Aimee Cloutier

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.

Motion capture experiments for validating optimization-based human models

Optimization-based digital human model research has gained significant momentum among various human models. Any task can be formulated to an optimization problem, and the model can predict not only postures but also motions. However, these optimization-based digital human models need validation using experiments. The motion capture system is one of the ways to validate predicted results. This paper summarizes the progress of motion capture experiment efforts at the Human-Centric Design Research (HCDR) Laboratory at Texas Tech University. An eight-camera motion capture system has been set up in our research lab. Marker placement protocols have been developed where markers are placed on the subjects to highlight bony landmarks and identify segments between joints in line with previously identified guidelines and suggestions in literature. A posture reconstruction algorithm has been developed to map joint angles from motion capture experiments to digital human models. Various studies have been conducted in the lab involving motion capture experiments for jumping, standing and seated reach, and pregnant womens walking, sit to standing, seated reach, and reach with external loads. The results showed that the posture reconstruction algorithm is useful and accurate to transfer motion capture experiment data to joint angles. Marker placement protocol is reliable to capture all joints. The main task of the motion caption system is to validate all optimization-based digital human models developed by other research members at the HCDR Lab.

Effect of human link length determination on posture reconstruction

Motion capture experiment results are often used as a means of validation for digital human simulations. Motion capture results are marker positions and joint centers in Cartesian space. However, joint angles are more intuitive and easy to understand compared to marker or joint center positions. Posture reconstruction algorithms are used to map Cartesian space to joint space by re-creating experimental postures with simulation models. This allows for direct comparison between the experimental results and digital human simulations. Besides the inherent experimental errors from motion capture system, one source of simulation error is the determination of the link lengths to be used in the simulation model. The link length errors can propagate through all simulation results. Therefore, it is critical to eliminate the link length errors. The objective of this study is to determine the best method of determining link lengths for the simulation model to best match the model to the experiment results containing errors. Specifically, the way that the link lengths are calculated in the posture reconstruction process from motion capture data has a significant effect on the recreated posture for the simulation model. Three link length calculation methods (experimental-average method, trial-specific method, and T-pose method) are developed and compared to a benchmark method (frame-specific method) for calculating link lengths. The results indicate that using the trial-specific method is the most accurate method when referring to calculating frame-specific link lengths.

Physical Design Factors Contributing to Patient Falls

OBJECTIVES The aim of this study was to identify physical design elements that contribute to potential falls in patient rooms. METHODS An exploratory, physical simulation-based approach was adopted for the study. Twenty-seven subjects, older than 70 years (11 male and 16 female subjects), conducted scripted tasks in a mockup of a patient bathroom and clinician zone. Activities were captured using motion-capture technology and video recording. After biomechanical data processing, video clips associated with potential fall moments were extracted and then examined and coded by a group of registered nurses and health care designers. Exploratory analyses of the coded data were conducted followed by a series of multivariate analyses using regression models. RESULTS In multivariate models with all personal, environmental, and postural variables, only the postural variables demonstrated statistical significance-turning, grabbing, pushing, and pulling in the bathroom and pushing and pulling in the clinician zone. The physical elements/attributes associated with the offending postures include bathroom configuration, intravenous pole, door, toilet seat height, flush, grab bars, over-bed table, and patient chair. CONCLUSIONS Postural changes, during interactions with the physical environment, constitute the source of most fall events. Physical design must include simultaneous examination of postural changes in day-to-day activities in patient rooms and bathrooms. Among discussed testable recommendations in the article, the followings design strategies should be considered: (a) designing bathrooms to reduce turning as much as possible and (b) designing to avoid motions that involve 2 or more of the offending postures, such as turning and grabbing or grabbing and pulling, and so on.

Control of Hand Prostheses: A Literature Review

In recent years, there has been a steep rise in the quality of prostheses for patients with upper limb amputations. One common control method, using electromyographic (EMG) signals generated by muscle contractions, has allowed for an increase in the degrees of freedom (DOFs) of hand designs and a larger number of available grip patterns with little added complexity for the wearer. However, it provides little sensory feedback and requires non-natural control which must be learned by the user. Another recent improvement in prosthetic hand design instead employs electroneurographic (ENG) signals, requiring an interface directly with the peripheral nervous system (PNS) or the central nervous system (CNS) to control a prosthetic hand. While ENG methods are more invasive than using surface EMG for control, an interface with the PNS has the potential to provide more natural control and creates an avenue for both efferent and afferent sensory feedback. Despite the recent progress in design and control strategies, however, prosthetic hands are still far more limited than the actual human hand. This review outlines the recent progress in the development of EMG and ENG controlled prosthetic hands, discussing advancements in the areas of sensory feedback and control. The potential benefits and limitations of both control strategies, in terms of signal classification, invasiveness, and sensory feedback, are examined. A brief overview of interfaces with the CNS is provided, and potential future developments for these control methods are discussed.Copyright

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.

An alternative formulation for determining weights of joint displacement objective function in seated posture prediction

The human posture prediction model is one of the most important and fundamental components in digital human models. The direct optimization-based method has recently gained more attention due to its ability to give greater insights, compared to other approaches, as how and why humans assume a certain pose. However, one longstanding problem of this method is how to determine the cost function weights in the optimization formulation. This paper presents an alternative formulation based on our previous inverse optimization approach. The cost function contains two components. The first is the weighted summation of the difference between experimental joint angles and neutral posture, and the second is the weighted summation of the difference between predicted joint angles and the neutral posture. The final objective function is then the difference of these two components. Constraints include (1) normalized weights within limits; (2) an inner optimization problem to solve for the joint angles, where joint displacement is the objective function; (3) the end-effector reaches the target point; and (4) the joint angles are within their limits. Furthermore, weight limits and linear weight constraints determined through observation are implemented. A 24 degree of freedom (DOF) human upper body model is used to study the formulation. An in-house motion capture system is used to obtain the realistic posture. Four different percentiles of subjects are selected and a total of 18 target points are designed for this experiment. The results show that using the new objective function in this alternative formulation can greatly improve the accuracy of the predicted posture.

Experimental identification of potential falls in older adult hospital patients

Patient falls within hospitals have been identified as serious but largely preventable incidents, particularly among older adult patients. Previous literature has explored intrinsic factors associated with patient falls, but literature identifying possible extrinsic or situational factors related to falls is lacking. This study seeks to identify patient motions and activities along with associated environmental design factors in a patient bathroom and clinician zone setting that may lead to falls. A motion capture experiment was conducted in a laboratory setting on 27 subjects over the age of seventy using scripted tasks and mockups of the bathroom and clinician zone of a patient room. Data were post-processed using Cortex and Visual3D software. A potential fall was characterized by a set of criteria based on the jerk of the upper body׳s center of mass (COM). Results suggest that only motion-related factors, particularly turning, pushing, pulling, and grabbing, contribute most significantly to potential falls in the patient bathroom, whereas only pushing and pulling contribute significantly in the clinician zone. Future work includes identifying and changing precise environmental design factors associated with these motions for an updated patient room and performing motion capture experiments using the new setup.

Vertical Ground Reaction Forces for Given Human Standing Posture With Uneven Terrains: Prediction and Validation

Ground reaction forces (GRFs) on individual support vary with posture and motion for bipedal mechanisms or systems due to the redundancy in the system. In digital human modeling, specifically posture prediction, the GRFs are predicted, as they are unknown in a virtual environment. Traditionally, models in which the GRFs are predicted have been presented; however, they are always assumed to be on flat ground. Little work has been done to predict the GRFs on uneven or arbitrary terrain. This paper presents a generic method to calculate the vertical GRFs for given standing postures with uneven terrain. The vertical GRFs are predicted based on the generalized forces (torque in revolute joints; force in prismatic joints) calculated using the recursive Lagrangian formulation and a 3-D zero moment point. Motion capture experiments were used to obtain postures for common standing reaching tasks. Force plates were employed to record GRF information for each task. Experimental postures were reconstructed, and the GRF prediction algorithm was used to predict the associated vertical GRFs for each task. Experimental and predicted vertical GRFs are compared to validate the prediction model. The prediction method proved to be valid, with an overall error of 6%.

Optimization-based posture reconstruction for digital human models

Digital human modeling provides a valuable tool for designers when implemented early in the design process. Motion capture experiments offer a means of validation of the digital human simulation models. However, there is a gap between the motion capture experiments and the simulation models, as the motion capture results are marker positions in Cartesian space and the simulation model is based on joint space. Therefore, it is necessary to map the motion capture data to simulation models by employing a posture reconstruction algorithm. Posture reconstruction is an inherently redundant problem where the collective distance error between experimental joint centers and simulation joint centers is minimized. This paper presents an optimization-based method for determining an accurate and efficient solution to the posture reconstruction problem. The procedure is used to recreate 120 experimental postures. For each posture, the algorithm minimizes the distance between the simulation model joint centers and the corresponding experimental subject joint centers which is called the mean measurement error.