Estimation Of Stiffness Research Articles

Coordinated Control Strategy for Stability Control and Trajectory Tracking with Wheel-Driven Autonomous Vehicles Under Harsh Situations

A coordinated strategy is proposed to prevent interference between trajectory tracking control and stability control in wheel-driven autonomous vehicles. A tire cornering stiffness estimate model is developed using the recursive least squares approach with a forgetting factor (FFRLS), resulting in precise estimation of tire cornering stiffness. An adaptive trajectory tracking control is developed, utilizing real-time updates of tire cornering stiffness; for the direct yaw moment required for stability control, an integral sliding-mode control is adopted, and the chatter problem of the integral sliding-mode controller is optimized by a fuzzy controller. The coordinated control of trajectory tracking and vehicle stability is ultimately attained through the application of the normalized stability index. The method’s practicality is validated by the hardware-in-the-loop simulation platform.

Stiffness estimation and finger-object impact detection with a robotic gripper using intrinsic sensors

Stiffness estimation and finger-object impact detection with a robotic gripper using intrinsic sensors

AS-Transfer: An arterial stiffness estimation approach from single photoplethysmography and physiological data

AS-Transfer: An arterial stiffness estimation approach from single photoplethysmography and physiological data

Robot stiffness modeling based on the rigid flexible coupling simulation and its application to trajectory planning

Robot stiffness modeling based on the rigid flexible coupling simulation and its application to trajectory planning

A Flatness Error Prediction Model in Face Milling Operations Using 6-DOF Robotic Arms

The current trend in machining with robotic arms involves leveraging Industry 4.0 technologies to propose solutions that reduce path deviation errors. This approach presents significant challenges alongside promising advancements, as well as a substantial increase in the cost of future industrial robotic cells, which is not always amortizable. As an alternative or complementary approach to this trend, methods encouraging the occasional use of Industry 4.0 devices for characterizing the behavior of the actual physical cell, calibration, or adjustment are proposed. One such method, called FlePFaM, predicts flatness errors in face milling operations using robotic arms. This is achieved by estimating tool path deviation errors through the integration of a simple model of the robot arm’s mechanics with the cutting forces vector of the process, thereby optimizing machining conditions. These conditions are determined through prior empirical estimations of mass, stiffness, and damping. The conducted tests enabled the selection of the most favorable combination of variables, such as the robot wrist configuration, the position and orientation of the workpiece, and the predominant milling orientation. This led to the identification of the configuration with the lowest absolute flatness error according to the model’s predictions. The results demonstrated a high degree of similarity—between 97% for the closest case and 57% for the farthest case—between simulated and experimental flatness error values. FlePFaM represents a significant step forward in adopting innovative robotic arm solutions for reliable and efficient production. FlePFaM includes dimensional flatness indicators that provide practical support for decision making.

Mode-shape-based tension and bending stiffness estimation method for Nielsen–Lohse bridge cables without removing intersection clamps

Mode-shape-based tension and bending stiffness estimation method for Nielsen–Lohse bridge cables without removing intersection clamps

Reliable and streamlined model setup for digital twin assessment of fracture healing.

Reliable and streamlined model setup for digital twin assessment of fracture healing.

Non-invasive estimation of left ventricular chamber stiffness using cardiovascular magnetic resonance and echocardiography.

Preclinical studies exploring the underlying mechanisms of elevated left ventricular (LV) chamber stiffness play a crucial role in developing new therapeutic strategies. However, there is a lack of systematic evaluation of imaging biomarkers of diastolic function against gold standard assessment of LV chamber stiffness in rodents. Therefore, we aimed to evaluate imaging biomarkers of diastolic function from cardiovascular magnetic resonance (CMR) and echocardiography in predicting the slope of the end-diastolic pressure-volume relationship (EDPVR) in rats. Sprague Dawley rats with varying degrees of myocardial stiffness induced by aortic constriction (n=38) and healthy controls (n=9) underwent echocardiography and CMR at approximately 13 weeks post-operation. Imaging biomarkers of diastolic function were evaluated for their ability to predict the EDPVR slope from pressure-volume recordings using regression analysis and receiver operating characteristics analysis. Both CMR and echocardiographic imaging biomarkers, in particular those related to the left atrium and mitral flow, were able to predict the EDPVR slope in a rat model with varying stiffness. From CMR, native T1 values, peak early diastolic longitudinal strain rate (SRe(long)) and E/SRe(long), left atrial (LA) ejection fraction, isovolumetric relaxation time (IVRT), E/A and peak LA strain, correlated best with the EDPVR slope (|r|=0.54-0.72). From echocardiography, E/A, E, LA diameter, e'/a', E/SRe(long) and IVRT correlated with the EDPVR slope (|r|=0.49-0.67), while E/e', e' and E-wave deceleration time demonstrated poor correlation (|r|=0.17-0.27). Receiver operating characteristics analysis indicated better performance of CMR imaging biomarkers than echocardiography in predicting increased EDPVR slope. Several diastolic imaging biomarkers commonly employed in preclinical studies have poor ability to predict cardiac chamber stiffness. Our study identifies several imaging biomarkers obtained from both echocardiography and CMR that are able to estimate LV chamber stiffness non-invasively, providing an important tool for future mechanistic research on myocardial stiffness.

ROLE OF IMAGING INVESTIGATIONS IN DETECTING THYROID NODULE MALIGNANCY. A RETROSPECTIVE STUDY

Nodular thyroid pathology is a topical public health problem that is increasingly targeted and discussed by the general population. The prevalence is around 10%, but rates up to 68% are reported in the literature. In Romania, according to statistics from the National Institute of Public Health, the incidence of thyroid diseases has tripled between 2010 and 2016 and is increasing, which can also be explained by improved patient access to specialized medical services and diagnostic tests. High-resolution thyroid ultrasound is considered the screening test of choice for the diagnosis of thyroid nodules, which employs the Ti-RADS score to determine the type of nodules and risk of malignancy. Elastography is a recently developed dynamic technique that uses software within ultrasound to provide an estimate of tissue stiffness by measuring the degree of distortion under the application of an external force, such as palpation, in the evaluation of the thyroid during physical examination. These tests used both could improve the accuracy of diagnosis of thyroid nodules.

Estimation of Ankle Joint Stiffness During Quiet Standing Using Only a Force Plate Without Perturbation

Estimation of Ankle Joint Stiffness During Quiet Standing Using Only a Force Plate Without Perturbation

Study on equivalent stiffness estimation mfethods and dynamic impact response of rockfall protection canopies

Study on equivalent stiffness estimation mfethods and dynamic impact response of rockfall protection canopies

Aortic Wall Biomechanical Assessment Depends on Ultrasound Probe Pressure: In-Vivo and In-Silico Insights.

Ultrasound-based aortic stiffness estimation has the potential to improve the diagnosis and prognosis of patients with abdominal aortic aneurysms (AAA), as a complement to routinely used diameter surveillance. However, existing methods have shown limited reproducibility, possibly influenced by the unknown effects of the highly variable ultrasound probe pressure. This proof-of-concept study addressed this gap by analyzing time-resolved ultrasound sequences from AAA patients. Two-dimensional ultrasound sequences were acquired from 10 AAA patients, applying light and firm probe pressure. Diameter variations and stiffness were evaluated and compared. An in-silico simulation was performed to support the in-vivo observations. Measured stiffness decreased with an increased probe pressure. Specifically, in the most responsive patient group, the cyclic diameter variation between diastole and systole changed from 1% at light probe pressure to 5% at firm probe pressure, and the estimated stiffness decreased by a factor of 6.3. Another group of patients showed a marginal increase in the diameter variations and a smaller decrease in stiffness (factor of 1.5) when transitioning from light to firm probe pressure. These two behaviors were reproduced via numerical simulations, showing that the different responses to probe pressure depend on the stress-strain relationship of the wall material. Varying the ultrasound probe pressure can alter the in-vivo mechanics of AAAs. This proof-of-concept study suggests potential implications for AAA mechanical characterization via ultrasound imaging.

Adaptive Position/Force Controller Design Using Fuzzy Neural Network and Stiffness Estimation for Robot Manipulator

AbstractThis paper proposes an adaptive hybrid position/force control approach using fuzzy neural networks (FNNs) for a robot manipulator with joint friction compensation. The dynamics model and system uncertainties are estimated by FNNs. For force tracking control, an adaptive impedance controller is employed with an online stiffness estimator, wherein the stiffness of the contacted environment is estimated using a gradient descent algorithm. The adaptive update laws of the FNNs and the stability of the controller are obtained using the Lyapunov stability theorem. Finally, the proposed adaptive hybrid controller is implemented on the AR605, a 6-axis articulated robot manipulator manufactured by the Industrial Technology Research Institute (ITRI). The effectiveness and robustness of the proposed control strategies are verified by the simulation and experimental results.

Autonomous Vehicle Motion Control Considering Path Preview with Adaptive Tire Cornering Stiffness Under High-Speed Conditions

The field of autonomous vehicle technology has experienced remarkable growth. A pivotal trend in this development is the enhancement of tracking performance and stability under high-speed conditions. Model predictive control (MPC), as a prevalent motion control method, necessitates an extended prediction horizon as vehicle speed increases and will lead to heightened online computational demands. To address this, a path preview strategy is integrated into the MPC framework that temporarily freezes the vehicle state within the prediction horizon. This approach assumes that the vehicle state will remain consistent for a specified preview distance and duration, effectively extending the prediction horizon for the MPC controller. In addition, a stability controller is designed to maintain handling stability under high-speed conditions, in which a square-root cubature Kalman filter (SRCKF) estimator is employed to predict tire forces to facilitate the cornering stiffness estimation of vehicle tires. The double lane change maneuver under high-speed conditions is conducted through the Carsim/Simulink co-simulation. The outcomes demonstrate that the SRCKF estimator could provide a reasonably accurate estimation of lateral tire forces throughout the whole traveling process and facilitates the stability controller to guarantee the handling stability. On the premise of ensuring handling stability, integrating the preview strategy could nearly double the prediction horizon for MPC, resulting in the limited increase of online computation burden brought while maintaining path tracking accuracy.

Learning-based object's stiffness and shape estimation with confidence level in multi-fingered hand grasping.

When humans grasp an object, they are capable of recognizing its characteristics, such as its stiffness and shape, through the sensation of their hands. They can also determine their level of confidence in the estimated object properties. In this study, we developed a method for multi-fingered hands to estimate both physical and geometric properties, such as the stiffness and shape of an object. Their confidence levels were measured using proprioceptive signals, such as joint angles and velocity. We have developed a learning framework based on probabilistic inference that does not necessitate hyperparameters to maintain equilibrium between the estimation of diverse types of properties. Using this framework, we have implemented recurrent neural networks that estimate the stiffness and shape of grasped objects with their uncertainty in real time. We demonstrated that the trained neural networks are capable of representing the confidence level of estimation that includes the degree of uncertainty and task difficulty in the form of variance and entropy. We believe that this approach will contribute to reliable state estimation. Our approach would also be able to combine with flexible object manipulation and probabilistic inference-based decision making.

Evaluation of the shear stiffness and load redistribution of framed structures affected by tunnelling

Evaluation of the shear stiffness and load redistribution of framed structures affected by tunnelling

Abstract 4137465: Estimating Arterial Stiffness Using a Smart Ring and the Impact of HIIT Exercise: A Pilot Study

Introduction: Arterial stiffness is an important marker of cardiovascular health, but measuring it requires specialized equipment and trained personnel. This study aimed to evaluate a novel method for estimating arterial stiffness using photoplethysmography (PPG) signals recorded by a commercially available device (Oura Ring) and to study the impact of a high-intensity interval training (HIIT) intervention on arterial stiffness. Research Questions: 1. Can a smart ring provide accurate estimates of arterial stiffness comparable to the reference device (Sphygmocor)? 2. Does a three-month supervised HIIT cycling protocol reduce arterial stiffness? Aims: 1. Evaluate a PPG-based arterial stiffness estimation method using a smart ring. 2. Assess the efficacy of HIIT in reducing arterial stiffness and improving VO2max. Methods: The study comprised two parts: 1. A cross-sectional study (N=300, age 18-78) for collecting arterial stiffness data using the Sphygmocor and the Smart Ring to evaluate the PPG-based algorithm. 2. A randomized exercise intervention pilot study (N=80, a subset of the cross-sectional study), assigned to either a three-month HIIT protocol or a control group. Arterial stiffness was measured pre- and post-intervention using both devices. Pearson’s correlation coefficients assessed the agreement between the novel method and reference device, and effects of the intervention were analyzed using ANCOVA. Results: The novel PPG-based method demonstrated good correlation with the Sphygmocor device (R = 0.77 and 0.80 in pre- and post-timepoints, respectively). However, despite improving the participants’ aerobic fitness (average increase in VO2max of 1.41 ml/min/kg; 95% CI: [0.15, 2.67], P = 0.029) the HIIT intervention did not significantly improve arterial stiffness, as measured by both methods. Conclusions: The developed smart ring method provides a reliable, non-invasive, and continuous estimation of arterial stiffness. This innovative approach could significantly impact how arterial stiffness is monitored in an everyday setting. The null results of the HIIT intervention suggest that HIIT may not be effective in reducing arterial stiffness, highlighting the need for further research into different exercise modalities for cardiovascular health improvement. This study underscores the potential of wearable technology in cardiovascular health monitoring and the importance of evaluating diverse exercise protocols for arterial health.

Compliant Grasp Control Method for the Underactuated Prosthetic Hand Based on the Estimation of Grasping Force and Muscle Stiffness with sEMG.

Human muscles can generate force and stiffness during contraction. When in contact with objects, human hands can achieve compliant grasping by adjusting the grasping force and the muscle stiffness based on the object's characteristics. To realize humanoid-compliant grasping, most prosthetic hands obtain the stiffness parameter of the compliant controller according to the environmental stiffness, which may be inconsistent with the amputee's intention. To address this issue, this paper proposes a compliant grasp control method for an underactuated prosthetic hand that can directly obtain the control signals for compliant grasping from surface electromyography (sEMG) signals. First, an estimation method of the grasping force is established based on the Huxley muscle model. Then, muscle stiffness is estimated based on the muscle contraction principle. Subsequently, a relationship between the muscle stiffness of the human hand and the stiffness parameters of the prosthetic hand controller is established based on fuzzy logic to realize compliant grasp control for the underactuated prosthetic hand. Experimental results indicate that the prosthetic hand can adjust the desired force and stiffness parameters of the impedance controller based on sEMG, achieving a quick and stable grasp as well as a slow and gentle grasp on different objects.

A 3D fast MR elastography sequence with interleaved multislab acquisition and Hadamard encoding.

To enhance the functional capability of MRI, this study aims to develop a novel MR elastography (MRE) sequence that achieves rapid acquisition without distortion artifacts. A displacement-encoded stimulated echo (DENSE) with multiphase acquisition scheme was used to capture wave images. A center-out golden-angle stack-of-stars sampling pattern was introduced for improved SNR and data incoherence. A combination of Hadamard encoding and interleaved multislab acquisition schemes was used to increase the acquisition efficiency of MRE data with multiple directions and phase offsets. A generalized parallel-imaging and compressed-sensing method was further applied to accelerate the acquisition process. The imaging results of the proposed sequence were compared with those from six gradient echo (GRE)/EPI/DENSE-based MRE sequences via phantom and brain acquisitions. The proposed sequence achieved a 6-fold acceleration compared with GRE MRE. With the application of a conventional parallel-imaging and compressed-sensing algorithm, the scanning speed was further accelerated by 8-fold, matching the speed of EPI-based MRE. Phantom tests revealed small variances in stiffness measurements across the seven sequences (< 9.23%). The proposed sequence exhibited a higher contrast-to-noise ratio (1.38) than the two EPI-based sequences (0.61/0.76) and similar to GRE-based sequences (1.34/1.22/1.58). Brain imaging validated the effectiveness of the proposed sequence in accurate stiffness estimation and distortion artifact avoidance. A rapid DENSE-based MRE sequence with interleaved multislab acquisition and Hadamard encoding was developed at a speed matching EPI-based sequences, without compromising SNR or introducing distortion artifacts.

Remote Monitoring of Sympathovagal Imbalance During Sleep and Its Implications in Cardiovascular Risk Assessment: A Systematic Review.

Nocturnal sympathetic overdrive is an early indicator of cardiovascular (CV) disease, emphasizing the importance of reliable remote patient monitoring (RPM) for autonomic function during sleep. To be effective, RPM systems must be accurate, non-intrusive, and cost-effective. This review evaluates non-invasive technologies, metrics, and algorithms for tracking nocturnal autonomic nervous system (ANS) activity, assessing their CV relevance and feasibility for integration into RPM systems. A systematic search identified 18 relevant studies from an initial pool of 169 publications, with data extracted on study design, population characteristics, technology types, and CV implications. Modalities reviewed include electrodes (e.g., electroencephalography (EEG), electrocardiography (ECG), polysomnography (PSG)), optical sensors (e.g., photoplethysmography (PPG), peripheral arterial tone (PAT)), ballistocardiography (BCG), cameras, radars, and accelerometers. Heart rate variability (HRV) and blood pressure (BP) emerged as the most promising metrics for RPM, offering a comprehensive view of ANS function and vascular health during sleep. While electrodes provide precise HRV data, they remain intrusive, whereas optical sensors such as PPG demonstrate potential for multimodal monitoring, including HRV, SpO2, and estimates of arterial stiffness and BP. Non-intrusive methods like BCG and cameras are promising for heart and respiratory rate estimation, but less suitable for continuous HRV monitoring. In conclusion, HRV and BP are the most viable metrics for RPM, with PPG-based systems offering significant promise for non-intrusive, continuous monitoring of multiple modalities. Further research is needed to enhance accuracy, feasibility, and validation against direct measures of autonomic function, such as microneurography.