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Experimental investigation coupled with numerical simulations is commonly used for solving multi-physical problems. In the field of biomechanics, in which the aim is to understand the mechanics of living system, the main difficulties are to provide experimental data reflecting the multi-physical behavior of the system of interest. These experimental data are used as input data for numerical simulations to quantify output responses through physical and/or biological laws expressed by constitutive mathematical equations. However, uncertainties on the experimentally available data exist from factors such as human variability and differences in protocols parameters and techniques. Thus, the true values of these data could never be experimentally measured. The objective of this study was to develop a modeling workflow to assess and account for the parameter uncertainty in rigid musculoskeletal simulation. A generic musculoskeletal model was used. Data uncertainties of the right thigh mass, physiological cross-sectional area (pCSA) and muscle tension coefficient of the rectus femoris were accounted to estimate their effect on the joint moment and muscle force computing, respectively. A guideline was developed to fuse data from multiple sources into a sample variation space leading to establish input data distribution. Uncertainty propagation was performed using Monte Carlo and most probable point methods. A high degree of sensitivity of 0.98 was noted for the effect of thigh mass uncertainty on the hip joint moment using inverse dynamics method. A strong deviation of rectus femoris muscle force (around 260 N) was found under effect of pCSA and muscle tension coefficient on the force estimation using static optimization method. Accounting parameter uncertainty into rigid musculoskeletal simulation plays an essential role in the evaluation of the confidence in the model outputs. Thus, simulation outcome may be computed and represented in a more reliable manner with a global range of plausible values.

The aim of this paper is to present the results of an assessment of internal loads in the joints of the lower limbs during the snatch performed by young weightlifters. A planar model of a weightlifter composed of 7 rigid segments (the lower trunk, thighs, lower legs and feet) connected by six hinge joints was used in the computations. The dynamic equations of the motion of the model were obtained using a projective technique. Kinematic data were recorded by a Vicon system with a sampling frequency of 200 Hz. The ground reactions were measured independently for the left and right limbs on two force platforms. The inverse dynamics problem was solved to assess the internal loads (the muscle forces and joint reactions) in the lower limbs. Relatively high differences in the reactions in the joints and muscle forces in the left and right lower extremities were identified. The obtained results also reveal that the snatch, a lift which tends to be geometrically symmetrical in the sagittal plane, is not necessarily characterized by symmetry of internal loads. Thus, this study has shown that a kinematics analysis of the lifter’s movement, which is commonly used to assess the technique of the snatch, is insufficient and should be supplemented with a dynamics analysis.

Inverse dynamics is a standard analysis in biomechanics to reconstruct time histories of internal driving forces and torques from measured external forces and segmental kinematics. The main sources of inconsistency leading to analytical artefacts in this process are skin marker and soft tissue motion. These potentially artificial high frequency fluctuations in the joint torques may serve as an erroneous basis of (misleading) assumptions with respect to muscular activity. Here we suggest techniques to reduce these errors. In both parts of this study, high-speed video and force platform data were acquired. In one part, 69 sequences of human barefoot running were sampled followed by an inverse dynamic analysis of the stance leg. The time history of the hip joint torque in the sagittal plane served as a sensitive "detector" of dynamic analysis artefacts. We show that the most important error — the relative skin to bone motion especially of the knee marker — can be reduced significantly by processing kinematic data using bone rigidity (constant segment lengths) and bony contour (frontal knee edge) information. Further on, neglecting significantly initiated soft tissue dynamics in the inverse dynamic model introduces another inconsistency in the analytical process. Therefore, in a second part of this study, soft tissue kinematics from 14 jumping sequences were identified. These data provided a set of coupling parameters of wobbling masses to the bone that were ready to be implemented in the inverse dynamic model. Using realistic bone kinematics mainly avoids phase shifts in the acceleration scenario within the leg, and thus artifical hip torque fluctuations within the whole contact period. In human running, accounting for soft tissue dynamics mainly affects the calculated timing of the hip joint torque during the impact phase.

Our study quantifies the amount of error induced in the calculated energy balances of the joints if a trigger offset between the measurement of ground reaction force and of video data occurs. Joint energy balances constitute the basis for an adequate interpretation of muscular activity. An estimation of the amount of this error introduced by deficient synchronization has not been published so far but currently seems to be essential in the face of commercial providers offering complete solutions from data acquisition up to inverse dynamics analyses. As an example, we applied an inverse dynamics process to a data set of the contact phase of human running where the synchronization was disturbed artificially. We compared the amount of error for different methods of inverse dynamics. We found that a time offset of 5 ms results in almost 100% error (compared to zero offset) in the energy balance of each joint (up to 28 J in the hip). A kinematic event appearing later on the time scale than the respective kinetics shifts the calculated main source of energy production from the ankle to the hip, and vice versa if appearing precipitate. This 5 ms synchronization error is even higher than the methodical error introduced when synchronizing correctly but using the static torque equilibrium instead of complex inverse dynamics for the calculation of joint torques. We conclude that when buying professional analysis systems a strong urge to prove exact synchronization should be put on the provider.

A method based on inverse dynamics modeling for the subject specific in vivo evaluation of muscle activity in one subject during the execution of chair rising is being described in this article.

The calculation of net joint loads and moments is performed through the solution of Newton–Euler mechanics applied to each body segment. Accuracy in the calculation of these quantities is a prerequisite for the reliability of the final results. This accuracy depends on several modeling factors: the choice of the inertial parameters data-set was specifically investigated here. This choice did not result in relevantly affecting the accuracy of the calculated net joint loads for stair ascending and descending, thus even more for a slow motor task like chair rising. Thus, the choice of an inertial parameter data set from the literature was acceptable for the specific application. In order to solve the undetermined problem, the method for the estimation of the contribution of each muscle from internal joint loads and moments was based on static optimization. The static optimization allowed for the estimation of the time histories of the force exerted by each muscolo-tendineous modeled actuator. Among these, the Rectus Femoris exerted the maximum force. In conclusion, the preliminary results obtained with the proposed method allowed for obtaining a realistic estimate of the force exerted in vivo by each muscle during the execution of chair rising-sitting motor task. This is promising in gaining a better understanding on the biomechanical role of muscles during every day living motor tasks, and for the planning and evaluation of surgical and/or rehabilitative procedures.

A recent survey of epidemiological studies lists ankle injuries as one common sport injury. However, the details of the injury mechanisms of ankle sprains — the majority of ankle injuries — remain not well understood.

The purpose of the presented study is twofold. The first aim is to introduce a new, widely applicable method to calculate ankle joint torques during movement using inverse dynamics. The subtalar and talocrural joint are modeled as anatomically based revolute joints. The kinematics of the lower extremities and ground reaction force are used as input data. Second, a comparison of two calculation approaches (dynamic versus static) is reported, aimed at verifying and simplifying the introduced method to have a more convenient tool at hand for applications in the field. For one first movement measurement (hopping), the calculated joint torques show a good match for the two calculation approaches.

After further application, the evaluation of the resulting joint torques will provide further insights into the joint mechanics and can contribute to a better understanding of the respective injury mechanisms.

Hence, this approach is interesting for researchers to be used in order to understand ankle injuries and to determine the influence of landing grounds and shoes on ankle joint torques.

The aim of the present study was to identify the phases of gait and the joints where the "ground reaction vector technique" (GRVT) can represent an acceptable alternative to the use of inverse dynamics (ID), when considering subjects with a lower-limb amputation. First, an analytical investigation of the ID of the three joints of the lower limb is given, distinguishing the gravitational, the inertial and the ground reaction contributions. The first two contributions require inertial parameters estimation; for this purpose, literature anthropometric data are typically used, both for the unimpaired and prosthetic limb, as accurate specific inertial parameters for the prosthetic limb are difficult to obtain from companies or require time consuming estimation. This assumption potentially leads to errors in the three-dimensional (3D) joint moment estimation. Second, the results of two case studies, a trans-femoral amputee with two different prostheses and a trans-tibial amputee, showed that the GRVT can explain the most part of the net joint moment for the ankle and the knee in the whole stance phase, and for the hip in the first part of the stance, leading to a similar clinical evaluation without any assumptions on inertial parameters.

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.

The purpose of this study was to estimate the ability of joint moments of force in transferring mechanical energy through all the leg segments during a cyclic hopping sequence, performed until exhaustion. The technique was applied to data from four healthy active students to characterize the relative contribution of the lower limb net joint moments of force to accelerate the ankle, knee, and hip joints. Our findings showed that the strategies used to maintain the same jumping height rely on the balance between the net joint moments to guarantee the acceleration of the joints. It seems that while the ankle and knee moments reduce their contribution to accelerate the ankle and the knee joints, the hip moments increase their participation and have an important influence in the re-arrangement of the musculoskeletal system to maintain the same mechanical output.

A biomechanics model of six-link kinematic chain of human body is developed by using Kane's method. The kinematic data comprise of six segments; foot, calf, thigh, trunk, upper arm and forearm, are obtained through data collection of walking, running and jumping using the Vicon Nexus system. The motion capture system uses 12 Vicon MX-3+ cameras and 12 Vicon MX-F40 cameras, two DV (50 Hz) cameras and a force plate (100 Hz). Inverse dynamics approach is used to obtain the unknown value of torques produced by joint segments during walking, running and jumping activities. The results show that the largest value of torques produced occurs at the foot segment.

A rehabilitation robot is a device that has been proving its positive effectiveness in the process of helping patients recover quickly after a stroke. Researching, designing, and manufacturing robot models in general and upper limb rehabilitation robots in particular are very practical. In this study, we proposed to combine the use of an algorithm and a physical modeling method to shorten the calculation process and design an upper limb rehabilitation robot. First, an exoskeleton upper limb rehabilitation robot model (UExosVN) was briefly described. Next, in turn, all the important problems including inverse kinematics, inverse dynamics for this robot model were proposed and solved by using optimization algorithms and physical modeling methods. The model was evaluated in the critical movement of daily operations. The results after the testing process have proven the accuracy and effectiveness of the proposed methods.

Rehabilitation of lower limbs with high-level amputation (Trans-femoral Cut) requires an alternative method of inverse and forward dynamics to be employed. Inverse and forward dynamics are capable of solving the problem of redundancy of muscles in human lower limbs. Furthermore, inverse and forward dynamics can be used for estimating joints moments, predicting body segment kinematics, and estimating the distribution of forces between the agonist/antagonist muscle group with reasonable accuracy [Riener R, Lünenburger L, Colombo G, Human-Centered robotics applied to gait training and assessment robert, J Rehabil Res Dev43:679, 2006; Keller T, Perry JC, Rehabilitation robotics for outpatient clinical and domestic use, WC IFMBE Proc25:291, 2009; Crespo LM, Reinkensmeyer DJ, Review of control strategies for robotic movement training after neurologic injury, J Neuroeng Rehabil6:20, 2009; Hillman MI, Rehabilitation robotics from past to present — A historical prespective, Adv Rehabil Robotics, Lect Notes Contr Inf306:25, 2004.]. However, the application of these methods, in rehabilitation, is still limited due to their dependence on multiple input data that cannot be measured using conventional measurement tools. This study is aimed at verifying and validating new models which are capable of predicting body segment kinematics accurately using features obtained from electromyography (EMG) and adaptive neuro fuzzy inference system (ANFIS). These features are used to model a set of (input\output) dynamic data. EMG signals obtained from full gait cycle trials of a subject are used to calculate joint moments. Kinematics of the body segments are obtained by applying Euler numerical integration. The relative percentage root mean square (RMS) error between predicted kinematics from ANFIS and measured values for hip, knee, and ankle were 2.23%, 0.98%, and 0.69%, respectively. However, the achieved minimal reasonable accuracy for minimum number of measured inputs depends on the characteristics of the gait parameters such as cyclic nature and delayed symmetric activities between right and left leg about mid sagittal plane. Results of the combined model show that higher prediction accuracy of the gait cycle is achieved and so rehabilitation of lower limbs is feasible.

We describe a method for computing inverse dynamics forces suitable for robots locomoting along (or manipulating) sticky and slippery surfaces. Our method assumes rigid body dynamics, rigid body contact, and Coulomb friction, and is sufficiently fast for computation in real-time servos. We assess our method's performance on the HyQ quadrupedal robot in both simulation and in situ.