Motion Profiles Research Articles

Projection-based reduced-order modelling of time-periodic problems, with application to flow past flapping hydrofoils

Simulating forced time-periodic flows in industrial applications presents significant computational challenges, partly due to the need to overcome costly transients before achieving time-periodicity. Reduced-order modelling emerges as a promising method to speed-up computations. We extend upon the work of Lotz et al. (2024) where a time-periodic space–time model is introduced. We present a time-periodic reduced-order model that directly finds the time-periodic solution without requiring extensive time integration. The reduced-order model gives a reduction in variables in both space and time. Our approach involves a POD-Galerkin reduced-order model based on a time-periodic full-order model that employs isogeometric analysis, residual-based variational multiscale turbulence modelling and weak boundary conditions. The projection-based reduced-order model inherits these features. We evaluate the reduced-order model with numerical experiments on moving hydrofoils. The motion is known a priori and we restrict ourselves to two spatial dimensions. In these experiments we vary the Strouhal and Reynolds numbers, and the motion profile respectively. Reduced-order model solutions agree well with those of the full-order model. The errors over the entire time period of thrust and lift forces are less than 0.2%. This includes complex scenarios such as the transition from drag to thrust production with increasing Strouhal number. Our time-periodic reduced-order model offers speed-ups ranging from O(102) to O(103) compared to the full-order model, depending upon the basis size. This makes it an appealing solution for prescribed time-periodic problems, with potential for additional speedup through nonlinear reduction techniques such as hyper-reduction.

Optimal placement of inherent zeros in polynomial-based motion profiles for minimizing residual vibration and travel time under system uncertainties

Various industrial processes require motion profiles that determine machine movement over time for workpiece transfer, with the goal of minimizing transfer time to enhance productivity. However, pursuing the maximum speed can trigger excessive motion-induced vibration, which can reduce the manufacturing efficiency, accuracy and operational lifespan of the system. This study proposes a generalized motion profile optimization method that considers the strategic placement of inherent zeros within the motion profile: each inherent zero contributes to the reduction of residual vibration or optimization of arrival time. By offering freedom to the placement of zeros, four optimization options for polynomial-based motion profiles of each order are proposed, expanding on existing work that offered a single option. All proposed optimization options have closed-form solutions, making them easily applicable to industrial applications. The practical applicability of the suggested methodology is demonstrated through case studies and experiments.

Simultaneous Velocity and Texture Classification from a Neuromorphic Tactile Sensor Using Spiking Neural Networks

The neuroTac, a neuromorphic visuo-tactile sensor that leverages the high temporal resolution of event-based cameras, is ideally suited to applications in robotic manipulators and prosthetic devices. In this paper, we pair the neuroTac with Spiking Neural Networks (SNNs) to achieve a movement-invariant neuromorphic tactile sensing method for robust texture classification. Alongside this, we demonstrate the ability of this approach to extract movement profiles from purely tactile data. Our systems achieve accuracies of 95% and 83% across their respective tasks (texture and movement classification). We then seek to reduce the size and spiking activity of our networks with the aim of deployment to edge neuromorphic hardware. This multi-objective optimisation investigation using Pareto frontiers highlights several design trade-offs, where high activity and large network sizes can both be reduced by up to 68% and 94% at the cost of slight decreases in accuracy (8%).

Implementation of a motion planning technique for a low-frequency piezo-actuated inchworm drive

A novel piezoelectric inchworm drive capable of long-range motion has been designed, fabricated, and tested in this research work. To control the motion of the inchworm drive, trajectory planning has been proposed. The trajectory planning ensures that the inchworm drive achieves smooth and continuous motion with high accuracy. Two trajectory planning methods were incorporated for the developed inchworm drive: a linear function with a parabolic blend trajectory and a cubic polynomial trajectory. Simulations for both the cubic polynomial and trapezoidal trajectories were conducted, with the estimated displacement results closely verified through experimental validation. The fabricated inchworm drive is tested for varying input voltages and frequencies. The experimental findings demonstrate that the proposed piezoactuated Inchworm drive can achieve substantial displacement, constrained only by the linear slide’s length. When an input signal of 150 V peak to peak and frequency of 10 Hz is applied to the inchworm drive, it was capable of moving at a speed of 1425 μm/s. When incorporating trajectory planning for the Inchworm drive the experimental results show that the maximum percentage error for the trapezoidal motion profile is 1.56% and the cubic polynomial profile trajectory is within 1% for the corresponding target position for a travel range of 25 mm of the inchworm drive.

A neural network inverse problem solution for a MIMO magnetic levitation actuator

A multiple-input and multiple-output planar actuator is proposed, which utilizes mechanically driven stator magnet arrays to levitate a permanent magnet mover. A state of levitation and actuation is obtained by mechanically altering the orientation of the stator magnets to control the forces and torques on the mover. A challenge for the design and control of the actuator is inverting the relationship between the force and stator magnet rotation angles, as there is no closed-form analytical solution. In this study, a feed-forward neural network is applied to model the forward relation between stator magnet angle input and a force and torque output to reduce the forward computation time for the design process and for error estimation in real-time applications. Additionally, the neural network is considered for inverting the solution for a motion profile sampled at 1000 Hz. The developed forward model is able to calculate the forces and torques on the mover a factor 10 faster than the equivalent charge or Fourier model with an absolute error of 3 mN and 0.1 mNm for the forces and torques, respectively, and a feed-forward neural network is able to accurately learn an inverse solution for small motion profiles.

Design Methodology for a 3D Printable Multi‐Degree of Freedom Soft Actuator Using Geometric Origami Patterns

Soft pneumatic actuators (SPAs) have applications in various domains due to their compliance, low cost, and lightweight characteristics. Herein, a geometric origami design for 3‐degree of freedom (DoF) SPAs is presented, which enables manufacturing using 3D printing at one go without any support structure. The proposed method uses a general design parameterization that works for multiple types of cylindrical origami patterns (Kresling, cylindrical Miura, Yoshimura, and Accordion). It is shown that a 3‐DoF pneumatic actuator capable of bending and extension can be constructed by superimposing two cylindrical origami patterns to create a separation of chambers inside of the module. In addition, the design parameters are chosen to reduce the strain during deformation and maximize the output forces of the origami actuator. The actuator is manufactured, and its motion and force profiles are experimentally characterized. The designed origami actuator can bend from to and the length during operation can vary between and of the initial length.

Experimental‐Assisted Approach to Develop a Numerical Model for Simulating the Reaction Propagation in Reactive Multilayers

One outstanding feature of self‐propagating reactions is their ability to release heat of reaction over both temporal and spatial scales, enabling the sustained progression of the reaction after a local ignition. They propagate in the form of a continuous reaction front through the mixture of the starting materials. Previous research on reactive materials has predominantly focused on unraveling the microstructure property relationships influencing released energy in reacting multilayers. This involved considering coupled differential equations, including the heat conduction equation and Fick's law. In this study, the introduction of a purely thermal numerical macroscale model is made, incorporating two states of material properties that differentiate between the thermal characteristics before and after phase formation. The homogenization of material properties before the phase formation is accomplished through the consideration of directional‐temperature‐dependent thermal conductivity and temperature‐dependent‐specific heat capacity. The energy‐release function is derived using experimental data for the reaction velocity depending on bilayer thickness. This model allows for the exploration of reaction motion and temperature profiles, achieving qualitative conformity with experimental measurements for freestanding foil, and necessitating reasonable computational effort.

Application of Support Vector Machine in the Evaluation of Table Tennis Motion Profiles

Application of Support Vector Machine in the Evaluation of Table Tennis Motion Profiles

Designing a Laboratory Cam Profile Measuring Machine to Validate Follower Displacements

This study presents the design, build, and evaluation of a laboratory cam profile measuring machine tailored to demonstrate the mechanical principles and applications of various cam shapes. Utilizing a diverse set of cam profiles, the machine effectively converts rotational motion into measurable linear motion, achieving a range of motion profiles, including rising, declining, steady, and instantaneous actions. Key components of the machine include an angle gauge for precise rotational measurements and a linear dial indicator for accurately gauging the cam-induced displacement. This setup facilitates the measuring of displacement, and computation of velocity and acceleration for each cam shape, offering a dynamic visual and numerical aid for engineering and design.

Visual feedbacks influence short-term learning of torque versus motion profile with robotic guidance among young adults

Robotic assistance can improve the learning of complex motor skills. However, the assistance designed and used up to now mainly guides motor commands for trajectory learning, not dynamics learning. The present study explored how a complex motor skill involving the right arm can be learned without suppressing task dynamics, by means of an innovative device with robotic guidance that allows a torque versus motion profile to be learned with admittance control. In addition, we assessed how concurrent visual feedback on this profile can enhance learning without creating dependency, by means of a fading procedure (i.e., feedback reduction across trials). On Day 1, a Control group performed an acquisition session (6 blocks) featuring concurrent visual feedback, while a Fading group performed the session with a gradual reduction in feedback (from 100% to 0% over the 6 blocks). On Day 2, both groups performed a block first without feedback (i.e., Transfer test), then with feedback (i.e., Retention test). Results revealed that on Day 1, movement rehearsal induced a significant improvement in spatiotemporal parameters for the Control group, compared with the Fading group. On Day 2, the opposite was found when this visual feedback was removed, as the Fading group performed significantly better than the Control group on the Transfer test. Vision allows a relationship to be established between the required torque and the motion profile. Its suppression then forces the processing of more intrinsic information, leading to the development of a stable internal representation of the task.

In situ monitoring of the impacts of Melaleuca styphelioides transpiration on soil water dynamics in an urban environment

Expansive soils are susceptible to substantial cyclical moisture variations under the prevailing climate, experiencing considerable shrinkage during drought and swelling when water availability increases. These dynamic volumetric changes jeopardize the functionality of lightweight infrastructures with shallow foundations. The presence of trees may amplify seasonal soil movements, as extensive water uptake by root networks in the summer months can induce soil moisture depletion and the resulting desiccation-driven settlement around the trees. This study assessed the drying effect of mature Melaleuca styphelioides on soil movement and water dynamics at an urban experimental site in Melbourne, Australia, instrumented for in situ monitoring over 12 months. Sap flow instrumentation was used to quantify seasonal variations in tree transpiration. The monitoring results revealed that the drying influence of the tree has extended to a depth of 2.2 m. Tree root-induced soil desiccation occurred 4.0 m away from the tree, as evidenced by the seasonal soil movement profiles. Linear regression analysis showed that the soil water content explained 60% of the variability in tree transpiration. In contrast, the soil water content and suction exhibited a robust negative correlation (R2=0.96) within the active root zone.

Biomechanical Comparison of Anterior Cervical Plate Fixation Versus Integrated Fixation Cage for Anterior Cervical Discectomy and Fusion.

Cadaveric, biomechanic study. To compare the range of motion profiles of the cervical spine following one-level anterior cervical discectomy and fusion (ACDF) constructs instrumented with either an interbody cage and anterior plate or integrated fixation cage in a cadaveric model. While anterior plates with interbody cages are the most common construct of fixation in ACDF, newer integrated cage-plate devices seek to provide similar stability with a decreased implant profile. However, differences in postoperative cervical range of motion between the 2 constructs remain unclear. Six cadaveric spines were segmented into 2 functional spine units (FSUs): C2-C5 and C6-T2. Each FSU was nondestructively bent in flexion-extension (FE), right-left lateral bending (LB), and right-left axial rotation (AR) at a rate of 0.5°/s under a constant axial load until a limit of 2-Nm was reached to evaluate baseline range of motion (ROM). Matched pairs were then randomly assigned to undergo instrumentation with either the standard anterior cage and plate (CP) or the integrated fixation cage (IF). Following instrumentation, ROM was then remeasured as previously described. For CP fixation, ROM increased by 61.2±31.7% for FE, 36.3±20.4% for LB, and 31.7±19.1% for AR. For IF fixation, ROM increased by 64.2±15.5% for FE, 56.7±39.8% for LB, and 94.5±65.1% for AR. There was no significant difference in motion between each group across FE, LB, and AR. This biomechanical study demonstrated increased motion in both the CP and IF groups relative to the intact, un-instrumented state. However, our model showed no differences in ROM between CP and IF constructs in any direction of motion. These results suggest that either method of instrumentation is a suitable option for ACDF with respect to constructing stiffness at time zero.

Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles.

Taekwondo has evolved from a traditional martial art into an official Olympic sport. This study introduces a novel action recognition model tailored for Taekwondo unit actions, utilizing joint-motion data acquired via wearable inertial measurement unit (IMU) sensors. The utilization of IMU sensor-measured motion data facilitates the capture of the intricate and rapid movements characteristic of Taekwondo techniques. The model, underpinned by a conventional convolutional neural network (CNN)-based image classification framework, synthesizes action images to represent individual Taekwondo unit actions. These action images are generated by mapping joint-motion profiles onto the RGB color space, thus encapsulating the motion dynamics of a single unit action within a solitary image. To further refine the representation of rapid movements within these images, a time-warping technique was applied, adjusting motion profiles in relation to the velocity of the action. The effectiveness of the proposed model was assessed using a dataset compiled from 40 Taekwondo experts, yielding remarkable outcomes: an accuracy of 0.998, a precision of 0.983, a recall of 0.982, and an F1 score of 0.982. These results underscore this time-warping technique's contribution to enhancing feature representation, as well as the proposed method's scalability and effectiveness in recognizing Taekwondo unit actions.

Developing a Gantry Robot with Pre-calculated pure S-curve Motion Profiles for Delicate Egg Handling: Utilizing ESP32, FreeRTOS, and AS5600 Encoders

The poultry industry faces challenges due to egg breakage during transfer. This paper describes a gantry robot specifically designed for delicate egg handling. The robot utilizes pre-calculated pure S-curve motion profiles to achieve smooth and precise movements, minimizing stress on the eggs. This approach leverages the computational efficiency of pre-calculation, making it suitable for low-power microcontrollers like the ESP32. FreeRTOS(Free Real-Time Operating System) ensures real-time task management for profile execution and data collection every 4 milliseconds from the AS5600 encoders. These encoders provide high-resolution angular position feedback, allowing for comparison with the planned S-curve profile after each movement step. This system offers advantages such as reduced egg breakage, improved transfer efficiency, and a simpler design compared to real-time control. However, limitations include limited adaptability to significant environmental changes and disturbances. Future work may investigate incorporating real-time feedback control for enhanced robustness.

The Energetics of the Lagrangian Evolution of Tropical Convective Systems

Abstract The convective life cycle is often conceptualized to progress from congestus to deep convection and develop further to stratiform anvil clouds, accompanied by a systematic change in the vertical structure of vertical motion. This archetype scenario has been developed largely from the Eulerian viewpoint, and it has yet to be explored whether the same life cycle emerges itself in a moving system tracked in the Lagrangian manner. To address this question, Lagrangian tracking is applied to tropical convective systems in combination with a thermodynamic budget analysis forced by satellite-retrieved precipitation and radiation. A new method is devised to characterize the vertical motion profiles in terms of the column import or export of moisture and moist static energy (MSE). The bottom-heavy, midheavy, and top-heavy regimes are identified for every 1° × 1° grid pixel accompanying tracked precipitation systems, making use of the diagnosed column export/import of moisture and MSE. The major findings are as follows. The Lagrangian evolution of convective systems is dominated by a state of dynamic equilibrium among different convective regimes rather than a monotonic progress from one regime to the next. The transition from the bottom-heavy to midheavy regimes is fed with intensifying precipitation presumably owing to a negative gross moist stability (GMS) of the bottom-heavy regime, whereas the transition from the midheavy to top-heavy regimes dissipates the system. The bottom-heavy to midheavy transition takes a relaxation time of about 5 h in the equilibrating processes, whereas the relaxation time is estimated as roughly 20 h concerning the midheavy to top-heavy transition.

Age-related changes in motor planning for prior intentions: a mouse tracking reach-to-click task.

When we complete sequential movements with different intentions, we plan our movements and adjust ahead. Such a phenomenon is called anticipatory planning for prior intentions and is known to decline with age. In daily life activities, we often need to consider and plan for multiple demands in one movement sequence. However, previous studies only considered one dimension of prior intentions, either different types of onward actions or different precisions of fit or placement. Therefore, in this study, we investigated anticipatory planning for both extrinsic (movement direction) and intrinsic (fit precision) target-related properties in a computer-based movement task and analyzed the computer cursor movement kinematics of both young and older adults. We found that older people consider and adjust for different properties step-by-step, with movement direction being considered as a prior intention during reach movement and fit precision as a motor constraint during drop movement. The age-related changes in the completion of onward actions are constrained by one's general cognitive ability, sensorimotor performance and effective motor planning for prior intentions. Age-related decline in motor planning can manifest as counterproductive movement profiles, resulting in suboptimal performance of intended actions.

Modulation of DBS-induced cortical responses and movement by the directionality and magnitude of current administered

Subthalamic deep brain stimulation (STN-DBS) is an effective therapy for alleviating motor symptoms in people with Parkinson’s disease (PwP), although some may not receive optimal clinical benefits. One potential mechanism of STN-DBS involves antidromic activation of the hyperdirect pathway (HDP), thus suppressing cortical beta synchrony to improve motor function, albeit the precise mechanisms underlying optimal DBS parameters are not well understood. To address this, 18 PwP with STN-DBS completed a 2 Hz monopolar stimulation of the left STN during MEG. MEG data were imaged in the time-frequency domain using minimum norm estimation. Peak vertex time series data were extracted to interrogate the directional specificity and magnitude of DBS current on evoked and induced cortical responses and accelerometer metrics of finger tapping using linear mixed-effects models and mediation analyses. We observed increases in evoked responses (HDP ~ 3–10 ms) and synchronization of beta oscillatory power (14–30 Hz, 10–100 ms) following DBS pulse onset in the primary sensorimotor cortex (SM1), supplementary motor area (SMA) and middle frontal gyrus (MFG) ipsilateral to the site of stimulation. DBS parameters significantly modulated neural and behavioral outcomes, with clinically effective contacts eliciting significant increases in medium-latency evoked responses, reductions in induced SM1 beta power, and better movement profiles compared to suboptimal contacts, often regardless of the magnitude of current applied. Finally, HDP-related improvements in motor function were mediated by the degree of SM1 beta suppression in a setting-dependent manner. Together, these data suggest that DBS-evoked brain-behavior dynamics are influenced by the level of beta power in key hubs of the basal ganglia-cortical loop, and this effect is exacerbated by the clinical efficacy of DBS parameters. Such data provides novel mechanistic and clinical insight, which may prove useful for characterizing DBS programming strategies to optimize motor symptom improvement in the future.

Optimization of higher-order S-curve motion profile using unitization method aiming to reduce the vibration of a lightly damped system

Optimization of higher-order S-curve motion profile using unitization method aiming to reduce the vibration of a lightly damped system

SDA: A NOVEL SKEWED-DEEP-ARCHITECTURE FOR VEHICLE MOTION DETECTION IN DRIVING VIDEOS

Collision avoidance mechanisms are important topics for studies in the field of autonomous vehicles. We could obtain prior information about the collision from the movement angles of vehicles. Therefore, it is important issue to learn the movement angles of vehicles in motion. In the study, an architectural model is developed that learns the horizontal movement angles of vehicles to form a base for collision warning systems. YOLOv3 is modified and used on motion profiles. Thanks to the learned angle values, also the bounding boxes match the traces in the motion profiles smoothly. The results obtained have a mAP value of 79% and an operating speed of 36 FPS. These results are better than when trained on motion profiles of the YOLOv3 architecture. In addition, the use of the new architecture on motion profiles and factors such as noise and bad weather in the image do not adversely affect the results. With these features, a fundamental step has been taken for anti-collision systems.

Children and adolescents with all forms of shoulder instability demonstrate differences in their movement and muscle activity patterns when compared to age- and sex-matched controls

BackgroundShoulder instability is a complex impairment and identifying biomarkers which differentiate subgroups is challenging. There is limited fundamental movement and muscle activity data for identifying different mechanisms for shoulder instability in children and adolescents which may inform subgrouping and treatment allocation. HypothesisChildren and adolescents with shoulder instability (irrespective of etiology) have differences in their movement and muscle activity profiles compared to age- and sex-matched controls (two-tailed). MethodsYoung people between eight to 18 years were recruited into two groups of shoulder instability (SI) or and age- and sex-matched controls (CG). All forms of SI were included and young people with co-existing neurological pathologies or deficits were excluded. Participants attended a single session and carried out four unweighted and three weighted tasks in which their movements and muscle activity was measured using 3D-movement analysis and surface electromyography. Statistical parametric mapping was used to identify between group differences. ResultsData was collected for 30 young people (15 SI (6M:9F) and 15 CG (8M:7F)). The mean (SD) age for all participants was 13.6 years (3.0). The SI group demonstrated consistently more protracted and elevated sternoclavicular joint positions during all movements. Normalized muscle activity in Latissimus dorsi was lower in the SI group and had the most statistically significant differences across all movements. Where differences were identified, the SI group also had increased normalized activity of their middle trapezius, posterior deltoid and biceps muscles whilst activity of their latissimus dorsi, triceps and anterior deltoid were decreased compared to the CG group. No statistically significant differences were found for pectoralis major across any movements. Weighted tasks produced fewer differences in muscle activity patterns compared to unweighted tasks. DiscussionYoung people with SI may adapt their movements to minimize glenohumeral joint instability. This was demonstrated by reduced variability in acromioclavicular and sternoclavicular joint angles, adoption of different movement strategies across the same joints and increased activity of the scapular stabilizing muscles, despite achieving similar arm positions to the CG. ConclusionYoung people with shoulder instability demonstrated consistent differences in their muscle activity and movement patterns. Consistently observed differences at the shoulder girdle included increased sternoclavicular protraction and elevation accompanied by increased normalized activity of the posterior scapula stabilizing muscles. Existing methods of measurement may be used to inform clinical decision making, however, further work is needed evaluate the prognostic and clinical utility of derived 3D and sEMG data for informing decision making within shoulder instability.