An analytical approach to the problem of inverse optimization with additive objective functions: an application to human prehension

We consider the problem of what is being optimized in human actions with respect to various aspects of human movements and different motor tasks. From the mathematical point of view this problem consists of finding an unknown objective function given the values at which it reaches its minimum. This problem is called the inverse optimization problem. Until now the main approach to this problems has been the cut-and-try method, which consists of introducing an objective function and checking how it reflects the experimental data. Using this approach, different objective functions have been proposed for the same motor action. In the current paper we focus on inverse optimization problems with additive objective functions and linear constraints. Such problems are typical in human movement science. The problem of muscle (or finger) force sharing is an example. For such problems we obtain sufficient conditions for uniqueness and propose a method for determining the objective functions. To illustrate our method we analyze the problem of force sharing among the fingers in a grasping task. We estimate the objective function from the experimental data and show that it can predict the force-sharing pattern for a vast range of external forces and torques applied to the grasped object. The resulting objective function is quadratic with essentially non-zero linear terms.     

Modeling of control and learning in a stepping motion

In a previous study (Beuter et al. 1986) the authors modeled a stepping motion using a three-body linkage with four degrees of freedom. Stepping was simulated by using three task parameters (i.e., step height, length, and duration) and sinusoidal joint angular velocity profiles. The results supported the concept of a hierarchical control structure with open-loop control during normal operation. In this study we refine the dynamic model and improve the simulation technique by incorporating the dynamics of the leg after landing, adding a foot segment to the model, and preprogramming the complete step motion using cycloids. The equations of the forces and torques developed on the ground by the foot during the landing phase are derived using the Lagrangian method. Simulation results are compared to experimental data collected on a subject stepping four times over an obstacle using a Selspot motion analysis system. A hierarchical control model that incorporates a learning process is proposed. The model allows an efficient combination of open and closed loop control strategies and involves hardwired movement segments. We also test the hypothesis of cycloidal velocity profiles in the joint programs against experimental data using a novel curve-fitting procedure based on analytical rather than numerical differentiation. The results suggest multiob-jective optimization of the joint's motion. The control and learning model proposed here will help the understanding of the mechanisms responsible for assembling selected movement segments into goaldirected movement sequences in humans.     

A probabilistic method to estimate gait kinetics in the absence of ground reaction force measurements

Human joint torques during gait are usually computed using inverse dynamics. This method requires a skeletal model, kinematics and measured ground reaction forces and moments (GRFM). Measuring GRFM is however only possible in a controlled environment. This paper introduces a probabilistic method based on probabilistic principal component analysis to estimate the joint torques for healthy gait without measured GRFM. A gait dataset of 23 subjects was obtained containing kinematics, measured GRFM and joint torques from inverse dynamics in order to obtain a probabilistic model. This model was then used to estimate the joint torques of other subjects without measured GRFM. Only kinematics, a skeletal model and timing of gait events are needed. Estimation only takes 0.28 ms per time instant. Using cross-validation, the resulting root mean square estimation errors for the lower-limb joint torques are found to be approximately 0.1 Nm/kg, which is 6–18% of the range of the ground truth joint torques. Estimated joint torque and GRFM errors are up to two times smaller than model-based state-of-the-art methods. Model-free artificial neural networks can achieve lower errors than our method, but are less repeatable, do not contain uncertainty information on the estimates and are difficult to use in situations which are not in the learning set. In contrast, our method performs well in a new situation where the walking speed is higher than in the learning dataset. The method can for example be used to estimate the kinetics during overground walking without force plates, during treadmill walking without (separate) force plates and during ambulatory measurements.     

A physiology-based inverse dynamic analysis of human gait using sequential convex programming: a comparative study

This paper presents an enhanced version of the previously proposed physiological inverse approach (PIA) to calculate musculotendon (MT) forces and evaluates the proposed methodology in a comparative study. PIA combines an inverse dynamic analysis with an optimisation approach that imposes muscle physiology and optimises performance over the entire motion. To solve the resulting large-scale, nonlinear optimisation problem, we neglected muscle fibre contraction speed and an approximate quadratic optimisation problem (PIA-QP) was formulated. Conversely, the enhanced version of PIA proposed in this paper takes into account muscle fibre contraction speed. The optimisation problem is solved using a sequential convex programing procedure (PIA-SCP). The comparative study includes PIA-SCP, PIA-QP and two commonly used approaches from the literature: static optimisation (SO) and computed muscle control (CMC). SO and CMC make simplifying assumptions to limit the computational time. Both methods minimise an instantaneous performance criterion. Furthermore, SO does not impose muscle physiology. All methods are applied to a gait cycle of six control subjects. The relative root mean square error averaged over all subjects, ε(RMS), between the joint torques simulated from the optimised activations and the joint torques obtained from the inverse dynamic analysis was about twice as large for SO (ε(RMS) = 86) as compared with CMC (ε(RMS) = 39) and PIA-SCP (ε(RMS) = 50). ε(RMS) was at least twice as large for PIA-QP (ε(RMS) = 197) than for all other methods. As compared with CMC, muscle activation patterns predicted by PIA-SCP better agree with experimental electromyography (EMG). This study shows that imposing muscle physiology as well as globally optimising performance is important to accurately calculate MT forces underlying gait.     

Acceleration Workspace of Cooperating Multi-Finger Robot Systems

We present a mathematical method for acceleration workspace analysis of cooperating multi-finger robot systems using a model of point-contact with friction. A new unified formulation from dynamic equations of cooperating multi-finger robots is derived considering the force and acceleration relationships between the fingers and the object to be handled. From the dynamic equation, maximum translational and rotational acceleration bounds of an object are calculated under given constraints of contact conditions, configurations of fingers, and bounds on the torques of joint actuators for each finger. Here, the rotational acceleration bounds can be applied as an important manipulability index when the multi-finger robot grasps an object. To verify the proposed method, we used a set of case studies with a simple multi-finger mechanism system. The achievable acceleration boundary in task space can be obtained successfully with the proposed method and the acceleration boundary depends on the configurations of fingers.     

A neuromusculoskeletal tracking method for estimating individual muscle forces in human movement

A neuromusculoskeletal tracking (NMT) method was developed to estimate muscle forces from observed motion data. The NMT method combines skeletal motion tracking and optimal neuromuscular tracking to produce forward simulations of human movement quickly and accurately. The skeletal motion tracker calculates the joint torques needed to actuate a skeletal model and track observed segment angles and ground forces in a forward simulation of the motor task. The optimal neuromuscular tracker resolves the muscle redundancy problem dynamically and finds the muscle excitations (and muscle forces) needed to produce the joint torques calculated by the skeletal motion tracker. To evaluate the accuracy of the NMT method, kinematics and ground forces obtained from an optimal control (parameter optimization) solution for maximum-height jumping were contaminated with both random and systematic noise. These data served as input observations to the NMT method as well as an inverse dynamics analysis. The NMT solution was compared to the input observations, the original optimal solution, and a simulation driven by the inverse dynamics torques. The results show that, in contrast to inverse dynamics, the NMT method is able to produce an accurate forward simulation consistent with the optimal control solution. The NMT method also requires 3 orders-of-magnitude less CPU time than parameter optimization. The speed and accuracy of the NMT method make it a promising new tool for estimating muscle forces using experimentally obtained kinematics and ground force data.     

Optimization-based prediction of asymmetric human gait

An optimization-based formulation and solution method are presented to predict asymmetric human gait for a large-scale skeletal model. Predictive dynamics approach is used in which both the joint angles and joint torques are treated as unknowns in the equations of motion. For the optimization formulation, the joint angle profiles are treated as the primary unknowns, and velocities and accelerations are calculated using them. In numerical implementation, the joint angle profiles are discretized using the B-spline interpolation. An algorithm is presented to inversely calculate the joint torques and the ground reaction forces. The sum of the joint-torques squared, called the dynamic effort, is minimized as the human performance measure. Constraints are imposed on the joint strengths (torques) and joint ranges of motion along with other physical constraints. The formulation is validated by simulating a symmetric gait and comparing the results with the experimental data. Then asymmetric gait motion is simulated, where the left and right step lengths are different. The kinematics and kinetics results from the simulation are presented and discussed. Predicted ground reaction forces are explained by using the inverted pendulum model. Predicted kinematics and kinetics have trends that are similar to those reported in the literature. Potential practical applications of the formulation and the solution approach are discussed.     

Do patients with diabetic neuropathy use a higher proportion of their maximum strength when walking?

Diabetic patients have an altered gait strategy during walking and are known to be at high risk of falling, especially when diabetic peripheral neuropathy is present. This study investigated alterations to lower limb joint torques during walking and related these torques to maximum strength in an attempt to elucidate why diabetic patients are more likely to fall. 20 diabetic patients with moderate/severe peripheral neuropathy (DPN), 33 diabetic patients without peripheral neuropathy (DM), and 27 non-diabetic controls (Ctrl) underwent gait analysis using a motion analysis system and force plates to measure kinetic parameters. Lower limb peak joint torques and joint work done (energy expenditure) were calculated during walking. The ratio of peak joint torques and individual maximum joint strengths (measured on a dynamometer) was then calculated for 59 of the 80 participants to yield the ‘operating strength’ for those participants. During walking DM and DPN patients showed significantly reduced peak torques at the ankle and knee. Maximum joint strengths at the knee were significantly less in both DM and DPN groups than Ctrls, and for the DPN group at the ankle. Operating strengths were significantly higher at the ankle in the DPN group compared to the Ctrls. These findings show that diabetic patients walk with reduced lower limb joint torques; however due to a decrement in their maximum ability at the ankle and knee, their operating strengths are higher. This allows less reserve strength if responding to a perturbation in balance, potentially increasing their risk of falling.     

Point perturbation analysis of experimental data

A new method of data analysis is proposed. The method is based on discrete perturbation of experimental data points, which is used to probe the metric of the parameter hyperspace. Perturbation-induced fluctuations in the residual values are analysed by discrete Fourier transform to yield the autocorrelation function and a relaxation length for each experimental point. This parameter provides a quantitative measure of correlation and hence nonrandomness of residuals. The method is applied to the analysis of measurements of the shear viscosity of a 2,6-lutidine/water mixture near the critical point, and to the oxygen and carbon monoxide binding reactions to human hemoglobin. Relaxation profiles are constructed for several experimental data sets. Departure from random behavior in the residuals is discussed in connection with the theoretical interpretations of the phenomenon under consideration.     

Comparative analysis of methods for estimating arm segment parameters and joint torques from inverse dynamics

A common problem in the analyses of upper limb unfettered reaching movements is the estimation of joint torques using inverse dynamics. The inaccuracy in the estimation of joint torques can be caused by the inaccuracy in the acquisition of kinematic variables, body segment parameters (BSPs), and approximation in the biomechanical models. The effect of uncertainty in the estimation of body segment parameters can be especially important in the analysis of movements with high acceleration. A sensitivity analysis was performed to assess the relevance of different sources of inaccuracy in inverse dynamics analysis of a planar arm movement. Eight regression models and one water immersion method for the estimation of BSPs were used to quantify the influence of inertial models on the calculation of joint torques during numerical analysis of unfettered forward arm reaching movements. Thirteen subjects performed 72 forward planar reaches between two targets located on the horizontal plane and aligned with the median plane. Using a planar, double link model for the arm with a floating shoulder, we calculated the normalized joint torque peak and a normalized root mean square (rms) of torque at the shoulder and elbow joints. Statistical analyses quantified the influence of different BSP models on the kinetic variable variance for given uncertainty on the estimation of joint kinematics and biomechanical modeling errors. Our analysis revealed that the choice of BSP estimation method had a particular influence on the normalized rms of joint torques. Moreover, the normalization of kinetic variables to BSPs for a comparison among subjects showed that the interaction between the BSP estimation method and the subject specific somatotype and movement kinematics was a significant source of variance in the kinetic variables. The normalized joint torque peak and the normalized root mean square of joint torque represented valuable parameters to compare the effect of BSP estimation methods on the variance in the population of kinetic variables calculated across a group of subjects with different body types. We found that the variance of the arm segment parameter estimation had more influence on the calculated joint torques than the variance of the kinematics variables. This is due to the low moments of inertia of the upper limb, especially when compared with the leg. Therefore, the results of the inverse dynamics of arm movements are influenced by the choice of BSP estimation method to a greater extent than the results of gait analysis.     

Estimating in vivo passive forces of the index finger muscles: Exploring model parameters

We compared predicted passive finger joint torques from a biomechanical model that includes the exponential passive muscle force–length relationship documented in the literature with finger joint torques estimated from measures in ten adult volunteers. The estimated finger joint torques were calculated from measured right index fingertip force, joint postures, and anthropometry across 18 finger and wrist postures with the forearm muscles relaxed. The biomechanical model predicting passive finger joint torques included three extrinsic and three intrinsic finger muscles. The values for the predicted passive joint torques were much larger than the values calculated from the fingertip force and posture measures with an average RMS error of 7.6 N cm. Sensitivity analysis indicated that the predicted joint torques were most sensitive to passive force–length model parameters compared to anthropometric and postural parameters. Using Monte Carlo simulation, we determined a new set of values for the passive force–length model parameters that reduced the differences between the joint torques calculated from the two methods to an average RMS value of 0.5 N cm, a 94% average improvement of error from the torques predicted using the existing data. These new parameter values did vary across individuals; however, using an average set for the parameter values across subjects still reduced the average RMS difference to 0.8 N cm. These new parameters may improve dynamic modeling of the finger during sub-maximal force activities and are based on in vivo data rather than traditional in vitro data.     

Evidence for composite cost functions in arm movement planning: an inverse optimal control approach

An important issue in motor control is understanding the basic principles underlying the accomplishment of natural movements. According to optimal control theory, the problem can be stated in these terms: what cost function do we optimize to coordinate the many more degrees of freedom than necessary to fulfill a specific motor goal? This question has not received a final answer yet, since what is optimized partly depends on the requirements of the task. Many cost functions were proposed in the past, and most of them were found to be in agreement with experimental data. Therefore, the actual principles on which the brain relies to achieve a certain motor behavior are still unclear. Existing results might suggest that movements are not the results of the minimization of single but rather of composite cost functions. In order to better clarify this last point, we consider an innovative experimental paradigm characterized by arm reaching with target redundancy. Within this framework, we make use of an inverse optimal control technique to automatically infer the (combination of) optimality criteria that best fit the experimental data. Results show that the subjects exhibited a consistent behavior during each experimental condition, even though the target point was not prescribed in advance. Inverse and direct optimal control together reveal that the average arm trajectories were best replicated when optimizing the combination of two cost functions, nominally a mix between the absolute work of torques and the integrated squared joint acceleration. Our results thus support the cost combination hypothesis and demonstrate that the recorded movements were closely linked to the combination of two complementary functions related to mechanical energy expenditure and joint-level smoothness.     

Improving joint torque calculations: optimization-based inverse dynamics to reduce the effect of motion errors

The accuracy of joint torques calculated from inverse dynamics methods is strongly dependent upon errors in body segment motion profiles, which arise from two sources of noise: the motion capture system and movement artifacts of skin-mounted markers. The current study presents a method to increase the accuracy of estimated joint torques through the optimization of the angular position data used to describe these segment motions. To compute these angular data, we formulated a constrained nonlinear optimization problem with a cost function that minimizes the difference between the known ground reaction forces (GRFs) and the GRF calculated via a top-down inverse dynamics solution. To evaluate this approach, we constructed idealized error-free reference movements (of squatting and lifting) that produced a set of known “true” motions and associated true joint torques and GRF. To simulate real-world inaccuracies in motion data, these true motions were perturbed by artificial noise. We then applied our approach to these noise-induced data to determine optimized motions and related joint torques. To evaluate the efficacy of the optimization approach compared to traditional (bottom-up or top-down) inverse dynamics approaches, we computed the root mean square error (RMSE) values of joint torques derived from each approach relative to the expected true joint torques. Compared to traditional approaches, the optimization approach reduced the RMSE by 54% to 79%. Average reduction due to our method was 65%; previous methods only achieved an overall reduction of 30%. These results suggest that significant improvement in the accuracy of joint torque calculations can be achieved using this approach.     

Modeling and dynamic simulation of astronaut’s upper limb motions considering counter torques generated by the space suit

Extravehicular activity (EVA) is an inevitable task for astronauts to maintain proper functions of both the spacecraft and the space station. Both experimental research in a microgravity simulator (e.g. neutral buoyancy tank, zero-g aircraft or a drop tower/tube) and mathematical modeling were used to study EVA to provide guidance for the training on Earth and task design in space. Modeling has become more and more promising because of its efficiency. Based on the task analysis, almost 90% of EVA activity is accomplished through upper limb motions. Therefore, focusing on upper limb models of the body and space suit is valuable to this effort. In previous modeling studies, some multi-rigid-body systems were developed to simplify the human musculoskeletal system, and the space suit was mostly considered as a part of the astronaut body. With the aim to improve the reality of the models, we developed an astronauts’ upper limb model, including a torque model and a muscle-force model, with the counter torques from the space suit being considered as a boundary condition. Inverse kinematics and the Maggi-Kane’s method was applied to calculate the joint angles, joint torques and muscle force given that the terminal trajectory of upper limb motion was known. Also, we validated the muscle-force model using electromyogram (EMG) data collected in a validation experiment. Muscle force calculated from our model presented a similar trend with the EMG data, supporting the effectiveness and feasibility of the muscle-force model we established, and also, partially validating the joint model in kinematics aspect.     

Automated estimation of ankle muscle EMG envelopes and resulting plantar-dorsi flexion torque from 64 garment-embedded electrodes uniformly distributed around the human leg

The design of personalized movement training and rehabilitation pipelines relies on the ability of assessing the activation of individual muscles concurrently with the resulting joint torques exerted during functional movements. Despite advances in motion capturing, force sensing and bio-electrical recording technologies, the estimation of muscle activation and resulting force still relies on lengthy experimental and computational procedures that are not clinically viable. This work proposes a wearable technology for the rapid, yet quantitative, assessment of musculoskeletal function. It comprises of (1) a soft leg garment sensorized with 64 uniformly distributed electromyography (EMG) electrodes, (2) an algorithm that automatically groups electrodes into seven muscle-specific clusters, and (3) a EMG-driven musculoskeletal model that estimates the resulting force and torque produced about the ankle joint sagittal plane. Our results show the ability of the proposed technology to automatically select a sub-set of muscle-specific electrodes that enabled accurate estimation of muscle excitations and resulting joint torques across a large range of biomechanically diverse movements, underlying different excitation patterns, in a group of eight healthy individuals. This may substantially decrease time needed for localization of muscle sites and electrode placement procedures, thereby facilitating applicability of EMG-driven modelling pipelines in standard clinical protocols.     

Analysis of ligand-receptor binding by the difference method

A new method has been proposed for analysis of experimental data on ligand-receptor binding at equilibrium. This method makes it possible to detect heterogeneity of a receptor system in cases where the contribution of the high-affinity site to total binding is rather small and the problem of graphic discrimination of a model cannot be solved unambiguously by other methods. The difference method permits us to exclude experiments on measuring nonspecific binding. A computer program for analysis of ligand-receptor binding has been worked out in which the difference method and traditional methods of binding isotherm analysis are realized. Numerical modeling has shown that the best strategy in experimental data processing is the treatment of total binding isotherms by both the difference method and regression analysis, including the nonspecific binding constant as one of the regression parameters.     

Data analysis of plasmid maintenance in a CSTR

A simple procedure is developed to process experimental data from plasmid maintenance studies of recombinant cells in a chemostat with nonselective medium. This procedure, based on the model proposed by Imanaka and Aiba, provides quantitative information on the rate of plasmid loss and the difference in the specific growth rate between the plasmid-carrying and plasmid-free cells. The performance of the proposed method is evaluated through simulation studies. In addition, the method is applied to a set of previously reported experimental data. The two-parameter model, together with the estimated parameter values, provides an excellent fit to the experimental data.