanocparticle with insulin and uncoated gold nanoparticle have been considered; there, the dominant governing parameters in each case are changed to identify the optimized condition for crossing nanoparticles. These parameters are of biological (ligand-receptor binding affinity), biophysical (membrane surface receptor density ratio and non-specific interaction parameter) or geometrical (size of components) origin. The most important part of this study is MD simulation of nanoparticles under the effect of magnetic field and the result shows that for crossing through BBB what force profile must be provided by the magnetic field.

Parameter estimation and interval type-2 fuzzy sliding mode control of a z-axis MEMS gyroscope.

This paper reports a hybrid intelligent controller for application in single axis MEMS vibratory gyroscopes. First, unknown parameters of a micro gyroscope including unknown time varying angular velocity are estimated online via normalized continuous time least mean squares algorithm. Then, an additional interval type-2 fuzzy sliding mode control is incorporated in order to match the resonant frequencies and to compensate for undesired mechanical couplings. The main advantage of this control strategy is its robustness to parameters uncertainty, external disturbance and measurement noise. Consistent estimation of parameters is guaranteed and stability of the closed-loop system is proved via the Lyapunov stability theorem. Finally, numerical simulation is done in order to validate the effectiveness of the proposed method, both for a constant and time-varying angular rate.

Fuzzy control of a hand rehabilitation robot to optimize the exercise speed in passive working mode.

The robotic rehabilitation devices can undertake the difficult physical therapy tasks and provide improved treatment procedures for post stroke patients. During passive working mode, the speed of the exercise needs to be controlled continuously by the robot to avoid excessive injurious torques. We designed a fuzzy controller for a hand rehabilitation robot to adjust the exercise speed by considering the wrist angle and joint resistive torque, measured continuously, and the patient's general condition, determined by the therapist. With a set of rules based on an expert therapist experience, the fuzzy system could adapt effectively to the neuromuscular conditions of the patient's paretic hand. Preliminary clinical tests revealed that the fuzzy controller produced a smooth motion with no sudden change of the speed that could cause pain and activate the muscle reflexive mechanism. This improves the recovery procedure and promotes the robot's performance for wide clinical usage.

Robust adaptive backstepping control of uncertain Lorenz system.

In this paper, a novel robust adaptive control method is proposed for controlling the Lorenz chaotic attractor. A new backstepping controller for the Lorenz system based on the Lyapunov stability theorem is proposed to overcome the singularity problem that appeared in using the typical backstepping control method. By exploiting the property of the system, the resulting controller is shown to be singularity free and the closed loop system is globally stable. Due to unavailability of system states measurement in practice, the controller is selected such that only one system state is needed. To overcome the problem of parameter uncertainty, an additional term to Lyapunov function is added and an identification scheme is adopted to have a negative definite Lyapunov function derivative. The simulation results demonstrate the effectiveness of the proposed controllers and approaches.

Closed-form approximation and numerical validation of the influence of van der Waals force on electrostatic cantilevers at nano-scale separations.

In this paper the two-point boundary value problem (BVP) of the cantilever deflection at nano-scale separations subjected to van der Waals and electrostatic forces is investigated using analytical and numerical methods to obtain the instability point of the beam. In the analytical treatment of the BVP, the nonlinear differential equation of the model is transformed into the integral form by using the Green's function of the cantilever beam. Then, closed-form solutions are obtained by assuming an appropriate shape function for the beam deflection to evaluate the integrals. In the numerical method, the BVP is solved with the MATLAB BVP solver, which implements a collocation method for obtaining the solution of the BVP. The large deformation theory is applied in numerical simulations to study the effect of the finite kinematics on the pull-in parameters of cantilevers. The centerline of the beam under the effect of electrostatic and van der Waals forces at small deflections and at the point of instability is obtained numerically. In computing the centerline of the beam, the axial displacement due to the transverse deformation of the beam is taken into account, using the inextensibility condition. The pull-in parameters of the beam are computed analytically and numerically under the effects of electrostatic and/or van der Waals forces. The detachment length and the minimum initial gap of freestanding cantilevers, which are the basic design parameters, are determined. The results of the analytical study are compared with the numerical solutions of the BVP. The proposed methods are validated by the results published in the literature.