Dynamic Motion Research Articles

A dual wave structure triboelectric sensor for heart rate and exercise monitoring

Recently, research on wearable devices for physiological exercise monitoring has garnered significant attention. Here, we propose a dual wave-structured triboelectric nanogenerator (DW-TENG) integrated with a cotton cloth, developed for smart running applications. The DW-TENG sensor leverages a flexible wave triboelectric layer composed of polydimethylsiloxane and silicone, with a copper electrode layer between them for sensing. This structure allows for customizable pressure sensitivity by adjusting the silicone hardness. Experimental results show that the DW-TENG achieves a sensitivity of 0.4 V kPa−1, with response and recovery times of 75 and 90 ms, respectively. The sensor effectively measures heart rate changes during various physical activities, including walking, running, and jumping. Electrical performance tests reveal that the DW-TENG’s output is significantly influenced by the silicone hardness, operational frequency, and microstructure height. The DW-TENG sensor demonstrates high durability and stability, maintaining consistent voltage output over 40 000 cycles. This research highlights the potential of the DW-TENG in multifunctional physiological and physical activity monitoring, providing real-time data on respiratory patterns, heart rate, and movement dynamics, thus enhancing athletic training, performance assessment, and health monitoring.

Low-Reynolds-number droplet motion in shear flow micro-confined by a rough substrate

A three-dimensional spectral boundary element method has been employed to compute for the dynamics of the droplet motion driven by shear flow near a single solid substrate with a rough surface. The droplet size is comparable with the surface features of the substrate. This is a problem that has barely been explored but with applications in biomedical research and heat management. This work numerically investigated the influences of surface roughness features, such as the roughness amplitude and wavelength, on the droplet deformation and velocities. We observe that a greater amplitude or wavelength leads to larger variations in droplet velocity perpendicular to the substrate. The droplet velocity along the substrate increases when the amplitude is reduced or when the wavelength increases. The effects of capillary number and viscosity ratios have also been studied. The droplet deformation and its velocity increases as we increase the capillary number, while the viscosity ratio shows a non-monotonic influence on the droplet behavior. The predicted droplet behaviors, including deformation, velocities, and trajectories, can provide physical insight, help to understand the droplet behavior in microfluidic devices without a perfectly smooth surface, and contribute in the design and operation of those devices.

Unsupervised Learning Bioreactor Regimes

Efficient operation of bioreactors is pivotal for the success of biomanufacturing. Traditional Computational Fluid Dynamics (CFD) simulations, while detailed, often suffer from long computation times and complexity, making them impractical for real-time applications. This study introduces a novel multivariate unsupervised learning algorithm for clustering bioreactors into coherent regions based on CFD-generated and real-world data. The resulting clusters can be used to determine regimes inside a reactor, or can work as an initial step for compartment models. Our method employs a custom k-means clustering algorithm that not only ensures spatial continuity within clusters but also optimizes the number of compartments based on clustering scores. This optimization process focuses on defining compartments clearly while retaining maximum information from the continuous body, as demonstrated by a Pareto front analysis. The efficacy and adaptability of this approach were validated through two case studies: a 202 m³ Rushton impeller bioreactor and an 840 m³ airlift reactor. The results underscore the benefits of 3-D compartmentalization in capturing the complex dynamics of fluid motion and cellular activities. By establishing a robust method for scaling down experiments and enhancing bioreactor design, this study paves the way for more efficient industrial applications.

Interacting myosin head dynamics and their modification by 2’-deoxy-ADP

The contraction of striated muscle is driven by cycling myosin motor proteins embedded within the thick filaments of sarcomeres. In addition to cross-bridge cycling with actin, these myosin proteins can enter an inactive, sequestered state in which the globular S1 heads rest along the thick filament surface and are inhibited from performing motor activities. Structurally, this state is called the interacting heads motif (IHM) and is a critical conformational state of myosin that regulates muscle contractility and energy expenditure. Structural perturbation of the sequestered state can pathologically disrupt IHM structure and the mechanical performance of muscle tissue. Thus, the IHM state has become a target for therapeutic intervention. An ATP analogue called 2’-deoxy-ATP (dATP) is a potent myosin activator that destabilizes the IHM. Here, we use molecular dynamics simulations to study the molecular mechanisms by which dATP modifies the structure and dynamics of myosin in a sequestered state. Simulations of the IHM state containing ADP.Pi in both nucleotide binding pockets revealed dynamic motions of the blocked head – free head interface, light chain binding domain, and S2 in this ‘inactive’ state of myosin. Replacement of ADP.Pi by dADP.Pi triggered a series of structural changes that increased heterogeneity among residue contact pairs at the blocked head – free head interface and a 14% decrease in the interaction energy at the interface. Dynamic changes to this interface were accompanied by dynamics in the light chain binding region. A comparative analysis of these dynamics predicted new structural sites that may affect IHM stability.

Bundling instability of lophotrichous bacteria

We present a mathematical model of lophotrichous bacteria, motivated by Pseudomonas putida, which swim through fluid by rotating a cluster of multiple flagella extended from near one pole of the cell body. Although the flagella rotate individually, they are typically bundled together, enabling the bacterium to exhibit three primary modes of motility: push, pull, and wrapping. One key determinant of these modes is the coordination between motor torque and rotational direction of motors. The computational variations in this coordination reveal a wide spectrum of dynamical motion regimes, which are modulated by hydrodynamic interactions between flagellar filaments. These dynamic modes can be categorized into two groups based on the collective behavior of flagella, i.e., bundled and unbundled configurations. For some of these configurations, experimental examples from fluorescence microscopy recordings of swimming P. putida cells are also presented. Furthermore, we analyze the characteristics of stable bundles, such as push and pull, and investigate the dependence of swimming behaviors on the elastic properties of the flagella.

Exact optimization of inter-array dynamic cable networks for Floating Offshore Wind Farms

Optimization of inter-array dynamic cables for Floating Offshore Wind Farms (FOWFs) using three integer linear programs and a heuristic is presented. Design optimization of fixed-bottom offshore wind is a challenging research problem but the presence of dynamic components in FOWFs adds new complexity - as the Floating Offshore Wind Turbine (FOWT), the support structure including the station-keeping system, and the floating power cables all experience dynamic movement in reaction to wind, wave and even current forcing. In this study, dynamic modelling for the response of this system is first carried out to assess the risk of potential mechanical interference between movable elements. Subsequently, safety zones constraints are defined in the optimization to ensure minimally safe conditions for operation of the combined FOWT/support-structure/cables system. Likewise, additional constraints including maximum thermal limits, tree topology without branching, and others are incorporated. The programs follow an incremental approach. Model 1 proposes a simple way to avoid mechanical interference, Model 2 adds variables modelling mooring lines anchoring, and Model 3 increases the degrees of freedom through addition of the positioning of the touchdown point where the dynamic and static sections meet at the seabed. The applicability is illustrated through realistic case studies for a reference FOWF in Europe. Results show that: (i) Modern branch-and-cut solvers are able to solve Model 2 getting the global optimum in seconds, and (ii) further cost refining can be obtained after wrapping Model 3 in the heuristic, using Model 2 as the initial design, decreasing the cost of this layout by around 1.5% in few hours through a nonrectilinear topology.

Optimal Location for Tower Crane and Dynamic Trailer Parking in High-Rise Modular Building Construction Project

Optimization of tower crane positioning is essential in precast building construction to ensure that a crane is in the right position to offer the best value for placing precast elements. The first goal is the optimization of transportation distances made by tower cranes, which will address the issues of efficiency and cost. This is done in a way that provides for minimization of the distances that lie between the trailer parking where elements are parked, the tower crane, and the construction installation point. However, current research fails to address the dynamic movement efficiency of trailers, especially when handling multiple trailers and several models of tower cranes, making this scenario an optimization problem that is classified as NP-hard due to the likelihood of causing a combinatorial explosion. This work endeavors to present a new mathematical model that has been applied to the Genetic Algorithm (GA). The ideal demand-generated model is designed to solve the optimal model and position of tower cranes and the prospective positioning of trailers for parking. The feasibility and applicability of the method exploited in the study are also established through a project case study. When adopting the proposed planning model compared to the earlier planning scale, comparisons highlight a small yet significant saving of 6% in time and cost when lifting elements instead of providing them from the trailers by setting up a new on-site stockyard. In addition, a great deal of enhancement is also observed in the final solution aspects, where the developed GA has even reduced the operational time and cost by half a percent each. The results of this research offer best-practice approaches to the position analysis of tower cranes in precast building construction to bring scalability and cost optimization to bear on the construction business.

The effects of pressure on anticipatory postural adjustments during a jump shot in basketball

BackgroundAnticipatory postural adjustments (APAs) play an important role in feedforward control of dynamic movements, such as jump shot performance in basketball. It is known that jump shot performance declines under pressure from a defender (shot blocker). Research questionDoes pressure from a shot blocker affect the APAs of a jump shot in basketball? Is jump shot performance in basketball associated with APAs? MethodsFourteen healthy male university basketball players performed jump shots under pressure and non-pressure (free) conditions by a shot blocker. Using a force plate, the APAs were defined as occurring until the point of thrust (TH) phase, and ground reaction force (GRF) and center of pressure (COP) at that moment were assessed. To assess jump shot performance, the maximum GRF during the TH phase (THmax), jumping height, and success score of the shot (accuracy score: AS) were measured by using the vertical component of the force plate. Two-way ANOVA examined the effects of the phase and condition on APA duration. The effects of pressure from a shot blocker on each variable (subjective pressure intensity, AS, GRF, and COP) were analyzed using a pairwise t-test or Wilcoxon signed-rank tests. Relationships between changes in jump shot performance and GRF or COP variables were assessed via Spearman’s correlation analysis. Differences between the pressure and free conditions were denoted by p/f (pressure/free). ResultsThe APA duration was shorter under the pressure condition compared to the free condition. The THmax and jump height values were greater under the pressure condition relative to the free condition. Across conditions, changes in COP sway velocity of the APA phase were significantly and negatively correlated with AS. SignificanceThe results of this study suggest that pressure from a shot blocker shortens APA duration and decreases jump shot performance.

Characterization of Human Shoulder Joint Stiffness Across 3D Arm Postures and Its Sex Differences.

Understanding the characteristics of shoulder joint stiffness can offer insights into how the shoulder joint contributes to arm stability and assists in various arm postures and movements. This study aims to characterize posture-dependent shoulder stiffness in a three-dimensional (3D) space and investigate its potential sex differences. A multi-degree-of-freedom, parallel-actuated shoulder exoskeleton robot was used' to perturb the participant's shoulder joint and measure the resulting torque responses while participants relaxed their shoulder muscles. The group average results of 40 healthy individuals (20 males and 20 females) revealed that arm postures significantly affect shoulder stiffness, particularly in postures involving shoulder flexion/extension and horizontal flexion/extension. Shoulder stiffness consistently increased as the shoulder flexion angle decreased and the shoulder horizontal flexion/extension approached the limit of its range of motion. The comparative group results between males and females indicated that shoulder stiffness in males was greater than that in females across all 15 arm postures measured in this study. Even after normalizing the data by subject body mass, the female group showed significantly lower stiffness than the male group in 12 out of the 15 arm postures. The results highlight that 3D arm postures and sex significantly affect shoulder stiffness even under relaxed muscles. This study provides valuable foundations for future studies aimed at characterizing shoulder stiffness in the context of active muscles and dynamic movement tasks, evaluating changes in shoulder stiffness following neuromuscular injuries, and formulating rehabilitative training protocols for individuals suffering from shoulder problems.

Brain and eye movement dynamics track the transition from learning to memory-guided action.

Learning never stops. As we navigate life, we continuously acquire and update knowledge to optimize memory-guided action, with a gradual shift from the former to the latter as we master our environment. How are these learning dynamics expressed in the brain and in behavioral patterns? Here, we devised a spatiotemporal image learning task ("Memory Arena") in which participants learn a set of 50 items to criterion across repeated exposure blocks. Critically, brief task-free periods between successive image presentations allowed us to assess multivariate electroencephalogram (EEG) patterns representing the previous and/or upcoming image identity, as well as anticipatory eye movements toward the upcoming image location. As expected, participants eventually met the performance criterion, albeit with different learning rates. During task-free periods, we were able to readily decode representations of both previous and upcoming image identities. Importantly though, decoding strength followed opposing slopes for previous vs. upcoming images across time, with a gradual decline of evidence for the previous image and a gradual increase of evidence for the upcoming image. Moreover, the ratio of upcoming vs. previous image evidence directly followed behavioral learning rates. Finally, eye movement data revealed that participants increasingly used the task-free period to anticipate upcoming image locations, with target-precision slopes paralleling both behavioral performance measures as well as EEG decodability of the upcoming image across time. Together, these results unveil the neural and behavioral dynamics underlying the gradual transition from learning to memory-guided action.

Global motor dynamics - Invariant neural representations of motor behavior in distributed brain-wide recordings

Objective.Motor-related neural activity is more widespread than previously thought, as pervasive brain-wide neural correlates of motor behavior have been reported in various animal species. Brain-wide movement-related neural activity have been observed in individual brain areas in humans as well, but it is unknown to what extent global patterns exist.Approach.Here, we use a decoding approach to capture and characterize brain-wide neural correlates of movement. We recorded invasive electrophysiological data from stereotactic electroencephalographic electrodes implanted in eight epilepsy patients who performed both an executed and imagined grasping task. Combined, these electrodes cover the whole brain, including deeper structures such as the hippocampus, insula and basal ganglia. We extract a low-dimensional representation and classify movement from rest trials using a Riemannian decoder.Main results.We reveal global neural dynamics that are predictive across tasks and participants. Using an ablation analysis, we demonstrate that these dynamics remain remarkably stable under loss of information. Similarly, the dynamics remain stable across participants, as we were able to predict movement across participants using transfer learning.Significance.Our results show that decodable global motor-related neural dynamics exist within a low-dimensional space. The dynamics are predictive of movement, nearly brain-wide and present in all our participants. The results broaden the scope to brain-wide investigations, and may allow combining datasets of multiple participants with varying electrode locations or calibrationless neural decoder.

Vibration control of two portal frames type shear buildings through self-synchronous dynamics of two non-ideal sources indirectly coupled

The present paper proposes a new device where it is observed the self-synchronization between two unbalanced DC motors with a limited power supply when they are indirectly coupled. The vibrating system studied consists of two portal frames type shear buildings coupled, carrying each an unbalanced DC motor. The engines are supposed to rotate in the same direction and act on each as external excitation. The dynamics investigations are done with analytical and numerical methods to achieve this purpose. The synchronous solutions are derived and their stability conditions are also explored using the averaging method. The results of this analytical investigation are confirmed later by numerical simulations. The effects of some physical parameters on the self-synchronization of DC motors are presented. The impact of the nonlinear coupling between the floors on the DC motors dynamics is also explored. The Sommerfeld effect appearing in the system is reduced by taking into account the damping coming from the environment and the coupling between the portal frame. It is observed that in-phase synchronization of the non-ideal sources assures a low amplitude of vibration in the different floors compared to opposite-phase synchronization.

Using an integrated motor imagery and physical training intervention after knee injury: an interim analysis of the MOTIFS randomised controlled trial

ObjectivesPhysical function is often a main focus of knee injury rehabilitation, but recent recommendations include increasing attention to psychological factors. We have developed the MOTor Imagery to Facilitate Sensorimotor re-learning...

A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

Accurate recognition of human dynamic movement is essential for seamless human–machine interaction (HMI) across various domains. However, most of the existing methods are single‐modal movement recognition, which has inherent limitations, such as limited feature representation and instability to noise, which will affect its practical performance. To address these limitations, this article proposes a novel fusion approach that can integrate two biological signals, including electromyography (EMG) and bioelectrical impedance (BI). The fusion method combines EMG for capturing dynamic movement features and BI for discerning key postures representing discrete points within dynamic movements. In this method, the identification of key postures and their temporal sequences provide a guiding framework for the selection and weighted correction of probability prediction matrices in EMG‐based dynamic recognition. To verify the effectiveness of the method, six dynamic upper limb movements and nine key postures are defined, and a Universal Robot that can follow movements is employed for experimental validation. Experimental results demonstrate that the recognition accuracy of the dynamic movement reaches 96.2%, representing an improvement of nearly 10% compared with single‐modal signal. This study illustrates the potential of multimodal fusion of EMG and BI in movement recognition, with broad prospects for application in HMI fields.

Analysis of plantar pressure of midsole prepared by 3d printed biomimetic structures with different densities

AbstractThis study investigates the impact of 3D printed midsoles with biomimetic structures of varying densities on plantar pressure during static and dynamic motions. The midsoles were designed with three densities of Tyson polygon (TS) structures: 1TS, 2TS, and 3TS. Plantar pressure tests were conducted on midsoles during static and dynamic motions such as walking, running, and jumping. The data were analyzed based on hypotheses related to samples, motions, and 10 plantar pressure zones. As results, for static motion, all midsoles improved pressure distribution and reduced peak pressure compared to barefoot conditions, with 1TS being the most effective. During dynamic motions, 1TS and 2TS effectively distributed plantar pressure in the midfoot and heel areas, while 3TS provided better support and stability during high-intensity activities like jumping. Statistical analysis revealed that 1TS offered comfort and flexibility but lacked support, 2TS balanced support and cushioning, and 3TS provided superior support and stability but reduced elasticity during jumps. In dynamic motions, 1TS excelled in walking, and 2TS performed best in high-intensity activities such as running and jumping. In the meta areas (M2 and M3), 1TS reduced pressure by over 30% during walking and nearly 40% during running, while 3TS showed similar reductions during jumping, with BF showing higher pressures compared to running. Thus, this study highlights the effectiveness of 1TS and 2TS in reducing pressure in the meta and midfoot areas, emphasizing the importance of selecting the right midsole density for optimal comfort and performance across different activities.

Transferable Deep Learning Models for Accurate Ankle Joint Moment Estimation during Gait Using Electromyography

The joint moment is a key measurement in locomotion analysis. Transferable prediction across different subjects is advantageous for calibration-free, practical clinical applications. However, even for similar gait motions, intersubject variance presents a significant challenge in maintaining reliable prediction performance. The optimal deep learning models for ankle moment prediction during dynamic gait motions remain underexplored for both intrasubject and intersubject usage. This study evaluates the feasibility of different deep-learning models for estimating ankle moments using sEMG data to find an optimal intrasubject model against the inverse dynamic approach. We verified and compared the performance of 1302 intrasubject models per subject on 597 steps from seven subjects using various architectures and feature sets. The best-performing intrasubject models were recurrent convolutional neural networks trained using signal energy features. They were then transferred to realize intersubject ankle moment estimation.

Modeling and motion analysis of flexible legged robots using the finite particle method

Robotics with flexible legs have attracted significant attention. Engineers often design and analyze the motion of legged robots from kinematic and biomimetic perspective. However, the influence of flexibility of the feet on robot locomotion is often not given sufficient considerations, which is also very crucial to the motion posture of the flexible legged robots, especially as the soft robots design becomes increasingly popular. The mainly difficulties lies in the traditional numerical methods in handling the dynamic motion analysis with both large rigid motion and large deformation. In this paper, the finite particle method (FPM) is used to simulate the motion and deformation coupled problems of the flexible six-legged robot. A shell-based particle model of a six-leg robot and the contact model between legs and ground are built. Without iterative and modification of the FPM analytical framework, structural nonlinearity is efficiently handled after eliminating rigid body motions by a fictitious reverse motion. The motion and deformation of a single leg with varying leg thickness, locomotion speed, and leg-to-ground friction coefficients were simulated. By analyzing the stress distribution in the leg and the number of contact points with the ground, the mechanical leg was optimized in design. Furthermore, the motion and deformation of the entire six-legged robot were simulated using the FPM. The numerical results' feasibility was validated through comparison with experimental data obtained from robot walking tests. The proposed method effectively simulates the motion and deformation of flexible robots, providing significant insights for the design of soft robots.

Learning Underwater Intervention Skills Based on Dynamic Movement Primitives

Improving the autonomy of underwater interventions by remotely operated vehicles (ROVs) can help mitigate the impact of communication delays on operational efficiency. Currently, underwater interventions for ROVs usually rely on real-time teleoperation or preprogramming by operators, which is not only time-consuming and increases the cognitive burden on operators but also requires extensive specialized programming. Instead, this paper uses the intuitive learning from demonstrations (LfD) approach that uses operator demonstrations as inputs and models the trajectory characteristics of the task through the dynamic movement primitive (DMP) approach for task reproduction as well as the generalization of knowledge to new environments. Unlike existing applications of DMP-based robot trajectory learning methods, we propose the underwater DMP (UDMP) method to address the problem that the complexity and stochasticity of underwater operational environments (e.g., current perturbations and floating operations) diminish the representativeness of the demonstrated trajectories. First, the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) are used for feature extraction of multiple demonstration trajectories to obtain typical trajectories as inputs to the DMP method. The UDMP method is more suitable for the LfD of underwater interventions than the method that directly learns the nonlinear terms of the DMP. In addition, we improve the commonly used homomorphic-based teleoperation mode to heteromorphic mode, which allows the operator to focus more on the end-operation task. Finally, the effectiveness of the developed method is verified by simulation experiments.

Efficient DC motor speed control using a novel multi-stage FOPD(1 + PI) controller optimized by the Pelican optimization algorithm.

This paper introduces a novel multi-stage FOPD(1 + PI) controller for DC motor speed control, optimized using the Pelican Optimization Algorithm (POA). Traditional PID controllers often fall short in handling the complex dynamics of DC motors, leading to suboptimal performance. Our proposed controller integrates fractional-order proportional-derivative (FOPD) and proportional-integral (PI) control actions, optimized via POA to achieve superior control performance. The effectiveness of the proposed controller is validated through rigorous simulations and experimental evaluations. Comparative analysis is conducted against conventional PID and fractional-order PID (FOPID) controllers, fine-tuned using metaheuristic algorithms such as atom search optimization (ASO), stochastic fractal search (SFS), grey wolf optimization (GWO), and sine-cosine algorithm (SCA). Quantitative results demonstrate that the FOPD(1 + PI) controller optimized by POA significantly enhances the dynamic response and stability of the DC motor. Key performance metrics show a reduction in rise time by 28%, settling time by 35%, and overshoot by 22%, while the steady-state error is minimized to 0.3%. The comparative analysis highlights the superior performance, faster response time, high accuracy, and robustness of the proposed controller in various operating conditions, consistently outperforming the PID and FOPID controllers optimized by other metaheuristic algorithms. In conclusion, the POA-optimized multi-stage FOPD(1 + PI) controller presents a significant advancement in DC motor speed control, offering a robust and efficient solution with substantial improvements in performance metrics. This innovative approach has the potential to enhance the efficiency and reliability of DC motor applications in industrial and automotive sectors.

Uncovering conserved networks and global conformational changes in G protein-coupled receptor kinases

G protein-coupled receptor kinases (GRKs) are essential regulators of signaling pathways mediated by G protein-coupled receptors. Recent research suggests that GRK-mediated phosphorylation patterns dictate functional selectivity, leading to biased cellular responses. However, a comprehensive understanding of the structural mechanisms at the single-residue level remains elusive. This study aims to define the general conformational dynamics of GRKs with a particular focus on quantifying the transitions between the closed and open states. Specifically, we examined these transitions, classified based on the ionic lock between the regulatory G protein signaling homology domain and kinase domain. To facilitate a precise structural comparison, we assigned common labels to topologically identical positions across the 47 GRK structures retrieved from the Protein Data Bank. Our analysis identified both general and subfamily-specific dynamic movements within the networks and measured the conformational change scores between the two states. Elucidating these structural dynamics could provide significant insights into the regulatory mechanisms of GRK.