Femoral fracture risk prediction is a necessary step preceding effective pharmacological intervention or pre-operative planning. Current clinical methods for fracture risk prediction rely on 2D imaging methods and have limited predictive value. Researchers are therefore trying to find improved methods for fracture prediction. During last few decades, many studies have focused on integration of 3D imaging techniques and the finite element (FE) method to improve the accuracy of fracture assessment techniques. In this paper, we review the recent advances in FE and other techniques for predicting the risk of femoral fractures. Based on a number of selected studies, the different steps that are involved in generation of patient-specific FE models are reviewed with particular emphasis on the fracture criteria. The inaccuracies that might arise due to the imperfections of the involved steps are also discussed. It is concluded that compared to image- and geometry-based techniques, FE is a more promising approach for prediction of fracture loads. However, certain technological advancements in FE modeling protocols are required before FE modeling can be recruited in clinical settings.

The aim of this study is to quantify the relative contributions of two muscle energy consumption processes (the detachment of cross-bridges and calcium-pumping) incorporated in a recently developed muscle load sharing cost function, namely the energy-based criterion, by using in vivo measured glenohumeral-joint reaction forces (GH-JRFs). Motion data and in vivo GH-JRFs were recorded for four patients carrying an instrumented shoulder implant while performing abduction and forward flexion motions up to their maximum possible arm elevations. Motion data were used as the input to the delft shoulder and elbow model for the estimation of GH-JRFs. The widely used stress as well as the energy-based cost functions were adopted as the load sharing criteria. For the energy-based criterion, simulations were run for a wide range of different weight parameters (determining the relative contribution of the two energy processes) in the neighborhood of the previously assumed parameters for each subject and motion. The model-predicted and in vivo-measured GH-JRFs were compared for all model simulations. Application of the energy-based criterion with new identified parameters resulted in significant (two-tailed p < 0.05, post-hoc power ~ 0.3) improvement (on average ~20%) of the model-predicted GH-JRFs at the maximal arm elevation compared to when using either the stress or the pre-assumed form of the energy-based criterion. About 25% of the total energy consumption was calculated for the calcium-pumping process at maximal muscle activation level when using the new parameters. This value was comparable to the corresponding ones reported in the previous literature. The identified parameters are recommended to be used instead of their predecessors.

Body powered hand prostheses require high physical user effort. This is caused by the stiffness of the cosmetic covering, or cosmetic glove. This paper aims to present a new concept of a mechanism for the compensation of the nonlinear stiffness of body powered hand prostheses by using static balancers with a nonlinear behavior. This concept is based on a cooperative action of snap-through behavior in multiple bi-stable spring mechanisms to create the nonlinear balancing force. To demonstrate the efficiency of the concept, an optimized design for a case study of a child-size hand prosthesis is also presented. A pattern search method was applied for the optimization. As a result, the calculated stiffness and thereby the operating effort was reduced by 96%. It can be concluded from the conceptual and numerical results that the presented concept provides a highly efficient solution to the discussed problem.

This paper aims to develop an EMG-driven model of the shoulder that can consider possible muscle co-contractions. A musculoskeletal shoulder model (the original model) is modified such that measured EMGs can be used as model-inputs (the EMG-driven model). The model is validated by using the in-vivo measured glenohumeral-joint reaction forces (GH-JRFs). Three patients carrying instrumented hemi-arthroplasty were asked to perform arm abduction and forward-flexion up to maximum possible elevation, during which motion data, EMG, and in-vivo GH-JRF were measured. The measured EMGs were normalized and together with analyzed motions served as model inputs to estimate the GH-JRF. All possible combinations of input EMGs ranging from a single signal to all EMG signals together were tested. The 'best solution' was defined as the combination of EMGs which yielded the closest match between the model and the experiments. Two types of inconsistencies between the original model and the measurements were observed including a general GH-JRF underestimation and a GH-JRF drop above 90° elevation. Both inconsistencies appeared to be related to co-contraction since inclusion of EMGs could significantly (p < .05) improve the predicted GH-JRF (up to 45%). The developed model has shown the potential to successfully take the existent muscle co-contractions of patients into account.

There is currently no consensus in the literature on whether the magnitude of the ground reaction force or loading rate decreases or increases with muscle fatigue. In this article, the effects of lower extremity muscle fatigue on the magnitude of the ground reaction force and loading rate during running and drop landing are examined. Through a systematic search of the literature, 24 articles are identified that satisfy the inclusion criteria and study the relationship between fatigue and the ground reaction force variables during running, single-leg drop landing, and bilateral drop landing. The articles are categorized based on the type of locomotion they study. To determine whether or not the ground reaction force peaks/loading rate are markedly affected by fatigue, meta-analyses are conducted both separately for each type of locomotion and for an aggregate of all studies. The results of the meta-analyses show that the ground reaction force active peak significantly decreases for the following study groups: aggregate of all included studies, studies of drop landing (both single-leg and bilateral), and studies of bilateral landing only. The ground reaction force active peak did not significantly change in running and single-leg drop landing. The effects of muscle fatigue on the magnitude of the ground reaction force impact peak and loading rate was always insignificant.

A modeling approach is used in this paper to study the effects of fatigue on the ground reaction force (GRF) and the vibrations of the lower extremity soft tissues. A recently developed multiple degrees-of-freedom mass-spring-damper model of the human body during running is used for this purpose. The model is capable of taking the muscle activity into account by using a nonlinear controller that tunes the mechanical properties of the soft-tissue package based on two physiological hypotheses, namely, "constant force" and "constant vibration." In this study, muscle fatigue is implemented in the model as the gradual reduction of the ability of the controller to tune the mechanical properties of the lower body soft-tissue package. Simulations are carried out for various types of footwear in both pre- and postfatigue conditions. The simulation results show that the vibration amplitude of the lower body soft-tissue package may considerably increase (up to 20%) with muscle fatigue, while the effects of fatigue on the GRF are negligible. The results of this modeling study are in line with the experimental studies that found muscle fatigue does not significantly change the GRF peaks, but may increase the level of soft-tissue vibrations (particularly for hard shoes). A major contribution of the current study is the formulation of a hypothesis about how the central nervous system tunes the muscle properties after fatigue.

The Delft Shoulder and Elbow Model (DSEM), a musculoskeletal model of the shoulder and elbow has been extensively developed since its introduction in 1994. Extensions cover both model structures and anatomical data focusing on the addition of an elbow part and muscle architecture parameters. The model was also extended with a new inverse-dynamics optimization cost function and combined inverse-forward-dynamics models. This study is an update on the developments of the model over the last decade including a qualitative validation of the different simulation architectures available in the DSEM. To validate the model, a dynamic forward flexion motion was performed by one subject, of which the motion data and surface EMG-signals of 12 superficial muscles were measured. Patterns of the model-predicted relative muscle forces were compared with their normalized EMG-signals. Results showed relatively good agreement between forces and EMG (mean correlation coefficient of 0.66). However, for some cases, no force was predicted while EMG activity had been measured (false-negatives). The DSEM has been used and has the potential to be used in a variety of clinical and biomechanical applications.

This paper tries to improve a recently developed mass-spring-damper model of the human body during running. The previous model took the muscle activity into account using a nonlinear controller that tuned the mechanical properties of the soft-tissue package based on two physiological hypotheses, namely "constant-force" and "constant-vibration". Three cost functions were used, out of which one was based on the constant-force hypothesis and two others were based on the constant-vibration hypothesis. The results of the study showed that the proposed cost functions are only partially successful in capturing the experimentally observed trends of the ground reaction force and vibration. The current paper proposes an improved cost function that combines both above-mentioned hypotheses. It is shown that the improved cost function can capture all the trends that were observed in the measurements of the ground reaction force and vibration level. It is therefore advised to use the new cost function in place of the previous ones.

The publication and citation patterns of the journals published in the broad area of Biomechanical engineering are compared with those of the journals published in several other closely related areas of research. The data published in ISI Journal Citation Reports® (2003–2010) for different subject categories is used for this purpose. A subject category comprising of Biomechanics journals is defined in this article. It is shown that the aggregate impact factor of the journals included in the defined subject category has been increasing with a slower pace as compared to the aggregate impact factor of the journals belonging to all other subject categories considered in the current study. More extensive research is required to clarify the reasons for the observed patterns.

The effects of fatigue on the ground reaction force (GRF) and the vibrations of the human lower extremity soft tissues are studied in this paper. A recently developed multiple degreesof-freedom mass-spring-damper model of the human body during running is used for this purpose. The model is able to take the muscle activity into account by using a nonlinear controller that tunes the mechanical properties of the softtissue package (i.e. stiffness and damping) based on two physiological hypotheses, namely “constant-force” and “constant-vibration”. To model muscle fatigue, it is assumed that the range of variations of the mechanical properties of the lower body soft-tissue package reduces over time. Simulations are carried out for various shoe hardness parameters and various preand post-fatigue conditions. The simulation results show that the vibration amplitude of the lower body soft-tissue package increases with muscle fatigue. However, fatigue does not have any significant impact on the GRF.

Determination of an accurate glenohumeral-joint rotation center (GH-JRC) from marker data is essential for kinematic and dynamic analysis of shoulder motions. Previous studies have focused on the evaluation of the different functional methods for the estimation of the GH-JRC for healthy subjects. The goal of this paper is to compare two widely used functional methods, namely the instantaneous helical axis (IHA) and symmetrical center of rotation (SCoRE) methods, for estimating the GH-JRC in vivo for patients with implanted shoulder hemiarthroplasty. The motion data of five patients were recorded while performing three different dynamic motions (circumduction, abduction, and forward flexion). The GH-JRC was determined using the CT-images of the subjects (geometric GH-JRC) and was also estimated using the two IHA and SCoRE methods. The rotation centers determined using the IHA and SCoRE methods were on average 1.47±0.62 cm and 2.07±0.55 cm away from geometric GH-JRC, respectively. The two methods differed significantly (two-tailed p-value from paired t-Test ∼0.02, post-hoc power ∼0.30). The SCoRE method showed a significant lower (two-tailed p-value from paired t-Test ∼0.03, post-hoc power ∼0.68) repeatability error calculated between the different trials of each motion and each subject and averaged across all measured subjects (0.62±0.10 cm for IHA vs. 0.43±0.12 cm for SCoRE). It is concluded that the SCoRE appeared to be a more repeatable method whereas the IHA method resulted in a more accurate estimation of the GH-JRC for patients with endoprostheses.

Background Lower-limb stress fracture is one of the most common types of running injuries. There have been several studies focusing on the association between stress fractures and biomechanical factors. In the current study, the ground reaction force and loading rate are examined. There is disagreement in the literature about whether the history of stress fractures is associated with ground reaction forces (either higher or lower than control), or with loading rates. Methods A systematic review of the literature was conducted on the relationship between the history of tibial and/or metatarsal stress fracture and the magnitude of the ground reaction force and loading rate. Fixed-effect meta-analysis techniques were applied to determine whether or not the ground reaction force and/or loading rate are different between the stress fracture and control groups. Findings Thirteen articles were identified through a systematic search of the literature. About 54% of these articles reported significantly different vertical ground reaction force and/or loading rate between the stress fracture and control groups. Other studies (~46%) did not observe any significant difference between the two groups. Meta-analysis results showed no significant differences between the ground reaction force of the lower-limb stress fracture and control groups (P>0.05). However, significant differences were observed for the average and instantaneous vertical loading rates (P<0.05). Interpretation The currently available data does not support the hypothesis that there is a significant difference between the ground reaction force of subjects experiencing lower-limb stress fracture and control groups. Instead, the vertical loading rate was found to be significantly different between the two groups.

Although hand prostheses with a cosmetic covering are commercially available for disabled people, the operating effort due to the stiffness of the mechanism is high. This results in high power requirements. This paper aims to present a new concept of mechanisms for the compensation of the nonlinear stiffness of hand prostheses by using statically balanced mechanisms with a nonlinear behavior. This concept was based on a combination of stability phases of snap-through buckling in bistable spring mechanisms to create the nonlinear balancing force. To demonstrate the efficiency of the concept, an optimized design for a case study of a child-sized hand prosthesis is also presented. A pattern search method was applied for the optimization. As a result, the calculated stiffness and, thereby, the operating effort was reduced by 96%. It can be concluded from the conceptual and numerical results that the presented concept provides a highly efficient solution to the discussed problem.

A lumped-parameter model of the human body during running was previously developed. The model was used to study the effects of footwear on the impact force during running. However, the parameters of the model were considered constant regardless of the shoe type. Experimental studies have shown that the stiffness and damping of the human body are adjusted in accordance with the stiffness and damping of the shoe-ground system. In this paper, we study how the parameters of the model are adjusted, when the shoe parameters change. The original model is improved by adding a controller which resembles the function of the CNS. The effects of footwear on the impact force are studied by using the improved model. Three different bound limits are used for the model parameters to study the effects of the upper and lower parameter limits on the results. It has been shown that the model can explain the contradictory observations about the effects of footwear on the impact force.

The Delft Shoulder and Elbow Model (DSEM), a large-scale musculoskeletal model, is used for the estimation of muscle and joint reaction forces in the shoulder and elbow complex. Although the model has been qualitatively verified using EMG-signals, quantitative validation has until recently not been feasible. The development of an instrumented shoulder endoprosthesis has now made this possible. To this end, motion data, EMG-signals, external forces, and in-vivo glenohumeral joint reaction forces (GH-JRF) were recorded for two patients with an instrumented shoulder hemi-arthroplasty, during dynamic tasks (including abduction and anteflexion) and force tasks with the arm held in a static position. Motions and external forces served as the model inputs to estimate the GH-JRF. In the modeling process, the effect of two different (stress and energy) optimization cost functions and uniform size and mass scaling were evaluated. The model-estimated GH-JRF followed the in-vivo measured force for dynamic tasks up to about 90° arm elevations, but generally underestimates the peak forces up to 31%; whereas a different behavior (ascending measured but descending estimated force) was found for angles above 90°. For the force tasks the model generally overestimated the peak GH-JRF for most directions (on average up to 34%). Applying the energy cost function improved model predictions for the dynamic anteflexion task (up to 9%) and for the force task (on average up to 23%). Scaling also led to improvement of the model predictions during the dynamic tasks (up to 26%), but had a negligible effect (<2%) on the force task results. Although results indicated a reasonable compatibility between model and measured data, adjustments will be necessary to individualize the generic model with the patient-specific characteristics.

The Delft Shoulder and Elbow Model (DSEM), a large-scale musculoskeletal model, allows for estimation of individual muscle and joint reaction forces in the shoulder and elbow complex. Although the model has been qualitatively verified previously using EMG signals, quantitative validation has not yet been feasible. In this paper we report on the validation of the DSEM by comparing the GH-joint contact forces estimated by the DSEM with the in-vivo forces measured by a recently developed instrumented shoulder endoprosthesis, capable of measuring the glenohumeral (GH) joint contact forces in-vivo [1]. To validate the model, two patients with instrumented shoulder hemi-arthroplasty were measured. The measurement process included the collection of motion data as well as in-vivo joint reaction forces. Segment and joint angles were used as the model inputs to estimate the GH-joint contact forces. The estimated and recorded GH-joint contact forces for Range of Motion (RoM) and force tasks were compared based on the magnitude of the resultant forces. The results show that the estimated force follows the measured force for abduction and anteflexion motions up to 80 and 50 degrees arm elevations, respectively, while they show different behaviors for angles above 90 degrees (decrease is estimated but increase is measured). The DSEM underestimates the peak force for RoM (up to 38% for abduction motion and 64% for anteflexion motion), while overestimates the peak forces (up to 90%) for most directions of performing the force tasks.

The evaporative inlet cooling systems used for inlet cooling of gas turbines during hot summers do not work well in humid areas. However, desiccant wheels can be used to dehumidify the air before passing it trough the evaporative cooler. Since the desiccant wheels work adiabatically, the resulting air is hotter than the air introduced to the wheel and an evaporative cooling system is used to cool down the dehumidified air. Combined direct and indirect evaporative coolers have been already used to investigate the effects of dehumidification on the effectiveness of the evaporation cooling systems. It is shown that a single desiccant wheel does not offer much higher effectiveness compared to the multiple-stage evaporative systems. In this paper, an improved version of the desiccant inlet cooling system is presented. Additional dehumidification and indirect evaporative cooling stages are added to increase the effectiveness of the inlet cooling. A typical gas turbine cycle along with an industrial gas turbine with actual performance curves are used to simulate the thermal cycle in presence of the different inlet cooling systems. The simulations are carried out for three different climatic conditions. The improved and original desiccant-based systems are compared and it is shown that the added stages substantially improve the effectiveness of the desiccant-based inlet cooling.

This paper studies learning of reaching movements in a dynamically variable virtual environment specially designed for this purpose. Learning of reaching movements in the physical world has been extensively studied by several researchers. In most of those studies, the subjects are asked to exercise reaching movements while being exposed to real force fields exerted through a robotic manipulandum. Those studies have contributed to our understanding of the mechanisms used by the human cognitive system to learn reaching movements in the physical world. The question that remains to be answered is how the learning mechanism in the physical world relates to its counterpart in the virtual world where the real force fields are replaced by virtual force fields. A limited number of studies have already addressed this question and have shown that there are, actually, quite a number of relationships between the learning mechanisms in these two different environments. In this study, we are focused on gaining a more in-depth understanding of these relationships. In our experiments, the subjects are asked to guide a virtual object to a desired target on a computer screen using a mouse. The movement of the virtual object is affected by a viscous virtual force field that is sensed by the examinees through their visual system. Three groups of examinees are used for the experiments. All the examinees are first trained in the null-field condition. Then, the viscous force field is introduced either suddenly (for the two first groups) or gradually (for the last group). While the first and third groups of the examinees used their dominant arm to guide the virtual object in the second step, the second group used their nondominant arm. Generalization of the learning from the dominant to the nondominant arm and vice versa was studied in the third phase of the experiments. Finally, the force field was removed and the examinees were asked to repeat the reaching task to study the so-called aftereffects phenomenon. The results of the experiments are compared with the studies performed in the physical world. It is shown that the trends of learning and generalization are similar to what is observed in the physical world for a sudden application of the virtual force field. However, the generalization behavior of the examinees is somewhat different from the physical world if the force field is gradually applied.

This paper deals with the impact force during foot–ground impact activities such as the running. A previously developed model is used for this study. The model is a lumped-parameter one consisting of four masses connected to each other via linear springs and viscous dampers. A shoe-specific nonlinear function is used for representation of the ground reaction force. The authors have previously showed that the previous version of the model as well as its simulation is incorrect. This paper slightly modifies the previous model so as it is able to produce results in agreement with the experiments. Then, the modified model is simulated for two typical shoe types. A parametric study is also conducted. The parametric study concerns with the effects of masses, mass ratios, stiffness constants, and damping coefficients on the dynamics of the impact. It is shown that the impact forces increase as the rigid and wobbling masses increase. However, the increase in the impact forces is not the same for all the masses. It is found that the impact force increases as the touchdown velocities increase. Simulations imply that the variations of the damping coefficients result in larger variations of the impact force compared to the stiffness. The effect of the variation of gravity on the simulated impact force is also explored. It is concluded that both the first and the second peaks of the impact force are increased with gravity. An in-depth discussion is included to compare results of the current paper with results of other investigators.