Motion Trajectory Data Research Articles

A preliminary investigation of the key parameters of average value articulator based on mandibular movement trajectories in 100 adults with individual normal occlusion

Objective: To explore the method of obtaining the key parameters of the average value articulator in healthy people based on mandibular movement trajectory data, with a view to providing a reference for the clinical application of the average value articulator. Methods: One hundred healthy volunteers (42 males and 58 females) with individual normal occlusion, aged 18-55 years old, who met the inclusion criteria were recruited from Beijing, and their mandibular movement trajectory data were collected. The left and right sagittal condylar inclination(SCI) and transversal condylar inclination(TCI) were obtained from the values of the articulator parameters which were generated in the mandibular movement analysis system.The SCI and TCI were grouped by gender and calculated separately for the two groups and the overall sample; the gender differences in the two parameters and the differences between the mean values of the two parameters and the average value articulator empirical values (35° for SCI and 15° for TCI) for the overall sample were compared. Results: The differences between SCI (35.8°±7.4°) and TCI [11.2° (11.3°)] in males and the corresponding parameters [35.6°±8.3° and 10.8° (9.5°), respectively] in females were not statistically significant (t=0.10, P=0.922; Z=-0.60, P=0.552); the overall sample SCI (35.7°±7.9°) did not differ statistically from the average value articulator empirical value (t=1.23,P=0.221), and the overall sample TCI [10.9° (10.3°)] was significantly smaller than the average value articulator empirical value (W=5 825.00, P<0.001). Conclusions: The mandibular movement trajectory data of 100 adults with individual normal occlusion in this study shows that the gender factor does not affect the setting of the key parameters of the average value articulator, the SCI of the average value articulator empirical values is appropriate, and the TCI has the possibility of being on the large side. In the clinical use of the articulator to assist in the design of restorations, the parameter values should be rationally adjusted according to the actual situation of the patient's dentition and mandibular movement.

Construction Method of a Digital-Twin Simulation System for SCARA Robots Based on Modular Communication.

Due to the high cost of robots, the algorithm testing cost for physical robots is high, and the construction of motion control programs is complex, with low operation fault tolerance. To address this issue, this paper proposes a low-cost, cross-platform SCARA robot digital-twin simulation system based on the concept of digital twins. This method establishes a 5D architecture based on the characteristics of different platforms, classifies data and integrates functions, and designs a data-processing layer for motion trajectory calculation and data storage for a virtual-reality robot. To address the complexity of data interaction under different cross-platform communication forms, an editable, modular, cross-platform communication system is constructed, and various control commands are encapsulated into simple programming statements for easy invocation. Experimental results showed that, based on modular communication control, users can accurately control data communication and synchronous motion between virtual and physical models using simple command statements, reducing the development cost of control algorithms. Meanwhile, the virtual-robot simulation system, as a data mapping of the real experimental platform, accurately simulated the physical robot's operating state and spatial environment. The robot algorithms tested using the virtual simulation system can be successfully applied to real robot platforms, accurately reproducing the operating results of the virtual system.

2E4-3 Automating Scene Evaluation using Deep learning Model from Movement Trajectory Data of Team Sports

2E4-3 Automating Scene Evaluation using Deep learning Model from Movement Trajectory Data of Team Sports

TAT-HUM: Trajectory analysis toolkit for human movements in Python.

Human movement trajectories can reveal useful insights regarding the underlying mechanisms of human behaviors. Extracting information from movement trajectories, however, can be challenging because of their complex and dynamic nature. The current paper presents a Python toolkit developed to help users analyze and extract meaningful information from the trajectories of discrete rapid aiming movements executed by humans. This toolkit uses various open-source Python libraries, such as NumPy and SciPy, and offers a collection of common functionalities to analyze movement trajectory data. To ensure flexibility and ease of use, the toolkit offers two approaches: an automated approach that processes raw data and generates relevant measures automatically, and a manual approach that allows users to selectively use different functions based on their specific needs. A behavioral experiment based on the spatial cueing paradigm was conducted to illustrate how one can use this toolkit in practice. Readers are encouraged to access the publicly available data and relevant analysis scripts as an opportunity to learn about kinematic analysis for human movements.

Dynamical characteristics of flip-flow screen with crankshaft-link structure and mechanical analysis

Flip-flow screen with crankshaft-link structure (FFSCLS) is effective for screening of high-water and fine materials. In this paper, a dynamic model governing the vibration and mechanical characteristics of the FFSCLS is proposed. In addition, the motion trajectory and vibration data of the FFSCLS were investigated by MATLAB/Simulink method. The results show that the MATLAB/Simulink method can fully describe the vibration characteristics of the FFSCLS with the maximum deviation within 6.71 % between experiment results. The mechanical characteristics of the FFSCLS were systematically studied by automatic dynamic simulation analysis using the ADMAS software. The MBD simulation results indicated that the resultant forces of the inner and outer screen boxes are always equal to 0. Moreover, the rotational speed of the crankshaft, the stiffness coefficient of the guild springs and rubber springs determined the mechanical characteristics of the flip-flow screen, and these parameters should be optimized. This work is helpful for the understanding of the combined effects of parameters and provides essential references for optimal design and efficient operation of a flip-flow screen with crank-link structure.

Early Detection of Suspicious Behaviors for Safe Residence from Movement Trajectory Data

Early detection of people’s suspicious behaviors can aid in the prevention of crimes and make the community safer. Existing methods that are focused on identifying abnormal behaviors from video surveillance that are based on computer vision, which are more suitable for detecting ongoing behaviors. While criminals intend to avoid abnormal behaviors under surveillance, their suspicious behaviors prior to crimes will be unconsciously reflected in the trajectories. Herein, we characterize several suspicious behaviors from unusual movement patterns, unusual behaviors, and unusual gatherings of people, and analyze their features that are hidden in the trajectory data. Meanwhile, the algorithms for suspicious behavior detection are proposed based on the main features of the corresponding behavior, which employ spatiotemporal clustering, semantic annotation, outlier detection, and other methods. A practical trajectory dataset (i.e., TucityLife) containing more than 1000 suspicious behaviors was collected, and experiments were conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method for suspicious behavior detection has a recall of 93.5% and a precision of 87.6%, demonstrating its excellent performance in identifying the possible offenders and potential target places. The proposed methods are valuable for preventing city crime and supporting the appropriate allocation of police resources.

Research on dynamic and vibration behaviors of a flip-flow screen with crankshaft-link structure

A flip-flow screen with crankshaft-link structure (FFSCLS) is an effective solution for screening of high-water and fine materials due to its good performance. The dynamical characteristics largely affects the screen performance and the processing capacity of the FFSCLS. In this paper, a dynamic model governing the dynamical characteristics of the FFSCLS was proposed. In addition, the motion trajectory and vibration data of the FFSCLS were investigated by vibration experimental technology. The results show that the experiment results can be fully described by the dynamic model whit the maximum deviation was within 6.26 %. Moreover, the mass ratio between outer box and inner box determined the dynamical characteristics of the flip-flow screen, and these parameters should be optimized. This work can provide useful references for efficient operation and optimal design for the flip-flow screen.

Ship Path Planning Based on Buoy Offset Historical Trajectory Data

In the existing research on the intelligent navigation of ships, navigation route planning often regards light buoys as fixed obstructions. However, due to factors such as water ripples, the position of the buoys keeps periodically changing. If the buoys are set to a fixed range of avoidance areas in the process of ship navigation, it is easy to allow a collision between the ship and the light buoys. Therefore, based on historical motion trajectory data of the buoys, a SARIMA-based time-series prediction model is proposed to estimate the offset position of a given buoy in a specified time. Furthermore, the collision-free path planning approach is presented to dynamically recommend an accurate sailing path. The results of the simulation experiment show that this method can effectively deal with collisions of ships caused by the offset position of the light buoys during the navigation of the large and low-speed autonomous ships.

Control Strategy of Frequent Overflow at Intersection Based on Remote Sensing of Unmanned Aerial Vehicle and Vehicle Trajectory Data

This paper proposed an effective method, the overflow control strategy, for identifying and controlling the frequent overflow of signalized intersections based on the unmanned aerial vehicle (UAV) remote sensing and vehicle trajectory data that provide multi-dimensional vehicle dynamic data. This strategy can effectively control the overflow intersection and restore normal operation as soon as possible. The strategy was implemented as follows. First, UAV remote sensing was used to quickly find the overflow precursor at the intersection. Second, by using moving trajectory data, the second-flow model of the road section was constructed, and overflow identification indexes were established. Third, considering the traffic demand of controlled intersection and non-overflow phase from the global perspective, the queue length model of the non-overflow phase based on moving trajectory data was established and overflow identification indexes were taken as the objective function to design overflow control strategy set. Finally, according to the sampling rate requirement of moving trajectory data and the field traffic survey, the Changping District in Beijing, which meets the requirements, is selected as the case study for simulation and verification. Usually, when overflow occurs, total delay time and the average number of stop cycles at intersections are relatively high. However, by taking the proposed method, overflow at intersections was identified quickly, and a control strategy was implemented immediately, which alleviated the overflow and eventually eliminated it. The results show that the method proposed in this paper can accurately detect the occurrence of overflow, restrain and eliminate the overflow eventually.

A Study on the Prediction of the Surface Drifter Trajectories in the Korean Strait

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

Wayfinding behaviour in a multi-level building: A comparative study of HMD VR and Desktop VR

Virtual Reality (VR) provides the possibility to study pedestrian wayfinding behaviour in multi-level buildings. Although VR has been applied increasingly to study pedestrian behaviour, it has remained unclear how different VR technology would affect behavioural outcomes in a multi-level building. The study compares the adoption of different VR technologies for pedestrian wayfinding studies, via investigating the difference in pedestrian wayfinding behaviour and user experience. Wayfinding experiments with two groups of participants were conducted using either HMD VR or Desktop VR. Pedestrian movement trajectory data were collected via the VR system and user experience data were recorded via a questionnaire. These data allow for direct comparison and detailed analysis of pedestrian behaviour and user experience between the adoption of two VR technologies. The results showed that technological differences have a significant impact on wayfinding task performance and head rotation change. However, the route choice, exit choice and user experience were overall similar between the two groups. These results provide empirical evidence supporting researchers to choose between immersive and non-immersive VR when study pedestrian wayfinding behaviour.

Error motion trajectory-driven diagnostics of kinematic and non-kinematic machine tool faults

Error motion trajectory data are routinely collected on multi-axis machine tools to assess their operational state. There is a wealth of literature devoted to advances in modelling, identification and correction using such data, as well as the collection and processing of alternative data streams for the purpose of machine tool condition monitoring. Until recently, there has been minimal focus on combining these two related fields. This paper presents a general approach to identifying both kinematic and non-kinematic faults in error motion trajectory data, by framing the issue as a generic pattern recognition problem. Because of the typically-sparse nature of datasets in this domain – due to their infrequent, offline collection procedures – the foundation of the approach involves training on a purely simulated dataset, which defines the theoretical fault-states observable in the trajectories. Ensemble methods are investigated and shown to improve the generalisation ability when predicting on experimental data. Machine tools often have unique ‘signatures’ which can significantly-affect their error motion trajectories, which are largely repeatable, but specific to the individual machine. As such, experimentally-obtained data will not necessarily be easily defined in a theoretical simulation. A transfer learning approach is introduced to incorporate experimentally-obtained error motion trajectories into classifiers which were trained primarily on a simulation domain. The approach was shown to significantly improve experimental test set performance, whilst also maintaining all theoretical information learned in the initial, simulation-only training phase. The ultimate approach represents a viable and powerful automated classifier for error motion trajectory data, which can encode theoretical fault-states with efficacy whilst also remain adaptable to machine-specific signatures.

Neural Network-Based Indoor Autonomously-Navigated AGV Motion Trajectory Data Fusion

Neural Network-Based Indoor Autonomously-Navigated AGV Motion Trajectory Data Fusion

Intelligent Recognition System for the Curves Trajectory and Track Capture Using Artificial Intelligence Technology

Aiming at the problem of large deviation of the recognition results of the traditional recognition system, this article carried out the design and research of the intelligent recognition system of the football curve ball movement trajectory. The hardware structure design includes the acceleration sensor selection, the angular motion detection device selection and the power supply circuit design, and the software structure design includes the motion trajectory data filtering processing and the football curve ball motion speed displacement recognition. Experiments show that, compared with the traditional system, the design system has a smaller deviation of the recognition results, significantly fewer recognition errors, and higher accuracy.

Intelligent Mobile Wireless Network for Toxic Gas Cloud Monitoring and Tracking.

Intelligent wireless networks that comprise self-organizing autonomous vehicles equipped with punctual sensors and radio modules support many hostile and harsh environment monitoring systems. This work’s contribution shows the benefits of applying such networks to estimate clouds’ boundaries created by hazardous toxic substances heavier than air when accidentally released into the atmosphere. The paper addresses issues concerning sensing networks’ design, focussing on a computing scheme for online motion trajectory calculation and data exchange. A three-stage approach that incorporates three algorithms for sensing devices’ displacement calculation in a collaborative network according to the current task, namely exploration and gas cloud detection, boundary detection and estimation, and tracking the evolving cloud, is presented. A network connectivity-maintaining virtual force mobility model is used to calculate subsequent sensor positions, and multi-hop communication is used for data exchange. The main focus is on the efficient tracking of the cloud boundary. The proposed sensing scheme is sensitive to crucial mobility model parameters. The paper presents five procedures for calculating the optimal values of these parameters. In contrast to widely used techniques, the presented approach to gas cloud monitoring does not calculate sensors’ displacements based on exact values of gas concentration and concentration gradients. The sensor readings are reduced to two values: the gas concentration below or greater than the safe value. The utility and efficiency of the presented method were justified through extensive simulations, giving encouraging results. The test cases were carried out on several scenarios with regular and irregular shapes of clouds generated using a widely used box model that describes the heavy gas dispersion in the atmospheric air. The simulation results demonstrate that using only a rough measurement indicating that the threshold concentration value was exceeded can detect and efficiently track a gas cloud boundary. This makes the sensing system less sensitive to the quality of the gas concentration measurement. Thus, it can be easily used to detect real phenomena. Significant results are recommendations on selecting procedures for computing mobility model parameters while tracking clouds with different shapes and determining optimal values of these parameters in convex and nonconvex cloud boundaries.

A hierarchical machine learning framework for the analysis of large scale animal movement data

BackgroundIn recent years the field of movement ecology has been revolutionized by our ability to collect high-accuracy, fine scale telemetry data from individual animals and groups. This growth in our data collection capacity has led to the development of statistical techniques that integrate telemetry data with random walk models to infer key parameters of the movement dynamics. While much progress has been made in the use of these models, several challenges remain. Notably robust and scalable methods are required for quantifying parameter uncertainty, coping with intermittent location fixes, and analysing the very large volumes of data being generated.MethodsIn this work we implement a novel approach to movement modelling through the use of multilevel Gaussian processes. The hierarchical structure of the method enables the inference of continuous latent behavioural states underlying movement processes. For efficient inference on large data sets, we approximate the full likelihood using trajectory segmentation and sample from posterior distributions using gradient-based Markov chain Monte Carlo methods.ResultsWhile formally equivalent to many continuous-time movement models, our Gaussian process approach provides flexible, powerful models that can detect multiscale patterns and trends in movement trajectory data. We illustrate a further advantage to our approach in that inference can be performed using highly efficient, GPU-accelerated machine learning libraries.ConclusionsMultilevel Gaussian process models offer efficient inference for large-volume movement data sets, along with the fitting of complex flexible models. Applications of this approach include inferring the mean location of a migration route and quantifying significant changes, detecting diurnal activity patterns, or identifying the onset of directed persistent movements.

Research on the Method and Application of MapReduce in Mobile Track Big Data Mining

Background: Mass movement trajectory data with real scenarios has been evolved with big data mining to solve the data redundancy problem. Methods: This paper proposes a parallel path based on the Map Reduce compression method, using two kinds of piecewise point mutual crisscross, the classified method of trajectory, and then segment trajectory distribution to multiple nodes to parallelize the compression. Results: Finally, the results based on both compression methods have been simulated for the different real-time data by merging both techniques. Conclusion: The performance test results show that the parallel trajectory compression method proposed in this paper can greatly improve the compression efficiency and completely eliminate the error caused by the failure of the correlation between the segments.

Discovery of stay area in indoor trajectories of moving objects

With the rapid development of location-based services, a large amount of moving object trajectory data has been generated. Mining users’ stay area from moving trajectory data can not only improve location-based service, but also take precautions to ensure personal safety to better promote the development of location-based services. However, the research on trajectory of indoor moving objects is still in its infancy, and there is a lack of relevant research in this field. It is urgent to meet the needs of the location service industry. We have done some novel work on the stay area in indoor trajectories. Firstly, a novel algorithm named SVBDSA is proposed for the stability value-based discovery of stay area. The stability value is calculated from the distance and velocity between the two points. By comparing the stability value with the threshold value, the two points are judged whether they are in a stable state, and then the point in the stable state is extended. Thus, the stay area in the moving trajectories can be discovered. Secondly, this paper proposes an improved algorithm named T-OPTICS, which adds the time attribute and puts forward the concept of difference value, so that abnormal stay points can be detected in the discovered stay area. Finally, the trajectory generation tool Vita is used to conduct experiments and analysis and the effectiveness of the proposed algorithms in this paper is verified. Discovery of stay area is not only a new and difficult scientific issue that has not been studied in the field of indoor trajectories of the moving object, but also has important academic value and research significance in location-based service industry.

Research on User Behavior Prediction and Profiling Method Based on Trajectory Information

Aiming at the need to discover user behavior characteristics and knowledge from moving trajectory data, a user behavior profiling method based on moving trajectory information was proposed. Firstly, the trajectory coordinates were preprocessed to clean out good data. Secondly, the travel rules and the points of interest of the user were found by means of stay points detection, staying points’ semantics and frequent pattern mining. In the aspect of predicting user trajectory information, Key Points Long Short-Term Memory Networks (KP-LSTM) was proposed to predict the user’s future travel location; then the user’s important attribute characteristics were taken through the user profiling, intuitively depicting the characteristics and patterns of users’ lives. Finally, the availability of the method was proved by experiments, and the prediction accuracy was better than the traditional Linear regression and LSTM neural network.

THÖR: Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset

Understanding human behavior is key for robots and intelligent systems that share a space with people. Accordingly, research that enables such systems to perceive, track, learn and predict human behavior as well as to plan and interact with humans has received increasing attention over the last years. The availability of large human motion datasets that contain relevant levels of difficulty is fundamental to this research. Existing datasets are often limited in terms of information content, annotation quality or variability of human behavior. In this paper, we present TH\"OR, a new dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for position, head orientation, gaze direction, social grouping, obstacles map and goal coordinates. TH\"OR also contains sensor data collected by a 3D lidar and involves a mobile robot navigating the space. We propose a set of metrics to quantitatively analyze motion trajectory datasets such as the average tracking duration, ground truth noise, curvature and speed variation of the trajectories. In comparison to prior art, our dataset has a larger variety in human motion behavior, is less noisy, and contains annotations at higher frequencies.