Skeleton Model Research Articles

Parameters Affecting Seismic Performance of Longitudinally Connected Track Lateral Blocks on Bridges Under Low-Cycle Reciprocating Loads

In the current seismic analysis of high-speed railway track–bridge structures, the constitutive parameters of track lateral blocks (TLBs) are not uniform. A finite element model of the TLB’s main beam is established, which considers concrete plastic damage, interface bond between old and new concrete, and bond slip between anchorage steel bars (ASBs) and concrete. Quasi-static tests of nine TLBs with different numbers and ASB diameters at a 1:2 scale was carried out to verify the accuracy of the TLB finite element model in terms of the failure pattern, interface damage, hysteretic properties, and skeleton model. Based on the verified TLB finite element model, the influence of different TLB design parameters on its seismic performance parameters was studied. The results show that the established TLB finite element model is in good agreement with the test data in terms of failure pattern, interface damage, hysteretic properties, and skeleton model, which verifies the accuracy of the TLB finite element model. ASB number has the greatest influence on the TLB yield load, followed by ASB diameter, concrete strength, ASB strength, and ASB spacing. ASB diameter has the greatest influence on the TLB peak load, followed by ASB number, ASB strength, concrete strength, and ASB spacing. ASB diameter has the greatest influence on the TLB energy dissipation capacity, followed by ASB number, concrete strength, ASB strength, and ASB spacing. With the increase in concrete strength and ASB strength, TLB ductility decreases. When the ASB number is higher than eight or the diameter is higher than 12 mm and the corresponding interfacial steel bar ratio reaches 0.82%, the TLB ductility will decrease.

Motor delay image recognition based on deep learning and human skeleton model

Children with developmental delays often exhibit gross motor delay during their early childhood. Identifying such delay typically requires standardized assessments, but these assessments are susceptible to subjective interpretation. To enhance objectivity and reliability in these assessments, this study proposes a novel method utilizing an AI model for identifying gross motor delay in children. The proposed method divides the assessment of gross motor skills into two components: static balance and dynamic balance. First, the Yolo target detection model and the Mediapipe human posture detection model are employed for preprocessing to determine skeletal joints and body positioning information. Subsequently, dimensionality reduction techniques are applied to extract key features. A sliding window method is then used to extract action features identifying static balance features of the human body (e.g., standing on one foot, standing on the left foot) and dynamic balance features (e.g., lifting the heels while walking, jumping on a mat). The machine learning classifier and LSTM-CNN are then used to categorize the static and dynamic balance feature data. Experimental results demonstrate that the proposed method achieved accuracy scores of 80 % and 87 % for static and dynamic balancing skills, respectively.

Design and Implementation of Multi-mode Natural Interaction of Game Animation Characters in Mixed Reality: A Novel User Experience Method

To enhance users’ novel experiences in gaming, the design method of multi-mode natural interaction of game animation characters in mixed reality is studied. Through the process of skeleton modeling, interpolation of position and orientation, time alignment and time transformation of character posture, the game animation character in mixed reality is constructed. The multi-role interactive game animation is synthesized using a random graph and uniform network, and the edge of the depth of field image collected by the Kinect somatosensory sensor is extracted. According to the transfer model and observation model of the Kalman filter algorithm, the motion trajectory of the game animation characters is obtained and the game animation characters are tracked. The IRS recognition algorithm is used for optical multi-touch processing in mixed reality, and the natural interaction of multi-mode game animation characters in interaction–perception mode, natural interaction mode and interactive feedback mode is realized. The experimental results demonstrate that this method can achieve multi-mode natural interaction of game animation characters in mixed reality and provide users with a novel game experience.

Development of Compaction Localization in Leitha Limestone: Finite Element Modeling Based on Synchrotron X‐Ray Imaging

AbstractThe mechanical behavior and failure mode of porous rocks vary with their microstructures. The formation of compaction bands (CBs) has been captured with high precision via in situ synchrotron CT and kinematic characteristics can be attained by image analysis. However, the stress characteristics cannot be directly evaluated from images, and how porosity heterogeneity triggers local instability and leads to the formation of CBs is not yet fully understood. To address this problem, we established a finite element (FE) model of the solid skeleton of a Leitha limestone sample based on X‐ray μCT data, considering the heterogeneity of pores and plastic hardening, and reproduced the evolution of strain localization and CBs. Our results revealed that the heterogeneity of porosity has a profound influence on the formation and propagation of CBs. Precursory stresses always appear very early around the pores where compaction bands develop, and the stress state of most points in CBs is quasi‐uniaxial compression, which has significantly high maximum principal stress σ1 in a direction subparallel to the sample axis, causing yield then compaction failure. Also, using a simplified FE mesh and ignoring the fracture of particles underestimate the extreme stress and porosity reduction—these can be improved by using fine mesh and involving grain‐scale fracture mechanics. Our study proves the feasibility and reliability of the CT‐FE simulation scheme, which can be extended to investigating the stress distribution and evolution of different rock types with a spectrum of failure modes if in situ CT data of rock deformation is available.

Influence of muscle soft tissue and lower limbs on the vibration behavior of the entire spine inside the seated human body: A finite element study.

The objective of the study is to investigate the vibration behavior of the entire spine inside the human body and the influence of muscle soft tissue and lower limbs on spinal response under vertical whole-body vibration. This study conducted modal and random response analyses to simulate the modal displacements and stress of all intervertebral discs in the vertical principal mode in the skeleton, upper, and whole body. Additionally, the acceleration response of intervertebral discs under vertical random excitation was investigated. The results revealed that removing muscle soft tissue and lower limbs significantly changed the resonant frequency, modal displacement, and stress. Particularly, there was a rapid increase in vertical displacement of the lumbar spine in the skeleton model. The reason for that was due to the lack of soft tissue to provide stability, leading to significant lumbar spine bending. Under random excitation, the fore-aft acceleration of intervertebral discs in the skeleton model was considerably larger than that in the whole body, especially in the lumbar spine where it can reach up to four times higher. Conversely, the vertical response of the intervertebral discs inside the human body model was 1.4-2.4 times larger than that of the skeleton model. Muscle soft tissue contributes to the strength of the spine, reducing fore-aft response. The muscle soft tissue in the gluteal region, connected below the spine, can lower the vertical natural frequency and attenuate spinal impact. Although the lower limbs enhance spinal stability, stimulation from the feet can superimpose vibrational responses in the spine.

Titanium versus plasma electrolytic oxidation surface-modified magnesium miniplates in a forehead secondary fracture healing model in sheep

Magnesium as a biodegradable material offers promising results in recent studies of different maxillo-facial fracture models. To overcome adverse effects caused by the fast corrosion of pure magnesium in fluid surroundings, various alloys, and surface modifications are tested in animal models. In specified cases, magnesium screws already appeared for clinical use in maxillofacial surgery. The present study aims to compare the bone healing outcome in a non-load-bearing fracture scenario of the forehead in sheep when fixed with standard-sized WE43 magnesium fixation plates and screws with plasma electrolytic oxidation (PEO) surface modification in contrast to titanium osteosynthesis. Surgery was performed on 24 merino mix sheep. The plates and screws were explanted en-bloc with the surrounding tissue after four and twelve weeks. The outcome of bone healing was investigated with micro-computed tomography, histological, immunohistological, and fluorescence analysis. There was no significant difference between groups concerning the bone volume, bone volume/ total volume, and newly formed bone in volumetric and histological analysis at both times of investigation. The fluorescence analysis revealed a significantly lower signal in the magnesium group after one week, although there was no difference in the number of osteoclasts per mm2. The magnesium group had significantly fewer vessels per mm2 in the healing tissue. In conclusion, the non-inferiority of WE43-based magnesium implants with PEO surface modification was verified concerning fracture healing under non-load-bearing conditions in a defect model. Statement of significanceTitanium implants, the current gold standard of fracture fixation, can lead to adverse effects linked to the implant material and often require surgical removal. Therefore, degradable metals like the magnesium alloy WE43 with plasma electrolytic oxidation (PEO) surface modification gained interest. Yet, miniplates of this alloy with PEO surface modification have not been examined in a fracture defect model of the facial skeleton in a large animal model. This study shows, for the first time, the non-inferiority of magnesium miniplates compared to titanium miniplates. In radiological and histological analysis, bone healing was undisturbed. Magnesium miniplates can reduce the number of interventions for implant removal, thus reducing the risk for the patient and minimizing the costs.

3D imaging and flow analysis of sandstone pore structure

Abstract Micro CT technology can provide three-dimensional high-precision digital images with micrometer (or smaller) resolution, offering robust technical support for the study of the internal structure of rocks. Based on the data obtained from the CT scanning of Leopard sandstone, a series of image processes were carried out using the Avizo software to extract pore distribution. The watershed algorithm was employed to independently segment each pore, and the largest sphere algorithm was used to establish a sphere-tube model. Based on the constructed pore network model, quantitative analyses of the porosity, fractal dimensions, permeability, and other parameters of the sandstone sample were conducted. Moreover, the connectivity of the pores was analyzed using an automatic skeleton model. Results show: ① CT scanning digital core technology can not only visualize the three-dimensional connectivity and isolated pores of sandstone but also quantitatively characterize the pore structure parameters; ② The analyzed Leopard sandstone has a connected porosity of 13.76%, an isolated porosity of 0.53%, and the pore radius is mainly concentrated in the 0-70μm small pore range, occupying 76.1% of the total pore volume; ③ The lower the sample porosity, the higher the fractal dimension, indicating a more complex pore structure.

An Image-Based Interactive Training Method of an Upper Limb Rehabilitation Robot

Aimed at the problem of human–machine interaction between patients and robots in the process of using rehabilitation robots for rehabilitation training, this paper proposes a human–machine interactive control method based on an independently developed upper limb rehabilitation robot. In this method, the camera is used as a sensor, the human skeleton model is used to analyse the moving image, and the key points of the human body are extracted. Then, the three-dimensional coordinates of the key points of the human arm are extracted by depth estimation and spatial geometry, and then the real-time motion data are obtained, and the control instructions of the robot are generated from it to realise the real-time interactive control of the robot. This method can not only improve the adaptability of the system to individual patient differences, but also improve the robustness of the system, which is less affected by environmental changes. The experimental results show that this method can realise real-time control of the rehabilitation robot, and that the robot assists the patient to complete the action with high accuracy. The results show that this control method is effective and can be applied to the fields of robot control and robot-assisted rehabilitation training.

Multi-level structural optimization of thin-walled sections in steel/aluminum vehicle body skeletons

A multi-level matching transformation and optimization design method for complex sections of thin-walled structures in steel/aluminum vehicle body skeleton was proposed. The problems of lengthy body performance numerical analysis and low optimization efficiency were solved by the rapid collaborative optimization of simplified cross-sections and performance of the body skeleton. A complex cross-sections shape library of thin-walled structures in steel/aluminum body skeleton and formulas for solving section mechanical properties were established, which overcame the difficulty of translating simplified cross-sections of thin-walled structures in steel/aluminum body skeleton to actual complex sections. A precise optimization method for complex cross-sections of thin-walled structure controlled by proportional vectors was established, which solved the defect that it was difficult to precisely control the arbitrary change of complex cross-section shape, and provided a new way for lightweight optimization design of complex cross-sections. The proposed method achieved the rapid collaborative design of steel/aluminum body thin-walled structure with complex cross-sections and body skeleton performance through multi-level progressive cross-section matching transformation and optimization. A body skeleton model with actual complex cross-sections was established, and compared with the initial simplified structure, the weight reduction ratio of the body skeleton was up to 6.9 %, and the performance indicators of the body skeleton fully met the design requirements, which verified the effectiveness of the proposed method.

Algorithms to create realistic virtual asphalt mixtures

This paper presents a set of algorithms for creating realistic three-dimensional (3D) models of asphalt mixtures based on its composition. The process begins by measuring the shape of the aggregates in the asphalt mixture. Using these measurements, an algorithm creates 3D volumes that approximate the size and shape of the aggregates. These volumes are then added to a virtual mixer in the same proportions as the aggregates in the real mixture and are pressed together until the air void content of the 3D model matches that of the real asphalt. This results in realistic models of aggregate skeletons in the asphalt mixture. Next, sections are taken from the aggregate skeletons and a mortar mixture, made up of bitumen, filler and fine aggregates, is applied to the sections. Geometrical rules are used to determine which areas of the sections are covered in mortar and which are air voids. The validation of virtual aggregates and air voids has demonstrated good correlations with actual ones. The resulting models are highly realistic and can be used to predict the mechanical or physical properties of asphalt mixtures in future studies.

Augmented reality presentation system of skeleton image based on biomedical features

Aimed at limitations in the description and expression of three-dimensional (3D) physical information in two-dimentsional (2D) medical images, feature extraction and matching method based on the biomedical characteristics of skeletons is employed in this paper to map the 2D images of skeletons into a 3D digital model. Augmented reality technique is used to realize the interactive presentation of skeleton models. Main contents of this paper include: Firstly, a three-step reconstruction method is used to process the bone CT image data to obtain its three-dimensional surface model, and the corresponding 2D–3D bone library is established based on the identification index of the 2D image and the 3D model; then, a fast and accurate feature extraction and matching algorithm is developed to realize the recognition, extraction, and matching of 2D skeletal features, and determine the corresponding 3D skeleton model according to the matching result. Finally, based on the augmented reality technique, an interactive immersive presentation system is designed to achieve visual effects of the virtual human bone model superimposed and rendered in the world scenes, which improves the effectiveness of information expression and transmission, as well as the user's immersion and embodied experience.

Modeling of the Effect of Subperiosteal Hydrostatic Pressure Conductivity between Joints on Decreasing Contact Loads on Cartilage and of the Effect of Myofascial Relief in Treating Trigger Points: The Floating Skeleton Theory.

Chronic overloading of the cartilage can lead to its irreversible destruction, as observed in people with osteoarthritis. The floating skeleton model previously introduced postulates that overloading begins and progresses when a joint is isolated from the hydrostatical connection with other joints. Such a connection occurs via the interstitial fluid in subperiosteal space and allows for pressure transmission between synovial capsules modulating intra-articular pressure. In the current study, a simple experiment was performed to model an obstruction in the subperiosteal hydrostatic pressure conductivity between joints to illustrate the effect of that obstruction on loads borne by the joint. When the obstruction was removed, the load experienced by the joint was reduced as it was redistributed throughout the model structure. The experiment demonstrated that contact pressures can be redistributed when the conditions of Pascal's Law are met.

Abstract 3516: AI-derived features of 3D glandular networks are associated with likelihood of biochemical recurrence post radical prostatectomy

Abstract Introduction: Prostate cancer is the most common cancer in American men and is mainly diagnosed from two-dimensional histology sections. Recently, three-dimensional (3D) prostate histopathology has emerged as a valuable strategy to enhance the understanding of the disease by increasing microscopic sampling of specimens. This strategy enhances the microscopic examination of specimens by facilitating the analysis of the volumetric shape of cells and glands, as well as the pathway structures through entire biopsies and other morphological features. These aspects hold the potential to be linked with biochemical recurrence (BCR) outcomes. This study highlights the potential of features derived from the 3D structure of glands in samples of patients with prostate cancer for identifying patients who are at higher risk of BCR. Methods: Radical prostatectomy specimens were collected from 50 patients along with 5-year postoperative BCR follow-up from the University of Washington. A total of 118 ex vivo whole-biopsy 3D pathology images were obtained from RP by an open-top light-sheet microscopy platform. Images were converted into a synthetic CK8 immunofluorescence dataset using an image-sequence translation model. From these 3D images, a glandular structure mask was generated by a computer-vision algorithm. The 3D structure of prostate glands was modeled through a Tree-structure Extraction Algorithm for Accurate and Robust Skeletons (TEASER), which provides a simplification of the length of the gland, its shape, and its pathway. 91 features were then extracted from the glandular skeleton architecture. A 3-fold cross-validation approach was used in which patients were divided into training (D1) and test (D2) sets of 33 and 17 patients, respectively. Fisher’s score, used to select features based on their correlation to the outcome, was applied to D1 to identify the top 5 features most correlated with BCR. We then utilized these features to train a K-Nearest Neighbors classifier on D1, aimed at predicting whether a patient would experience BCR. The model’s performance was evaluated on D2 and averaged across all folds. Results: The classifier was able to accurately differentiate between BCR+ and BCR- patients, achieving an average ROCAUC of 0.73 (±0.04) in D2. Significant differences in 3D skeletal features were observed between BCR+ and BCR- groups (p<0.05). Among the five selected features, three were related to the length and diameter of the gland, with the longest glands observed in BCR- patients. The remaining two features pertained to gland curvature and tortuosity, revealing more twisted glands in BCR+ patients. Conclusion: Features derived from a skeleton model of the 3D glandular structure of biopsies showed promise in identifying patients with prostate cancer who are at a higher risk of BCR. Additional, multi-site independent validation of these findings is warranted. Citation Format: Jennifer Salguero, Hisham Abdeltawab, Kamal Hammouda, German Corredor, Sarah Hawley, Pushkar Mutha, Rohan Dhamdhere, Sebastian Medina, Tilak Pathak, Kevin Bishop, Rob Serafin, Tuomas Mirtti, Priti Lal, Lawrence True, Jonathan Liu, Anant Madabhushi. AI-derived features of 3D glandular networks are associated with likelihood of biochemical recurrence post radical prostatectomy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3516.

An automatic diagnosis model of otitis media with high accuracy rate using transfer learning.

Introduction: Chronic Suppurative Otitis Media (CSOM) and Middle Ear Cholesteatoma are two common chronic otitis media diseases that often cause confusion among physicians due to their similar location and shape in clinical CT images of the internal auditory canal. In this study, we utilized the transfer learning method combined with CT scans of the internal auditory canal to achieve accurate lesion segmentation and automatic diagnosis for patients with CSOM and middle ear cholesteatoma. Methods: We collected 1019 CT scan images and utilized the nnUnet skeleton model along with coarse grained focal segmentation labeling to pre-train on the above CT images for focal segmentation. We then fine-tuned the pre-training model for the downstream three-classification diagnosis task. Results: Our proposed algorithm model achieved a classification accuracy of 92.33% for CSOM and middle ear cholesteatoma, which is approximately 5% higher than the benchmark model. Moreover, our upstream segmentation task training resulted in a mean Intersection of Union (mIoU) of 0.569. Discussion: Our results demonstrate that using coarse-grained contour boundary labeling can significantly enhance the accuracy of downstream classification tasks. The combination of deep learning and automatic diagnosis of CSOM and internal auditory canal CT images of middle ear cholesteatoma exhibits high sensitivity and specificity.

On the modelling of the effective longitudinal diffusion in bi-continuous chromatographic beds

We report on the possibility to extend to bi-continuous packings the two models for the effective longitudinal diffusion Deff, or B-term band broadening, recently proposed for discontinuous chromatographic beds. In bi-continuous packings, like monolithic columns, solutes experience a connected end-to-end pathway in both the mobile and stationary zones, as opposed to discontinuous packings, wherein the stationary adsorptive zone is distributed over a set of isolated elements. Since it is unclear whether a densely packed bed of spherical particles should be treated as a continuous or a bi-continuous medium, this extension is also crucial to fully understand the behaviour of packed particle beds. The proposed models for the effective longitudinal diffusion Deff originate from the adoption of the Two Zone Moment Analysis (TZMA) method by which Deff can be expressed as a linear combination of two essential quantities γm and γs, referred to as effective zone-diffusion factors. In the present work we propose two analytical models for γm and γs that now cover both the discontinuous and the bi-continuous case. To validate the theory, several bi-continuous packings are investigated, including the tetrahedral skeleton model (TSM), six different Triple Periodic Minimal Surface (TPMS) monoliths and randomly packed beds of spheres. For all of these, the models provide highly accurate results for Deff over a wide range of porosities and zone retention factors k″. The comparison with literature experimental data for both monolithic silica columns and columns packed with fully porous and porous-shell particles is also presented.

LOCALIZATION METHOD OF HUMAN SKELETAL SEQUENCE MOVEMENTS IN ICE SPORTS

A method based on kernel extreme learning machine is proposed for accurate localization and recognition of human bones. In this method, the human skeleton is divided into 16 key nodes based on physiology, and the human skeleton model is formed by connecting lines between the nodes. The sequence images of human actions are taken as input and fed into the input and hidden layers of the extreme learning machine. During the learning and training process, a kernel function is used to construct a kernel matrix to complete weight allocation. The experimental study was conducted on the action sequences of figure skaters, and the results showed that our method achieved an accuracy rate of over 90% in recognizing human bones, with individual sequences achieving an accuracy rate of over 95%, which is superior to convolutional neural network (CNN) and long short-term memory (LSTM) methods.

JointTracker: Real-time inertial kinematic chain tracking with joint position estimation.

In-field human motion capture (HMC) is drawing increasing attention due to the multitude of application areas. Plenty of research is currently invested in camera-based (markerless) HMC, with the advantage of no infrastructure being required on the body, and additional context information being available from the surroundings. However, the inherent drawbacks of camera-based approaches are the limited field of view and occlusions. In contrast, inertial HMC (IHMC) does not suffer from occlusions, thus being a promising approach for capturing human motion outside the laboratory. However, one major challenge of such methods is the necessity of spatial registration. Typically, during a predefined calibration sequence, the orientation and location of each inertial sensor are registered with respect to the underlying skeleton model. This work contributes to calibration-free IHMC, as it proposes a recursive estimator for the simultaneous online estimation of all sensor poses and joint positions of a kinematic chain model like the human skeleton. The full derivation from an optimization objective is provided. The approach can directly be applied to a synchronized data stream from a body-mounted inertial sensor network. Successful evaluations are demonstrated on noisy simulated data from a three-link chain, real lower-body walking data from 25 young, healthy persons, and walking data captured from a humanoid robot. The estimated and derived quantities, global and relative sensor orientations, joint positions, and segment lengths can be exploited for human motion analysis and anthropometric measurements, as well as in the context of hybrid markerless visual-inertial HMC.

MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures

Motion tracking systems based on optical sensors typically suffer from poor lighting conditions, occlusion, limited coverage, and may raise privacy concerns. More recently, radiofrequency (RF) based approaches using commercial WiFi devices have emerged which offer low-cost ubiquitous sensing whilst preservin privacy. However, RF sensing systems typically output range-Doppler maps, time-frequency spectrograms, cross-range plots etc which cannot represent human motion intuitively and usually requires further processing. In this study, we propose MDPose, a novel framework for human skeletal motion reconstruction base on WiFi micro-Doppler signatures. MDPose provides an effective solution to represent human activity by reconstructing a skeleton model with 17 key points, which can assist with the interpretation of conventional RF sensing outputs in a more understandable way. Specifically, MDPose is implemented over three sequential stage to address a series of challenges: First, a denoising algorithm is employed to remove any unwanted noise that may affect feature extraction and enhance weak Doppler measurements. Secondly, a convolutional neural network (CNN)-recurrent neural network (RNN) architecture is applied to learn temporal spatial dependenc from clean micro-Doppler signatures and restore velocity information to key points under the supervision of the motion capture (Mocap) system. Finally, a pose optimisation mechanism based on learning optimisation vectors is employed to estimate the initial state of the skeleton and to limit additional errors. We hav conducted a comprehensive set of tests in a variety of environments using numerous subjects with a single receiver radar system to demonstrate the performance of MDPose, and report 29.4mm mean absolute error over all key points positions on several common daily activities, which has performance comparable to that of state-ofthe- art RF-based pose estimation systems.

Enhanced robot obstacle avoidance strategy: efficient distance estimation and collision avoidance for hidden robots

Human-robot interaction is crucial for the future of smart factories and new industrial systems. Safety in robotics has always been a top priority, with external sensors being studied to construct safety perception systems for robots. This paper proposes an obstacle avoidance strategy based on an efficient distance estimation method using a vision sensor to address the challenge of robot occlusion. The method fuses depth images with a predefined robot skeleton model to estimate robot pose in real time, and uses the optimized potential field model to achieve full-body collision avoidance. Comparative experiments validate the efficiency of the proposed method, which represents a significant contribution to enhancing human–robot interaction and safety in industrial settings.

Energy dissipation evaluation of butterfly-shaped steel plate shear keys under different loading protocols

This paper proposes a novel energy dissipation shear key (ED shear key) for small-to-medium-span highway bridges using butterfly-shaped steel plates for enhanced energy dissipation capacity. Reversed cyclic loading tests were conducted on two series of specimens which had different designs of structural dimensions. Four distinctive loading protocols were adopted to allow an in-depth examination of the seismic hysteretic behavior and energy dissipation capacity of the ED shear keys. The test results reveal that the ED shear keys exhibited distinctive characteristics before and after buckling of steel plates. The recorded force-displacement curves of the ED shear keys were plump before buckling and typically became pinched after buckling. The loading protocol had a significant effect on the energy dissipation of the ED shear key specimens, with an over 90% increase in energy dissipation observed under the constant-amplitude loading protocol compared to the incremental-amplitude ones. It was found from the test results that the profile of the force-displacement skeleton curves was hardly affected by the loading protocols, as a result a trilinear skeleton model is developed to characterize the force-displacement relationship of the ED shear keys. Furthermore, by decomposing the force-displacement hysteresis curve into a skeleton part and a Bauschinger part, an evaluation model is established for predicting the energy dissipation capacity of the ED shear keys. Validation against the test results indicates that the evaluation model can provide a satisfactory accuracy for predicting the energy dissipation of the ED shear keys subjected to various loading protocols.