A new one-time registration method was developed in this research for hand-eye calibration of a surgical robot to simplify the operation process and reduce the preparation time. And a new and practical method is introduced in this research to optimize the end-tool parameters of the surgical robot based on analysis of the error sources in this registration method. In the process with one-time registration method, firstly a marker on the end-tool of the robot was recognized by a fixed binocular camera, and then the orientation and position of the marker were calculated based on the joint parameters of the robot. Secondly the relationship between the camera coordinate system and the robot base coordinate system could be established to complete the hand-eye calibration. Because of manufacturing and assembly errors of robot end-tool, an error equation was established with the transformation matrix between the robot end coordinate system and the robot end-tool coordinate system as the variable. Numerical optimization was employed to optimize end-tool parameters of the robot. The experimental results showed that the one-time registration method could significantly improve the efficiency of the robot hand-eye calibration compared with the existing methods. The parameter optimization method could significantly improve the absolute positioning accuracy of the one-time registration method. The absolute positioning accuracy of the one-time registration method can meet the requirements of the clinical surgery.
Transcranial electrical stimulation (TES) is a non-invasive neuromodulation technique with great potential. Electrode optimization methods based on simulation models of individual TES field could provide personalized stimulation parameters according to individual variations in head tissue structure, significantly enhancing the stimulation accuracy of TES. However, the existing electrode optimization methods suffer from prolonged computation times (typically exceeding 1 d) and limitations such as disregarding the restricted number of output channels from the stimulator, further impeding their clinical applicability. Hence, this paper proposes an efficient and practical electrode optimization method. The proposed method simultaneously optimizes both the intensity and focality of TES within the target brain area while constraining the number of electrodes used, and it achieves faster computational speed. Compared to commonly used electrode optimization methods, the proposed method significantly reduces computation time by 85.9% while maintaining optimization effectiveness. Moreover, our method considered the number of available channels for the stimulator to distribute the current across multiple electrodes, further improving the tolerability of TES. The electrode optimization method proposed in this paper has the characteristics of high efficiency and easy operation, potentially providing valuable supporting data and references for the implementation of individualized TES.
To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately (P < 0.05). Next, using the relationship between multiple thresholds and network parameters, the area under the curve (AUC) of each network parameter was extracted as new features (P < 0.05). Finally, support vector machine (SVM) was used to classify the two groups with the network parameters and their AUC as features. The study results show that with strength, average characteristic path length, and average clustering coefficient as features, the classification accuracy in the θ band is increased from 69% to 71%, 66% to 77%, and 50% to 68%, respectively. In the α band, the accuracy is increased from 72% to 79%, 69% to 82%, and 65% to 75%, respectively. And from overall view, when AUC of network parameters was used as a feature in the α band, the classification accuracy is improved compared to the network parameter feature. In the θ band, only the AUC of average clustering coefficient was applied to classification, and the accuracy is improved by 17.6%. The study proved that based on graph theory, the method of feature optimization of brain function network could provide some theoretical support for the computer-aided diagnosis of adolescent depression.
An adaptive inertia weight particle swarm algorithm is proposed in this study to solve the local optimal problem with the method of traditional particle swarm optimization in the process of estimating magnetic resonance (MR) image bias field. An indicator measuring the degree of premature convergence was designed for the defect of traditional particle swarm optimization algorithm. The inertia weight was adjusted adaptively based on this indicator to ensure particle swarm to be optimized globally and to avoid it from falling into local optimum. The Legendre polynomial was used to fit bias field, the polynomial parameters were optimized globally, and finally the bias field was estimated and corrected. Compared to those with the improved entropy minimum algorithm, the entropy of corrected image was smaller and the estimated bias field was more accurate in this study. Then the corrected image was segmented and the segmentation accuracy obtained in this research was 10% higher than that with improved entropy minimum algorithm. This algorithm can be applied to the correction of MR image bias field.
Sudden cardiac arrest is one of the critical clinical syndromes in emergency situations. A cardiopulmonary resuscitation (CPR) is a necessary curing means for those patients with sudden cardiac arrest. In order to simulate effectively the hemodynamic effects of human under AEI-CPR, which is active compression-decompression CPR coupled with enhanced external counter-pulsation and inspiratory impedance threshold valve, and research physiological parameters of each part of lower limbs in more detail, a CPR simulation model established by Babbs was refined. The part of lower limbs was divided into iliac, thigh and calf, which had 15 physiological parameters. Then, these 15 physiological parameters based on genetic algorithm were optimized, and ideal simulation results were obtained finally.
Triply periodic minimal surface (TPMS) is widely used because it can be used to control the shape of porous scaffolds precisely by formula. In this paper, an I-wrapped package (I-WP) type porous scaffolds were constructed. The finite element method was used to study the relationship between the wall thickness and period, the morphology and mechanical properties of the scaffolds, as well as to study the compression and fluid properties. It was found that the porosity of I-WP type scaffolds with different wall thicknesses (0.1 ~ 0.2 mm) and periods (I-WP 1 ~ I-WP 5) ranged from 68.01% ~ 96.48%, and the equivalent elastic modulus ranged from 0.655 ~ 18.602 GPa; the stress distribution of the scaffolds tended to be uniform with the increase of periods and wall thicknesses; the equivalent elastic modulus of the I-WP type scaffolds was basically unchanged after the topology optimization, and the permeability was improved by 52.3%. In conclusion, for the I-WP type scaffolds, the period parameter can be adjusted first, then the wall thickness parameter can be controlled. Topology optimization can be combined to meet the design requirements. The I-WP scaffolds constructed in this paper have good mechanical properties and meet the requirements of repairing human bone tissue, which may provide a new choice for the design of artificial bone trabecular scaffolds.
Due to individual differences of the depth of anaesthesia (DOA) controlled objects, the drawbacks of monitoring index, the traditional PID controller of anesthesia depth could not meet the demands of nonlinear control. However, the adjustments of the rules of DOA fuzzy control often rely on personal experience and, therefore, it could not achieve the satisfactory control effects. The present research established a fuzzy closed-loop control system which takes the cerebral state index (CSI) value as a feedback controlled variable, and it also adopts the particle swarm optimization (PSO) to optimize the fuzzy control rule and membership functions between the change of CSI and propofol infusion rate. The system sets the CSI targets at 40 and 30 through the system simulation, and it also adds some Gaussian noise to imitate clinical disturbance. Experimental results indicated that this system could reach the set CSI point accurately, rapidly and stably, with no obvious perturbation in the presence of noise. The fuzzy controller based on CSI which has been optimized by PSO has better stability and robustness in the DOA closed loop control system.
The impeller, as a key component of artificial heart pumps, experiences high shear stress due to its rapid rotation, which may lead to hemolysis. To enhance the hemolytic performance of artificial heart pumps and identify the optimal combination of blade parameters, an optimization design for existing pump blades is conducted. The number of blades, outlet angle, and blade thickness were selected as design variables, with the maximum shear stress within the pump serving as the optimization objective. A back propagation (BP) neural network prediction model was established using existing simulation data, and a grey wolf optimization algorithm was employed to optimize the blade parameters. The results indicated that the optimized blade parameters consisted of 7 impeller blades, an outlet angle of 25 °, and a blade thickness of 1.2 mm; this configuration achieved a maximum shear stress value of 377 Pa—representing a reduction of 16% compared to the original model. Simulation analysis revealed that in comparison to the original model, regions with high shear stress at locations such as the outer edge, root, and base significantly decreased following optimization efforts, thus leading to marked improvements in hemolytic performance. The coupling algorithm employed in this study has significantly reduced the workload associated with modeling and simulation, while also enhancing the performance of optimization objectives. Compared to traditional optimization algorithms, it demonstrates distinct advantages, thereby providing a novel approach for investigating parameter optimization issues related to centrifugal artificial heart pumps.
By analyzing the physiological structure and motion characteristics of human ankle joint, a four degree of freedom generalized spherical parallel mechanism is proposed to meet the needs of ankle rehabilitation. Using the spiral theory to analyze the motion characteristics of the mechanism and based on the method of describing the position with spherical coordinates and the posture with Euler Angle, the inverse solution of the closed vector equation of mechanism position is established. The workspace of mechanism is analyzed according to the constraint conditions of inverse solution. The workspace of the moving spherical center of the mechanism is used to match the movement space of the tibiotalar joint, and the workspace of the dynamic platform is used to match the movement space of subtalar joint. Genetic algorithm is used to optimize the key scale parameters of the mechanism. The results show that the workspace of the generalized spherical parallel mechanism can satisfy the actual movement space of human ankle joint rehabilitation. The results of this paper can provide theoretical basis and experimental reference for the design of ankle joint rehabilitation robot with high matching degree.
The goal of this paper is to solve the problems of large volume, slow dynamic response and poor intelligent controllability of traditional gait rehabilitation training equipment by using the characteristic that the shear yield strength of magnetorheological fluid changes with the applied magnetic field strength. Based on the extended Bingham model, the main structural parameters of the magnetorheological fluid damper and its output force were simulated and optimized by using scientific computing software, and the three-dimensional modeling of the damper was carried out after the size was determined. On this basis and according to the design and use requirements of the damper, the finite element analysis software was used for force analysis, strength check and topology optimization of the main force components. Finally, a micro magnetorheological fluid damper suitable for wearable rehabilitation training system was designed, which has reference value for the design of lightweight, portable and intelligent rehabilitation training equipment.