Joint Angles Research Articles

Data-Aided Maximum Likelihood Joint Angle and Delay Estimator Over Orthogonal Frequency Division Multiplex Single-Input Multiple-Output Channels Based on New Gray Wolf Optimization Embedding Importance Sampling.

In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for "GWO embedding IS"), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs' positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramér-Rao lower bound (CRLB), even at low SNR levels.

The relationship between back handspring step out performance and take-off technique in female gymnasts

ABSTRACT Although the back handspring step out (BHS) is a foundational skill in balance beam routines, it can be performed using different take-off techniques. Back injuries are highly prevalent in the BHS due to the combination of high spine extension and joint loading. However, it is unclear which technique minimises injury risk or leads to better BHS performance. The purpose of the study was to identify techniques used for the BHS take-off and analyse the resulting BHS performance. Gymnasts were found to use either: Simultaneous Flexion—trunk and knees flex at the same time; Sequential Flexion—trunk reaches its maximum flexion followed by knee flexion; or Double-Bounce—knees and trunk both flex and then the knees extend and flex again. To assess performance, point deductions were calculated, and dynamic balance, ground reaction forces (GRFs) and relevant joint angles were analysed. The techniques had no differences in point deductions or dynamic balance, but there were differences in GRFs, spine extension and knee flexion. The Sequential Flexion technique had the lowest spine extension, which potentially reduces back injuries and the lowest knee flexion, which is a BHS requirement. These results support the use of Sequential Flexion technique when performing the BHS.

Comparative analysis of upper body kinematics in stroke, Parkinson's disease, and healthy subjects: An observational study using IMU-based targeted box and block test

BackgroundThe Box and Block Test (BBT) is an essential and widely used test in rehabilitation for the assessment of gross unilateral manual dexterity. Although it is a valid, simple, and ecological instrument, it does not provide a quantitative measure of the upper limb trajectories during the test. Research questionThe study introduces a new motion-capture-based method (using ecological Inertial Measurement Units - IMUs) to evaluate upper body kinematics while performing a targeted version of BBT (tBBT). MethodsThis observational study compares data from 35 healthy subjects, 35 subjects with Parkinson’s disease, and 35 post-stroke individuals to evaluate upper limb kinematics during tBBT quantitatively. Seven IMUs were placed on the trunk, head, and upper limb of each subject. The joint angles and kinematic scores were calculated and analyzed. Motor task execution time and kinematic scores were statistically correlated with clinical assessment measures. Kruskal-Wallis between groups test and Dunn-Bonferroni post-hoc were used. ResultsThe statistics revealed significant differences (p<0.05) among the three groups. The analyzed joint angles highlight various compensatory strategies in neurological subjects, such as using the trunk to complete a motor task instead of the shoulder and using the wrist instead of the elbow, along with differences in movement fluidity (DimensionLess-Jerk, p<0.05). A positive correlation was found between kinematics and the Fugl-Meyer Assessment-Upper Limb (r=0.7344; p<0.01), and a negative correlation between kinematics and the Unified Parkinson's Disease Rating Scale (r=-0.5286; p<0.01). SignificanceThe quantitative assessments of joint kinematics correlated to clinical assessments could guarantee a new method of assessment of the upper limb in subjects with motor deficits. This would allow to capture new insight into the characteristics of the subject’s disability, with implications for the choice of a personalized rehabilitation treatment focused on the motor recovery of the upper limb.

Effects of ankle joint degree of freedom of knee–ankle–foot orthoses on loading patterns and triceps surae muscle activity on the paretic side in individuals with subacute severe hemiplegia: a retrospective study

BackgroundIndividuals with subacute severe hemiplegia often undergo alternate gait training to overcome challenges in achieving walking independence. However, the ankle joint setting in a knee–ankle–foot orthosis (KAFO) depends on trunk function or paralysis stage for alternate gait training with a KAFO. The optimal degree of ankle joint freedom in a KAFO and the specific ankle joint conditions for effective rehabilitation remain unclear. Therefore, this study aimed to investigate the effects of different degrees of freedom of the ankle joint on center-of-pressure (CoP) parameters and muscle activity on the paretic side using a KAFO and to investigate the recommended setting of ankle joint angle in a KAFO depending on physical function.MethodsThis study included 14 participants with subacute stroke (67.4 ± 13.3 years). The CoP parameters and muscle activity of the gastrocnemius lateralis (GCL) and soleus muscles were compared using a linear mixed model (LMM) under two ankle joint conditions in the KAFO: fixed at 0° and free ankle dorsiflexion. We confirmed the relationship between changes in CoP parameters or muscle activity under different conditions and physical functional characteristics such as the Fugl–Meyer Assessment of Lower Extremity Synergy Score (FMAs) and Trunk Impairment Scale (TIS) using LMM.ResultsAnterior–posterior displacement of CoP (AP_CoP) (p = 0.011) and muscle activity of the GCL (p = 0.043) increased in the free condition of ankle dorsiflexion compared with that in the fixed condition. The FMAs (p = 0.004) and TIS (p = 0.008) demonstrated a positive relationship with AP_CoP. A positive relationship was also found between TIS and the percentage of medial forefoot loading time in the CoP (p < 0.001).ConclusionsFor individuals with severe subacute hemiplegia, the ankle dorsiflexion induction in the KAFO, which did not impede the forward tilt of the shank, promotes anterior movement in the CoP and muscle activity of the GCL. This study suggests that adjusting the dorsiflexion mobility of the ankle joint in the KAFO according to improvement in physical function promotes loading of the CoP to the medial forefoot.

Using markerless motion capture and musculoskeletal models: An evaluation of joint kinematics.

This study presents a comprehensive comparison between a marker-based motion capture system (MMC) and a video-based motion capture system (VMC) in the context of kinematic analysis using musculoskeletal models. Focusing on joint angles, the study aimed to evaluate the accuracy of VMC as a viable alternative for biomechanical research. Eighteen healthy subjects performed isolated movements with 17 joint degrees of freedom, and their kinematic data were collected using both an MMC and a VMC setup. The kinematic data were entered into the AnyBody Modelling System, which enables the calculation of joint angles. The mean absolute error (MAE) was calculated to quantify the deviations between the two systems. The results showed good agreement between VMC and MMC at several joint angles. In particular, the shoulder, hip and knee joints showed small deviations in kinematics with MAE values of 4.8∘, 6.8∘ and 3.5∘, respectively. However, the study revealed problems in tracking hand and elbow movements, resulting in higher MAE values of 13.7∘ and 27.7∘. Deviations were also higher for head and thoracic movements. Overall, VMC showed promising results for lower body and shoulder kinematics. However, the tracking of the wrist and pelvis still needs to be refined. The research results provide a basis for further investigations that promote the fusion of VMC and musculoskeletal models.

Data-physics hybrid-driven external forces estimation method on excavators

To enhance the accuracy of excavator external forces estimation, a data-physics hybrid-driven excavator external forces estimation method is proposed. MultiLayer Perceptron (MLP) model is applied for nonlinear component modeling and is used to compensate for Rigid Body Dynamics (RBD) models. Gaussian Process Regression (GPR) model is developed to estimate the external forces and uncertainties based on the joint angles, angular velocities, cylinder actuation forces, and actuation end velocities of the excavator. The external forces estimated by the GPR models are used as a new measurement in the Disturbance Kalman Filter (DKF), and the uncertainty is incorporated into the covariance of the process noise. The constructed GPR-DKF estimator is validated on a scaled-down excavator. The results demonstrate the robustness and effectiveness of the proposed method in estimating out-of-distribution samples.

A Hierarchical Control Method for Trajectory Tracking of Aerial Manipulators Arms

To address the control challenges of an aerial manipulator arm (AMA) mounted on a drone under conditions of model inaccuracy and strong disturbances, this paper proposes a hierarchical control architecture. In the upper-level control, Bézier curves are first used to generate smooth and continuous desired trajectory points, and the theory of singular trajectory lines along with a Radial Basis Function Neural Network (RBFNN) is introduced to construct a highly accurate multi-configuration inverse kinematic solver. This solver not only effectively avoids singular solutions but also enhances its precision online through data-driven methods, ensuring the accurate calculation of joint angles. The lower-level control focuses on optimizing the dynamic model of the manipulator. Using a Model Predictive Control (MPC) strategy, the dynamic behavior of the manipulator is predicted, and a rolling optimization process is executed to solve for the optimal control sequence. To enhance system robustness, an RBFNN is specifically introduced to compensate for external disturbances, ensuring that the manipulator maintains stable performance in dynamic environments and computes the optimal control commands. Physical prototype testing results show that this control strategy achieves a root mean square (RMS) error of 0.035, demonstrating the adaptability and disturbance rejection capabilities of the proposed method.

Gait Generation of a 10-DOF Humanoid Robot on Deformable Terrain Based on Spherical Inverted Pendulum Model

Abstract Gait generation of a humanoid robot on deformable terrain is a complex problem as the foot and terrain interaction and terrain deformation has to be included in the dynamics. In order to simplify the dynamics of walk on deformable terrain, we used a spherical inverted pendulum (SIP) to represent the single support phase, in which the effect of terrain deformation is represented by a spring and damper contact model. The impact model for leg transition is derived from angular momentum conservation. In order to minimize the energy loss due to impact, the double support phase is modeled as a suspended pendulum. Based on the motion of the SIP model, the hip and leg trajectories of a 10 DOF humanoid robot are generated. The joint angles of the humanoid are obtained from inverse kinematics. The motion of the center of mass is analyzed by inverse dynamics of a floating-base robot. The proposed gait generation method has been experimentally validated using a Kondo KHR-3HV humanoid robot on deformable terrain. The results show that the humanoid can effectively track the trajectories of the SIP model.

Differences in the stride time and lower limb joint angles and their variability during distance running between treadmill and over-ground: a crossover study.

Treadmills have been used in laboratories to assess various measures related to walking and running. However, there has been some skepticism regarding their reliability as a representation of outdoor running. While marathon running has gained popularity as a form of physical activity, there have been few studies examining stride-to-stride variability after distance running, especially in relation to the duration and surface of running. This study compared stride time and lower limb joint angles during distance treadmill running and running over-ground. The hypothesis was that stride-to-stride variability would be influenced by running duration and surface, with greater variability observed during outdoor running. Eleven runners participated in the study, running on a treadmill and over-ground for 31 minutes at their preferred speed. Inertial measurement units were used to measure stride time, total range of motion, and joint angles of the hip, knee, and ankle in different phases of the gait cycle in the sagittal plane movements. Mean and coefficient of variation of each parameter were compared between the initial and final 5 minutes of running on the treadmill and over-ground. There were no significant differences in stride time or its variability based on running duration or surface. However, mean and variability of certain lower limb joint angles were higher during outdoor running, supporting the hypothesis. Variability was higher in the initial duration of running as compared to final phase of running. These findings suggest that treadmill may not fully reflect the dynamics of running over-ground. It is important to consider variability in gait analysis and research, as well as the potential impact on training and clinical practice.

Inverse kinematics of six degrees of freedom robot manipulator based on improved dung beetle optimizer algorithm

Inverse kinematics is a basic problem in robotics, which aims to solve the robot’s joint angles according to the end effector’s position and orientation. This paper proposed an improved spiral search multi-strategy dung beetle optimizer (DBO) algorithm for solving the inverse kinematics problem. The improved DBO algorithm considers not only the error between the target value and the current value but also the previous position of the robot to ensure minimum displacement during the movement. To solve the end position error and orientation error of the robot end effector more accurately, the quaternion is introduced as a penalty factor in the optimization objective function, which is of great significance for reducing the orientation error. Through the improved DBO algorithm, the position error is still accurate, and the orientation error is reduced from 9.5901 to 1.8718. Experimental results show that the proposed algorithm outperforms other swarm-intelligent algorithms in terms of accuracy and convergence speed. Overall, the proposed spiral search multi-strategy DBO algorithm provides an effective and efficient solution to the inverse kinematics problem in robotics.

Locomotion trajectory optimization for quadruped robots with kinematic parameter calibration and compensation

It is significant to improve the locomotion trajectory accuracy of a quadruped robot for more applications. A compensation approach with kinematic parameter calibration is proposed to enhance the trajectory accuracy. The motion equation of the foot ends in coordinated gait is given. The related kinematic error model is deduced. The model for identifying the joint parameter errors is implemented via the trajectory error in the Z-direction. The trunk trajectory accuracy enhancement is facilitated by compensating the joint parameter error with joint angles as tuning variables. The performance of the methodology is validated in the simulation system by predetermining ten random sets of joint variable errors. The results indicate that both the trajectory and the attitude accuracy of the calibrated robot can be significantly improved. In practical experiments, it is shown that an error reduction rate of 80% in positional deviation and 58% in attitude error can be achieved.

Gait planning based on bionic quadruped robot

Based on the ankylosaurus, a four-legged robot structure with 14 degrees of freedom was designed in this study. The kinematic model was established using the Denavit-Hartenberg (D-H) method. The inverse kinematics of the robot was analyzed, and the angle equations of each leg joint were obtained. In order to reduce the kinetic energy generated when leaving the ground and landing, the compound cycloid was modified. The modified foot curve effectively reduces energy and meets the kinematic requirements. On the basis of the foot trajectory, the diagonal leg phase difference was set to 0.5, and the diagonal gait was adopted as the gait of the bionic quadrupled robot. Simulink software was used to construct the simulation environment, and the maximum error of the simulation results and the theoretical step height was 2.05%, and the step length maximum error was 2.39%. During walking, the thigh joint angle was −92.82° to −80.21°, and the hip joint angle was 22.23° to 43.83°. Compared with the simulation trajectory and the theoretical curve, because the experiment did not consider the balance of the fuselage, there was a certain error when the leg was raised to the highest point. Overall, the gait planning strategy designed in this study basically achieves the expected effect, lays a certain foundation for the practical application of the bionic quadruped dinosaur robot, and also provides a reference for the subsequent research.

Using Genetic Algorithms and Bézier Curves for Automatic Path Optimization of a 6-DOF Robot

This paper describes a novel method for automatically planning point-to-point motion paths for a robot with six degrees of freedom (6-DOF). A linear motion path between two points on such robots may be impractical due to joint angle constraints or exceeding the manipulator's operational range. The proposed method employs a genetic algorithm to generate suitable motion paths based on the second-, third-, and fourth-orders of Bézier curves. The control points of Bézier curves are determined using a genetic algorithm, which can adjust the fitness function as the end-effector moves closer to the obstacle. As a result, the algorithm can adjust its motion path planning in response to obstacles. The motion paths are generated with the goal of optimizing the robot's inverse kinematic configuration. The results show that using a genetic algorithm and Bézier curves can produce motion paths with smooth transitions, minimal changes in joint angles, and no sudden jerks within the robot's operational area in both obstacle-free and obstacle avoidance scenarios. This solution may be useful for intelligent robots with automated path-planning capabilities in unknown environments.

Managing lower extremity loading in distance running by altering sagittal plane trunk leaning

BackgroundTrunk lean angle is an underrepresented biomechanical variable for modulating and redistributing lower extremity joint loading and potentially reducing the risk of running-related overuse injuries. The purpose of this study was to systematically alter the trunk lean angle in distance running using an auditory real-time feedback approach and to derive dose–response relationships between sagittal plane trunk lean angle and lower extremity (cumulative) joint loading to guide overuse load management in clinical practice. MethodsThirty recreational runners (15 males and 15 females) ran at a constant speed of 2.5 m/s at 5 systematically varied trunk lean conditions on a force-instrumented treadmill while kinematic and kinetic data were captured. ResultsA change in trunk lean angle from –2° (extension) to 28° (flexion) resulted in a systematic increase in stance phase angular impulse, cumulative impulse, and peak moment at the hip joint in the sagittal and transversal plane. In contrast, a systematic decrease in these parameters at the knee joint in the sagittal plane and the hip joint in the frontal plane was found (p < 0.001). Linear fitting revealed that with every degree of anterior trunk leaning, the cumulative hip joint extension loading increases by 3.26 Nm·s/kg/1000 m, while simultaneously decreasing knee joint extension loading by 1.08 Nm·s/kg/1000 m. ConclusionTrunk leaning can reduce knee joint loading and hip joint abduction loading, at the cost of hip joint loading in the sagittal and transversal planes during distance running. Modulating lower extremity joint loading by altering trunk lean angle is an effective strategy to redistribute joint load between/within the knee and hip joints. When implementing anterior trunk leaning in clinical practice, the increased demands on the hip musculature, dynamic stability, and the potential trade-off with running economy should be considered.

Trajectory tracking control based on deep reinforcement learning and ensemble random network distillation for robotic manipulator

Abstract In general, the trajectory tracking of robotic manipulator is exceptionally challenging due to the complex and strongly coupled mechanical architecture. In this paper, precise track control of the robotic manipulator is formulated as a dense reward problem for reinforcement learning(RL). A deep RL(DRL) approach combining the soft actor-critic (SAC) algorithm and ensemble random network distillation (ERND) is proposed to address the tracking control problem for robotic manipulator. Firstly, an ERND model is designed, consisting of a module list of multiple RND models. Each RND model obtains the error by learning the target features and the predicted features of the environment. The resulting error serves as internal rewards that drive the robotic agent to explore unknown and unpredictable environmental states. The ensemble model obtains the total internal reward by summing the internal rewards of each RND model, thereby obtaining more accurately reflecting the characteristics of the manipulator in tracking control tasks and improving control performance. Secondly, combining the SAC algorithm with ERND facilitates more robust exploration capabilities in environments with input saturation and joint angle constraints, thereby enabling faster learning of effective policies and enhancing the performance and efficiency of robotic manipulator tracking control tasks. Finally, the simulation results demonstrate that the robotic manipulator tracking control task is effectively completed in dense reward problems through the combination of the SAC algorithm and ERND.

Effect of Gait Speed and Dynamic Time-Warping on the prediction of Lower-Limb Joint Angles

Effect of Gait Speed and Dynamic Time-Warping on the prediction of Lower-Limb Joint Angles

The Effectiveness of Warm-Up Using an Assistive Device in Wheelchair Basketball: A Feasibility Study of Able-Bodied Participants.

Introduction A warm-up is often performed to prevent injury and prepare for optimal performance. Nonetheless, research on its impact on performance, particularly in para-sports, remains limited. We hypothesized that the use of an assistive device during warm-up would enable wheelchair basketball players to perform full-body movements efficiently and effectively, contributing to enhanced wheelchair mobility. Therefore, this feasibility study aimed to assess the safety of warm-up exercise with an assistive device and the changes in wheelchair mobility performance before and after warm-up in able-bodied participants. Methods Thirteen able-bodied participants (nine males and four females; mean age: 34.3 ± 6.11 years) were analyzed. Warm-up consisted of a five-minute stand-up exercise using the lumbar-type Hybrid Assistive Limb®. Before and after warm-up, a 3-3-6 m sprint was performed as a wheelchair mobility performance test. The 3-3-6 m sprint is a test in which the athlete repeatedly accelerates, decelerates, and stops while driving at maximum effort for a total of 12 m (0-3 m, 3-6 m, and 6-12 m). The time required for the 3-3-6 m sprint and maximum instantaneous speed, acceleration time, deceleration time, hip joint angle, and average muscle activity of the lower limb and trunk muscles during the acceleration/deceleration phase of each section were compared before and after warm-up exercise. Results Warm-up with an assistive device was safe in healthy participants. The time required for the 3-3-6 m sprint was significantly reduced after the warm-up compared to that before the warm-up (p=0.005). Although not significant, there was a trend toward shorter deceleration times after the warm-up for participants herein. Conclusions In able-bodied participants, warm-up with an assistive device is safe; it may improve wheelchair mobility performance. Further research is required to determine its impact on para-athletes with disabilities.

Longitudinal effects of stroke rehabilitation: A new deep learning method on joint angle latent space

Longitudinal effects of stroke rehabilitation: A new deep learning method on joint angle latent space

Delineating aquitard characteristics within a Silurian dolostone aquifer using high-density hydraulic head and fracture datasets

Fractured aquifers are heterogeneous due to the variable frequency, orientation, and intersections of rock discontinuities. A ~100-m-thick Silurian dolostone sequence provides a bedrock aquifer supplying the city of Guelph, Canada. Here, fracture network characteristics and associated influences on hydraulic head were examined using several data types obtained from 24 cored holes in a study that is novel for the quantity and quality of data. High (50–90°) angle joint orientations, heights, and terminations relative to bedding features were determined from acoustic televiewer logs and outcrop scanlines. These data were compared to high-resolution hydraulic head profiles showing head loss over depth-discrete intervals identifying zones with lower vertical hydraulic conductivity. This study reveals that the marl-rich Vinemount Member, traditionally considered the principal aquitard, corresponds to head loss in only 62% of the 24 boreholes. The vertical position of head loss varies across the 90-km2 study area and occurs in any of the lithostratigraphic units of the Lockport Group. Within this sedimentary sequence, aquitards are laterally discontinuous or “patchy” at variable depths and relate to: (1) the frequency of the high-angle joints; (2) shorter joint height; and (3) the type of joint terminations. The head loss occurs in thin (2–2.5 m) intervals where the frequency of the high-angle joints is low. Where a large proportion of small joints cross-cut marl bedding planes, head loss is negligible, suggesting that the vertical hydraulic conductivity is not reduced. Overall, these findings are potentially applicable to assessing aquitard and cap rock integrity in carbonate sedimentary sequences worldwide.

Optimizing PID control for enhanced stability of a 16‐DOF biped robot during ditch crossing

AbstractThe current research article discusses the design of a proportional–integral–derivative (PID) controller to obtain the optimal gait planning algorithm for a 16‐degrees‐of‐freedom biped robot while crossing the ditch. The gait planning algorithm integrates an initial posture, position, and desired trajectories of the robot's wrist, hip, and foot. A cubic polynomial trajectory is assigned for wrist, hip, and foot trajectories to generate the motion. The foot and wrist joint angles of the biped robot along the polynomial trajectory are obtained by using the inverse kinematics approach. Moreover, the dynamic balance margin was estimated by using the concept of the zero‐moment point. To enhance the smooth motion of the gait planner and reduce the error between two consecutive joint angles, the authors designed a PID controller for each joint of the biped robot. To design a PID controller, the dynamics of the biped robot are essential, and it was obtained using the Lagrange–Euler formulation. The gains, that is, KP, KD, and KI of the PID controller are tuned with nontraditional optimization algorithms, such as particle swarm optimization (PSO), differential evolution (DE), and compared with modified chaotic invasive weed optimization (MCIWO) algorithms. The result indicates that the MCIWO‐PID controller generates more dynamically balanced gaits when compared with the DE and PSO‐PID controllers.