Posture Prediction Research Articles

Optimizing the Rear Seat Environment for Older Children, Adults, and Infants

Objective: Our recent rear seat safety research found that more-forward and higher lap belt anchorage locations and much shorter and stiffer seat cushions can improve the protection of older children from 6 to 12 years old who are using the vehicle belt without a booster. The objective of this study was to investigate whether the optimal rear seat restraint systems for adults and infants are consistent with those for older children. Methods: We conducted sensitivity analyses and design optimizations for adults and for infants in a rear-facing child restraint system (CRS) using a set of MADYMO models, an automated simulation framework, and occupant belt-fit and posture prediction models from our previous studies. A series of 12 sled tests was also used to validate the computational models. Results: The optimal belt anchorage locations and the seat cushion length for older children, adults, and rear-facing CRS-seated infants conflict with each other. In particular, more-forward lap belt anchorage locations that prevent submarining for older children would reduce the protection to both adults and CRS-seated infants, although the protection is still acceptable based on regulated injury criteria. A shorter seat cushion could provide optimal protection to older children and adults but would significantly increase the CRS rotation. Conclusions: The findings of this study suggested that adaptive/adjustable restraint systems are necessary to simultaneously improve the rear seat occupant protection for all age groups. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

Hand Posture Prediction Using Neural Networks within a Biomechanical Model

This paper proposes the use of artificial neural networks (ANNs) in the framework of a biomechanical hand model for grasping. ANNs enhance the model capabilities as they substitute estimated data for the experimental inputs required by the grasping algorithm used. These inputs are the tentative grasping posture and the most open posture during grasping. As a consequence, more realistic grasping postures are predicted by the grasping algorithm, along with the contact information required by the dynamic biomechanical model (contact points and normals). Several neural network architectures are tested and compared in terms of prediction errors, leading to encouraging results. The performance of the overall proposal is also shown through simulation, where a grasping experiment is replicated and compared to the real grasping data collected by a data glove device.

Towards Bridging the Gap between Biomechanics and Motor Control for Virtual Ergonomics Applications

Posture prediction algorithms, particularly those that consider comfort (or discomfort), are typically based on gross movements rather than on the finer movements of the upper extremity. However, understanding these finer movements, particularly during goal-directed reaching tasks, is critical to accurately predicting postures, and the associated potential injury risks, when using virtual ergonomic tools. Furthermore, it is expected that these finer movements will be highly sensitive to the planning processes used to perform sequential tasks, particularly when start and terminal orientation, terminal precision, and terminal force are manipulated. Thus, the purpose of this proposed study is to challenge the theory of the end-state comfort effect under conditions of varying terminal precision and exertion force requirements during both discrete and sequential goal-directed reaching tasks. It is expected that each of these manipulations (i.e. start and terminal orientation, terminal precision, terminal force, and number of movement sequences) will influence the chosen movement patterns. Understanding how each of these variables affects movement patterns will provide important information for the development of posture prediction algorithms for use with virtual ergonomics tools.

Physics-based seated posture prediction for pregnant women and validation considering ground and seat pan contacts.

An understanding of human seated posture is important across many fields of scientific research. Certain demographics, such as pregnant women, have special postural limitations that need to be considered. Physics-based posture prediction is a tool in which seated postures can be quickly and thoroughly analyzed, as long the predicted postures are realistic. This paper proposes and validates an optimization formulation to predict seated posture for pregnant women considering ground and seat pan contacts. For the optimization formulation, the design variables are joint angles (posture); the cost function is dependent on joint torques. Constraints include joint limits, joint torque limits, the distances from the end-effectors to target points, and self-collision avoidance constraints. Three different joint torque cost functions have been investigated to account for the special postural characteristics of pregnant women and consider the support reaction forces (SRFs) associated with seated posture. Postures are predicted for three different reaching tasks in common reaching directions using each of the objective function formulations. The predicted postures are validated against experimental postures obtained using motion capture. A linear regression analysis was used to evaluate the validity of the predicted postures and was the criteria for comparison between the different objective functions. A 56 degree of freedom model was used for the posture prediction. Use of the objective function minimizing the maximum normalized joint torque provided an R² value of 0.828, proving superior to either of two alternative functions.

Nonlinear inverse optimization approach for determining the weights of objective function in standing reach tasks

This paper presents a nonlinear inverse optimization approach to determine the weights for the joint displacement function in standing reach tasks. This inverse optimization problem can be formulated as a bi-level highly nonlinear optimization problem. The design variables are the weights of a cost function. The cost function is the weighted summation of the differences between two sets of joint angles (predicted posture and the actual standing reach posture). Constraints include the normalized weights within limits and an inner optimization problem to solve for joint angles (predicted standing reach posture). The weight linear equality constraints, obtained through observations, are also implemented in the formulation to test the method. A 52 degree-of-freedom (DOF) human whole body model is used to study the formulation and visualize the prediction. An in-house motion capture system is used to obtain the actual standing reach posture. A total of 12 subjects (three subjects for each percentile in stature of 5th percentile female, 50th percentile female, 50th percentile male and 95th percentile male) are selected to run the experiment for 30 tasks. Among these subjects one is Turkish, two are Chinese, and the rest subjects are Americans. Three sets of weights for the general standing reach tasks are obtained for the three zones by averaging all weights in each zone for all subjects and all tasks. Based on the obtained sets of weights, the predicted standing reach postures found using the direct optimization-based approach have good correlation with the experimental results. Sensitivity of the formulation has also been investigated in this study. The presented formulation can be used to determine the weights of cost function within any multi-objective optimization (MOO) problems such as any types of posture prediction and motion prediction.

A bio-inspired grasp optimization algorithm for an anthropomorphic robotic hand

A fundamental requirement for assistive robots is to guarantee a safe and human-like way to perform their tasks. In particular, the ability to realize smooth movements and obtain a stable grasp is of primary importance. In this perspective, this paper aims at studying human grasping and developing a bio-inspired method for power-grip posture prediction and finger trajectory planning for a robotic hand. The developed method is based on neuroscientific assumptions and experimental evidence coming from the observation of the human behavior during power grip. It is based on the minimization of a suitably defined function to identify the optimal grasp configuration and the choice of a logarithmic spiral trajectory for moving the fingers. The behavior of ten different subjects during the grasping action has been analyzed with the CyberGlove motion capture data glove. A common thumb posture has been observed and has been introduced in the grasping algorithm. The algorithm performance has been tested on an anthropomorphic robotic hand by means of simulation trials. The results demonstrate the effectiveness of the approach and pave the way for the implementation on a real robotic hand.

Joint discomfort human performance measure for driver posture prediction: some insights

Digital human models have proven to be valuable tools for understanding human reach envelope inside a vehicle. Typical digital human posture prediction simulations employ optimisation techniques that find the most likely posture that a human would realise to achieve a given task. Human performance measures are included as an objective function in the optimisation formulation. A previous study (Yang et al., 2004) defined a joint discomfort human performance measure based on joint angles that incorporates three distinct aspects of joint discomfort. However, the previously defined joint discomfort function is poorly understood. This paper investigates the properties of the joint discomfort function and how each parameter in the function affects the predicted posture. An alternate formulation of the joint discomfort human performance measure is proposed. The parameters of the new joint discomfort function are investigated through graphical analysis, ANOVA analysis, and sensitivity analysis. The joint discomfort function is then employed in several posture prediction simulations that pertain to reach space inside a vehicle. The postures are given to demonstrate the effect that the parameters of joint discomfort have on predicted postures.

Prediction of Occupant Posture in Pre-crash by a Muscle Control Method Using a Whole Body Musculo-skeletal FE Model and a Mathematical Model of Learning in Brain

Prediction of Occupant Posture in Pre-crash by a Muscle Control Method Using a Whole Body Musculo-skeletal FE Model and a Mathematical Model of Learning in Brain

An inverse optimization approach for determining weights of joint displacement objective function for upper body kinematic posture prediction

SUMMARYHuman posture prediction can often be formulated as a nonlinear multiobjective optimization (MOO) problem. The joint displacement function is considered as a benchmark of human performance measures. When the joint displacement function is used as the objective function, posture prediction is a MOO problem. The weighted-sum method is commonly used to find a Pareto solution of this MOO problem. Within the joint displacement function, the relative value of the weights associated with each joint represents the relative importance of that joint. Usually, weights are determined by trial and error approaches. This paper presents a systematic approach via an inverse optimization approach to determine the weights for the joint displacement function in posture prediction. This inverse optimization problem can be formulated as a bi-level optimization problem. The design variables are joint angles and weights. The cost function is the summation of the differences between two set of joint angles (the design variables and the realistic posture). Constraints include (1) normalized weights within limits and (2) an inner optimization problem to solve for joint angles (predicted posture). Additional constraints such as weight limits and weight linear equality constraints, obtained through observations, are also implemented in the formulation to test the method. A 24 degree of freedom human upper body model is used to study the formulation and visualize the prediction. An in-house motion capture system is used to obtain the realistic posture. Four different percentiles of subjects are selected to run the experiment. The set of weights for the general seated posture prediction is obtained by averaging all weights for all subjects and all tasks. On the basis of obtained set of weights, the predicted postures match the experimental results well.

Optimisation-based approach for determining driver seat adjustment range for vehicles

When designing for human variability in the interior cab design for a vehicle, it becomes difficult to predict an optimum driver seat adjustment range. This paper proposes an optimisation–based approach to determine the seat adjustment range without the need for population sampling and stochastic posture prediction. This paper uses boundary anthropometric digital human models, a 95% male and a 5% female, to establish the driver seat adjustment range for vehicles. The simulation predicts the optimum posture of the seated driver inside the vehicle, and also gives an indication of how comfortable the driver is while seated in the predicted posture.

A finger motion model for reach and grasp

This study aimed to develop a model that describes human finger motion for simulation of reach and grasp for selected objects and tasks. Finger joint angles and timing of their changes were measured for six subjects as they reached 20–40 cm and grasped cylindrical handles (1.3–10.2 cm D) of varying orientation (vertical/axial). The empirical results from multiple regression analyses served as inputs to allow a fourth order polynomial to predict motion of each finger joint. The proposed model showed good fit with observations, with high coefficients of determination from 0.54 to 1 and reasonable errors from 0.04° to 5.44° for all conditions considered. The proposed finger motion model was implemented in an existing kinematic hand model to employ a contact algorithm for refined prediction of grip posture and to illustrate its predictive power by graphically displaying the opening and closing of the hand. Relevance to industry Finger joint motions during reach and grasp are needed for prediction of (1) tendon excursions for study of work-related musculoskeletal disorders, (2) required space for the hand, (3) finger locations on work objects, and (4) hand grip postures and strength.

Hybrid method for driver accommodation using optimization-based digital human models

Many applications exist where humans are required to perform a task in a seated position, such as operating a vehicle. Seated posture inside a vehicle influences driver performance and control of the vehicle. For people of extreme stature, tall or short, and for people of extreme width, obese or pregnant populations, it can be difficult to safely operate a vehicle if there is not enough room in the cab or if some controls cannot be reached. This study proposes a hybrid method for predicting the optimum driver seat adjustment range to accommodate diverse drivers in any vehicle based on a direct optimization-based posture prediction method. The proposed hybrid method combines three approaches: a boundary manikin approach, a population sampling approach, and a special population approach. The boundary manikin approach places two boundary manikins (5% female and 95% male) inside a virtual vehicle cab to perform tests. The population sampling approach spans a multitude of test subjects ranging in stature from 158 to 185 cm, determining the range from a plot of predicted hip point distances from the point of contact of the right heel and the floor. The special population approach studies the effect that size and shape changes, such as pregnancy, have on seated posture inside a vehicle. Also given is an indication of discomfort through the output values of the multi-objective function in the optimization formulation. A combination of the three approaches is used to determine an optimal adjustment range for the driver seat, thus allowing most people to safely operate the vehicle. Results of the simulations are validated using experimental determinations of the driver seat adjustment range from the literature. The main benefit of using this method is that the human aspect of design can be included early in the design process, thereby reducing or eliminating prototypes. Another benefit of the simulation is that it can be adapted to other seating tasks such as: occupant seat check inside a vehicle; workstation design; and issues related to other special populations such as obese individuals, dwarfs, and children.

Multi-objective optimization-based method for kinematic posture prediction: development and validation

SUMMARYPosture prediction plays an important role in product design and manufacturing. There is a need to develop a more efficient method for predicting realistic human posture. This paper presents a method based on multi-objective optimization (MOO) for kinematic posture prediction and experimental validation. The predicted posture is formulated as a multi-objective optimization problem. The hypothesis is that human performance measures (cost functions) govern how humans move. Twelve subjects, divided into four groups according to different percentiles, participated in the experiment. Four realistic in-vehicle tasks requiring both simple and complex functionality of the human simulations were chosen. The subjects were asked to reach the four target points, and the joint centers for the wrist, elbow, and shoulder and the joint angle of the elbow were recorded using a motion capture system. We used these data to validate our model. The validation criteria comprise R-square and confidence intervals. Various physics factors were included in human performance measures. The weighted sum of different human performance measures was used as the objective function for posture prediction. A two-domain approach was also investigated to validate the simulated postures. The coefficients of determinant for both within-percentiles and cross-percentiles are larger than 0.70. The MOO-based approach can predict realistic upper body postures in real time and can easily incorporate different scenarios in the formulation. This validated method can be deployed in the digital human package as a design tool.

Use of multi-objective optimization for digital human posture prediction

With sufficient fidelity, the use of virtual humans can save time, money, and lives through improved product design, process design, and understanding of behaviour. Optimization-based posture prediction is a unique tool, and this article presents a study that advances posture prediction with a multi-objective optimization (MOO) approach. MOO is used to both develop and combine the following human performance measures: joint displacement; musculoskeletal discomfort; and a variation on potential energy. The following MOO methods are studied in the context of human modelling: objective sum; min–max; and global criterion. Using MOO yields realistic results. Of the independent performance measures, discomfort generally provides the most accurate postures. Potential energy, however, is not a significant factor in governing human posture and should be combined with other performance measures. The three MOO methods for combining performance measures yield similar results, but the objective sum provides slightly more realistic postures.

Optimization-based posture prediction for human upper body

SUMMARYA general methodology and associated computational algorithm for predicting postures of the digital human upper body is presented. The basic plot for this effort is an optimization-based approach, where we believe that different human performance measures govern different tasks. The underlying problem is characterized by the calculation (or prediction) of the human performance measure in such a way as to accomplish a specified task. In this work, we have not limited the number of degrees of freedom associated with the model. Each task has been defined by a number of human performance measures that are mathematically represented by cost functions that evaluate to a real number. Cost functions are then optimized, i.e., minimized or maximized, subject to a number of constraints, including joint limits. The formulation is demonstrated and validated. We present this computational formulation as a broadly applicable algorithm for predicting postures using one or more human performance measures.

Multi-objective optimisation method for posture prediction and analysis with consideration of fatigue effect and its application case

Automation techniques have been widely used in the manufacturing industry, but there are still many manual handling operations required in assembly and maintenance work. Inappropriate posture and physical fatigue might result in musculoskeletal disorders (MSDs) in such physical jobs. In ergonomics and occupational biomechanics, virtual human modelling techniques have been employed to optimize manual operations in the design stage so as to avoid or decrease risk of MSD. In these methods, physical fatigue is only considered to minimize muscle or joint stress, while the effect of fatigue along time on posture is not often considered, although worker’s motion strategies and postures under physical fatigue are different from those under non-fatigue conditions. In this paper, based on related literatures and multiple-objective optimisation method (MOO), a new posture prediction and analysis method is proposed for predicting the optimal posture under non-fatigue and fatigue conditions and evaluating the physical fatigue in manual material handling operation. The posture prediction and analysis problem is mathematically described, and a special application case is demonstrated for analyzing a drilling assembly operation in European Aeronautic Defence and Space Company (EADS).

Posture Prediction with External Loads – A Pilot Study

Posture Prediction with External Loads – A Pilot Study

A New Methodology for Human Grasp Prediction

Grasping is an essential requirement for digital human models (DHMs). It is a complex process and thus a challenging problem for DHMs, involving a skeletal structure with many degrees-of-freedom (DOFs), cognition, and interaction between the human and objects in the environment. Furthermore, grasp planning involves not only finding the shape of the hand and the position and orientation of the wrist but also the posture of the upper body required for producing realistic grasping simulations. In this paper, a new methodology is developed for grasping prediction by combining a shape-matching method and an optimization-based posture prediction technique. We use shape matching to pick a hand shape from a database of stored grasps, then position the hand around the object. The posture prediction algorithm then calculates the optimal posture for the whole upper body necessary to execute the grasp. The proposed algorithm is tested on a variety of objects in a 3-D environment. The results are realistic and suggest that the new method is more suitable for grasp planning than conventional methods. This improved performance is particularly apparent when the nature of the grasped objects is not known <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">priori</i> , and when a complex high-DOF hand model is necessary.

A physics-based digital human model

This paper presents a comprehensive human modelling and simulation environment. This environment, called Santos™, is a new generation of digital human simulation systems that allows a user to interact with a digital character with full and accurate biomechanics and a complete muscular system, subject to the laws of physics. Major results in the areas of dynamic motion prediction, advanced posture prediction and comfort level assessment, physiology model, modelling of clothing and muscle wrapping and force assessment will be presented. This paper will feature the various modules that comprise the Santos environment.

Workspace zone differentiation tool for visualization of seated postural comfort

Over the past several years, significant advances have been made in the area of posture prediction. However, to make simulations more useful for vehicle design, additional unique tools are needed. This research focuses on the development of one such tool, called workspace zone differentiation or zone differentiation. This new tool allows users to visualize not only the complete reach envelope but also the interior comfort levels of the envelope. It uses a color map to display the relative values of various human performance measures (i.e., comfort) at points surrounding an avatar. This is done by leveraging an optimization-based approach to seated posture prediction. Using this tool, a vehicle designer can visually display the impact that the placement of a control (switch, button, etc.) has on a driver's seated postural comfort. The comfort values are displayed in a manner similar to how finite element analysis (FEA) programs display stress and strain results. Within multi-objective optimization (MOO) for seated posture prediction, the objective function is defined as the mathematical rating model for discomfort. A direct mapping method is used to correlate subjective (survey) comfort levels with the mathematical rating model. Based on the correlated mathematical model, we develop the graphical user interface to display the comfort zones.