Intricate Motion Research Articles

An adhesive, highly stretchable and low-hysteresis alginate-based conductive hydrogel strain sensing system for motion capture

A strain sensor stands as an indispensable tool for capturing intricate motions in various applications, ranging from human motion monitoring to electronic skin and soft robotics. However, existing strain sensors still face difficulties in simultaneously achieving superior sensing performance sufficing for practical applications like high stretchability and low hysteresis, as well as seamless device fabrication like desirable interfacial adhesion and system-level integration. Herein, we develop a highly stretchable and low-hysteresis strain sensor with adhesive poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS)/polyacrylamide (PAAm)‑sodium alginate (SA) composite hydrogel, allowing the successful construction of a wireless motion capture sensing system that can provide precise data collection within a large deformation range. The resultant composite hydrogel displays favorable interfacial adhesion and robust mechanical stability, and the fabricated strain sensor demonstrates a wide working strain range (up to 500 %) with high sensitivity (gauge factor = 11) and ultra-low hysteresis (1.52 %), outperforming previous PEDOT-based hydrogel strain sensors. Enabled by the intriguing material properties and high sensing performance, we further demonstrate the fabrication and integration of a wireless motion capture sensing system for diverse applications like human motion monitoring, gesture recognition, and interactive communication.

Multi‐sensors edge computing for tennis stroke recognition in IoT environment

AbstractThis paper proposes a multi‐sensors edge computing approach for tennis stroke recognition in an IoT environment. By applying the edge computing capabilities to this framework, we can ensure swift local processing of sensor data, thereby significantly reducing latency and enhancing system scalability for tennis stroke. In this way, we develop an advanced multi‐sensor based tennis stroke recognition mechanism, which employs dedicated measuring sensors mounted on the player's forearm to comprehensively capture intricate motion patterns and send the collected data to the edge computing center. Through rigorous analysis at the edge, we can determine the optimal actions in stroke detection with minimum delay, in which the optimal training datasets for stroke classification are also determined. Experimental results indicate that the proposed mechanism surpasses the state‐of‐the‐art methods and achieves effective adaptability and scalability for tennis stroke recognition in IoT‐driven environments, evidenced by their commendable performance even when applied to athletes not included in the training datasets.

Rigid-foldable spiral origami with compression-torsion coupled motion mode

Rigid foldable origami enables smooth and precise folding without stretching or bending its constituent panels and is promising for applications such as reprogrammable matter, self-folding machines, reconfigurable antennas, and deployable spacecraft. The diverse range of potential applications necessitates the need for the design and detailed analysis of different rigid-foldable origami structures, especially those with intricate motion modes. In this paper, we introduce a rigid-foldable spiral origami design that features a compression-torsion coupled motion mode. This design exhibits rich static and dynamic properties. Under static conditions, the compression-torsion coupled motion mode creates multiple self-locking positions and allows for the development of mechanical static diodes. Under dynamic conditions, the compression-torsion coupling effect in the spiral origami facilitates precise control of wave modes within the origami chain when impacted by a ball with a moderate initial velocity. In the case of large initial velocities of the ball, the spiral origami can function as a wave generator, producing rarefaction solitary waves or compressive solitary waves. The proposed spiral origami design provides an opportunity to explore new applications of rigid-foldable origami with compression-torsion coupling effects.

Velocity-resolved Ionization Mapping of Broad Line Region. I. Insights into Diverse Geometry and Kinematics

Broad emission lines of active galactic nuclei (AGNs) originate from the broad-line region (BLR), consisting of dense gas clouds in orbit around an accreting supermassive black hole. Understanding the geometry and kinematics of this region is crucial for gaining insights into the physics and evolution of AGNs. Conventional velocity-resolved reverberation mapping may face challenges in disentangling the degeneracy between intricate motion and geometry of this region. To address this challenge, new key constraints are required. Here, we report the discovery of an asymmetric BLR using a novel technique: velocity-resolved ionization mapping, which can map the distance of emitting gas clouds by measuring Hydrogen line ratios at different velocities. By analyzing spectroscopic monitoring data, we find that the Balmer decrement is anticorrelated with the continuum and correlated with the lags across broad emission line velocities. Some line ratio profiles deviate from the expectations for a symmetrically virialized BLR, suggesting that the redshifted and blueshifted gas clouds may not be equidistant from the supermassive black hole (SMBH). This asymmetric geometry might represent a formation imprint, provide new perspectives on the evolution of AGNs, and influence SMBH mass measurements.

Quantitative assessment of human motion for health and rehabilitation: A novel fuzzy comprehensive evaluation approach

In the pursuit of advancing health and rehabilitation, the quintessence of human motion recognition technology has been underscored through its quantitative contributions to physical performance assessment. This research delineates the inception of a novel fuzzy comprehensive evaluation-based recognition method that stands at the forefront of such innovative endeavours. By synergistically fusing multi-sensor data and advanced classification algorithms, the proposed system offers a granular quantitative analysis with implications for health and fitness monitoring, particularly rehabilitation processes. Our methodological approach, grounded in the modal separation technique and Empirical Mode Decomposition (EMD), effectively distills the motion acceleration component from raw accelerometer data, facilitating the extraction of intricate motion patterns. Quantitative analysis revealed that our integrated framework significantly amplifies the accuracy of motion recognition, achieving an overall recognition rate of 90.03 %, markedly surpassing conventional methods, such as Support Vector Machines (SVM), Decision Trees (DT), and K-Nearest Neighbors (KNN), which hovered around 80 %. Moreover, the system demonstrated an unprecedented accuracy of 97 % in discerning minor left-right swaying motions, showcasing its robustness in evaluating subtle movement nuances—a paramount feature for rehabilitation and patient monitoring. This marked precision in motion recognition heralds a new paradigm in health assessment, enabling objective and scalable analysis pertinent to individualized therapeutic interventions. The experimental evaluation accentuates the system's adeptness at navigating the dichotomy between complex, intense motions and finer, subtler movements with a high fidelity rate. It substantiates the method's utility in delivering sophisticated, data-driven insights for rehabilitation trajectory monitoring.

On the Response of the Transition Region and the Corona to Rapid Excursions in the Chromosphere

Spicules are the thin hair/grass-like structures that are prominently observed at the chromospheric solar limb. It is believed that fibrils and rapid blueshifted and redshifted excursions (RBEs and RREs; collectively referred to as REs) correspond to on-disk counterparts of type I spicules and type II spicules, respectively. Our investigation focuses on observing the response of these REs alongside similar spectral features in the chromosphere, transition region (TR), and corona, utilizing space–time plots derived from coordinated observations from the Swedish 1 m Solar Telescope/Hα, Interface Region Imaging Spectrograph (IRIS), and Solar Dynamics Observatory. Our analysis reveals upflowing REs, promptly reaching temperatures characteristic of the TR and corona, indicating a multithermal nature. Similarly, downflowing features exhibiting similar spectral signatures over the disk display plasma motion from the corona to chromospheric temperatures, demonstrating a multithermal nature. In addition to distinct upflows and downflows, we observe sequential upflow and downflow along the same path, depicting a distinctive parabolic trajectory in space–time plots of observations sampling TR and various coronal passbands. Similar to isolated upflows and downflows, these REs also exhibit a multithermal nature throughout their trajectory. Furthermore, our results reveal a more intricate motion of the REs in which both upflow and downflow coexist at the same spatial location. On a different note, our analysis, utilizing coordinated IRIS spectral observations, shows spatiotemporal redshifts/downflows in both the TR and chromosphere, suggesting that at least subsets of the strong redshifts/downflows observed in TR temperature spectra result from the return from the upper atmosphere flow of plasma in the form of bundles of spicules or features exhibiting similar spectra.

Multimodal spatiotemporal aggregation for point cloud accumulation

Point cloud accumulation is a crucial technique in point cloud analysis, facilitating various downstream tasks like surface reconstruction. Current methods merely rely on raw LiDAR points, yielding unsatisfactory performance due to the limited geometric information, particularly in complex scenarios characterized by intricate motions, diverse objects, and an increased number of frames. In this paper, we introduce camera modality data, which is usually acquired alongside LiDAR data at minimal expense. To this end, we present the Multimodal Spatiotemporal Aggregation solution (termed MSA) to thoroughly explore and aggregate these two distinct modalities (sparse 3D points and multi-view 2D images). Concretely, we propose a multimodal spatial aggregation module to bridge the data gap between different modalities in the Bird’s-Eye-View (BEV) space and further fuse them by learnable adaptive channel-wise weights. By assembling their respective strengths, this module generates a reliable and consistent scene representation. Subsequently, we design a temporal aggregation module to capture continuous motion information across consecutive sequences, which is beneficial for identifying the motion state of the foreground scene and enabling the model to extend robustly to longer sequences. Experiments demonstrate MSA outperforms state-of-the-art (SoTA) point cloud accumulation methods across all evaluation metrics in the public benchmark, especially with more frames.

Motion-Aware Video Frame Interpolation

Video frame interpolation methodologies endeavor to create novel frames betwixt extant ones, with the intent of augmenting the video’s frame frequency. However, current methods are prone to image blurring and spurious artifacts in challenging scenarios involving occlusions and discontinuous motion. Moreover, they typically rely on optical flow estimation, which adds complexity to modeling and computational costs. To address these issues, we introduce a Motion-Aware Video Frame Interpolation (MA-VFI) network, which directly estimates intermediate optical flow from consecutive frames by introducing a novel hierarchical pyramid module. It not only extracts global semantic relationships and spatial details from input frames with different receptive fields, enabling the model to capture intricate motion patterns, but also effectively reduces the required computational cost and complexity. Subsequently, a cross-scale motion structure is presented to estimate and refine intermediate flow maps by the extracted features. This approach facilitates the interplay between input frame features and flow maps during the frame interpolation process and markedly heightens the precision of the intervening flow delineations. Finally, a discerningly fashioned loss centered around an intermediate flow is meticulously contrived, serving as a deft rudder to skillfully guide the prognostication of said intermediate flow, thereby substantially refining the precision of the intervening flow mappings. Experiments illustrate that MA-VFI surpasses several representative VFI methods across various datasets, and can enhance efficiency while maintaining commendable efficacy.

Advancements in Motion Detection Within Video Streams Through the Integration of Optical Flow Estimation and 3D-Convolutional Neural Network Architectures

Motion detection in video streams is important for many applications, such as autonomous navigation, human-computer interaction, and spying. Conventional techniques, which depend on backdrop removal or frame differencing, frequently break down when dealing with intricate motion patterns and occlusions. Current techniques struggle to handle occlusions and correctly capture complex motion patterns. Furthermore, these techniques could be computationally expensive, especially when dealing with big amounts of video data. An integrated strategy combining optical flow estimation with 3D convolutional neural network (3D-CNN) architectures is presented to address these limitations and boost motion detection systems' accuracy and efficiency. The suggested technique is unusual as it combines 3D CNNs with optical flow estimates. Motion vectors representing dynamic scene changes are produced by optical flow estimates, and spatiotemporal characteristics are extracted by 3D CNNs processing together with optical flow information. By using the complementing capabilities of both approaches, the method performs better in motion detection applications such as object tracking, action recognition, and anomaly detection in video streams. The efficiency of the strategy is assessed by experiments carried out on benchmark datasets. The results show that it is more accurate, resistant against occlusions, and computationally efficient than existing approaches. The suggested approach offers a viable way to improve motion detection capabilities for a range of practical uses. The results show a 98% improvement in processing efficiency and motion detection accuracy when compared to baseline methods. More research and advancement in this area are made possible by the attainable way that MATLAB's suggested approach offers to enhance motion detection skills in a variety of real-world applications.

Gaitdlf: global and local fusion for skeleton-based gait recognition in the wild

A new trend in long-range biometrics, gait recognition, is finding application in a number of different fields including video surveillance. Recently, with the increase in robustness of the pose estimator and the presence of various unpredictable factors in realistic gait recognition, skeleton-based methods with higher robustness have emerged to better meet the challenging gait recognition needs. However, existing approaches primarily focus on extracting global skeletal features, neglecting the intricate motion information of local body parts and overlooking inter-limb relationships. Our solution to these challenges is the dynamic local fusion network (GaitDLF), a novel gait neural network for complex environments that includes a detail-aware stream in addition to the previous direct extraction of global skeleton features, which provides an enhanced representation of gait features. To extract discriminative local motion information, we introduce predefined body part assignments for each joint in the skeletal structure. By segmenting and mapping the overall skeleton based on these limb site divisions, limb-level motion features can be obtained. In addition, we will dynamically fuse the motion features from different limbs and enhance the motion feature representation of each limb by global context information and local context information of the limb-level motion features. The ability to extract gait features between individuals can be improved by aggregating local motion features from different body parts. Based on experiments on CASIA-B, Gait3D, and GREW, we show that our model extracts more comprehensive gait features than the state-of-the-art skeleton-based method, demonstrating that our method is better suited to detecting gait in complex environments in the wild than the appearance-based method.

Simplified neural architecture for efficient human motion prediction in human-robot interaction

Human motion prediction is essential for safe and effective human-robot interaction, but modeling the intricate spatio-temporal dynamics inherent in human movement remains challenging. While recent methods have advanced motion prediction accuracy, they rely on complex neural architectures such as Graph Convolutional Networks and Recurrent Neural Networks, demanding extensive hyperparameter tuning. To overcome this limitation, we propose a streamlined multi-stage layer incremental perceptron (MSLP) architecture that achieves competitive results with far fewer parameters, improving efficiency. The MSLP eschews the complexity of prevalent networks and instead takes a stepped refinement approach to predict intricate motions using a lightweight model. This simplified yet effective design enables nuanced learning of spatio-temporal relationships without exhaustive tuning. Specifically, the MSLP incorporates two key components: a multi-channel feature extraction and enhancement block (MFE-block) and a temporal feature extraction module (TFE-block). The MFE-block strengthens the representation of each action node by integrating multi-dimensional action features. The TFE-block then captures the contextual relationships between actions over time. Together, the MFE-block and TFE-block allow the MSLP to model the complex spatial and temporal dynamics of human movement using a streamlined architecture and minimal parameter tuning. When evaluated on established datasets including Human3.6 M, CMU, 3DPW, and AMASS, the proposed MSLP method achieves improved accuracy gains of 16.7%-67.0% over existing state-of-the-art techniques. Additionally, the MSLP significantly reduces the number of parameters by 35.7%-99.5% compared to prior architectures. We find that the proposed MSLP significantly improves both long-term prediction and generalization capabilities of the model.

Efficient and accurate flexible multibody dynamics modeling for complex spacecraft with integrated control applications

This study presents a novel flexible multibody dynamics modeling method tailored to spacecraft with intricate motion features such as multi-axis antenna actuation, robotic manipulations, and space station module rotation. Using a floating base and a tree-topology structure, the model effectively simulates the kinematics and dynamics of large-angle joint rotations and elastic deformations, leveraging the Lagrangian framework and finite element analysis. An advanced augmented proportional–derivative (APD) control scheme is also introduced, incorporating system dynamics for enhanced predictive control, accommodating complex interactions and nonlinear system behaviors. Our modeling approach, compared with the commercial Adams software, shows similar system responses while reducing computation time by about 70%, highlighting its efficiency and accuracy. Moreover, incorporating the APD strategy into our system enhances tracking precision, achieving a 90% reduction in error compared with conventional proportional–derivative controls, underscoring the benefits of combining dynamic modeling with control improvements.

Semantic Lens: Instance-Centric Semantic Alignment for Video Super-resolution

As a critical clue of video super-resolution (VSR), inter-frame alignment significantly impacts overall performance. However, accurate pixel-level alignment is a challenging task due to the intricate motion interweaving in the video. In response to this issue, we introduce a novel paradigm for VSR named Semantic Lens, predicated on semantic priors drawn from degraded videos. Specifically, video is modeled as instances, events, and scenes via a Semantic Extractor. Those semantics assist the Pixel Enhancer in understanding the recovered contents and generating more realistic visual results. The distilled global semantics embody the scene information of each frame, while the instance-specific semantics assemble the spatial-temporal contexts related to each instance. Furthermore, we devise a Semantics-Powered Attention Cross-Embedding (SPACE) block to bridge the pixel-level features with semantic knowledge, composed of a Global Perspective Shifter (GPS) and an Instance-Specific Semantic Embedding Encoder (ISEE). Concretely, the GPS module generates pairs of affine transformation parameters for pixel-level feature modulation conditioned on global semantics. After that the ISEE module harnesses the attention mechanism to align the adjacent frames in the instance-centric semantic space. In addition, we incorporate a simple yet effective pre-alignment module to alleviate the difficulty of model training. Extensive experiments demonstrate the superiority of our model over existing state-of-the-art VSR methods.

Robot-Assisted Radical Prostatectomy by the Hugo Robotic-Assisted Surgery (RAS) System and the da Vinci System: A Comparison between the Two Platforms.

In a previous study, we proved that an experienced urologist is more likely to adapt to the Hugo RAS system. Based on this, we further examine various parameters in this study. Parameters included in this study consisted of console time, functional outcomes, and oncological outcomes. A total of 60 patients who underwent robot-assisted radical prostatectomy (RARP) performed by a single surgeon using the da Vinci (DV) system (n = 30) or the Hugo RAS system (n = 30) between March 2023 and August 2023 were included in the analysis. The intraoperative operative time was categorized into vesicourethral anastomosis time and overall console time. Functional and oncological outcomes were documented at the 1st and 3rd postoperative months. Parametric and non-parametric methods were adopted after checking skewness and kurtosis, and an α value of 5% was used to determine the significance. The vesicourethral anastomosis time was significantly lengthened (Hedge's g: 0.87; 95% confidence interval (CI): 0.34-1.39; J factor = 0.987). However, the overall console time was not affected. The functional (postoperative 3rd month: p = 0.130) and oncological outcomes (postoperative 3rd month: p = 0.103) were not significantly different. We also found that the adverse effect on surgical specimens and positive surgical margins was not affected (p = 0.552). During the process of adaptation, although intricate motions (such as the vesicourethral anastomosis time) would be lengthened, the overall console time would not change remarkably. In this process, the functional and oncological outcomes would not be compromised. This encourages urologists to adopt the Hugo RAS system in RARP if they have previous experiences of using the DV system, since their trifecta advantage would not be compromised.

A Multifactor Combination Optimization Design Based on Orthogonality for a Two-Degree-of-Freedom Floating Machine Gun Vibration System

This paper introduces a novel type of floating machine gun that can be simplified as a self-balancing two-degree-of-freedom mechanical system with distinct vibration characteristics. The model accounts for intricate motion patterns and encompasses numerous potential influencing factors. Multifactor combination optimization of the system represents a pressing engineering challenge. After establishing a simulation model for the machine gun and validating it experimentally, seven factors were chosen as optimization variables. The maximum recoil displacement of the inner receiver (MRD) and the firing rate were chosen to be indicators. Orthogonal combinations and variance analyses were used, and the effects of multiple factors were analyzed using SPSS software; these processes led to a determination of the optimal combination. The results indicated that the piston cylinder pressure, the bi-directional buffer spring energy storage, and the inner receiver mass significantly affected the MRD. Furthermore, the automaton mass and the reset spring energy storage were found to substantially affect the firing rate. Careful analysis of the variance results facilitated the determination of the optimal combination of parameter values. Remarkably, the optimal combination chosen resulted in an MRD reduction of approximately 20.2% and a firing rate increase of approximately 26.6%.

Investigation of the soybean infiltration process utilizing low-field nuclear magnetic resonance technology.

This paper employs low-field nuclear magnetic resonance (LF-NMR) technology to meticulously analyze and explore the intricate soybean infiltration process. The methodology involves immersing soybeans in distilled water, with periodic implementation of Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence experiments conducted at intervals of 20 to 30 minutes to determine the relaxation time T2. Currently, magnetic resonance imaging (MRI) is conducted every 30 minutes. The analysis uncovers the existence of three distinct water phases during the soybean infiltration process: bound water denoted as T21, sub-bound water represented by T22, and free water indicated as T23. The evolution of these phases unfolds as follows: bound water T21 displays a steady oscillation within the timeframe of 0 to 400 minutes; sub-bound water T22 and free water T23 exhibit a progressive pattern characterized by a rise-stable-rise trajectory. Upon scrutinizing the magnetic resonance images, it is discerned that the soybean infiltration commences at a gradual pace from the seed umbilicus. The employment of LF-NMR technology contributes significantly by affording an expeditious, non-destructive, and dynamic vantage point to observe the intricate motion of water migration during soybean infiltration. This dynamic insight into the movement of water elucidates the intricate mass transfer pathway within the soybean-water system, thus furnishing a robust scientific foundation for the optimization of processing techniques.

Programming hierarchical anisotropy in microactuators for multimodal actuation.

Microactuators, capable of executing tasks typically repetitive, hazardous, or impossible for humans, hold great promise across fields such as precision medicine, environmental remediation, and swarm intelligence. However, intricate motions of microactuators normally require high complexity in design, making it increasingly challenging to realize at small scales using existing fabrication techniques. Taking inspiration from the hierarchical-anisotropy principle found in nature, we program liquid crystalline elastomer (LCE) microactuators with multimodal actuation tailored to their molecular, shape, and architectural anisotropies at (sub)nanometer, micrometer, and (sub)millimeter scales, respectively. Our strategy enables diverse deformations with individual LCE microstructures, including expanding, contracting, twisting, bending, and unwinding, as well as re-programmable shape transformations of assembled LCE architectures with negative Poisson's ratios, locally adjustable actuation, and changing from two-dimensional (2D) to three-dimensional (3D) structures. Furthermore, we design tetrahedral microactuators with well-controlled mobility and precise manipulation of both solids and liquids in various environments. This study provides a paradigm shift in the development of microactuators, unlocking a vast array of complexities achievable through manipulation at each hierarchical level of anisotropy.

Trajectory-BERT: Trajectory Estimation Based on BERT Trajectory Pre-Training Model and Particle Filter Algorithm.

In the realm of aviation, trajectory data play a crucial role in determining the target's flight intentions and guaranteeing flight safety. However, the data collection process can be hindered by noise or signal interruptions, thus diminishing the precision of the data. This paper uses the bidirectional encoder representations from transformers (BERT) model to solve the problem by masking the high-precision automatic dependent survey broadcast (ADS-B) trajectory data and estimating the mask position value based on the front and rear trajectory points during BERT model training. Through this process, the model acquires knowledge of intricate motion patterns within the trajectory data and acquires the BERT pre-training Model. Afterwards, a refined particle filter algorithm is utilized to generate alternative trajectory sets for observation trajectory data that is prone to noise. Ultimately, the BERT trajectory pre-training model is supplied with the alternative trajectory set, and the optimal trajectory is determined by computing the maximum posterior probability. The results of the experiment show that the model has good performance and is stronger than traditional algorithms.

Framework Models for Theme Park Design: Incorporating Transportability Models and Visitor Preferences

Analyzing human movement is crucial for evaluating designs in transportation and urban planning. Our expertise lies in the realm of designing and assessing wireless networks and services tailored for cutting-edge theme park experiences. The effectiveness of specific wireless networks, such as mobile ad hoc networks, is significantly impacted by human mobility. In response to this challenge, we've developed an advanced program known as ParkSim, dedicated to replicating the intricate motion patterns of guests within a theme park environment. ParkSim utilizes an activity-based mobility model, influenced by the diverse activities guests engage in throughout the park. The calibration of this tool is based on GPS data collected from an amusement theme park, ensuring its accuracy and reliability. It has undergone rigorous verification against various performance metrics relevant to wireless ad hoc networks. Our primary goal is to enhance ParkSim further, enabling it to evaluate innovative strategies for equitably distributing visitors across different sections of the theme park. This expansion will provide valuable insights into optimizing wireless network performance and enhancing the overall theme park experience for guests.

Nanomagnetic Elastomers for Realizing Highly Responsive Micro- and Nanosystems.

Evolution has produced natural systems that generate motion and sense external stimuli at the micro- and nanoscales. At extremely small scales, the intricate motions and large deformations shown by these biosystems are due to a tipping balance between their structural compliance and the actuating force generated in them. Artificially mimicking such ingenious systems for scientific and engineering applications has been approached through the development and use of different smart materials mostly limited to microscale dimensions. To push the application range down to the nanoscale, we developed a material preparation process that yields a library of nanomagnetic elastomers with high magnetic particle concentrations. Through this process, we have realized a material with the highest magnetic-to-elastic force ratio, as is shown by an extensive mechanical and magnetic characterization of the materials. Furthermore, we have fabricated and actuated micro- and nanostructures mimicking cilia, demonstrating the extreme compliance and responsiveness of the developed materials.