Motion Prediction Model Research Articles

A ship motion forecasting approach based on Fourier transform, regularized Bi-LSTM and chaotic quantum adaptive WOA

Ship motion forecasting is of great significance to assist ship navigation and ensure ship operation. However, due to the coupling influence of wind, waves, currents and other factors, the ship motion has a strong time variation, which brings great challenges to the improvement of the ship motion forecasting accuracy. This paper proposes a novel approach for ship motion forecasting. Firstly, Fourier Transform (FT) is used for data pre-processing to deal with the data noise. Secondly, the Bi-LSTM is applied to simulate the nonlinear dynamical system of ship motion, and the Dropout mechanism and l2 regularization method are used to improve the generalization performance of the network, and a novel ship motion prediction model (FRB model) is constructed. Thirdly, in view of the inherent defects of Whale Optimization Algorithm (WOA), based on chaotic mapping and quantum computing, a new Chaotic Quantum Adaptive Whale Optimization Algorithm (CQAWOA) is proposed to construct a new ship motion forecasting approach, namely FRB&CQAWOA. Finally, based on the measured pitch and roll data of ship, the feasibility and superiority of the established FRB model and the proposed CQAWOA are tested. The test results indicate that the proposed model have better prediction accuracy than other algorithms selected in this paper.

Deep-neural-network model for predicting ground motion parameters using earthquake horizontal-to-vertical spectral ratios

This study proposed a deep-neural-network (DNN) model for seismic ground motion prediction by utilizing a unified strong motion database by the National Research Institute for Earth Science and Disaster Resilience, and earthquake horizontal-to-vertical spectral ratio (EHVR) database in Japan. The model aims to enhance the accuracy of predictions by incorporating the EHVRs for complementing site effects, and utilizing existing ground motion prediction equations (GMPE) as the base model for source and propagation path effects. The hybrid approach enables the prediction of peak ground accelerations (PGAs), peak ground velocities (PGVs), and 5% damped absolute acceleration response spectra (SAs). After classifying the training and test sets from the database, the trained DNN models were applied on the test set to evaluate the performance of the predicted results. The accuracy assessment by the residuals, R-squared ( R2), and root mean square error (RMSE) between the predicted and observed values in the test set revealed the superior performance of the proposed model compared with the traditional GMPE with proxy-based site effects such as V S30s especially in predicting both the spectral amplitude and shape of SAs.

Prediction of abnormal gait behavior of lower limbs based on depth vision

Abstract As a kind of lower-limb motor assistance device, the intelligent walking aid robot plays an essential role in helping people with lower-limb diseases to carry out rehabilitation walking training. In order to enhance the safety performance of the lower-limb walking aid robot, this study proposes a deep vision-based abnormal lower-limb gait prediction model construction method for the problem of abnormal gait prediction of patients’ lower limbs. The point cloud depth vision technique is used to acquire lower-limb motion data, and a multi-posture angular prediction model is trained using long and short-term memory networks to build a model of the user’s lower-limb posture characteristics during normal walking as well as a real-time lower-limb motion prediction model. The experimental results indicate that the proposed lower-limb abnormal behavior prediction model is able to achieve a 97.4% prediction rate of abnormal lower-limb movements within 150 ms. Additionally, the model demonstrates strong generalization ability in practical applications. This paper proposes further ideas to enhance the safety performance of lower-limb rehabilitation robot use for patients with lower-limb disabilities.

Research on LSTM-Based Maneuvering Motion Prediction for USVs

Maneuvering motion prediction is central to the control and operation of ships, and the application of machine learning algorithms in this field is increasingly prevalent. However, challenges such as extensive training time, complex parameter tuning processes, and heavy reliance on mathematical models pose substantial obstacles to their application. To address these challenges, this paper proposes an LSTM-based modeling algorithm. First, a maneuvering motion model based on a real USV model was constructed, and typical operating conditions were simulated to obtain data. The Ornstein–Uhlenbeck process and the Hidden Markov Model were applied to the simulation data to generate noise and random data loss, respectively, thereby constructing a sample set that reflects real experiment characteristics. The sample data were then pre-processed for training, employing the MaxAbsScaler strategy for data normalization, Kalman filtering and RRF for data smoothing and noise reduction, and Lagrange interpolation for data resampling to enhance the robustness of the training data. Subsequently, based on the USV maneuvering motion model, an LSTM-based black-box motion prediction model was established. An in-depth comparative analysis and discussion of the model’s network structure and parameters were conducted, followed by the training of the ship maneuvering motion model using the optimized LSTM model. Generalization tests were then performed on a generalization set under Zigzag and turning conditions to validate the accuracy and generalization performance of the prediction model.

Bio-Inspired Motion Emulation for Social Robots: A Real-Time Trajectory Generation and Control Approach.

Assistive robotic platforms have recently gained popularity in various healthcare applications, and their use has expanded to social settings such as education, tourism, and manufacturing. These social robots, often in the form of bio-inspired humanoid systems, provide significant psychological and physiological benefits through one-on-one interactions. To optimize the interaction between social robotic platforms and humans, it is crucial for these robots to identify and mimic human motions in real time. This research presents a motion prediction model developed using convolutional neural networks (CNNs) to efficiently determine the type of motions at the initial state. Once identified, the corresponding reactions of the robots are executed by moving their joints along specific trajectories derived through temporal alignment and stored in a pre-selected motion library. In this study, we developed a multi-axial robotic arm integrated with a motion identification model to interact with humans by emulating their movements. The robotic arm follows pre-selected trajectories for corresponding interactions, which are generated based on identified human motions. To address the nonlinearities and cross-coupled dynamics of the robotic system, we applied a control strategy for precise motion tracking. This integrated system ensures that the robotic arm can achieve adequate controlled outcomes, thus validating the feasibility of such an interactive robotic system in providing effective bio-inspired motion emulation.

Node Adjustment Scheme of Underwater Wireless Sensor Networks Based on Motion Prediction Model

Node Adjustment Scheme of Underwater Wireless Sensor Networks Based on Motion Prediction Model

Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data

Background and purposeActive breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies. However, they require usually a large dataset for training. The objective of this work was to demonstrate that explicitly encoding the cyclic nature of the breathing signal into the training data enables significant reduction of training datasets which can be obtained from healthy volunteers. Material and methodsSeventy surface scanner breathing signals from 25 healthy volunteers in anterior-posterior direction were used for training and validation (ratio 4:1) of long short-term memory models. The model performance was compared to a model using decomposition into phase, amplitude and a time-dependent baseline. Testing of the models was performed on 55 independent breathing signals in anterior-posterior direction from surface scanner (35 lung, 20 liver) of 30 patients with a mean breathing amplitude of (5.9 ± 6.7)mm. ResultsUsing the decomposed breathing signal allowed for a reduction of the absolute root-mean square error (RMSE) from 0.34 mm to 0.12 mm during validation. Testing using patient data yielded an average absolute RMSE of the breathing signal of (0.16 ± 0.11)mm with a prediction horizon of 500 ms. ConclusionIt was demonstrated that a motion prediction model can be trained with less than 100 datasets of healthy volunteers if breathing cycle parameters are considered. Applied to 55 patients, the model predicted breathing motion with a high accuracy.

CASTNet: A Context-Aware, Spatio-Temporal Dynamic Motion Prediction Ensemble for Autonomous Driving

Autonomous vehicles are cyber-physical systems that combine embedded computing and deep learning with physical systems to perceive the world, predict future states, and safely control the vehicle through changing environments. The ability of an autonomous vehicle to accurately predict the motion of other road users across a wide range of diverse scenarios is critical for both motion planning and safety. However, existing motion prediction methods do not explicitly model contextual information about the environment, which can cause significant variations in performance across diverse driving scenarios. To address this limitation, we propose CASTNet : a dynamic, context-aware approach for motion prediction that (i) identifies the current driving context using a spatio-temporal model, (ii) adapts an ensemble of motion prediction models to fit the current context, and (iii) applies novel trajectory fusion methods to combine predictions output by the ensemble. This approach enables CASTNet to improve robustness by minimizing motion prediction error across diverse driving scenarios. CASTNet is highly modular and can be used with various existing image processing backbones and motion predictors. We demonstrate how CASTNet can improve both CNN-based and graph-learning-based motion prediction approaches and conduct ablation studies on the performance, latency, and model size for various ensemble architecture choices. In addition, we propose and evaluate several attention-based spatio-temporal models for context identification and ensemble selection. We also propose a modular trajectory fusion algorithm that effectively filters, clusters, and fuses the predicted trajectories output by the ensemble. On the nuScenes dataset, our approach demonstrates more robust and consistent performance across diverse, real-world driving contexts than state-of-the-art techniques.

A review on motion sickness of autonomous driving vehicles

The objective of this study is to investigate the symptoms, types, etiology, and assessment methods of motion sickness in autonomous vehicles in order to gain a comprehensive understanding of its occurrence mechanism and emphasize the significance of enhancing autonomous vehicle algorithms for improved ride comfort. Thus, this paper provides a synthesis and discussion of various theories while exploring strategies for mitigating motion sickness from three perspectives: passengers, vehicles, and external equipment. Firstly, it summarizes the clinical manifestations and classification of motion sickness while conducting an in-depth analysis of associated factors. Secondly, it evaluates different approaches for quantitatively measuring the severity and extent of motion sickness. Subsequently, it analyzes the reasons behind increased motion sickness caused by autonomous vehicles and emphasizes the importance of algorithmic improvements to enhance travel comfort. Finally, mitigation strategies are proposed considering passengers' needs as well as advancements in accurate motion prediction models and optimization techniques for autonomous planning and control algorithms that can effectively reduce the risk of motion sickness. As application scenarios for autonomous technology continue to expand, meeting user requirements while ensuring safety has become a benchmark for assessing technical proficiency. Therefore, promoting unmanned travel services necessitates a thorough analysis of existing issues related to autonomous technology along with prioritizing algorithm design enhancements through effective means to achieve an enhanced user experience.

Vertical Ground Motion Prediction Equations for Interplate and Intermediate-Depth Intraslab Earthquakes at Mexico City’s Hill Zone

ABSTRACT We present ground motion prediction models for spectral pseudo acceleration (5% damping), peak ground velocity, and peak ground acceleration values from interplate and intermediate-depth intraslab earthquakes at the so-called hill zone of Mexico City for the vertical component and the vertical-to-horizontal ratio case. There are no previous models for the vertical component from these events in Mexico City. The prediction equations were developed as a function of magnitude and also as a function of distance to the fault surface of earthquakes by using 30 and 26 seismic recordings from interplate (1965–2022) and intermediate-depth intraslab (1964–2017) earthquakes at the hill zone of the city by using Bayesian regression analyses. The attenuation models are developed for interplate events for distances ranging from 280 km to over 500 km and Mw from 6 to 8.1, and for intraslab events for distances to the fault surface ranging from 100 km up to about 600 km, focal depths from 40 km to over 120 km and Mw from 5 to 8.2. Nevertheless, since at large distances ground motions are generally weak, we recommend the applicability of the proposed models to be restricted to up to 400 km. The models are adequate to estimating the seismic hazard at the hill zone of Mexico City, as proved by contrasting analytical versus empirical seismic exceedance rates for the vertical component.

Premonition-driven deep learning model for short-term ship violent roll motion prediction based on the hull attitude premonition mechanism

Ship transit forecasting can provide advanced movement information to a multidimensional wave motion compensation system. However, traditional deep learning models based on data-driven or physical data have difficulty in accurately predicting the rapid variations in ship motion attitudes that frequently occur in complex ocean environments. Therefore, a premonition-driven deep learning model is proposed in this paper to significantly improve the prediction accuracy of ship violent roll motion. This model applies precursor information to predict premonitory values of ship prospective motion attitudes based on an integrated statistical regression model and then employs these premonitory values to revise the outputs of the deep learning time series model. First, a hull attitude premonition mechanism is presented based on biological premonition cognitive behaviour, which captures the correlation relationships between extreme values by downgrading the globally continuous time series prediction problem to a time-independent regression problem. The corresponding methods for predicting local extreme values and deep feedforward neural network cluster are established based on these correlation relationships between the extreme values to achieve precise premonitory values. Second, the time and amplitude variables of the extreme angle values are transformed to countdown time variables and sequential sets of premonitory angle values. Two types of premonition units and their respective premonitory long short-term memory neural networks are constructed to upgrade the regression problem to a globally continuous time series prediction problem. Finally, a single-step output recurrent strategy is applied to forecast the short-term ship motion attitude for fifteen seconds by considering eleven initially predicted points across the two datasets. In sixty-six simulation experiments, the mean absolute error, root mean squared error, and mean absolute percentage error of the premonition-driven long short-term memory neural networks are considerably lower than those of traditional deep learning models. These simulation experiments verify that the premonition-driven deep learning model can effectively forecast short-term violent ship roll motion and provide safeguards for cargo transfer.

Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift Experiment

Annually, hundreds of individuals tragically lose their lives at sea due to shipwrecks or aircraft accidents. For search and rescue personnel, the task of locating the debris of a downed aircraft in the vastness of the ocean presents a formidable challenge. A primary task these teams face is determining the search area, which is a critical step in the rescue operation. The movement of aircraft wreckage on the ocean surface is extremely complex, influenced by the combined effects of surface winds, waves, and currents. Establishing an appropriate drift motion prediction model is instrumental in accurately determining the search area for the wreckage. This article initially conducts maritime drift observation experiments on wreckage, and based on the results of these experiments, analyzes the drift characteristics and patterns of the debris. Subsequently, employing a wealth of observational experimental data, three types of drift prediction models for the wreckage are established using the least squares method. These models include the AP98 model, the dynamics model, and an improved model. In conclusion, the effectiveness and accuracy of the three models is evaluated and analyzed using Monte Carlo techniques. The results indicate that the probability of positive crosswind leeway (CWL) is 47.4%, while the probability of negative crosswind leeway (CWL) is 52.6%. The jibing frequency is 7.7% per hour, and the maximum leeway divergence angle observed is 40.4 degrees. Among the three drift prediction models, the refined AP98 drift model demonstrates the highest forecasting precision. The findings of this study offer a more accurate drift prediction model for the search of an aircraft lost at sea. These results hold significant guiding importance for maritime search and rescue operations in the South China Sea.

Mean period prediction models for Mexican interplate and intermediate-depth intraslab earthquake ground motions

The aim of this paper is to predict the mean period, Tm, as a measure of the frequency content of interplate and intermediate-depth intraslab earthquake ground motions recorded at rock sites due to subduction earthquakes in Mexico. For this purpose, both Ground Motion Prediction Models (GMPMs), which use a parametric regression, and Support Vector Machine (SVM) regression, a type of machine learning algorithm used for regression analysis, are employed in this investigation. Therefore, two databases which include 484 and 300 earthquake ground motions recorded during interplate and intraslab historical Mexican earthquakes were assembled in this study. It is shown that Tm estimates depend on the type of earthquake (i.e., interplate or intraslab). While the SVM regression yields the lowest total standard deviation in predicting Tm, the GMPM still yields an acceptable total standard deviation similar to that reported in other studies. A comparison of the GMPM for intraslab earthquakes in Mexico with another GMPMs for predicting Tm recently introduced in the literature showed differences, which suggests that GMPMs for predicting Tm should be developed for specific regions.

Considering Cyclists' Aggressiveness and Bounded Rationality: A Self-learning Motion Prediction Model for Cyclists

Considering Cyclists' Aggressiveness and Bounded Rationality: A Self-learning Motion Prediction Model for Cyclists

Modeling and Prediction of Nonstationary Ground Motions as Time–Frequency Images

Proper modeling of nonstationary characteristics of ground motions is critical in estimating the seismic performance of structures. The importance of ground motion nonstationarity is being increasingly recognized in seismic design codes and provisions. This paper develops a new method for predicting seismic ground motions in the joint time–frequency domain based on recorded ground motions. We treat wavelet energy maps of acceleration records as images and extract essential patterns from them using principal component analysis. These patterns are then linked to the seismic source, path, and site variables using general regression neural network. The resulting model predicts an image representing the expected evolutionary power spectrum conditioned on the input variables. An example application is presented using records from the Next Generation Attenuation of Ground Motions Database of the Pacific Earthquake Engineering Center. As opposed to conventional ground motion prediction models, the proposed approach retains the time domain characteristics of ground motions. The results show that the proposed model can predict significant patterns in the seismic energy distribution, acceleration time series, and 5% damped elastic spectral accelerations.

LLMT: A Transformer-Based Multi-Modal Lower Limb Human Motion Prediction Model for Assistive Robotics Applications

LLMT: A Transformer-Based Multi-Modal Lower Limb Human Motion Prediction Model for Assistive Robotics Applications

A dynamic spatiotemporal model for fall warning and protection.

Early detection of falls is important for reducing fall injuries. However, existing fall detection strategies mostly focus on reducing impact injuries rather than avoiding falls. This study proposed the concept of identifying "Imbalance Point" to warn the body imbalance, allowing sufficient time to recover balance. And if falling cannot be avoided, an impact sign is released by detecting the "Fall Point" prior to the impact. To achieve this goal, motion prediction model and balance recovery model are integrated into a spatiotemporal framework to analyze dynamic and kinematic features of body motion. Eight healthy young volunteers participated in three sets of experiment: Normal trial, Recovery trial and Fall trial. The body motion in the trials was recorded using Microsoft Azure Kinect. The results show that the developed algorithm for Fall Point detection achieved 100% sensitivity and 98.6% specificity, along with an average lead time of 297ms. Moreover, Imbalance Point was successfully detected in all Fall trials, and the average time interval between Imbalance Point and Fall Point was 315ms, longer than reported step reaction time for elderly (approximately 270ms). The experiment results demonstrate that the developed algorithm have great potential for fall warning and protection in the elderly.

Ground motion prediction model for Himalayan region: a comprehensive review

Ground motion prediction model for Himalayan region: a comprehensive review

Online advance respiration prediction model for percutaneous puncture robotics.

Surgical robots have significant research value and clinical significance in the field of percutaneous punctures. There have been numerous studies on ultrasound-guided percutaneous surgical robots; however, addressing the respiratory compensation problem of deep punctures remains a significant obstacle. Herein we propose a robotic system for percutaneous puncture with respiratory compensation. We proposed an online advance respiratory prediction model based on Bidirectional Gate Recurrent Unit (Bi-GRU) for the respiratory prediction requirements of surgical robot systems. By analyzing the main factors governing the accuracy of the respiratory motion prediction models, various parameters of the online advance prediction model were optimized. Subsequently, we integrated and developed ultrasound-guided percutaneous puncture robot software and a hardware platform to implement respiratory compensation, thus verifying the effectiveness and reliability of various key technologies in the system. The proposed respiratory prediction model has a significantly reduced update time, with an average root mean square error (RMSE) of less than 0.4mm. This represents a reduction of ~ 20% compared to the online training long short-term memory(LSTM). By conducting puncture experiments based on a respiratory phantom, the average puncture error was 2.71 ± 0.65mm and the average single-round puncture time was 65.00 ± 6.67s. Herein we proposed and optimized an online training respiratory prediction network model based on Bi-GRU. The stability and reliability of this system are verified by conducting puncture experiments on a respiratory phantom.

Ground motion prediction model for shallow crustal earthquakes in Japan based on XGBoost with Bayesian optimization

Ground motion prediction is an important and complex research subject in earthquake engineering and traditional approaches based on statistical regression have much room for improvement in prediction accuracy. Utilizing the 67,164 ground motion records from KiK-net and K-Net for 777 shallow crustal earthquakes between 1997 and 2019 in Japan, this paper proposes a ground motion prediction model XGBoost-SC based on the machine learning algorithm of eXtreme Gradient Boosting (XGBoost) for Japan. Magnitude, focal depth, hypo-central distance, Vs30, site altitude, and focal mechanism were used as the feature parameters, and XGBoost, Random Forest, and Deep Neural Networks (DNN) algorithms were selected for model training while Bayesian optimization was used to search for optimized hyperparameters to improve the prediction accuracy. XGBoost algorithm was selected for further study based on the comparison of results from the three algorithms. Residual change with magnitude and hypo-central distance, the probability distribution of residuals, residual standard deviation (σ), residual mean squared error (MSE), and Pearson correlation coefficient (R) were used as the evaluation parameters, and a comparison study was performed against the ground motion prediction equation based on traditional approaches. Actual earthquake events were selected to compare the prediction results against the observation records. To further validate and explain the proposed model, SHapley Additive exPlanations (SHAP) analysis was performed to explain the impact of selected feature parameters on the proposed model. The results demonstrate that the proposed XGBoost-SC model has good prediction stability for all periods, and its residual errors are smaller than those of other models. Therefore, the proposed model can better reflect the ground motion attenuation for shallow crustal earthquakes in Japan and can serve as a better model for ground motion prediction in future aseismic design and earthquake disaster mitigation efforts.