Finite Element Modeling of the Human Wrist: A Review.

Background Understanding wrist biomechanics is important to appreciate and treat the wrist joint. Numerical methods, specifically, finite element method (FEM), have been used to overcome experimental methods' limitations. Due to the complexity of the wrist and difficulty in modeling, there is heterogeneity and lack of consistent methodology in the published studies, challenging our ability to incorporate information gleaned from the various studies. Questions/Purposes This study summarizes the use of FEM to study the wrist in the last decade. Methods We included studies published from 2012 to 2022 from databases: EBSCO, Research4Life, ScienceDirect, and Scopus. Twenty-two studies were included. Results FEM used to study wrist in general, pathology, and treatment include diverse topics and are difficult to compare directly. Most studies evaluate normal wrist mechanics, all modeling the bones, with fewer studies including cartilage and ligamentous structures in the model. The dynamic effect of the tendons on wrist mechanics is rarely accounted for. Conclusion Due to the complexity of wrist mechanics, the current literature remains incomplete. Considering published strategies and modeling techniques may aid in the development of more comprehensive and improved wrist model fidelity.

Human behavior determinants of exposure to Anopheles vectors of malaria in Sumba, Indonesia.

Malaria vector control interventions in Sumba, Indonesia, have not been able to eliminate malaria. Human drivers of exposure to Anopheles bites were investigated as part of a larger clinical trial evaluating the impact of a spatial repellent product on malaria incidence. Human behavioral observations (HBOs) evaluating temporal and spatial presence, sleeping behaviors, and insecticide treated net (ITN) use, were collected parallel to entomological collections-indoor and outdoor human landing catches (HLCs), and house hold surveys. Data demonstrates that mosquito access to humans, enabled by structurally open houses, is evident by the similar entomological landing rates both inside and outside households. The presence of animals inside houses was associated with increased mosquito entry-however, the number of humans present inside houses was not related to increased mosquito landing. Analyzing mosquito landing rates with human behavior data enables the spatial and temporal estimation of exposure to Anopheles bites, accounting for intervention (ITN) presence and usage. Human behavior adjusted exposure to Anopheles bites was found to be highest in the early in the evening, but continued at lower levels throughout the night. Over the night, most exposure (53%) occurred when people were indoors and not under the protection of nets (asleep or awake) followed by exposure outside (44%). Characterized gaps in protection are outdoor exposure as well as exposure indoors-when awake, and when asleep and not using ITNs. Interestingly, in the primary trial, even though there was not a significant impact of the spatial repellent on vector biting rates by themselves (16%), when factoring in human behavior, there was approximately 28% less exposure in the intervention arm than in the placebo arm. The treated arm had less human behavior adjusted bites in all spaces evaluated though there was proportionally higher exposure indoors. This analysis points to the importance of using HBOs both towards understanding gaps in protection as well as how interventions are evaluated. To mitigate ongoing transmission, understanding context specific spatial and temporal exposure based on the interactions of vectors, humans and interventions would be vital for a directed evidence-based control or elimination strategy.

Optimization-based subject-specific planar human vertical jumping prediction: Effect of elbow flexion and weighted vest.

Jumping strategies differ considerably depending on athletes' physical activity demands. In general, the jumping motion is desired to have excellent performance and low injury risk. Both of these outcomes can be achieved by modifying athletes' jumping and landing mechanics. This paper presents a consecutive study on the optimization-based subject-specific planar human vertical jumping to test different loading conditions (weighted vest) during jumping with or without elbow flexion during the arm-swing based on the validated prediction model in the first part of this study. The sagittal plane skeletal model simulates the weighting, unweighting, breaking, propulsion phases and considers four loading conditions: 0%, 5%, 10%, and 15% body weight. Results show that the maximum ground reaction forces, the body center of mass position, and velocities at the take-off instant are different for different loading conditions and with/without elbow flexion. The optimization formulation is solved using MATLAB® with 35 design variables with 197 nonlinear constraints for a five-segment body model and 42 design variables with 227 nonlinear constraints for a six-segment body model. Both models are computationally efficient, and they can predict ground reaction forces, the body center of mass position, and velocity. This work is novel in the sense that presents a simulation model capable of considering different external loading conditions and the effect of elbow flexion during arm swing.

A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision: Part 2-Application.

Occupational ApplicationDigital human models have been widely used for occupational assessments to reduce potential injury risk, such as automotive assembly lines, box lifting, and in the mining industry. Human motion prediction is one of the important capabilities in digital human models, and collision avoidance is involved in human motion prediction. An algorithm proposed earlier was implemented for human motion prediction, and simulated results were found to have a good correlation with the experimental studies. Use of this algorithm can help ensure that human motion is predicted realistically, and thus can impact the accuracy of injury risk assessments.

TECHNICAL ABSTRACTBackground: With any type of human movement, there is the potential for a collision with other objects. In addition to the objects presented in the environment surrounding one's body and surrounding the objects to be manipulated, one's own body can become an obstacle. Therefore, consideration of the methods available for avoiding obstacles is necessary to comprehensively describe the way human movements are planned.Purpose: This paper evaluates a collision avoidance algorithm for human motion prediction based on the perceived risk of collision, specifically the application to human motion prediction.Method: Human motion prediction is formulated as an optimization problem with dynamic effort as the cost function, and the perceived risk of collision is considered as one constraint among other constraints. Performance using the new formulation was compared to observed performance from an experiment.Result: Based on the results, the new formulation can account for the suboptimal behavior observed in real subjects while still optimizing biomechanical cost. The predicted motion is much more realistic compared with that from purely biomechanically optimized formulation.Application: The developed collision avoidance algorithm can be applied to optimization-based manual movement prediction in which obstacles need to be navigated.

A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision: Part 1-Model Development.

OCCUPATIONAL APPLICATIONSDigital human models have been widely used in occupational biomechanics assessments to prevent potential injury risks, such as automotive assembly lines, box lifting, patient repositioning, and the mining industry. Motion prediction is one of the important capabilities in digital human models, and collision avoidance is involved in human motion prediction. We propose an algorithm that will ensure human motions are predicted realistically, and finally, use of this algorithm could help enhance the accuracy of injury risk assessments using digital human models.

TECHNICAL ABSTRACTBackground: Humans perform daily tasks such as reaching around an obstacle with ease, even though the complexities of such behavior are largely hidden from those performing them. Optimization-based motion prediction has employed numerical methods in order to predict human movements. However, these movements are heavily constrained, such that the planning of the motion is explicitly provided in the optimization formulation of the problem. This implies that for each task a unique optimization formulation is needed, which relies heavily on the experience of the code developer to provide these constraints.Purpose: Cognitive psychology has focused on the reasoning or motivation behind the planning of movements and provides an opportunity for digital human modeling to adopt these theories to provide a more general or versatile motion prediction framework. Humans tend to overestimate the risk associated with colliding with objects during movement. We present the formulation of a collision avoidance algorithm that considers the perceived risk, for future use in a human motion prediction application.Methods: An experiment was completed to evaluate human performance when avoiding obstacles during movement. Using Bayesian inference, perceived risk was modeled and minimized for use in human motion prediction.Results: The experimental results were used to derive a formula in which the perceived risk associated with the task could be quantified in a gain/loss context. Overestimation of risk by a subject was modeled using the observed behavior and the results of simulations based on the parameterized risk model are presented.Conclusions: The algorithm presented, based on the perceived risk of collision, can be integrated into human motion prediction to generate realistic human motion considering collision avoidance.

Optimization-based subject-specific planar human vertical jumping prediction: Model development and validation.

Jumping biomechanics may differ between individuals participating in various sports. Jumping motion can be divided into different phases for research purposes when seeking to understand performance, injury risk, or both. Experimental-based methods are used to study different jumping situations for their capabilities of testing other conditions intended to improve performance or further prevent injuries. External loading training is commonly used to simulate jumping performance improvement. This paper presents the optimization-based subject-specific planar human vertical jumping to develop the prediction model with and without a weighted vest and validate it through experiments. The skeletal model replicates the human motion for jumping (weighting, unweighting, breaking, propulsion) in the sagittal plane considering four different loading conditions (0% and 10% body mass): unloaded, split-loaded, front-loaded, and back-loaded. The multi-objective optimization problem is solved using MATLAB® with 35 design variables and 197 nonlinear constraints. Results show that the model is computationally efficient, and the predicted jumping motion matches the experimental data trend. The simulation model can predict vertical jumping motion and can test the effect of different loading conditions with weighted vests and arm-swing strategy on the ground reaction forces. This work is novel in the sense that it can predict ground reaction forces, joints angles, and center of mass position without any experimental data.

Jump and Landing Biomechanical Variables and Methods: A Literature Review.

Noncontact lower extremity injuries are commonly related to jumping and landing activities. This review presents an overview of relevant biomechanical variables that can be modified in training to improve jumping performance, landing mechanics, and consequently, reduce injury risks. Relevant studies from the last 2 decades in the Compendex, Pubmed, and Scopus databases were considered for this review. Studies related to jumping and landing kinetics, kinematics, injuries, performance, and/or simulation were included. The use of experimental methods as the drop jump landing and jumping countermovement are widely used to measure biomechanical variables. At the same time, there has been a continuous development of simulation models that could present results without the need for testing on human subjects, with the final objective of exploring the limits of an athlete's performance without increasing the risk of any injury. The most common injuries occur in the knee and ankle ligaments and are directly related to joint angles and moments (i.e., torque or joint loading) at the hip, ankle, and knee joints. Jumping and landing biomechanics are considerably different between male and female subjects for different experimental methods and in both cases, these kinematics factors can be improved over shorter- or longer-time training to develop a better landing strategy.