Motion System Research Articles

Landmark-Based Generation of Common Ultrasound Views and 17-Segment Model from Cardiac Computed Tomography

Abstract Complex medical therapies can require a multitude of imaging modalities and are often supervised by a team with different medical backgrounds. This necessitates the conversion of medical data between technical systems and visualizations. In the case of stereotactic arrhythmia radioablation therapy (STAR-therapy) of the left ventricle, electroanatomical mapping, ultrasound (US) and computed tomography (CT) are the central imaging modalities that are needed for defining the target volume as well as for the examination and validation pre and post treatment. In the interest of developing a motion management system for STAR-therapy, a way to transfer information and visualizations between these modalities and to compare data from different patients is needed. For this purpose, we present a landmark-based approach for the generation of commonly used ultrasound views and the 17-segment model from cardiac computed tomography (CCT) data. The developed tool can already be used to aid the examination process by extracting function-based views from CT datasets, comparing them to live US imaging, localize CT structures within the 17-segment model or to transfer information like motion or strain data from one modality to the other. In the future, it will be used in the development of a live or predictive management system for cardiac and respiratory motion.

A Cerebellum-Inspired Prediction and Correction Model for Motion Control of a Musculoskeletal Robot

It is an important issue that how to regulate the existing control models of musculoskeletal robots to improve the ability of motion learning and generalization. In this paper, based on the motion modulation function of the cerebellum, a cerebellum-inspired prediction and correction model is proposed to carry out feedforward regulation of the original controller. Firstly, drawing on the reservoir computing mechanism of the cerebellar granular layer, the cerebellum prediction model is established by using the echo state network. Incremental learning for the network is achieved using the replay method, which is able to process control signals with different distributions. The cerebellum prediction network can accurately predict the motion results of the robot under the action of the time-series control signals. Secondly, referring to the neural pathways of the cerebellum, the cerebellum correction model is constructed. The network learning rules are designed by drawing on the long-term potentiation and depression processes of cerebellar synaptic plasticity. The characteristics of the parameters in the network weight update equations are further analyzed. And the hyperparameter update rules of the correction network weights are proposed. To simulate the function of the cerebellum involved in the limb ballistic movement, the adaptive adjustment method for cerebellum correction duration is proposed. The cerebellum correction network can accurately modulate the original control signals by using the motion prediction results. In experiments, a musculoskeletal robot is used to verify the movement effects under the control of the cerebellum model. The results show that the cerebellum-inspired model can effectively improve the motion accuracy and learning efficiency of the musculoskeletal robot, and enhance the motion generalization ability and system robustness of the robot.

Performance‐Based Assessment of a Long Span Bridge

AbstractThe Normandy Bridge is an important asset in northern France, as it is a road cable‐stayed bridge that links Honfleur to Le Havre, spanning over the Seine River in the region of Normandy. When it was opened in 1995, it was the longest cable‐stayed bridge in the world with a 2143‐meters total length and had the greatest distance between cable‐stayed bridge piers (250 meters), its height is 214.77 meters. The bridge benefited for a while from a Bridge Weight in Motion (BWIM) system to monitor its traffic, to assess the fatigue of the bridge due to increased traffic.The paper aims to assess the performance of the Normandy bridge. For that purpose, first, the Normandy bridge is introduced, along with available monitoring helping assess the state of the bridge and manage it. Second, the performance indicators and the key performance indicators are estimated for this bridge. The objective is to verify if the bridge fulfills the pre‐specified performance goals. In the end, a quality control plan for the Normandy bridge is suggested for implementation.

Assessment of Bilateral Shoulder Range of Motion in Firefighter Trainees Using a Markerless Motion Capture System

The unpredictable environments firefighters face paired with biomechanically compromising shoulder movements, such as overhead and lifting movements, place this population at an increased risk for shoulder injury. The purpose of this study was to assess firefighter trainees’ bilateral shoulder range of motion (ROM) using the Dynamic Athletic Research Institute Motion system. Retrospective anthropometric and ROM data for 31 male firefighter trainees were analyzed. Firefighter trainees’ mean shoulder ROM for bilateral external rotation, internal rotation, and extension were lower than previously published values. External rotation demonstrated the lowest percentage of trainees within normal ROM (left—6.67%, right—16.67%). Noting the susceptibility of upper extremity injuries among firefighters, establishing baseline ROM measurements for reference may improve musculoskeletal evaluations, training interventions, and injury rehabilitation.

Non-stability of a bogie moving along a specific infinite complex flexibly beam-layer structure

A comprehensive investigation was conducted to analyze the vibration stability of a bogie that moves uniformly along a complexly modeled flexibly supported infinite Reddy-Bickford coupled beam system situated on a viscoelastic base. The bogie was simulated as a non-deformable body of finite length, connected through a specific system of two springs and two dampers with identical stiffness and damping, interacting with masses within a complex beam-layer system commonly encountered in technical practice. These masses, through which the bogie interacts with the high-order shear deformable beam-layer system, form part of a mechanical system consisting of a double oscillator in constant contact with the infinitely long flexible structure. As it is well-known that when the velocity of masses, such as the bogie or any other complex oscillator, exceeds the minimum velocity at which waves propagate in the beam, the system's oscillation may become unstable, it is crucial to determine the conditions and stable regions of motion that involve specific combinations of system parameters. The region of instability is identified within the parameter space of the system using the D-decomposition technique and the argument principle. A comprehensive analysis was conducted to investigate the impacts of the viscous damping in the supports of the bogie, the mass of the bogie bar, and the wheelbase on the stability of the model. The derived conclusions, focused on determining the stable regions of motion based on the considered parameters, represent a significant novelty and contribution to the research previously unknown for the motion system of a bogie along a complexly modeled flexibly supported infinite high-order shear deformable coupled beam-layer system on a viscoelastic base.

Wearable Recognition System for Complex Motions Based on Hybrid Deep‐Learning‐Enhanced Strain Sensors

Wearable recognition systems based on flexible electronics present immense potential for applications in human–machine interfaces, medical care, soft robots, etc. However, they experience challenges in terms of the nonideal consistency and stability of flexible sensors, which are responsible for detecting physical signals from human motions. These challenges hinder the improvement of recognition precision and capability in the wearable systems. Furthermore, the computational consumption for the recognition increases as more sensors are used to extensively gather information for distinguishing between complex motions. Herein, a wearable recognition system based on deep‐learning‐enhanced strain sensors for distinguishing between the complex motions of the human body is presented. A strain sensor based on peak–valley microstructures is fabricated and packaged to improve consistency and stability. Moreover, a lightweight hybrid convolutional neural network long short‐term memory model is designed to lower the computational costs of the deep learning process. In particular, by designing Butterworth filtering and Z‐score normalization algorithms, the error in feature extraction caused by sensor signal fluctuation is reduced, thereby improving the recognition accuracy of the proposed wearable system to 95.72% for seven gait motions and 100% for four different continuous series of Tai Chi forms.

The effect of android-based augmented reality lab coats on students with different academic abilities on understanding of skeletal system

The understanding of students' concepts of SMP Negeri 1 Kandangan on the material of the skeletal system is still low. This study aims to determine the effect of applying Android-based Augmented Reality laboratory coats to students with different academic abilities on understanding the concept of skeletal system material for students of SMP Negeri 1 Kandangan. This research is a quasi-experiment or quasi-experiment with a pretest-posttest control group design. The population used in this study were all seventh-grade students of SMP Negeri 1 Kandangan. In contrast, the samples in this study were class VII A as the control class and class VII B as the experimental class, which would be treated as applying an Augmented Reality Android-based. The sample was determined using a purposive side technique. The ANCOVA test results show a significant difference in student's conceptual understanding of the skeletal system material between the control and the experimental, with a significance value or p of 0.006, which is <0.05. Meanwhile, the follow-up test results showed that the use of android-based Augmented Reality Laboratory Coat learning media significantly differed in understanding the concepts of seventh-grade students of SMP Negeri 1 Kandangan. So it can be concluded that the android-based Augmented Reality Laboratory Coat affects increasing the understanding of the concepts of students with different academic abilities at SMP Negeri 1 Kandangan as a learning medium on skeletal system material to provide a better visualization effect in observing bony structures in the passive motion system (bones).

On the Use of Metal Sinter Powder in Laser Powder Bed Fusion Processing (PBF-LB/M).

Metal Laser Powder Bed Fusion (PBF-LB/M Powder Bed Fusion, Laser-Based/Metal) offers decisive advantages over conventional manufacturing processes. Complex geometries can be produced that cannot, or only to a limited extent, be manufactured with conventional manufacturing processes. One of the main disadvantages of the process are high investment and operating costs. In order to make the PBF-LB/M process accessible to new research areas, the costs need to be reduced. Therefore, this work investigates whether laser beam sources and motion systems in currently established PBF-LB/M systems can be replaced by more cost-effective components. To reduce the operating costs for PBF-LB/M, the studies are carried out based on previous work with water-atomized, process-foreign sinter powder instead of gas-atomized, spherical PBF-LB/M powders. A cost-efficient, low-alloyed powder is selected (Höganäs HP1) and processed on two different PBF-LB/M machines with a restricted process window using process parameter values that current low-cost machines can achieve. The results show that a multimode fiber laser leads to a more stable process and wider melt pools compared to a single mode fiber laser. In addition, a lower sensitivity of the process with respect to modified process parameters is observed for the multimode laser, resulting in a wider range of stable process windows. A Cartesian motion system (gantry) is suitable for use in PBF-LB/M despite lower scan speeds compared to galvanometer scanners. Beam guidance in the XY-plane offers new possibilities for machine and process design that are not possible with usual scanner systems.

Retracted: Research on Tennis Motion Evaluation Method and System Based on Deep Learning

Retracted: Research on Tennis Motion Evaluation Method and System Based on Deep Learning

Penerapan Model Discovery Learning untuk Meningkatkan Hasil Belajar pada Konsep Sistem

The observation results at class there is a problem in learning, that is students are less able to understand the learning concept. This can be seen from the average value of students who are still below the minimum learning completeness score. The purpose of research was to improve student achievement on the concept of motion systems after learning with the discovery learning model. The researcher uses an action research method in three cycles. Each cycle consists of stages of planning, action, observation, and reflection. The research subjects were students of class XI social 4 at Mangunjaya senior high school, with a total of 32 students, consisting of 13 boys and 19 girls. Data collection uses two techniques, that is test techniques and observation techniques. The conclusion of this research is that the application of discovery learning models can improve student achievement on the concept of motion systems. This is evidenced by an increase in the number of students who complete the standard minimum by more than 75%. In the first cycle, students who get the standard minimum were 18 students from 32 students (56.25%). In cycle II, students who get the standard minimum were 24 students from 32 students (75%). In cycle III, students who get the standard minimum were 32 students from 32 students.

On‐line instrumental variable‐based feedforward tuning for non‐resetting motion tasks

SummaryData‐driven feedforward control for tracking of varying and non‐resetting point‐to‐point references requires continuous updating of feedforward parameters instead of task‐by‐task updating. The aim of this paper is to develop an adaptive feedforward controller for non‐resetting point‐to‐point motion tasks by a data‐driven feedforward controller. An approximate optimal instrumental variable (IV) estimator with real‐time bootstrapping is employed in a closed‐loop setting to update the feedforward parameters. A case study on a wafer‐stage and experimental validation on a benchmark motion system show the performance benefit.

Evaluation of meniscus extrusion during stair ambulation in healthy volunteers using dynamic ultrasonography: a feasibility study

PurposeThis study aimed to investigate the feasibility of evaluating medial meniscus extrusion (MME) during stair ambulation.MethodsTwenty healthy young participants (mean age, 22.4 ± 0.9 years) were recruited for this cross-sectional study. Synchronization between the three-dimensional motion system and ultrasonography was used to quantify the extent of meniscal extrusion and knee angles during different tasks, including gait, stair ascent, and stair descent. In particular, ultrasonography was used to record the movements of both the middle and posterior segments of the meniscus to obtain detailed information about these movements in relation to the knee angle. The difference between the maximum MME and the MME at the initial contact (ΔMME) was evaluated during each task in the stance phase.ResultsVisualization of the meniscus in the middle segment was limited with increasing knee flexion angle, whereas the posterior segments were visible during all tasks. ΔMME of the posterior segment during stair ascent and descent was higher than that during gait (gait: 0.68 ± 0.20 mm, ascent: 1.00 ± 0.39 mm, descent: 0.90 ± 0.27 mm, gait-ascent: p = 0.009, gait-descent: p = 0.004).ConclusionsEvaluation that includes the posterior segment enables visualization of the medial meniscus and detection of its specific behavior during stair ambulation. These findings demonstrate the feasibility of evaluating meniscus dynamics during stair ambulation, and could contribute to a better understanding of these dynamics.

Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images.

Forests are traditionally characterized by stand-level descriptors, such as basal area, mean diameter, and stem density. In recent years, there has been a growing interest in enhancing the resolution of forest inventory to examine the spatial structure and patterns of trees across landscapes. The spatial arrangement of individual trees is closely linked to various non-monetary forest aspects, including water quality, wildlife habitat, and aesthetics. Additionally, associating individual tree positions with dendrometric variables like diameter, taper, and species can provide data for highly optimized, site-specific silvicultural prescriptions designed to achieve diverse management objectives. Aerial photogrammetry has proven effective for mapping individual trees; however, its utility is limited due to the inability to directly estimate many dendrometric variables. In contrast, terrestrial mapping methods can directly observe essential individual tree characteristics, such as diameter, but their mapping accuracy is governed by the accuracy of the global satellite navigation system (GNSS) receiver and the density of the canopy obstructions between the receiver and the satellite constellation. In this paper, we introduce an integrated approach that combines a camera-based motion and tree detection system with GNSS positioning, yielding a stem map with twice the accuracy of using a consumer-grade GNSS receiver alone. We demonstrate that large-scale stem maps can be generated in real time, achieving a root mean squared position error of 2.16 m. We offer an in-depth explanation of a visual egomotion estimation algorithm designed to enhance the local consistency of GNSS-based positioning. Additionally, we present a least squares minimization technique for concurrently optimizing the pose track and the positions of individual tree stem[s].

Design and test of the motion system for HEPS storage ring horizontal collimator

Design and test of the motion system for HEPS storage ring horizontal collimator

Biomechanical properties of suprapectoral biceps tenodesis with double-anchor knotless luggage tag sutures vs. subpectoral biceps tenodesis with single-anchor whipstitch suture using all-suture anchors

As the use of all-suture anchors continues to increase, limited biomechanical data on the use of these anchors in various configurations for tenodesis of the long head biceps tendon (LHBT) exists. The aim of this study was to compare the biomechanical properties of a 2-anchor luggage tag suprapectoral biceps tenodesis (Sup-BT) vs. a single-anchor whipstitch subpectoral biceps tenodesis (Sub-BT) using all-suture anchors. The hypothesis was that the Sub-BT will have a higher ultimate load to failure and less creep relative to the Sup-BT construct. Eighteen fresh frozen cadaveric humeri were used. The specimens were randomly divided into 2 groups of 9; i) The Sup-BT were performed with 2 1.8 mm knotless all-suture anchors using a luggage-tag fixation configuration, ii) The Sub-BT were performed using a single 1.9 mm all-suture anchor and a whipstitch suture configuration with a tied knot. The humeri were tested on a hydraulic MTS machine where the specimens were preloaded at 5 N for 2 minutes and then cyclically loaded from 5 to 50 N for 1000 cycles at 1 Hz while maximum displacement was recorded with a motion system and markers attached to the bone and bicep tendon. The tendon was then tensioned at a rate of 1 mm/s to obtain the ultimate load to failure. CT scans of the specimens were used to calculate the bone mineral density at the site of the anchor/bone interface and video recordings were captured during load to failure to document all modes of failure. There was no significant difference in the average load to failure of the Sup-BT and Sub-BT groups (197 N±45 N (SD), 164 N±68 N (SD) respectively; P=.122) or creep under fatigue between the Sup-BT vs. Sub-BT specimens (3.1 mm, SD=1.5 vs. 2.2 mm, SD=0.9; P=.162). The bone mineral density was statistically different between the 2 groups (P<.001); however, there were no observed failures at the anchor/bone interface and no correlation between failure load and bone mineral density. The ultimate load to failure and creep between a Sup-BT with 2 knotless all-suture anchors using a luggage tag suture configuration was equivalent to a Sub-BT with 1 all-suture anchor using a whipstitched suture configuration and a tied knot. Surgeons can perform either technique confidently knowing that they are biomechanically equivalent in a cadaver model at time zero, and they offer similar strength to other fixation methods cited in the literature.

Distinct rules for binding in position-based and velocity-based motion systems

Distinct rules for binding in position-based and velocity-based motion systems

Motion estimation and system identification of a moored buoy via physics-informed neural network

This paper explores the use of physics-informed neural networks (PINNs) to estimate motion and identify system parameters of a moored buoy under three different sea states. PINNs are a deep learning architecture that incorporates physical information to provide an interpretable and physically-meaningful neural network model, making it well-suited for modeling offshore moored structures with high nonlinearity. The moored buoy is modeled as a nonlinear ordinary differential equation (ODE), and the general formulation of PINN for the system of ODEs is established. Two new metrics for motion estimation and system identification are proposed to evaluate the accuracy and efficiency of the implementation of PINN. The results demonstrate that PINN can accurately estimate motion and identify system parameters by choosing appropriate hyperparameters (HPs). This paper also investigates the effects of the number of layers, nodes, and learning rate on motion estimation and system identification to provide a benchmark for selecting optimal HPs. This study finds that hyperparameter optimization can reduce the relative error of identified parameters by up to two thousand times compared to no optimization. Motion estimation prefers large neural networks for all sea states, while at least three layers of neural networks are needed for accurate parameter identification. The paper also provides a look-up table to investigate further implementing PINN on moored floating offshore structures. Proper selection of HPs is crucial as it can incur up to three orders of magnitude PINN loss and exceedingly-high identification error. Overall, this study highlights the applicability of PINN in modeling complex offshore structures and provides insights into selecting optimal HPs for accurate and efficient estimation of motion and system parameters.

A sensor fault detection, isolation, and estimation method for intelligent vehicles

Accurate and reliable sensor information is crucial for intelligent vehicles to ensure safety and reliability. It is significant to devise effective solutions to diagnose possible sensor faults. In this paper, a sensor fault detection, isolation, and estimation approach is developed for intelligent vehicle-integrated motion systems. The Luenberger observer is designed and embedded into the fault diagnosis framework to real-time estimate the system states. Interval observers are constructed for vehicle subsystems to estimate the interval bounds of the corresponding observation errors. The estimation errors enter into the corresponding intervals generated by the adaptive laws when the sensor fault is free. In the event of a sensor fault, it can be detected efficiently and the faulty sensor can be isolated. Moreover, occurred sensor fault can be estimated by modifying the interval observer structure. The simulation results in the standard J-Turn test scenario are provided to evaluate and verify the effectiveness of the proposed method.

The application of molecular dynamic simulations

Molecular dynamic(MD) simulations have been developed into a mature tool for researchers and have had a huge impact on the aspects of drug discovery, molecular biology, and prediction of specific structures and related functions of macromolecules in recent years. The aim of this article is to provide some essential knowledge about the theories and applications of this useful technology. This introduction to molecular dynamics starts from the definition of some basic concepts, including the molecular mechanics force fields and how they are applied to the motion systems of molecular dynamics simulations and their developments recently. Also, the outstanding strengths and limitations of the MD simulations are discussed, as are some possible solutions to them. This article shows the wide applications of MD simulations, including drug discovery, prediction of structure, and allostery. Due to the growing power of computational calculations and its important role in simulating the biological process between receptors and ligands like allosteric binding sites, MD simulations are thought to be more and more important in the near future.

Application of AI and ML

Artificial inteligence(AI) and machine learning(ML) are arguably the most distruptive technologies in the modern world. The possible applications in robotics and automation are virtually limitless. Thus, this paper explores uses in two key areas: servo motors and the kinematics and dynamics models of robotics and drones. Reliable studies providing insights into these topics were used to develop the report. The study found that AI and ML enable precise control, obstacle avoidance, and adaptive performance in servo motors and motion planning, snesor fusion, fault detection, and system identification in kinematics and dynamics models. These findings confirm the vitality of AI and ML for the functionality of intelligent robots and drones used across different industries, such as construction and manufacturing.