bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.05.08.652806

Modeling glenohumeral stability in musculoskeletal simulations: A validation study with in vivo contact forces

Abstract

Common optimization approaches to solve the muscle redundancy problem in musculoskeletal simulations can predict shoulder contact forces that violate joint stability with lines of action outside the glenohumeral joint border. Approaches with simple joint stability constraints were previously introduced imposing an upper limit on the direction of the contact force to stay within a specified stability perimeter. Such approaches predicted higher rotator cuff muscle activation than without constraints, but the estimated joint contact forces were oriented along the specified perimeter, raising questions about validity. In this study, several glenohumeral stability formulations were investigated, and tested against in vivo measurements of glenohumeral contact forces from the Orthoload dataset on one participant data in three dumbbell tasks: lateral raise, posterior raise, and and anterior raise. The investigated formulations either imposed inequality constraints on the contact force direction to remain within a stability perimeter whose shape was varied, or added a penalty term as a criterion measure to the objective function that made the objective function costly for contact force directions to deviate from the glenoid cavity center. All stability formulations predicted contact force magnitudes that agreed relatively well to the in vivo measured forces except for the strictest formulation that constrained the joint contact force to be directed at the glenoid cavity center. Models that restricted the force direction to lie within a specified shape estimated force vectors that largely lay along the perimeters. Models that instead penalized force directions that deviated from the glenoid cavity center estimated relatively more accurate contact force directions within the glenoid cavity, though still not entirely in agreement with in vivo measurements. Our findings support the proposed penalty formulations as more reasonable and accurate than other investigated existing glenohumeral stability formulations. Author summaryIn musculoskeletal models, the glenohumeral joint is often simplified as a purely rotational joint with no translation, whereas the actual joint movement involves both rotation and some translation, requiring stabilizing forces to prevent dislocation. Models that compute muscle forces based on a minimal effort strategy without specifically addressing glenohumeral stability may underestimate co-activation of the stabilizing rotator cuff muscles, and inaccurately predict that the contact force between the humerus and the glenoid is directed outside the articular surface of the joint. In this study, we compared existing stability models and proposed a new approach that penalized joint contact force whose direction deviates from the glenoid cavity center. We integrated these approaches into a muscle redundancy solver and estimated muscle and joint forces using the thoracoscapular shoulder musculoskeletal model. We compared model estimates to in vivo measurements of joint contact forces. We found that the penalty formulation reproduced the contact force direction most accurately, and promoted greater muscle co-contraction. These findings support the proposed penalty approach as a benchmark for more accurate analysis of shoulder biomechanics using musculoskeletal simulations.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hasan, I. M. I., Belli, I., Seth, A., Gutierrez-Farewik, E. M.. 2025-05-12. Modeling glenohumeral stability in musculoskeletal simulations: A validation study with in vivo contact forces. https://doi.org/10.1101/2025.05.08.652806

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Dynamic Compression Platform for Live Imaging of Scaffold-Transmitted Cellular Mechanoresponses

Mechanical characterization of biomaterial scaffolds is essential to evaluate their capacity to meet the functional demands of target tissues in tissue engineering and regenerative medicine applications. Scaffolds designed to interface with living tissues must support the transmission of mechanical cues to resident cells and stimulate mechanosignaling pathways that are essential to their function. In joints, bone and cartilage cells act as primary mechanosensors, converting mechanical stimuli into biochemical signals that regulate tissue homeostasis and remodelling. Therefore, evaluating cellular mechanoresponses to scaffold-transmitted compression in vitro can inform the development of functional tissue-engineered constructs. For example, poly({epsilon}-caprolactone) (PCL) scaffolds are highly relevant for bone and cartilage tissue engineering due to their biocompatibility, stable mechanical properties and slow degradation. Here, we applied a custom-built device to study compression-induced mechanosignaling in MC3T3-E1 pre-osteoblast cells. The device is composed of a polydimethylsiloxane (PDMS) pillar, a force-sensing load cell, and a piezoelectric linear track. A protocol is described in which MC3T3-E1 cells are repeatedly compressed, while in parallel live tracking of force measurements and live imaging of intracellular calcium dynamics in MC3T3-E1 cells are recorded. PCL scaffolds fabricated by melt electrowriting (MEW) were subsequently integrated into the platform. Scaffold-transmitted compression triggered dynamic increases in cytosolic calcium; in MC3T3-E1 cells located directly under the PCL microfibers, but also in cells located in the interfiber spaces. This device and workflow facilitate in vitro investigations of real-time cellular mechanoresponses to dynamic compression applied with biomaterial scaffolds, and provides a testing platform for evaluating the mechanotransductive properties of scaffolds intended for tissue engineering applications.

bioengineering↗

Ultrasound Tracking Reveals Progressive Regional Strain Differences in Human Achilles Tendons During Fatigue Loading

Ultrasound is commonly used to assess structural changes in symptomatic Achilles tendons, but quantitative biomechanical metrics for progressive tendon deterioration remain limited. The goal of this study was to develop and validate an automated ultrasound tracking algorithm for regional tendon deformation and evaluate strain progression in survived and ruptured tendons during fatigue loading. We hypothesized that maximum strain, average strain, and strain heterogeneity would exhibit different trajectories between groups. Ten cadaveric Achilles tendons underwent cyclic loading with stress tests every 500 cycles until rupture or 150,000 cycles. Ultrasound images acquired during stress tests were analyzed using an automated tracking algorithm to generate spatially resolved regional strain fields. Ultrasound-derived bulk strain was highly correlated with actuator-derived strain in survived (R^2 = 0.968 +/- 0.017) and ruptured tendons (R^2 = 0.972 +/- 0.014). Maximum and average longitudinal strains progressively diverged between groups across fatigue life (Group x FatigueLife: p = 0.003 and p < 0.0001, respectively). During the first 10,000 cycles, average strain decreased in survived tendons ({beta} = -0.0268%, p = 0.0215) but not ruptured tendons ({beta} = 0.0147%, p = 0.1197), with a significant Group x Cycle interaction (p = 0.0061). This study demonstrates that the algorithm quantified Achilles tendon deformation with high fidelity and enabled spatially resolved strain assessment throughout fatigue loading. Maximum and average strain followed different trajectories between groups, whereas strain heterogeneity did not. Early differences in tendon biomechanics suggest that regional strain behavior may change before pronounced differences in absolute magnitude develop.

bioengineering↗

Brain organoid computing for robotic decision-making

Biomimicry has inspired the evolution of robotics toward greater autonomy, adaptability, and symbiosis with humans and dynamic environments. However, current robotic systems still face major challenges in recapitulating the high-efficiency decision-making capabilities of the human brain under complex and dynamic conditions. Here, we present Brainobot, a biohybrid robotic system that establishes a brain organoid controller as a high-level robotic decision-making layer for closed-loop embodiment. By leveraging brain organoid reservoir computing, Brainobot interacts with dynamic environments by receiving and processing sensory inputs and generating motor actions. As a proof-of-concept demonstration, Brainobot is implemented in a humanoid robotic system to perform real-world tasks, including object grasping and laser chasing. Interestingly, Brainobot exhibits unique features, including cross-task adaptivity, high computing efficiency, and low energy consumption. Thus, our approach may provide insights for advancing robotic embodiment and understanding biological decision-making.

bioengineering↗