Body Joints Research Articles

Heel riser height influence on kinematics and muscle activity of ski mountaineering – A field based study

Introduction The ski binding plays an important role in ski mountaineering. When traveling uphill, the binding has an adjustable heel height known as the riser. Previous laboratory research reported joint kinematics and kinetics are influenced by riser height, however little is known about changes to muscle activity associated with differing joint motion. The purpose of this work was to assess riser height influence on kinematics and muscle activity at different slopes during on-snow skiing. Methods Three female and nine male recreational ski mountaineers (19-26 y) were tested on 5o and 16o gradients using no riser (0 cm) and riser (5.3 cm) at a submaximal 80% HRmax. Each subject used Backland 85 UL skis and Backland Bindings (Atomic Skis, Altenmarkt, Austria). Subjects skied for 6 min at each binding setting with the last 10 gait-cycles evaluating lower limb joint motion gathered from 2D-sagittal plane motion capture. Electromyography (EMG) collected unilaterally on the rectus femoris, biceps femoris, medial gastrocnemius and triceps brachii also. Results 5o slope: hip range of motion (ROM) decreased (p = .003), ankle ROM decreased (p = .005), stride length decreased (p = .004), rating of perceived exertion (RPE) increased (p = .02) for riser compared to no riser. At 16o slope: hip ROM decreased (p = .001), and RPE decreased (p = .004) for riser compared to no riser. HR, glide distance, velocity, EMG, and net mechanical efficiency were not different between riser heights on either slope. Discussion/Conclusion Lower body joint kinematics, step length and RPE varied significantly with riser height. Kinematic differences did not impact velocity or muscle activity when controlling pace. These results agree with previous findings showing minimal differences in EMG and HR while lower body kinematics and RPE changed with riser height.

EPIC: Emotion Perception by Spatio-Temporal Interaction Context of Gait.

Recently, psychophysiological computing has received considerable attention. Due to easy acquisition at a distance and less conscious initiation, gait-based emotion recognition is considered as a valuable research branch in the field of psychophysiological computing. However, most existing methods rarely explore the spatio-temporal context of gait, which limits the ability to capture the higher-order relationship between emotion and gait. In this paper, we utilize a range of research, including psychophysiological computing and artificial intelligence, to propose an integrated emotion perception framework called EPIC, which can find novel joint topology and generate thousands of synthetic gaits by spatio-temporal interaction context. First, we analyze the joint coupling among non-adjacent joints by calculating Phase Lag Index (PLI), which can discover the latent connection among body joints. Second, to synthesize more sophisticated and accurate gait sequences, we explore the effect of spatio-temporal constraints, and propose a new loss function that utilizes the Dynamic Time Warping (DTW) algorithm and pseudo-velocity curve to constrain the output of Gated Recurrent Units (GRU). Finally, Spatial Temporal Graph Convolution Networks (ST-GCN) is used to classify emotions using the generation and the real data. Experimental results demonstrate our approach achieves the accuracy of 89.66%, and outperforms the state-of-the-art methods on Emotion-Gait dataset.

A wearable flexible triboelectric nanogenerator for bio-mechanical energy harvesting and badminton monitoring

Recently, textile materials used for wearable flexible sensors have received much attention. Wearable textile based triboelectric nanogenerator (TENG) not only has unique advantages in mechanical energy harvesting, but also has application value in the direction of motion sensing. Here, we proposed a non-woven fabric triboelectric nanogenerator (NW-TENG) for mechanical energy harvesting and badminton monitoring. The non-woven fabric play the role of positive triboelectric, and the fluffy fiber structure endows NW-TENG with a sensitive response to pressure. The pressure sensing sensitivity of NW-TENG sensor can reach 1.22 V N−1 (Pressure range: 0–7 N) and 0.18 V N−1 (Pressure range: 8 N–55 N). Furthermore, the NW-TENG can be installed on the body joints of badminton players for analyzing joint movements, thereby achieving data-driven badminton training and facilitating the evaluation of training effectiveness. This research provide a new path to promote TENG to the badminton monitoring field.

Multi-hop graph transformer network for 3D human pose estimation

Accurate 3D human pose estimation is a challenging task due to occlusion and depth ambiguity. In this paper, we introduce a multi-hop graph transformer network designed for 2D-to-3D human pose estimation in videos by leveraging the strengths of multi-head self-attention and multi-hop graph convolutional networks with disentangled neighborhoods to capture spatio-temporal dependencies and handle long-range interactions. The proposed network architecture consists of a graph attention block composed of stacked layers of multi-head self-attention and graph convolution with learnable adjacency matrix, and a multi-hop graph convolutional block comprised of multi-hop convolutional and dilated convolutional layers. The combination of multi-head self-attention and multi-hop graph convolutional layers enables the model to capture both local and global dependencies, while the integration of dilated convolutional layers enhances the model’s ability to handle spatial details required for accurate localization of the human body joints. Extensive experiments demonstrate the effectiveness and generalization ability of our model, achieving competitive performance on benchmark datasets.

Jump Performance and Its Relationship with Lower Body Joint Kinetics and Kinematics in Children with Cerebral Palsy.

The aim was to quantify jump performance in children with cerebral palsy (CP) and determine if the expected deficit is related to their lower body joint kinetics and kinematics. Twenty-four ambulatory ( n = 17 level I and 7 level II in the Gross Motor Function Classification System) children with spastic CP ( n = 13 unilateral and 11 bilateral) and 24 age-, sex-, and race-matched typically developing controls were studied. Jump height and peak power and range of motion at the hip, knee, and ankle of the more affected limb in children with CP and the nondominant limb in controls were assessed during a countermovement jump using three-dimensional motion capture and a force platform. Compared with controls, children with CP had lower jump height (33%, Cohen's d ( d ) = 1.217), peak power at the knee (39%, d = 1.013) and ankle (46%, d = 1.687), and range of motion at the hip (32%, d = 1.180), knee (39%, d = 2.067), and ankle (46%, d = 3.195; all P < 0.001). Jump height was positively related to hip, knee, and ankle power and range of motion in children with CP ( rs range = 0.474-0.613, P < 0.05), and hip and ankle power and knee and ankle range of motion in controls ( rs range = 0.458-0.630, P < 0.05). The group difference in jump height was no longer detected when ankle joint power, ankle range of motion, or knee range of motion was statistically controlled ( P > 0.15). Jump performance is compromised in children with CP and is associated with low power generation and range of motion in the lower limb, especially at the ankle.

Every "Body" Gets a Say: An Augmented Optimization Metric to Preserve Body Pose during Avatar Adaptation in Mixed/Augmented Reality.

User-Avatar interaction within augmented reality applications is rapidly increasing in frequency. Applications routinely place users in rooms with other, remote users embodied by photorealistic avatars, or require users to work with an avatar of a remote user to complete a task. During these types of interactions, it is often required to modify or redirect the posture of an avatar to achieve goals such as contact with or pointing at an object or maintaining eye gaze with the local user. A key limitation of modern redirection techniques is successfully preserving body posture, a critical component of nonverbal communication. This paper presents a new pose-preserving objective function to be used in the multi-objective optimization of an avatar's kinematic configuration. This objective function not only mimics the correct placement of body joints, but also preserves their orientation in space. We have tested this approach against several commonly used and current state-of-the-art redirection techniques and have found that our new approach achieves a significant reduction in targeted redirection error while simultaneously reducing body posture error. Additionally, human subject testing has shown that our new technique provides both a significantly more natural looking redirection and a significantly more realistic and believable overall body posture.

High power triboelectric nanogenerator based on nanofibers of silk protein and PVBVA and its motion sensing applications

Rapid expansion of the Internet of Things (IoT)-based healthcare and biomedical research has surged the demand for versatile electronics with seamless human–machine interfaces. However, chemical batteries hinder interfacing with the human body, and this limitation can be mitigated by using self-powered devices. Here, we introduce a novel strategy to develop a high-power and protein-based triboelectric nanogenerator (TENG) suitable for self-powered human–machine interfacial applications in daily life. The TENG consists of electrospun PEO-Silk nanofibers (PEO-Silk-NFs) and poly(vinyl butyral-co-vinyl alcohol-co-vinyl acetate) nanofibers (PVBVA-NFs). Owing to large surface areas and electrical affinity differences between the two NFs, a high TENG performance with an open-circuit voltage (Voc) of ∼ 2.1 kV, a short-circuit current (Isc) of ∼ 6.5 µA, and an impressive power density of ∼ 196 mW/cm2 are obtained. The PVBVA-NF/PEO-Silk-NF TENG is highly sensitive to impact forces and thus allows effective and continuous monitoring of body joint movements. Additionally, the PVBVA-NF/PEO-Silk-NF TENG is water-resistant, completely recyclable, and wearable, which make it an excellent option for future IoT applications. Our research paves the way for high-voltage output and motion-sensing solutions for the next phase of wearable electronics based on self-powered and sustainable technology.

Does IMU redundancy improve multi-body optimization results to obtain lower-body kinematics? A preliminary study says no

Inertial Measurement Units (IMUs) have been proposed as an ecological alternative to optoelectronic systems for obtaining human body joint kinematics. Tremendous work has been done to reduce differences between kinematics obtained with IMUs and optoelectronic systems, by improving sensor-to-segment calibration, fusion algorithms, and by using Multibody Kinematics Optimization (MKO). However, these improvements seem to reach a barrier, particularly on transverse and frontal planes. Inspired by marker-based MKO approach performed via OpenSim, this study proposes to test whether IMU redundancy with MKO could improve lower-limb kinematics obtained from IMUs. For this study, five subjects were equipped with 11 IMUs and 30 reflective markers tracked by 18 optoelectronic cameras. They then performed gait, cycling, and running actions. Four different lower-limb kinematics were computed: one kinematics based on markers after MKO, one kinematics based on IMUs without MKO, and two based on IMUs after MKO performed with OpenSense (one with, and one without, sensor redundancy). Kinematics were compared via Root Mean Square Difference and correlation coefficients to kinematics based on markers after MKO. Results showed that redundancy does not reduce differences with the kinematics based on markers after MKO on frontal and transverse planes comparatively to classic IMU MKO. Sensor redundancy does not seem to impact lower-limb kinematics on frontal and transverse planes, due to the likelihood of the “rigid component” of soft-tissue artefact impacting all sensors located on one segment.

Considerations beyond spine pain: do different co-occurring lower body joint pains differentially influence physical function and quality of life ratings?

BackgroundPatients seeking medical care for back pain often have coexisting painful joints and the effects of different combinations and number of coexisting pain sites (hip, knee, foot/ankle) to back pain on physical function domains and quality of life rating are not yet established. The purpose of this study was to determine the differences in functional outcomes and QOL among individuals with back pain who have concurrent additional pain sites or no pain sites.MethodsData from the Osteoarthritis Initiative (OAI) cohort were used for this cross-sectional analysis. Men and women aged 45–79 years with back pain were binned into nine groups by presence or not of coexisting hip, knee, ankle/foot pain and combinations of these sites (N = 1,642). Healthy controls reported no joint pain. Main outcomes included Knee Injury and Osteoarthritis Outcome score (KOOS; quality of life and function-sports-and-recreation), Western Ontario McMaster Universities Osteoarthritis Index (WOMAC; Activities of Daily Living, Pain), Medical Outcomes Short Form-12 (SF-12) Physical Component score, and self-reported function in last 7–30 days (lifting 25-pound objects, housework). 20-m and 400-m walk times and gait speed and repeated chair rise test times were collected.ResultsCompared to back pain alone, pain at all five sites was associated with 39%—86% worse KOOS, WOMAC, and SF-12 scores (p < .0001). Back-Hip and Back-Knee did not produce worse scores than Back pain alone, but Back-Hip-Knee and Back-Knee-Ankle/Foot did. The 20-m, 400-m walk, and repeated chair times were worse among individuals with pain at all five sites. Additional hip and knee sites to back pain, but not ankle/foot, worsened performance-based walk times and chair rise scores.ConclusionsThe number and type of coexistent lower body musculoskeletal pain among patients with back pain may be associated with perceived and performance-based assessments. Management plans that efficiently simultaneously address back and additional coexistent pain sites may maximize treatment functional benefits, address patient functional goals in life and mitigate disability.

AI Trainer : Video-Based Squat Analysis

This research proposes a video-based system for analyzing human squats and providing real-time feedback to improve posture. The system leverages MediaPipe, an open-source pose estimation library, to identify key body joints during squats. By calculating crucial joint angles (knee flexion, hip flexion, ankle dorsiflexion), the system assesses squat form against established biomechanical principles. Deviations from these principles trigger real-time feedback messages or visual cues to guide users towards optimal squat posture. The paper details the system architecture, with a client-side application performing pose estimation and feedback generation. The methodology outlines data collection with various squat variations, system development integrating MediaPipe, and evaluation through user testing with comparison to expert evaluations. Key features include real-time feedback and customizable thresholds for user adaptation. Potential applications encompass fitness training, physical therapy, and sports training. Finally, the paper explores future work possibilities like mobile integration, advanced feedback mechanisms, and machine learning for automatic threshold adjustments. This research offers a valuable tool for squat analysis, empowering users to achieve their fitness goals with proper form and reduced injury risk.

3D Convolutional Neural Networks based Movement Evaluation System for Gymnasts in Computer Vision Applications

This study introduces an innovative Movement Evaluation System, which utilizes state of art 3D CNNs in grassland of computer vision. This system presents a significant advancement in analyzing and assessing complex gymnastic movements, providing a comprehensive understanding of both spatial and temporal dynamics. By incorporating pose estimation algorithms, it accurately identifies body joints and positions to extract detailed spatial features. The 3D CNN further captures the temporal evolution of these features, enabling a precise analysis of the fluidity, rhythm, and synchronization of gymnastic movements over time. The effectiveness of this system is evaluated through performance metrics such as precision, recall, and accuracy. Aimed at enhancing sports science and athlete training, this research offers coaches, judges, and gymnasts a sophisticated tool for objective and standardized movement assessments. It has the potential to streamline the evaluation process in gymnastics and provide constructive feedback efficiently. The integration of 3D CNNs in this system marks a paradigm shift in utilizing advanced computer vision techniques to improve the understanding and refinement of gymnastic performances.

Interfacing legitimacy – health and social care integration in Scotland

As people, particularly those ageing and living with disabilities, struggle with how care is enacted, integrated care has gained policy purchase in the United Kingdom. Despite integration’s apparent popularity, its contribution to improved care for people has been questioned, exposing uncertainties about its associated benefits. Nonetheless, over decades a remarkably consistent approach to integrated care has advanced partnerships between the NHS and local government. Accordingly, in 2014 the Scottish Government mandated Health and Social Care Integration (HSCI) via the Scottish Public Bodies (Joint Working) (Scotland) Act. Emerging from an interorganisational ethnography of the implementation of a Health and Social Care Partnership in 2016, in a place I call ‘Kintra’, I interrogated what happened when NHS Kintra and Kintra Council endeavoured to implement HSCI according to the precepts of ‘the Act’. Immersed in the everyday arrangements in the spaces of governance, I attended to how these policy actors worked to both (re)configure and held things together behind care frontiers, and away from the bodywork of direct care. I charted their efforts to comply with regulations, plan, and build governance apparatuses through documents. In following documents, I show the ways in which HSCI was materialised through documentation. I reveal how, in the mundane mattering of document manufacturing, possibilities for (re)forming the carescape emerged. I deploy a posthuman practice stance to show not only the way in which ‘papery’ partnerships between the NHS and local government ‘enact’ care, but also how they make worlds through a sociomaterial politics of anticipation.

Investigation of Thuja orientalis Leaves Extract's Anti-Arthritic Activity in Freund's Complete Adjuvant-Induced Arthritis in Wistar Rats

A severe auto-immune disease that destroys the body's joints and may render a person physically incapable is rheumatoid arthritis. In Ayurveda, herbs have been used to cure a wide range of illnesses. Herbal medicines are widely available and have fewer side effects. As a result, many people find herbal treatments to be intriguing. The Thuja orientalis is a valuable plant for Chinese and Indian medicine. The leaves of Thuja orientalis have long been used in traditional medicine to treat rheumatoid arthritis (RA). However, The current study investigates the anti-arthritic mechanism and phytochemical evaluations of methanolic extract of Thuja orientalis leaves. Researchers employed rats that had Freund's Complete Adjuvant (FCA)-induced arthritis to test the anti-arthritic potential of methanol extracts from Thuja orientalis. Among other things, we have examined paw edoema, body mass index, locomotor activity, haematological observations, biochemical assessment, arthritic score, radiography, etc. Rats were given 0.05ml of FCA subcutaneously to cause arthritis. Four groups of arthritic rats and one group of normal rats, each with six animals, were created. The arthritic rats in Groups IV and V got 200mg per kg and 400mg per kg of thuja orientalis extract, respectively. Diclofenac sodium (10mg per kg) was given as a routine medication to Groups III of the arthritic rats, respectively, and CMC (1ml/kg) was given to Group I of the normal animals. The disease control groups were Group II respectively. Paw volumes were measured using a plethysmometer at 1, 7, 14, 21 days. When compared to arthritis control rats, the Thuja orientalis extract (200 and 400mg/kg orally) significantly (p&lt;0.05, p&lt;0.01) decreased the size of the paws and changed the body weight in CFA rats at day 21. In statistical analysis, all data were reported as mean SEM. The mean values of the test groups and the control group were compared using one-way ANOVA and Dunnett's test. The results of the current investigation showed that a methanol extract from Thuja orientalis leaves may have anti-arthritic properties.

MiSleep: Human Sleep Posture Identification from Deep Learning Augmented Millimeter-wave Wireless Systems

In this work, we propose MiSleep , a deep learning augmented millimeter-wave (mmWave) wireless system to monitor human sleep posture by predicting the 3D location of the body joints of a person during sleep. Unlike existing vision- or wearable-based sleep monitoring systems, MiSleep is not privacy-invasive and does not require users to wear anything on their body. MiSleep leverages knowledge of human anatomical features and deep learning models to solve challenges in existing mmWave devices with low-resolution and aliased imaging and specularity in signals. MiSleep builds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since a practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction, MiSleep designs a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn and predict the posture only during the rest states. We evaluate MiSleep with real data collected from Commercial-Off-The-Shelf mmWave devices for eight volunteers of diverse ages, genders, and heights performing different sleep postures . We observe that MiSleep identifies the toss-turn events start time and duration within 1.25 s and 1.7 s of the ground truth, respectively, and predicts the 3D location of body joints with a median error of 1.3 cm only and can perform even under the blankets, with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.

DC-NAS: Divide-and-Conquer Neural Architecture Search for Multi-Modal Classification

Neural architecture search-based multi-modal classification (NAS-MMC) methods can individually obtain the optimal classifier for different multi-modal data sets in an automatic manner. However, most existing NAS-MMC methods are dramatically time consuming due to the requirement for training and evaluating enormous models. In this paper, we propose an efficient evolutionary-based NAS-MMC method called divide-and-conquer neural architecture search (DC-NAS). Specifically, the evolved population is first divided into k+1 sub-populations, and then k sub-populations of them evolve on k small-scale data sets respectively that are obtained by splitting the entire data set using the k-fold stratified sampling technique; the remaining one evolves on the entire data set. To solve the sub-optimal fusion model problem caused by the training strategy of partial data, two kinds of sub-populations that are trained using partial data and entire data exchange the learned knowledge via two special knowledge bases. With the two techniques mentioned above, DC-NAS achieves the training time reduction and classification performance improvement. Experimental results show that DC-NAS achieves the state-of-the-art results in term of classification performance, training efficiency and the number of model parameters than the compared NAS-MMC methods on three popular multi-modal tasks including multi-label movie genre classification, action recognition with RGB and body joints and dynamic hand gesture recognition.

Expressive Forecasting of 3D Whole-Body Human Motions

Human motion forecasting, with the goal of estimating future human behavior over a period of time, is a fundamental task in many real-world applications. However, existing works typically concentrate on foretelling the major joints of the human body without considering the delicate movements of the human hands. In practical applications, hand gesture plays an important role in human communication with the real world, and expresses the primary intention of human beings. In this work, we are the first to formulate whole-body human pose forecasting task, which jointly predicts future both body and gesture activities. Correspondingly, we propose a novel Encoding-Alignment-Interaction (EAI) framework that aims to predict both coarse (body joints) and fine-grained (gestures) activities collaboratively, enabling expressive and cross-facilitated forecasting of 3D whole-body human motions. Specifically, our model involves two key constituents: cross-context alignment (XCA) and cross-context interaction (XCI). Considering the heterogeneous information within the whole-body, XCA aims to align the latent features of various human components, while XCI focuses on effectively capturing the context interaction among the human components. We conduct extensive experiments on a newly-introduced large-scale benchmark and achieve state-of-the-art performance. The code is public for research purposes at https://github.com/Dingpx/EAI.

Shoulder Impingement Syndrome, A Common Shoulder Disease: A Comprehensive Review

The shoulder joint is a flexible but unstable joint in the human body. Also, it has complex structures including coracoacromial arch, synovial bursa, head of femur and so on. Based on these traits, shoulder joints have a wide range of activity, but some lesions accompany with, especially some overhand motions like throwing baseball, swimming, lifting the weight above head repetitively. Diversification of pathogenic factors are significant traits of this kind of injury. In addition, lesions of shoulder impingement syndrome (SIS) can occur in multiple locations even happening at same time. Under normal conditions, this type of disease is often delimited by rotator cuff. Because of the complexity of this injury, different tests of diagnosis can be applied, including Hawkins-Kennedy test, Neer sign, Jobe test, Painful arc of motion. For treatment options, these can be divided into conservative treatment including conservative methods and surgical treatment like arthroscopic subacromial decompression (ASD). SIS causes shoulder pain to disrupt and reduce life’s quality of patients. Based on relevant research and literature, this article is designed to offer a general review of the above content.

CDKN1A down-regulation of inflammasomes and pro-inflammatory cytokines in pyroptosis of cartilage cells

Osteoarthritis is the most common chronic degenerative disease worldwide; it mainly affects elderly people. This disease can involve nearly any joint in the human body, and the most common symptoms include joint pain and disordered articular functions. Inflammasomes (NLRP3) which are induced by nuclear factor kappa B (NF-κB) signaling and can convert interleukin-1β (IL-1β) and IL-18 into mature proinflammatory cytokines are considered a factor in low-grade inflammatory pathology. This study aimed to explore the mechanisms underlying CDKN1A-in osteoarthritis. Chondrocytes were collected and isolated from 22 patients with osteoarthritis (average age 50.22 ± 2.15) and healthy volunteers (average age 51.12 ± 2.34) were enrolled as the control group from June 2021 to June 2023. mRNA expression levels of CDKN1A, NLRP3, and cleaved-Caspase1) were detected by real-time PCR. Cell activity was calculated with CCK-8. It has been found that CDKN1A regulates DNA damage repair, which contributes to the improvement of osteoarthritis by regulating the pyroptosis of cartilage cells. However, the exact mechanistic effects are still unknown.

Human–machine interaction and implementation on the upper extremities of a humanoid robot

Estimation and tracking the various joints of the human body in a dynamic environment plays a crucial role and it is a challenging task. Based on human–machine interaction, in the current research work the authors attempted to explore the real-time positioning of a humanoid arm using a human pose estimation framework. Kinect depth sensor and media pipe framework are used to obtain the three-dimensional position information of human skeleton joints. Further, the obtained joint coordinates are used to calculate the joint angles using the inverse kinematics approach. These joint angles are helpful in controlling the movement of the neck, shoulder, and elbow of a humanoid robot by using Python-Arduino serial communication. Finally, a comparison study was conducted between the Kinect, MediaPipe, and real-time robots while obtaining the joint angles. It has been found that the obtained result from the MediaPipe framework yields a minimum standard error compared to Kinect-based joint angles.

Production-related effects on the adhesive bondline performance of structural adhesives joining dissimilar materials

Adhesive bonding is a commonly used technology for joining dissimilar materials. However, production-related effects on the performance of the bondline have to be considered for an accurate joint design. In case of an adhesive joint in an automotive multi-material body in white, these effects arise from the so-called mismatch in thermal expansion coefficients, which leads to distortions of the adhesive in an uncured state or even damage in the cured one. The distortion of the uncured adhesive in the normal direction is called the ‘viscous fingering effect’, which reduces the adhesively bonded cross section by changing the adhesive bondline's shape to thin ‘fingers’ and therefore influences the materials properties. To investigate the effect of viscous fingering on the modulus, strength and energy release rate, linear butt bonded specimens and Tapered Double Cantilever Beams (TDCB) with different elongations of the adhesive bondline in the viscous state are investigated. The results are used to parameterize a cohesive zone model (CZM) and perform numerical analysis of the TDCB specimen for validation and to build up a model of a dissimilar joint consisting of a steel hatprofile and an adhesively bonded aluminum panel subjected to thermal distortions.