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In this communication, the Standardization and Terminology Committee (STC) of the International Society of Biomechanics proposes a definition of a joint coordinate system (JCS) for the shoulder, elbow, wrist, and hand. For each joint, a standard for the local axis system in each articulating segment or bone is generated. These axes then standardize the JCS. The STC is publishing these recommendations so as to encourage their use, to stimulate feedback and discussion, and to facilitate further revisions. Adopting these standards will lead to better communication among researchers and clinicians.

A well-preserved and articulated partial foot and ankle of Australopithecus sediba, including an associated complete adult distal tibia, talus, and calcaneus, have been discovered at the Malapa site, South Africa, and reported in direct association with the female paratype Malapa Hominin 2. These fossils reveal a mosaic of primitive and derived features that are distinct from those seen in other hominins. The ankle (talocrural) joint is mostly humanlike in form and inferred function, and there is some evidence for a humanlike arch and Achilles tendon. However, Au. sediba is apelike in possessing a more gracile calcaneal body and a more robust medial malleolus than expected. These observations suggest, if present models of foot function are correct, that Au. sediba may have practiced a unique form of bipedalism and some degree of arboreality. Given the combination of features in the Au. sediba foot, as well as comparisons between Au. sediba and older hominins, homoplasy is implied in the acquisition of bipedal adaptations in the hominin foot.

The Ardipithecus ramidus hand and wrist exhibit none of the derived mechanisms that restrict motion in extant great apes and are reminiscent of those of Miocene apes, such as PROCONSUL: The capitate head is more palmar than in all other known hominoids, permitting extreme midcarpal dorsiflexion. Ar. ramidus and all later hominids lack the carpometacarpal articular and ligamentous specializations of extant apes. Manual proportions are unlike those of any extant ape. Metacarpals 2 through 5 are relatively short, lacking any morphological traits associable with knuckle-walking. Humeral and ulnar characters are primitive and like those of later hominids. The Ar. ramidus forelimb complex implies palmigrady during bridging and careful climbing and exhibits none of the adaptations to vertical climbing, forelimb suspension, and knuckle-walking that are seen in extant African apes.

To date it has been thought that shoe midsole hardness does not affect vertical impact peak forces during running. This conclusion is based partially on results from experimental data using homogeneous samples of participants that found no difference in vertical impact peaks when running in shoes with different midsole properties. However, it is currently unknown how apparent joint stiffness is affected by shoe midsole hardness. An increase in apparent joint stiffness could result in a harder landing, which should result in increased vertical impact peaks during running. The purpose of this study was to quantify the effect of shoe midsole hardness on apparent ankle and knee joint stiffness and the associated vertical ground reaction force for age and sex subgroups during heel-toe running. 93 runners (male and female) aged 16-75 years ran at 3.33 ± 0.15 m/s on a 30 m-long runway with soft, medium and hard midsole shoes. The vertical impact peak increased as the shoe midsole hardness decreased (mean(SE); soft: 1.70BW(0.03), medium: 1.64BW(0.03), hard: 1.54BW(0.03)). Similar results were found for the apparent ankle joint stiffness where apparent stiffness increased as the shoe midsole hardness decreased (soft: 2.08BWm/º x 100 (0.05), medium: 1.92 BWm/º x 100 (0.05), hard: 1.85 BWm/º x 100 (0.05)). Apparent knee joint stiffness increased for soft (1.06BWm/º x 100 (0.04)) midsole compared to the medium (0.95BWm/º x 100 (0.04)) and hard (0.96BWm/º x 100 (0.04)) midsoles for female participants. The results from this study confirm that shoe midsole hardness can have an effect on vertical impact force peaks and that this may be connected to the hardness of the landing. The results from this study may provide useful information regarding the development of cushioning guidelines for running shoes.

Skeleton-based action recognition has emerged as a promising field within computer vision, offering structured representations of human motion. While existing Graph Convolutional Network (GCN)-based approaches primarily rely on raw 3D joint coordinates, these representations fail to capture higher-order spatial and temporal dependencies critical for distinguishing fine-grained actions. In this study, we introduce novel geometric features for joints, bones, and motion streams, including multi-level spatial normalization, higher-order temporal derivatives, and bone-structure encoding through lengths, angles, and anatomical distances. These enriched features explicitly model kinematic and structural relationships, enabling the capture of subtle motion dynamics and discriminative patterns. Building on this, we propose two architectures: (i) an Enhanced Multi-Stream AGCN (EMS-AGCN) that integrates joint, bone, and motion features via a weighted fusion at the final layer, and (ii) a Multi-Branch AGCN (MB-AGCN) where features are processed in independent branches and fused adaptively at an early layer. Comprehensive experiments on the NTU-RGB+D 60 benchmark demonstrate the effectiveness of our approach: EMS-AGCN achieves 96.2% accuracy and MB-AGCN attains 95.5%, both surpassing state-of-the-art methods. These findings confirm that incorporating higher-order geometric features alongside adaptive fusion mechanisms substantially improves skeleton-based action recognition.

Computational models of musculoskeletal systems are essential tools for understanding how muscles, tendons, bones, and actuation signals generate motion. In particular, the OpenSim family of models has facilitated a wide range of studies on diverse human motions, clinical studies of gait, and even non-human locomotion. However, biological structures with many joints, such as fingers, necks, tails, and spines, have been a longstanding challenge to the OpenSim modeling community, especially because these structures comprise numerous bones and are frequently actuated by extrinsic muscles that span multiple joints—often more than three—and act through a complex network of branching tendons. Existing model building software, typically optimized for limb structures, makes it difficult to build OpenSim models that accurately reflect these intricacies. Here, we introduce ArborSim , customized software that efficiently creates musculoskeletal models of highly jointed structures and can build branched muscle-tendon architectures. We used ArborSim to construct toy models of articulated structures to determine which morphological features make a structure most sensitive to branching. By comparing the joint kinematics of models constructed with branched and parallel muscle-tendon units, we found that among various parameters—the number of tendon branches, the number of joints between branches, and the ratio of muscle fiber length to muscle tendon unit length—the number of tendon branches and the number of joints between branches are most sensitive to branching modeling method. Notably, the differences between these models showed no predictable pattern with increased complexity. As the proportion of muscle increased, the kinematic differences between branched and parallel models units also increased. Our findings suggest that stress and strain interactions between distal tendon branches and proximal tendon and muscle greatly affect the overall kinematics of a musculoskeletal system. By incorporating complex muscle-tendon branching into OpenSim models using ArborSim , we can gain deeper insight into the interactions between the axial and appendicular skeleton, model the evolution and function of diverse animal tails, and understand the mechanics of more complex motions and tasks.

Preoperative simulation of the elbow joint motion has been proposed for prosthesis alignment, ligament reconstruction or treatment of impingement-inducing osteophytes. However, its daily application remains seldom. This study proposes an algorithm to simulate the ulno-humeral joint motion in flexion/extension. Four observers placed reference points on 3D surface models of elbows. The algorithm generated five spheres representing specific joint surface: medial and lateral trochlea humeri (MT and LT), capitellum (CAP), medial and lateral trochlear notch (MED- and LAT NOTCH). Three rotational axes were defined: MT-LT, CAP-MT and MED-LAT NOTCH. A fourth axis, COMB, was computed using the average 3D distance between MT-MED NOTCH and LT-LAT NOTCH. Interobserver average distance between the reference points and the computed sphere as well as the average interobserver 3D angle between the axis were analysed. The dynamic articular congruence of the axes in relation to the MED-LAT NOTCH axis was assessed by calculating their respective 3D angle variation from 0° extension to 150° flexion. The number of patients needed to reach stable dynamic articular congruence was assessed. The computed spheres exhibit lower interobserver average translation compared to the reference points. The CAP-MT axis shows the lowest interobserver variation of 3D angle (4.8°). However, COMB axis has the lowest dynamic articular incongruency (3D angle variation of 7.4°, p < 0.001). Once a learning curve of six patients is reached, an average congruence of 4.8° can be achieved. An algorithm based on multiple articular references can reduce observer-induced inaccuracies in simulation of elbow joint motion.

ObjectiveIn the evolving landscape of medical research and radiology, effective communication of intricate ideas is imperative, with visualizations playing a crucial role. This study explores the transformative potential of ChatGPT4, a powerful Large Language Model (LLM), in automating the creation of schematics and figures for radiology research papers, specifically focusing on its implications for musculoskeletal studies.Materials and methodsDeploying ChatGPT4, the study aimed to assess the model’s ability to generate anatomical images of six large joints—shoulder, elbow, wrist, hip, knee, and ankle. Four variations of a text prompt were utilized, to generate a coronal illustration with annotations for each joint. Evaluation parameters included anatomical correctness, correctness of annotations, aesthetic nature of illustrations, usability of figures in research papers, and cost-effectiveness. Four panellists performed the assessment using a 5-point Likert Scale.ResultsOverall analysis of the 24 illustrations encompassing the six joints of interest (4 of each) revealed significant limitations in ChatGPT4’s performance. The anatomical design ranged from poor to good, all of the illustrations received a below-average rating for annotation, with the majority assessed as poor. All of them ranked below average for usability in research papers. There was good agreement between raters across all domains (ICC = 0.61).ConclusionWhile LLMs like ChatGPT4 present promising prospects for rapid figure generation, their current capabilities fall short of meeting the rigorous standards demanded by musculoskeletal radiology research. Future developments should focus on iterative refinement processes to enhance the realism of LLM-generated musculoskeletal schematics.

When reconstructing bone geometry to calculate joint kinematics, shape-model scaling can be more accurate and repeatable than linear scaling given the same anatomical landmarks. This study perturbed anatomical landmarks from optical motion capture and determined the robustness of shape-model scaling to misplaced markers compared to a traditional approach of linear scaling. We hypothesised that shape-model scaling would be less susceptible to variance in marker positions compared to linear scaling. The positions of hip joint centres and femoral/tibial segment lengths across perturbations were compared to determine each scaling method’s range of geometric variation. The standard deviation (SD) of the hip joint centre location from the shape model had a maximum of 1.4 mm, compared to 4.2 mm for linear scaling. Femoral and tibial segments displayed SD’s of 5.4 mm and 5.2 mm when shape-model scaled, compared to 9.2 mm and 9.5 mm with linear scaling, respectively, thus supporting our hypothesis. Geometric constraints within a shape model provide robustness to marker misplacement providing potential improvements in repeatability and data exchange.