Predicting the influence of hip and lumbar flexibility on lifting motions using optimal control

Computational models of the human body coupled with optimization can be used to predict the influence of variables that cannot be experimentally manipulated. Here, we present a study that predicts the motion of the human body while lifting a box, as a function of flexibility of the hip and lumbar joints in the sagittal plane. We modeled the human body in the sagittal plane with joints actuated by pairs of agonist-antagonist muscle torque generators, and a passive hamstring muscle. The characteristics of a stiff, average and flexible person were represented by co-varying the lumbar range-of-motion, lumbar passive extensor-torque and the hamstring passive muscle-force. We used optimal control to solve for motions that simulated lifting a 10 kg box from a 0.3 m height. The solution minimized the total sum of the normalized squared active and passive muscle torques and the normalized passive hamstring muscle forces, over the duration of the motion. The predicted motion of the average lifter agreed well with experimental data in the literature. The change in model flexibility affected the predicted joint angles, with the stiffer models flexing more at the hip and knee, and less at the lumbar joint, to complete the lift. Stiffer models produced similar passive lumbar torque and higher hamstring muscle force components than the more flexible models. The variation between the motion characteristics of the models suggest that flexibility may play an important role in determining lifting technique.     

A novel approach to predicting human ingress motion using an artificial neural network

Due to the increased availability of digital human models, the need for knowing human movement is important in product design process. If the human motion is derived rapidly as design parameters change, a developer could determine the optimal parameters. For example, the optimal design of the door panel of an automobile can be obtained for a human operator to conduct the easiest ingress and egress motion. However, acquiring motion data from existing methods provides only unrealistic motion or requires a great amount of time. This not only leads to an increased time consumption for a product development, but also causes inefficiency of the overall design process. To solve such problems, this research proposes an algorithm to rapidly and accurately predict full-body human motion using an artificial neural network (ANN) and a motion database, as the design parameters are varied. To achieve this goal, this study refers to the processes behind human motor learning procedures. According to the previous research, human generate new motion based on past motion experience when they encounter new environments. Based on this principle, we constructed a motion capture database. To construct the database, motion capture experiments were performed in various environments using an optical motion capture system. To generate full-body human motion using this data, a generalized regression neural network (GRNN) was used. The proposed algorithm not only guarantees rapid and accurate results but also overcomes the ambiguity of the human motion objective function, which has been pointed out as a limitation of optimization-based research. Statistical criteria were utilized to confirm the similarity between the generated motion and actual human motion. Our research provides the basis for a rapid motion prediction algorithm that can include a variety of environmental variables. This research contributes to an increase in the usability of digital human models, and it can be applied to various research fields.     

Comparative Analysis of Modeling Techniques for Coliform Organisms in Streams

The use of models for predicting changes in water quality parameters is currently considered an integral part of river basin management. The application of modeling techniques to coliform organisms is in its infancy due to the complexities involved and the lack of definitive information on coliform populations in natural environments. The purpose of this study was to make a comparative analysis of the available models for coliform organisms in order to improve on the state of the art of this subject. The available coliform models may be classified into deterministic or statistical types. In this study, six different models, three of each type, were selected for analysis and were applied to coliform data available on the Leaf River. Results of comparing the models indicated that a deterministic model was best suited for total coliform and a statistical model was best suited for fecal coliform. Ultimate selection of a model for coliform organisms is dependent not only on the accuracy of the model but on ease of implementation. Current technology would probably dictate the use of a deterministic model because of the lack of a complete data base on which to base statistical models.     

Analysis of surface-to-surface distance mapping during three-dimensional motion at the ankle and subtalar joints

Joint surface interaction and ligament constraints determine the kinematic characteristics of the ankle and subtalar joints. Joint surface interaction is characterized by joint contact mechanics and by relative joint surface position potentially characterized by distance mapping. While ankle contact mechanics was investigated, limited information is available on joint distance mapping and its changes during motion. The purpose of this study was to use image-based distance mapping to quantify this interaction at the ankle and subtalar joints during tri-planar rotations of the ankle complex. Five cadaveric legs were scanned using Computed Tomography and the images were processed to produce 3D bone models of the tibia, fibula, talus and calcaneus. Each leg was tested on a special linkage through which the ankle complex was loaded in dorsiflexion/plantarflexion, inversion/eversion, and internal/external rotation and the resulting bone movements were recorded. Fiduciary bone markers data and 3D bone models were combined to generate color-coded distance maps for the ankle and subtalar joints. The results were processed focusing on the changes in surface-to-surface distance maps between the extremes of the range of motion and neutral. The results provided detailed insight into the three-dimensional highly coupled nature of these joints showing significant and unique changes in distance mapping from neutral to extremes of the range of motion. The non-invasive nature of the image-based distance mapping technique could result, after proper modifications, in an effective diagnostic and clinical evaluation technique for application such as ligament injuries and quantifying the effect of arthrodesis or total ankle replacement surgery.     

Genetic prediction of complex traits: integrating infinitesimal and marked genetic effects

Genetic prediction for complex traits is usually based on models including individual (infinitesimal) or marker effects. Here, we concentrate on models including both the individual and the marker effects. In particular, we develop a “Mendelian segregation” model combining infinitesimal effects for base individuals and realized Mendelian sampling in descendants described by the available DNA data. The model is illustrated with an example and the analyses of a public simulated data file. Further, the potential contribution of such models is assessed by simulation. Accuracy, measured as the correlation between true (simulated) and predicted genetic values, was similar for all models compared under different genetic backgrounds. As expected, the segregation model is worthwhile when markers capture a low fraction of total genetic variance.     

A computational model for dynamic analysis of the human gait

Biomechanical models are important tools in the study of human motion. This work proposes a computational model to analyse the dynamics of lower limb motion using a kinematic chain to represent the body segments and rotational joints linked by viscoelastic elements. The model uses anthropometric parameters, ground reaction forces and joint Cardan angles from subjects to analyse lower limb motion during the gait. The model allows evaluating these data in each body plane. Six healthy subjects walked on a treadmill to record the kinematic and kinetic data. In addition, anthropometric parameters were recorded to construct the model. The viscoelastic parameter values were fitted for the model joints (hip, knee and ankle). The proposed model demonstrated that manipulating the viscoelastic parameters between the body segments could fit the amplitudes and frequencies of motion. The data collected in this work have viscoelastic parameter values that follow a normal distribution, indicating that these values are directly related to the gait pattern. To validate the model, we used the values of the joint angles to perform a comparison between the model results and previously published data. The model results show a same pattern and range of values found in the literature for the human gait motion.     

Ultra-high-field MAS NMR assay of a multispin labeled ligand bound to its G-protein receptor target in the natural membrane environment: electronic structure of the retinylidene chromophore in rhodopsin

11-Z-[8,9,10,11,12,13,14,15,19,20-(13)C10]Retinal prepared by total synthesis is reconstituted with opsin to form rhodopsin in the natural lipid membrane environment. The 13C shifts are assigned with magic angle spinning NMR dipolar correlation spectroscopy in a single experiment and compared with data of singly labeled retinylidene ligands in detergent-solubilized rhodopsin. The use of multispin labeling in combination with 2-D correlation spectroscopy improves the relative accuracy of the shift measurements. We have used the chemical shift data to analyze the electronic structure of the retinylidene ligand at three levels of understanding: (i) by specifying interactions between the 13C-labeled ligand and the G-protein-coupled receptor target, (ii) by making a charge assessment of the protonation of the Schiff base in rhodopsin, and (iii) by evaluating the total charge on the carbons of the retinylidene chromophore. In this way it is shown that a conjugation defect is the predominant ground-state property governing the molecular electronics of the retinylidene chromophore in rhodopsin. The cumulative chemical shifts at the odd-numbered carbons (Delta(sigma)odd) of 11-Z-protonated Schiff base models relative to the unprotonated Schiff base can be used to measure the extent of delocalization of positive charge into the polyene. For a series of 11-Z-protonated Schiff base models and rhodopsin, Delta(sigma)odd appears to correlate linearly with the frequency of maximum visible absorption. Since rhodopsin has the largest value of Delta(sigma)odd, the data contribute to existing and converging spectroscopic evidence for a complex counterion stabilizing the protonated Schiff base in the binding pocket.     

Three-dimensional spine kinematics during multidirectional,target-directed trunk movement in sitting

The current study provides a quantitative assessment of three-dimensional spine motion during target-directed trunk movements in sitting. Subjects sat on an elevated surface, without foot support, and targets were placed in five directions, at three subject-specific distances (based on trunk height). Subjects were asked to lean toward the target, touch it with their head, and return to upright sitting. A retro-reflective motion analysis system was used to measure spine motion, using three kinematic trunk models (1, 3 and 7 segments). Significant differences were noted in the total trunk motion measured between the models, as well as between target distances and directions. In the most segmented model, inter-segmental trunk motion was also found to differ between trunk levels, with complex interaction effects involving target distance and direction. These findings suggest that inter-segmental spine motion is complex, task dependent, and often unevenly distributed between spine levels, with motion patterns differing between subjects, even in the absence of pathology. Use of a multi-segmental model provides the most interpretable findings, allowing for differentiation of individual motion patterns of the spine. Such an approach may be beneficial to the understanding of movement-related spine pathologies.     

How Can a Patient Blind to Radial Motion Discriminate Shifts in the Center-of-Motion?

Within biologically constrained models of heading and complex motion processing, localization of the center-of-motion (COM) is typically an implicit property arising from the precise computation of radial motion direction associated with an observers forward self-motion. In the work presented here we report psychophysical data from a motion-impaired stroke patient, GZ, whose pattern of visual motion deficits is inconsistent with this view. We show that while GZ is able to discriminate direction in circular motions she is unable to discriminate direction in radial motion patterns. GZs inability to discriminate radial motion is in stark contrast with her ability to localize the COM in such stimuli and suggests that recovery of the COM does not necessarily require an explicit representation of radial motion direction. We propose that this dichotomy can be explained by a circular template mechanism that minimizes a global motion error relative to the visual motion input, and we demonstrate that a sparse population of such templates is computationally sufficient to account for human psychophysical performance in general and in particular, explains GZs performance. Recent re-analysis of the predicted receptive field structures in several existing heading models provides additional support for this type of circular template mechanism and suggests the human visual system may have available circular motion mechanisms for heading estimation.     

Geographical location, climate and land use influences on the phenology and numbers of the aphid, Myzus persicae, in Europe

Aim The purpose of this study was to improve understanding of the relationship between the spatial patterns of an important insect pest, the aphid Myzus persicae, and aspects of its environment. The main objectives were to determine the predominant geographical, climatic and land use factors that are linked with the aphid's distribution, to quantify their role in determining that distribution, including their interacting effects and to explore the ability of artificial neural networks (ANNs) to provide predictive models. Location The study focused on four spatial scales to account for the aphid data base characteristics and available land use data sets: Europe; a broad zone over Europe covering Belgium, Denmark, France, Ireland, Italy, The Netherlands, Scotland, Sweden and Wales (Regio data base coverage); North‐West Europe (i.e. Belgium, France and the United Kingdom); and England with Wales. Methods Multiple linear regression (MLR) was used to identify the variables in the Geographic location, Climate and Land use groups, that explained significant proportions of the variance in M. persicae total annual numbers and Julian date of first capture. A variance partitioning procedure was used to measure the fraction of the variation that can be explained by each environmental factor and of shared variation between the different factors. Finally, ANNs were employed as an alternative modelling approach for the two largest study areas, i.e. Europe and the Regio data base coverage, to determine whether the relationship between aphid and environmental variables was better described by more complex functions as well as their ability to generalize to new data. Results Land use variables are shown to play a significant role in explaining aphid numbers. The area of agricultural crops, in particular oilseed rape, is positively correlated with M. persicae annual numbers. Among the climatic variables, rainfall is negatively correlated with aphid numbers and temperature is positively correlated. The geographical components also explain a significant part of aphid annual numbers. However, the variance partitioning procedure indicates that while each group has an effect, none is dominant. Aphid first capture is mainly explained by climate where rainfall tends to delay migration and warmer conditions tend to advance it. Climate accounts for the greatest part of the variance when considered separately from the other factors. The geographical and land use components also have a significant effect on first capture at each scale, but their direct contribution is negligible. The ability of the ANN models to generalize to new total numbers and phenological data compared with MLR models was less for Europe (9 and 6% increase in the variance accounted for, respectively) than for the Regio data coverage where an increase of 44% in the variance accounted for was observed. Main conclusions This research supports the hypothesis that climate, land use and geographical location play a role in determining patterns of aphid annual numbers and phenology. The ability of ANN models to predict aphid distribution is improved by the inclusion of temporal land use data. However, identification of the processes involved in such relationships is difficult due to numerous interactions between the environmental factors.     

A two-step procedure for coupling development and usage of a pair of human neck models

Both finite element models and multi-body models of human head-neck complex had been widely used in neck injuries analysis, as the former could be used to generate detailed stress strain information and the later could generate dynamic responses with high efficiency. Sometimes, detailed stress and strain information were hoped to be obtained more efficiently, but current methods were not effective enough when they were used to analyze responses of human head neck complex to long duration undulate accelerations. In this paper, a two-step procedure for ‘parallel’ development and ‘sequential’ usage of a pair of human head neck models was discussed. The pair of models contained a finite element model and a multi-body model, which were developed based on the coupling ‘parallel’ procedure using the same bio-realistic geometry. After being validated using available data, the pair of human neck models were applied to analyze biomechanical responses of pilot’s neck during arrested landing operation according to the ‘sequential’ procedure, because typical sustained undulate accelerations usually appeared during such processes. The results, including both kinematic and detailed biomechanical responses of human head-neck complex, were obtained with preferable efficiency. This research provided an effective way for biomechanical analysis of human head neck responses to sustained undulate accelerations.     

Subject-specific axes of the ankle joint complex

The aim of this study was to use a two-axis ankle joint model and an optimisation process (van den Bogert et al., 1994) to calculate and compare the talocrural and subtalar hinge axes for non-weight-bearing ankle motion, weight-bearing ankle motion, and walking in normal, healthy adult subjects and to see which of the first two sets of axes better fit the walking data. Motion data for the foot and shank were collected on eight subjects whilst they performed the activities mentioned. After choosing the best marker sets for motion tracking, a two-hinge ankle joint model was fit to the motion data. Ankle joint ranges of motion were also calculated. It was found that the model fit the experimental data well, with non-weight-bearing motion achieving the best fit. Despite this, the calculated axis orientations were highly variable both between motion types and between subjects. No significant difference between the fit of the non-weight-bearing and weight-bearing models to the walking data was found, which implies that either set of functional axes is adequate for modeling walking; however, the subtalar deviation angle was significantly closer for the weight-bearing activity and walking than for the non-weight-bearing activity and walking, which suggests that it is marginally better to use the weight-bearing functional motions. The results lead to questions about the appropriateness of the two-hinge ankle model for use in applications in which the behaviour of the individual joints of the ankle complex, rather than simply the relative motion of the leg and foot, is important.     

The apparent mass of the seated human exposed to single-axis and multi-axis whole-body vibration

Most workplaces where workers are exposed to whole-body vibration involves simultaneous motion in the fore-and-aft (x-), lateral (y-) and vertical (z-) directions. Previous studies reporting the biomechanical response of people exposed to vibration have almost always used single-axis vibration stimuli. This paper reports a study where apparent masses of 15 subjects were measured whilst exposed to single-axis and tri-axial whole-body vibration. Each subject was exposed to 28 vibration conditions comprising every combination of single-axis and tri-axial vibration with magnitudes of 0.4 and 0.8 ms(-2) r.m.s. in each direction, once with backrest contact and once without backrest contact. Results show that increasing the magnitude of vibration in directions orthogonal to that being measured affects the apparent mass, causing a reduction in the resonance frequency as the total magnitude of vibration increases. It is demonstrated that the apparent mass resonance frequency is a function of the total vibration magnitude in all axes rather than a function of the vibration magnitude in the direction being measured. It is also shown that, for individuals, the frequency of the peak in the apparent mass in one direction is not related to the frequency of the peak in another direction. It is concluded that more complex biomechanical models are required in order to simulate human response to multi-axis vibration.     

Computational modelling of motion detectors: responses to two-frame displays

Schemes for motion detection fall into two classes. Reichardt correlators compare spatial luminance patterns at two locations at different times; gradient detectors compare spatial and temporal luminance gradients. Both are candidate operators for biological and machine vision systems. A large body of perceptual data exists, defining the properties of motion detectors used by human observers, which can form a basis for determining which class of detector is appropriate for the human visual system. Plausible versions of each detector were implemented, and their responses to a variety of two-frame stimuli were computed. Results indicated that both detectors can predict most of the data, but on balance gradient detectors offer the best working hypothesis for motion detection by human observers. This conclusion is necessarily limited to the type of stimuli used, and may require modification in the light of responses to continuously moving stimuli.