Dynamics simulation of human box delivering task

Thesis (M.S.) University of Alaska Fairbanks, 2018The dynamic optimization of a box delivery motion is a complex task. The key component is to achieve an optimized motion associated with the box weight, delivering speed, and location. This thesis addresses one solution for determining the optimal delivery of a box. The delivering task is divided into five subtasks: lifting, transition step, carrying, transition step, and unloading. Each task is simulated independently with appropriate boundary conditions so that they can be stitched together to render a complete delivering task. Each task is formulated as an optimization problem. The design variables are joint angle profiles. For lifting and carrying task, the objective function is the dynamic effort. The unloading task is a byproduct of the lifting task, but done in reverse, starting with holding the box and ending with it at its final position. In contrast, for transition task, the objective function is the combination of dynamic effort and joint discomfort. The various joint parameters are analyzed consisting of joint torque, joint angles, and ground reactive forces. A viable optimization motion is generated from the simulation results. It is also empirically validated. This research holds significance for professions containing heavy box lifting and delivering tasks and would like to reduce the chance of injury.Chapter 1 Introduction -- Chapter 2 Skeletal Human Modeling -- Chapter 3 Kinematics and Dynamics -- Chapter 4 Lifting Simulation -- Chapter 5 Carrying Simulation -- Chapter 6 Delivering Simulation -- Chapter 7 Conclusion and Future Research -- Reference

Maximum weight lifting prediction considering dynamic joint strength

Thesis (M.S.) University of Alaska Fairbanks, 2018This thesis describes an efficient optimization method for predicting the maximum lifting weight considering dynamic joint strength in symmetric box lifting using a skeletal model. Dynamic joint strength is modeled as a three-dimensional function of joint angle and joint angular velocity based on experimentally obtained joint strength data. The function is further formulated as the joint torque limit constraint in an inverse dynamics optimization formulation to predict the lifting motion. In the proposed optimization formulation, external load is treated as design variables along with joint angle profiles, which are represented by control points of B-spline curves. By using this new formulation, dynamic lifting motion and strategy can be predicted for a symmetric maximum weight box lifting task with given initial and final box locations. Results show that incorporating dynamic strength is critical in predicting the lifting motion in extreme lifting conditions. The prediction outputs in joint space are incorporated in OpenSim software to find out muscles force and activity during the movement. Electromyography data are collected for a regular weight lifting to validate the integration process between the predictive model (joint model) and OpenSim model (muscle model). The proposed algorithm and analysis method based on motion prediction and OpenSim can be further developed as a useful ergonomic tool to protect workers from injury in manual material handling.Chapter 1 Introduction -- 1.1 Motivation and Objectives -- 1.2 Background -- 1.2.1 Lifting Simulation -- 1.2.2 Muscle modelling -- 1.3 Overview of thesis and specific contribution. Chapter 2 Human Modelling, Kinematics, and Dynamics. Chapter 3 Optimization Formulation -- 3.1 Basic optimization formulation -- 3.2 New optimization formulation -- 3.2.1 External force as design variable -- 3.2.2 Time grid points as design variables -- 3.2.3 Dynamic joint strength. Chapter 4 Maximum Weight Prediction. Chapter 5 OpenSim Simulation for Maximum Weight Lifting -- 5.1 OpenSim -- 5.2 OpenSim simulation and processing -- 5.3 Data processing -- 5.4 Post processing and analysis -- 5.5 Results and comparison. Chapter 6 Validation of Electromyography -- 6.1 Electromyography -- 6.2 Experimental setup -- 6.3 Procedure -- 6.4 Data acquisition -- 6.5 Results and conclusion. Chapter 7 Conclusion and Future Work -- 7.1 Conclusion -- 7.2 Future Work -- Reference -- Appendix

Biomechanical analysis of asymmetric and dynamic lifting task

Lifting tasks is one of the leading causes of occupational lower back disorders (LBD). Aimed at deriving internal forces of human musculoskeletal system during lifting, biomechanical models are utilized to address this problem. This thesis provides an indepth literature review of such modeling, and the results of experiments used to address LBD issues. An isometric pulling experiment was conducted to study the correlation between electromyography (EMG) and predicted muscle forces by AnyBody Modeling System™ with increasing hand loads. An infinite order polynomial (min/max) optimization criterion predicted percentage of maximum muscle forces, which achieved 98% correlation with normalized EMG. In a separate study, motion data during lifting of 13.6 kg (30 lb) weight at 0°, 30° and 60° asymmetry was collected by the OptiTrack™ sixcamera motion capture system to drive the AnyBody™ model. Erector spinae was the most activated muscle during lifting. When the lifting origin became more asymmetric toward the right direction, the right external oblique was more activated, and complementarily the right Internal oblique was less activated. Since oblique muscles can support an external moment more efficiently, and in addition the subject squatted more as the lifting origin became more asymmetric, L5/S1 joint forces decreased. This study contributes to the design and evaluation of lifting tasks to minimize LBD

Sex-Dependent Estimation of Spinal Loads During Static Manual Material Handling Activities — Combined in vivo and in silico Analyses

Manual material handling (MMH) is considered as one of the main contributors to low back pain. While males traditionally perform MMH tasks, recently the number of females who undertake these physically-demanding activities is also increasing. To evaluate the risk of mechanical injuries, the majority of previous studies have estimated spinal forces using different modeling approaches that mostly focus on male individuals. Notable sex-dependent differences have, however, been reported in torso muscle strength and anatomy, segmental mass distribution, as well as lifting strategy during MMH. Therefore, this study aimed to use sex-specific models to estimate lumbar spinal and muscle forces during static MHH tasks in 10 healthy males and 10 females. Motion-capture, surface electromyographic from select trunk muscles, and ground reaction force data were simultaneously collected while subjects performed twelve symmetric and asymmetric static lifting (10 kg) tasks. AnyBody Modeling System was used to develop base-models (subject-specific segmental length, muscle architecture, and kinematics data) for both sexes. For females, female-specific models were also developed by taking into account for the female's muscle physiological cross-sectional areas, segmental mass distributions, and body fat percentage. Males showed higher absolute L5-S1 compressive and shear loads as compared to both female base-models (25.3% compressive and 14% shear) and female-specific models (41% compressive and 23.6% shear). When the predicted spine loads were normalized to subjects' body weight, however, female base-models showed larger loads (9% compressive and 16.2% shear on average), and female-specific models showed 2.4% smaller and 9.4% larger loads than males. Females showed larger forces in oblique abdominal muscles during both symmetric and asymmetric lifting tasks, while males had larger back extensor muscle forces during symmetric lifting tasks. A stronger correlation between measured and predicted muscle activities was found in females than males. Results indicate that female-specific characteristics affect the predicted spinal loads and must be considered in musculoskeletal models. Neglecting sex-specific parameters in these models could lead to the overestimation of spinal loads in females

On the origin of back pain

Dieen, J.H. van [Promotor]Bongers, P.M. [Promotor]Kingma, I. [Copromotor]Boot, C.R.L. [Copromotor

Reliability And Validity Of Virtual Build Methodology For Ergonomics Analyses

This study was conducted to assess the validity and reliability of the Virtual Build methodology for ergonomics design and analysis. Thirty-six human subjects participated in this study and performed a set of six tasks. The tasks were performed twice in both real and virtual environment. The subject?s motion in performing tasks was analyzed by ergonomics assessments by using Virtual Build methodology. Criteria-related validity was evaluated by comparing the Virtual Build ergonomic assessment results with manual calculation. Test-retest reliability was evaluated by correlating ergonomics assessment results between two trials. The result shows that the Virtual Build methodology is reliable for ergonomic assessments. 48 out of 51 reliability index scores are higher than 0.8. The Virtual Build with virtual environment has lower over-time reliability performance than the real environment. The t-test shows that the Virtual Build is valid for 1991 NIOSH lifting equation assessment when using real environment. Some improvements in enhancing human perception need to be done to make Virtual Build valid when using virtual environment

Analysis of a Biomechanical Model for Safe Lifting using MATLAB Simulation

This paper describes the development of a multi-body biomechanical model that can be used to assess the risk of low back disorders. A multi-segment link model is considered in this paper which represents a human body in which links represent various limbs such as arms, leg, foot, thigh, thorax etc. Force balance and moment balance equations are formed at different joints. Equations formed are written in form of a MATLAB program to determine the relationship between load lifted and muscle moment generated due to load. This biomechanical model was employed to clarify the role of various biomechanical factors such as magnitude of load, shape, size and location of load involved in the load lifting process. To determine safe lifting postures on the basis of model such that the reaction force at the L4 / L5 joint is minimum subjected to other joints not being overstressed is carried out. Various moment-load relationships between various joints are computed along with moment-moment relationships between various joints. The model is able to suggest the safe posture in manual material handling tasks. A geometric model for simulations of postural control is obtained with Matlab/Simulink software

Understanding the effect of objective function weightings on posture prediction: application to predicting lifting postures in un-fatigued and fatigued states

Physical prototyping is often required when developing new equipment and workspaces so that human-systems integration can be considered. Digital human modelling facilitates more efficient, upstream assessment of human interactions with new equipment within a virtual environment. However, predicting how humans will behave (i.e., move and position) to perform industry relevant tasks, requires the ability to consider state-based influences, like fatigue, on predicted behaviours (Davidson, Graham, Beck, Marler, & Fischer, 2021). The overarching objective of this thesis was to understand how objective function weightings should be configured within a multi-objective task-focused human behaviour prediction model to simulate postures within a floor-to-shoulder height lifting task under different fatigue states. To achieve this aim, simulated lifting postures were generated by systematically altering objective function weightings where each simulation was compared with experimentally captured lifting posture data to determine the closest match and subsequent ideal optimal weighting configuration. Fifteen participants were recruited and completed a lifting protocol at 30% of their EPIC predicted maximum box lifting capacity. Participant fatigue level was evaluated based on their RPE and task completion time. Changes in participants lifting strategy over the course of the fatigue lifting protocol was also verified. Participants who were deemed fatigued and exhibited movement strategy changes were included in the simulation portion of the thesis where participant’s first and last lift kinematic data were used as inputs to a multi-objective optimization digital human model. Avatar’s were generated to match participant anthropometry and were constrained to maintain the same hand and foot position as the participants. Simulations were run while altering three objective functions including the minimization of: Discomfort, Total Joint Torque and, Maximum Joint Torque. The different objective functions represent performance measures to be minimized in order to predict the design variables, in this case joint angles, within the avatar’s available degrees of freedom. Simulations were run for objective function weighting coefficients between 0-100% at 10% intervals for each subject, each posture and each fatigue state. Each simulation was compared to the simulation that was generated using the participant-specific motion capture data and the error between the motion capture and optimization predicted data were calculated. A total root mean squared error (RMSE) value was calculated including the ankle, knee, hip, trunk, shoulder, and elbow errors. Error was modeled as a function of objective function weightings using participant specific multivariate regression equations. Multivariate regression equations were then used to determine the objective function weighting configuration that would results in the lowest error for each participant, at lift origin and destination and for un-fatigued and fatigued lifting states. A two-way repeated measures Friedman’s test was used to detect for difference in optimal objective functions weightings between locations (origin vs. destination) and fatigue state (un-fatigued vs. fatigued lift). Results showed a median objective function weighting of sixty, zero, and zero for the discomfort, maximum joint torque and total joint torque objective functions, respectively. Friedman’s test did not detect significant differences between fatigue states or location for any of the three objective function weightings. The discomfort objective function alone tended to predict box lifting postures best. Discomfort may include aspects of the torque based objective functions leading to its increased priority for predicting postures over the minimization of maximum and total joint torque. Future studies should build on the current suite of objective functions to improve predictive capabilities of digital human models for novel tasks. More accurate digital human models will allow for earlier consideration of humans-in-the-loop and significantly reduce industry costs of physical prototypes