Journal of Biomechanical Engineering-Transactions of the Asme最新文献

The majority of manual wheelchair users (MWCU) develop shoulder pain or injuries, which is often caused by impingement. Because propulsion mechanics are influenced by the recovery hand pattern used, the pattern may affect shoulder loading and susceptibility to injury. Shoulder muscle weakness is also correlated with shoulder pain, but how shoulder loading changes with specific muscle group weakness is unknown. Musculoskeletal modeling and simulation were used to compare glenohumeral joint contact forces (GJCFs) across hand patterns and determine how GJCFs vary when primary shoulder muscle groups are weakened. Experimental data were analyzed to classify individuals into four hand pattern groups. A representative musculoskeletal model was then developed for each group and simulations generated to portray baseline strength and six muscle weakness conditions. Three-dimensional GJCF peaks and impulses were compared across hand patterns and muscle weakness conditions. The semicircular pattern consistently had lower shear (anterior-posterior and superior-inferior) GJCFs compared to other patterns. The double-loop pattern had the highest superior GJCFs, while the single-loop pattern had the highest anterior and posterior GJCFs. These results suggest that using the semicircular pattern may be less susceptible to shoulder injuries such as subacromial impingement. Weakening the internal rotators and external rotators resulted in the greatest increases in shear GJCFs and decreases in compressive GJCF, likely due to decreased force from rotator cuff muscles. These findings suggest that strengthening specific muscle groups, especially the rotator cuff, is critical for decreasing the risk of shoulder overuse injuries.

This paper presents an innovative approach to the design optimization of valved holding chambers (VHCs), crucial devices for aerosol drug delivery. We present the design of an optimal cylindrical VHC body and introduce a novel valve based on particle impaction theory. The research combines computational simulations and physical experiments to assess the performance of various VHCs, with a special focus on the deposition patterns of medication particles within these devices. The methodology incorporates both experimental and simulation approaches to validate the reliability of the simulation. Emphasis is placed on the deposition patterns observed on the VHC walls and the classification of fine and large particles for salbutamol sulfate particles. The study reveals the superior efficacy of our valve design in separating particles compared to commercially available VHCs. In standard conditions, our valve design allows over 95% of particles under 7 μm to pass through while effectively filtering those larger than 8 μm. The optimized body design accomplishes a 60% particle mass flow fraction at the outlet and an average particle size reduction of 58.5%. When compared numerically in terms of size reduction, the optimal design outperforms the two commercially available VHCs selected. This study provides valuable insights into the optimization of VHC design, offering significant potential for improved aerosol drug delivery. Our findings demonstrate a new path forward for future studies, aiming to further optimize the design and performance of VHCs for enhanced pulmonary drug delivery.

The human body represents a collection of interacting systems that range in scale from nanometers to meters. Investigations from a systems perspective focus on how the parts work together to enact changes across spatial scales, and further our understanding of how systems function and fail. Here, we highlight systems approaches presented at the 2022 Summer Biomechanics, Bio-engineering, and Biotransport Conference in the areas of solid mechanics; fluid mechanics; tissue and cellular engineering; biotransport; and design, dynamics, and rehabilitation; and biomechanics education. Systems approaches are yielding new insights into human biology by leveraging state-of-the-art tools, which could ultimately lead to more informed design of therapies and medical devices for preventing and treating disease as well as rehabilitating patients using strategies that are uniquely optimized for each patient. Educational approaches can also be designed to foster a foundation of systems-level thinking.

Shoe manufacturing technology is advancing faster than new shoe designs can viably be evaluated in human subject trials. To aid in the design process, this paper presents a model for estimating how new shoe properties will affect runner performance. This model assumes runners choose their gaits to optimize an intrinsic, unknown objective function. To learn this objective function, a simple two-dimensional mechanical model of runners was used to predict their gaits under different objectives, and the resulting gaits were compared to data from real running trials. The most realistic model gaits, i.e., the ones that best matched the data, were obtained when the model runners minimized the impulse they experience from the ground as well as the mechanical work done by their leg muscles. Using this objective function, the gait and thus performance of running under different shoe conditions can be predicted. The simple model is sufficiently sensitive to predict the difference in performance of shoes with disruptive designs but cannot distinguish between existing shoes whose properties are fairly similar. This model therefore is a viable tool for coarse-grain exploration of the design space and identifying promising behaviors of truly novel shoe materials and designs.

Current in vitro emissions and exposure systems lack biomimicry, use unrealistic flow conditions, produce unrealistic dose, and provide inaccurate biomechanical cues to cell cultures, limiting ability to correlate in vitro outcomes with in vivo health effects. A biomimetic in vitro system capable of puffing aerosol and clean air inhalation may empower researchers to investigate complex questions related to lung injury and disease. A biomimetic aerosol exposure system (BAES), including an electronic cigarette adapter, oral cavity module (OCM), and bifurcated exposure chamber (BEC) was designed and manufactured. The fraction of aerosol deposited in transit to a filter pad or lost as volatiles was 0.116±0.021 in a traditional emissions setup versus 0.098 ± 0.015 with the adapter. The observed flowrate was within 5% of programed flowrate for puffing (25 mL/s), puff-associated respiration (450 mL/s), and tidal inhalation (350 mL/s). The maximum flowrate observed in the fabricated BAES was 450 mL/s, exceeding the lower target nominal wall shear stress of 0.025 Pa upstream of the bifurcation and fell below the target of 0.02 Pa downstream. This in vitro system addresses several gaps observed in commercially available systems and may be used to study many inhaled aerosols. The current work illustrates how in silico models may be used to correlate results of an in vitro study to in vivo conditions, rather than attempting to design an in vitro system that performs exactly as the human respiratory tract.

X-rays are widely used in mammography and radiotherapy of breast cancer. The research has focused on the effects of X-rays on cells in breast tissues, instead of the tissues' nonliving material, extracellular matrix. It is unclear what the influence of X-ray irradiation is on the matrix's mechanical cues, known to regulate malignant cancer-cell behaviors. Here, we developed a technique based on magnetic microrheology that can quantify the influence of X-ray irradiation on matrix viscoelasticity--or (solid-like) elastic and (liquid-like) viscous characteristics--at cell-size scales. To model breast-tissue extracellular matrix, we used the primary component of the tissue matrix, collagen type 1, as it is for control, and as irradiated by X-rays (tube voltage 50 kV). We used a magnetic microrheometer to measure collagen matrices using 10-μm-diameter magnetic probes. In each matrix, the probes were nanomanipulated using controlled magnetic forces by the microrheometer while the probes' displacements were detected to measure the viscoelasticity. The collagen-matrix data involve with a typical spatial variation in viscoelasticity. We find that higher irradiation doses (320 Gy) locally reduce stiffness (soften) collagen matrices and increase their loss tangent, indicating an elevated liquid-like nature. For lower, clinically relevant irradiation doses (54 Gy), we find insignificant matrix-viscoelasticity changes. We provide this irradiation-related technique for detection, and modification, of matrix viscoelastic cues at cell-size scales. The technique enables enhanced characterization of irradiated tissue constituents in a variety of breast-cancer radiotherapy types.

This paper describes the development and evaluation of a novel, threshold-based gait event detection algorithm utilizing only one thigh inertial measurement unit (IMU) and unilateral, sagittal plane hip and knee joint angles. The algorithm was designed to detect heel strike (HS) and toe off (TO) gait events, with the eventual goal of detection in a real-time exoskeletal control system. The data used in the development and evaluation of the algorithm were obtained from two gait databases, each containing synchronized IMU and ground reaction force (GRF) data. All database subjects were healthy individuals walking in either a level-ground, urban environment or a treadmill lab environment. Inertial measurements used were three-dimensional thigh accelerations and three-dimensional thigh angular velocities. Parameters for the TO algorithm were identified on a per-subject basis. The GRF data were utilized to validate the algorithm's timing accuracy and quantify the fidelity of the algorithm, measured by the F1-Score. Across all participants, the algorithm reported a mean timing error of -41±20 ms with an F1-Score of 0.988 for HS. For TO, the algorithm reported a mean timing error of -1.4±21 ms with an F1-Score of 0.991. The results of this evaluation suggest that this algorithm is a promising solution to inertial based gait event detection; however, further refinement and real-time evaluation are required for use in exoskeletal control.

The rationality of gait analysis directly affects the dynamics of human lower limbs in the sagittal plane, and recent studies on gait stage redivision lack the stage when both feet are not in complete contact with the ground. This paper proposes a novel variable pivot gait, which includes the stage when the heel of one foot and the toe of the other are in contact with the ground and a dynamics correction method based on this gait. First, the relative motion data between the foot and the ground are measured by motion capture experiments, and then a variable pivot gait is proposed in terms of the pivot transformation between the foot and the ground. Second, the dynamics modeling is conducted based on the principle of mechanisms of human lower limbs in each stage of the variable pivot gait. Third, a dynamics correction method is proposed to correct the foot dynamics when the foot is not in complete contact with the ground. The experiment and simulation show that the variable pivot gait is consistent with the actual motion of the foot relative to the ground. The effectiveness of the dynamics correction method is proved by comparing dynamics results (hip, knee, and ankle moments) with previous studies. The variable pivot gait and the dynamics correction method can be applied to the human lower limbs and lower-limb robots, providing a new avenue.

The maximum actuation joint torques that operators can perform at the workplace are essential parameters for biomechanical risk assessment. However, workstation designers generally only have at their disposal the imprecise and sparse estimates of these quantities provided with digital manikin digital human model (DHM) software. For instance, such tools consider only static postures and ignore important specificities of the human musculoskeletal system such as interjoints couplings. To alleviate the weaknesses of existing approaches implemented in digital human modeling tools relying on torque databases, this paper describes a methodology based on a class of polytopes called zonotopes and musculoskeletal simulation to assess maximum actuation torques. It has two main advantages, the ability to estimate maximum joint torques for any posture and taking into account musculoskeletal specificities unlike existing digital human modeling tools. As a case study, it also compares simulated maximum actuation torques to those recorded during an experiment described in the literature, focusing on an isometric task of the upper limb. This simulation has led to similar or smaller errors than DHM software tools. Hence, this methodology may help in interpreting interjoint couplings, choosing appropriate mathematical models or design experimental protocols. It may also be implemented in DHM software to provide designers with more comprehensive and more reliable data.

The stress-strain curve of biological soft tissues helps characterize their mechanical behavior. The yield point on this curve is when a specimen breaches its elastic range due to irreversible microstructural damage. The yield point is easily found using the offset yield method in traditional engineering materials. However, correctly identifying the yield point in soft tissues can be subjective due to its nonlinear material behavior. The typical method for yield point identification is visual inspection, which is investigator-dependent and does not lend itself to automation of the analysis pipeline. An automated algorithm to identify the yield point objectively assesses soft tissues' biomechanical properties. This study aimed to analyze data from uniaxial extension testing on biological soft tissue specimens and create a machine learning (ML) model to determine a tissue sample's yield point. We present a trained machine learning model from 279 uniaxial extension curves from testing aneurysmal/nonaneurysmal and longitudinal/circumferential oriented tissue specimens that multiple experts labeled through an adjudication process. The ML model showed a median error of 5% in its estimated yield stress compared to the expert picks. The study found that an ML model could accurately identify the yield point (as defined) in various aortic tissues. Future studies will be performed to validate this approach by visually inspecting when damage occurs and adjusting the model using the ML-based approach.