Biomechanical simulation of manual lifting using spacetime optimization

Multi-phase optimisation model predicts manual lifting motions with less reliance on experiment-based posture data

Optimisation-based predictive models are widely-used to explore the lifting strategies. Existing models incorporated empirical subject-specific posture constraints to improve the prediction accuracy. However, over-reliance on these constraints limits the application of predictive models. This paper proposed a multi-phase optimisation method (MPOM) for two-dimensional sagittally symmetric semi-squat lifting prediction, which decomposes the complete lifting task into three phases-the initial posture, the final posture, and the dynamic lifting phase. The first two phases are predicted with force- and stability-related strategies, and the last phase is predicted with a smoothing-related objective. Box-lifting motions of different box initial heights were collected for validation. The results show that MPOM has better or similar accuracy than the traditional single-phase optimisation (SPOM) of minimum muscular utilisation ratio, and MPOM reduces the reliance on experimental data. MPOM offers the opportunity to improve accuracy at the expense of efforts to determine appropriate weightings in the posture prediction phases. Practitioner summary: Lifting optimisation models are useful to predict and explore the human motion strategies. Existing models rely on empirical subject-specific posture constraints, which limit their applications. A multi-phase model for lifting motion prediction was constructed. This model could accurately predict 2D lifting motions with less reliance on these constraints.

Hybrid musculoskeletal model-based 3D asymmetric lifting prediction and comparison with symmetric lifting

In this study, a 3D asymmetric lifting motion is predicted by using a hybrid predictive model to prevent potential musculoskeletal lower back injuries for asymmetric lifting tasks. The hybrid model has two modules: a skeletal module and an OpenSim musculoskeletal module. The skeletal module consists of a dynamic joint strength based 40 degrees of freedom spatial skeletal model. The skeletal module can predict the lifting motion, ground reaction forces (GRFs), and center of pressure (COP) trajectory using an inverse dynamics-based motion optimization method. The musculoskeletal module consists of a 324-muscle-actuated full-body lumbar spine model. Based on the predicted kinematics, GRFs and COP data from the skeletal module, the musculoskeletal module estimates muscle activations using static optimization and joint reaction forces through the joint reaction analysis tool in OpenSim. The predicted asymmetric motion and GRFs are validated with experimental data. Muscle activation results between the simulated and experimental EMG are also compared to validate the model. Finally, the shear and compression spine loads are compared to NIOSH recommended limits. The differences between asymmetric and symmetric liftings are also compared.

Optimization-based biomechanical lifting models for manual material handling: A comprehensive review

Lifting is a main task for manual material handling (MMH), and it is also associated with lower back pain. There are many studies in the literature on predicting lifting strategies, optimizing lifting motions, and reducing lower back injury risks. This survey focuses on optimization-based biomechanical lifting models for MMH. The models can be classified as two-dimensional and three-dimensional models, as well as skeletal and musculoskeletal models. The optimization formulations for lifting simulations with various cost functions and constraints are reviewed. The corresponding equations of motion and sensitivity analysis are briefly summarized. Different optimization algorithms are utilized to solve the lifting optimization problem, such as sequential quadratic programming, genetic algorithm, and particle swarm optimization. Finally, the applications of the optimization-based lifting models to digital human modeling which refers to modeling and simulation of humans in a virtual environment, back injury prevention, and ergonomic safety design are summarized.

Different Phases in Manual Materials Handling Have Different Performance Criteria: Evidence From Multi-Objective Optimization

A manual material handling task involves the phases of reaching, lifting, unloading, and standing up (RLUS). Understanding the mechanisms of manual material handling is important for occupational health and the development of assist devices. Predictive models are becoming popular in exploring which performance criterion is appropriate in the lifting phase. However, limited attempts have been performed on the other phases. The associated performance criterion for predicting other phases is unknown. In this study, an optimization model for predicting RLUS has been developed with the multi-objective optimization method. Two performance criteria (minimum dynamic effort and maximum balance) were studied to explore their importance in each phase. The result shows that maximum balance leads to joint angle errors 27.6% and 40.9% smaller than minimum dynamic effort in reaching and unloading phases, but 40.4% and 65.9% larger in lifting and standing up phases. When the two performance criteria are combined, the maximum balance could help improve the predicting accuracy in the reaching, lifting, and unloading phases. These findings suggest that people prefer different performance criteria in different phases. This study helps understand the differences in motion strategies in manual materials handling (MMH), which would be used to develop a more accurate predictive model.

Cost Function Determination for Human Lifting Motion via the Bilevel Optimization Technology

Investigating the optimal control strategy involved in human lifting motion can provide meritorious insights on designing and controlling wearable robotic devices to release human low-back pain and fatigue. However, determining the latent cost function regarding this motion remains challenging due to the complexities of the human central nervous system. Recently, it has been discovered that the underlying cost function of a biological motion can be identified from an inverse optimization control (IOC) issue, which can be handled via the bilevel optimization technology. Inspired by this discovery, this work is dedicated to studying the underlying cost function of human lifting tasks through the bilevel optimization technology. To this end, a nested bilevel optimization approach is developed by integrating particle swarm optimization (PSO) with the direction collocation (DC) method. The upper level optimizer leverages particle swarm optimization to optimize weighting parameters among different predefined performance criteria in the cost function while minimizing the kinematic error between the experimental data and the result predicted by the lower level optimizer. The lower level optimizer implements the direction collocation method to predict human kinematic and dynamic information based on the human musculoskeletal model inserted into OpenSim. Following after a benchmark study, the developed method is evaluated by experimental tests on different subjects. The experimental results reveal that the proposed method is effective at finding the cost function of human lifting tasks. Thus, the proposed method could be regarded as a paramount alternative in the predictive simulation of human lifting motion.

Hybrid Predictive Model for Lifting by Integrating Skeletal Motion Prediction with an OpenSim Musculoskeletal Model

OBJECTIVE

In this study, a novel hybrid predictive musculoskeletal model is proposed which has both motion prediction and muscular dynamics assessment capabilities.

METHODS

First, a two-dimensional (2D) skeletal model with 10 degrees of freedom is used to predict a symmetric lifting motion, outputting joint angle profiles, ground reaction forces (GRFs), and center of pressure (COP). These intermediate outputs are input to the scaled musculoskeletal model in OpenSim for muscle activation and joint reaction load analysis. Finally, the experimental validation is carried out.

RESULTS

Static Optimization tool is used to estimate the muscle activation data in OpenSim for the predicted lifting motion. Joint reaction forces of the lumbosacral joint (L5-S1) are generated using the OpenSim Joint Reaction analysis tool. The predicted joint angles, muscle activations, and peak joint reaction forces are compared with experimental data and data from literature to validate the hybrid model.

CONCLUSION

The proposed hybrid model combines the skeletal models rapid motion prediction with OpenSims complex muscular dynamics assessment, and it can serve as a new generic tool for motion prediction and injury analysis in ergonomics and biomechanics.

Dynamic-joint-strength-based two-dimensional symmetric maximum weight-lifting simulation: Model development and validation

This article presents an optimization formulation and experimental validation of a dynamic-joint-strength-based two-dimensional symmetric maximum weight-lifting simulation. Dynamic joint strength (the net moment capacity as a function of joint angle and angular velocity), as presented in the literature, is adopted in the optimization formulation to predict the symmetric maximum lifting weight and corresponding motion. Nineteen participants were recruited to perform a maximum-weight-box-lifting task in the laboratory, and kinetic and kinematic data including motion and ground reaction forces were collected using a motion capture system and force plates, respectively. For each individual, the predicted spine, shoulder, elbow, hip, knee, and ankle joint angles, as well as vertical and horizontal ground reaction force and box weight, were compared with the experimental data. Both root-mean-square error and Pearson's correlation coefficient (r) were used for the validation. The results show that the proposed two-dimensional optimization-based motion prediction formulation is able to accurately predict all joint angles, box weights, and vertical ground reaction forces, but not horizontal ground reaction forces.

A reach motion generation algorithm based on posture memories

BACKGROUND

Most existing models/algorithms for simulating goal-directed human motions were designed to generate a single "realistic" motion for a given input scenario.

OBJECTIVE

This study presents a novel reach motion generation algorithm utilizing multiple posture memories. The algorithm aims to compute and visualize a set of human reach motions that approximates the full range of physically and physiologically feasible human motions for a given input scenario.

METHODS

The algorithm utilizes posture memories constructed specifically for an individual worker using a probabilistic posture generation and registration process. The posture memories relate a hand position to the set of postures that place the individual's hand in its vicinity. When given an input scenario, the algorithm first generates different hand paths connecting the starting and ending hand positions specified in the scenario. Then, for each hand path, the algorithm produces different "feasible" motions by selecting and connecting multiple postures stored in the posture memories; the postures corresponding to the hand positions along the hand path are utilized.

CONCLUSIONS

The proposed algorithm helps understand the impacts of workplace design on the range of feasible human motion behaviors, and, thereby, contributes to the computer-aided ergonomics design of work tasks and workplaces.

Differences between obese and non-obese drivers in preferred vehicle interior components setting and driving posture

This study compared obese and non-obese drivers in the preferred seat and steering wheel setting and preferred driving posture. Twenty-one extremely obese and 23 non-obese drivers participated. Each participant determined the most preferred setting of the interior components using an adjustable vehicle mock-up; the preferred components setting and corresponding preferred driving posture were recorded. The participant groups exhibited significant differences in the preferred interior components setting. The obese group created larger steering wheel-seat space than the non-obese, with greater rearward seat displacement, more upright steering wheel angle and smaller steering wheel column displacement. It also exhibited more upright seatback angle deemed necessary for facilitating steering wheel reach with the increased steering wheel-seat distance. The between-group differences in the preferred driving posture were less pronounced: no significant group mean angle differences were found except for the elbow joint angles. Also, the mean hip joint centre positions did not significantly differ. Practitioner Summary: To contribute to larger driver packaging, this study compared obese and non-obese drivers in the preferred vehicle interior components setting and driving posture. The obese group created significantly larger space between the steering wheel and seat than the non-obese, through interior components adjustments. The between-group postural differences were less pronounced.

Robot body self-modeling algorithm: a collision-free motion planning approach for humanoids

Motion planning for humanoid robots is one of the critical issues due to the high redundancy and theoretical and technical considerations e.g. stability, motion feasibility and collision avoidance. The strategies which central nervous system employs to plan, signal and control the human movements are a source of inspiration to deal with the mentioned problems. Self-modeling is a concept inspired by body self-awareness in human. In this research it is integrated in an optimal motion planning framework in order to detect and avoid collision of the manipulated object with the humanoid body during performing a dynamic task. Twelve parametric functions are designed as self-models to determine the boundary of humanoid's body. Later, the boundaries which mathematically defined by the self-models are employed to calculate the safe region for box to avoid the collision with the robot. Four different objective functions are employed in motion simulation to validate the robustness of algorithm under different dynamics. The results also confirm the collision avoidance, reality and stability of the predicted motion.

Using mutual information to capture major concerns of postural control in a tossing activity

Human body motion for load-tossing activity was partitioned into three phases using four critical events based on the load position viz. lift-off, closest to body, peak and release. For each phase, three objective functions values, viz. mobilization, stabilization and muscular torque utilization, used to control the motion patterns, were then calculated. We hypothesize that the relationships between different objective functions can be extracted using information theory. The kinematic data obtained with 36 treatment combinations (2 tossing distances, 2 tossing heights, 3 weights, and 3 target clearances) was used to estimate the mutual information between each pair of objective functions and construct Chow-Liu trees. Results from this research indicate that there was no dominant concern in the first two phases of the activity; however, torque utilization and mobilization were found to be important factors in the third phase of the load tossing activity.

Lifting motion simulation using a hybrid approach

In this study, a hybrid dynamic model for lifting motion simulation is presented. The human body is represented by a two-dimensional (2D) five-segment model. The lifting motions are predicted by solving a nonlinear optimisation problem, the objective function of which is defined based on a minimal-effort performance criterion. In the optimisation procedure, the joint angular velocities are bounded by time-functional constraints that are determined by actual motions. Symmetric lifting motions performed by younger and older adults under varied task conditions were simulated. Comparisons between the simulation results and actual motion data were made for model evaluation. The results showed that the mean and median joint angle errors were less than 10°, which suggests the proposed model is able to accurately simulate 2D lifting motions. The proposed model is also comparable with the existing motion simulation models in terms of the prediction accuracy. Strengths and limitations of this hybrid model are discussed. Practitioner Summary: Human motion simulation is a useful tool in assessing the risks of occupational injuries. Lifting motions are associated with low-back pain. A hybrid model for lifting motion simulation was constructed. The model was able to accurately simulate 2D lifting motions in varied task scenarios for younger and older subjects.

Trajectory of human movement during sit to stand: a new modeling approach based on movement decomposition and multi-phase cost function

The purpose of this work is to develop a computational model to describe the task of sit to stand (STS). STS is an important movement skill which is frequently performed in human daily activities, but has rarely been studied from the perspective of optimization principles. In this study, we compared the recorded trajectories of STS with the trajectories generated by several conventional optimization-based models (i.e., minimum torque, minimum torque change and kinetic energy cost models) and also with the trajectories produced by a novel multi-phase cost model (MPCM). In the MPCM, we suggested that any complex task, such as STS, is decomposable into successive motion phases, so that each phase requires a distinct strategy to be performed. In this way, we proposed a multi-phase cost function to describe the STS task. The results revealed that the conventional optimization-based models failed to correctly predict the invariable features of STS, such as hip flexion and ankle dorsiflexion movements. However, the MPCM not only predicted the general features of STS with a sufficient accuracy, but also showed a potential flexibility to distinguish between the movement strategies from one subject to the other. According to the results, it seems plausible to hypothesize that the central nervous system might apply different strategies when planning different phases of a complex task. The application areas of the proposed model could be generating optimized trajectories of STS for clinical applications (such as functional electrical stimulation) or providing clinical and engineering insights to develop more efficient rehabilitation devices and protocols.

OPTIMIZATION-BASED DYNAMIC PREDICTION OF HUMAN POSTURAL RESPONSE UNDER TILTING OF BASE OF SUPPORT

The purpose of this paper is to study formulations and computational procedures for prediction of natural human response to tilting of its base of support. The human skeletal structure is modeled as a five-segment, four-degree-of-freedom mechanical system standing on sinusoidally driven tilting platform in the sagittal plane. The problem is formulated based on predictive dynamics method that leads to an optimization problem. The joint torque square is included in the performance measure and the dynamic stability is achieved by satisfying the vertical forces criterion. The constrained nonlinear optimization problem is solved using an algorithm based on the sequential quadratic programming (SQP) approach. The results which are joint trajectories and torques are characterized in terms of two main types of movement strategies observed in humans, namely, the ankle and hip strategies. Moreover, the effect of arms on the stability of the model is studied. The results obtained with the formulation are validated with the experimental data. Simulation results demonstrate the effectiveness of the proposed formulation in prediction of natural motion of human in response to tilting of the base plate.

The development of a model to predict the effects of worker and task factors on foot placements in manual material handling tasks

Accurate prediction of foot placements in relation to hand locations during manual materials handling tasks is critical for prospective biomechanical analysis. To address this need, the effects of lifting task conditions and anthropometric variables on foot placements were studied in a laboratory experiment. In total, 20 men and women performed two-handed object transfers that required them to walk to a shelf, lift an object from the shelf at waist height and carry the object to a variety of locations. Five different changes in the direction of progression following the object pickup were used, ranging from 45° to 180° relative to the approach direction. Object weights of 1.0 kg, 4.5 kg, 13.6 kg were used. Whole-body motions were recorded using a 3-D optical retro-reflective marker-based camera system. A new parametric system for describing foot placements, the Quantitative Transition Classification System, was developed to facilitate the parameterisation of foot placement data. Foot placements chosen by the subjects during the transfer tasks appeared to facilitate a change in the whole-body direction of progression, in addition to aiding in performing the lift. Further analysis revealed that five different stepping behaviours accounted for 71% of the stepping patterns observed. More specifically, the most frequently observed behaviour revealed that the orientation of the lead foot during the actual lifting task was primarily affected by the amount of turn angle required after the lift (R(2) = 0.53). One surprising result was that the object mass (scaled by participant body mass) was not found to significantly affect any of the individual step placement parameters. Regression models were developed to predict the most prevalent step placements and are included in this paper to facilitate more accurate human motion simulations and ergonomics analyses of manual material lifting tasks. STATEMENT OF RELEVANCE: This study proposes a method for parameterising the steps (foot placements) associated with manual material handling tasks. The influence of task conditions and subject anthropometry on the foot placements of the most frequently observed stepping pattern during a laboratory study is discussed. For prospective postural analyses conducted using digital human models, accurate prediction of the foot placements is critical to realistic postural analyses and improved biomechanical job evaluations.

Prediction accuracy in estimating joint angle trajectories using a video posture coding method for sagittal lifting tasks

This study investigated prediction accuracy of a video posture coding method for lifting joint trajectory estimation. From three filming angles, the coder selected four key snapshots, identified joint angles and then a prediction program estimated the joint trajectories over the course of a lift. Results revealed a limited range of differences of joint angles (elbow, shoulder, hip, knee, ankle) between the manual coding method and the electromagnetic motion tracking system approach. Lifting range significantly affected estimate accuracy for all joints and camcorder filming angle had a significant effect on all joints but the hip. Joint trajectory predictions were more accurate for knuckle-to-shoulder lifts than for floor-to-shoulder or floor-to-knuckle lifts with average root mean square errors (RMSE) of 8.65 degrees , 11.15 degrees and 11.93 degrees , respectively. Accuracy was also greater for the filming angles orthogonal to the participant's sagittal plane (RMSE = 9.97 degrees ) as compared to filming angles of 45 degrees (RMSE = 11.01 degrees ) or 135 degrees (10.71 degrees ). The effects of lifting speed and loading conditions were minimal. To further increase prediction accuracy, improved prediction algorithms and/or better posture matching methods should be investigated. STATEMENT OF RELEVANCE: Observation and classification of postures are common steps in risk assessment of manual materials handling tasks. The ability to accurately predict lifting patterns through video coding can provide ergonomists with greater resolution in characterising or assessing the lifting tasks than evaluation based solely on sampling with a single lifting posture event.

Multifinger prehension: an overview

The authors review the available experimental evidence on what people do when they grasp an object with several digits and then manipulate it. The article includes three parts, each addressing a specific aspect of multifinger prehension. In the first part, the authors discuss manipulation forces (i.e., the resultant force and moment of force exerted on the object) and the digits' contribution to such forces' production. The second part deals with internal forces defined as forces that cancel each other and do not disturb object equilibrium. The authors discuss the role of the internal forces in maintaining the object stability, with respect to such issues as slip prevention, tilt prevention, and resistance to perturbations. The third part is devoted to the motor control of prehension. It covers such topics as prehension synergies, chain effects, the principle of superposition, interfinger connection matrices and reconstruction of neural commands, mechanical advantage of the fingers, and the simultaneous digit adjustment to several mutually reinforcing or conflicting demands.

A memory-based model for planning target reach postures in the presence of obstructions

Existing posture prediction and motion simulation models generally lack the capability of simulating human obstruction avoidance during target reach. This compromises the utility of digital human models for ergonomics, as many design problems involve interactions between humans and obstructions. To address this problem, this paper presents a novel memory-based posture planning (MBPP) model, which plans reach postures that avoid obstructions. In this model, the task space is partitioned into small regions called cells. For a given human figure, each cell is linked to a memory that stores various alternative postures for reaching the cell. When a posture planning problem is given in terms of a target and an obstruction configuration, the model examines postures belonging to the relevant cell, selects collision-free ones and modifies them to exactly meet the hand target acquisition constraint. Simulation results showed that the MBPP model is capable of rapidly and robustly planning reach postures for various scenarios.

Posture and motion variability in non-repetitive manual materials handling tasks

In developing a motion prediction model it is important to initially consider the sources of variability that a model should reproduce. This initial step is followed by model evaluation, where the variability predicted by the model can be a useful test parameter. An existing lifting-motion dataset collected under controlled laboratory conditions was employed here to evaluate quantitatively some important sources of variability for lift motion modeling. The main source of variability was the segment being analyzed, which accounted for more than 20% of the overall variability. There was substantial left-right symmetry in individual segment variability estimates, which were largest for the upper arm segment and tended to be larger for the upper limbs than the lower limbs. Task-related factors accounted for variability mainly as a function of the segment being considered. Within-participant variability contributions to the dataset were relatively small, whereas the contribution of between-participants variability was dependent on the segment (as large as 50%) and could indicate different lifting strategies across participants. Variability was found to remain relatively constant across the different stages of the lifting movements. Implications of these results for the development and evaluation of motion prediction models are presented. Specifically, while task characteristics may be important modifiers of the mean segment trajectory during a lifting movement, their influence on variability differs based on the segment that is being considered. The relevance of the findings is discussed in terms of their utility in the ergonomic design of tasks and work spaces.

Optimization model predictions for postural coordination modes

This paper examines the ability of the dynamic optimization model to predict changes between in-phase and anti-phase postural modes of coordination and to evaluate influence of two particular environmental and intentional constraints on postural strategy. The task studied was based on an experimental paradigm that consisted in tracking a target motion with the head. An original optimal procedure was developed for cyclic problems to calculate hip and ankle angular trajectories during postural sway with a minimum torque change criterion. Optimization results give a good description of the sudden bifurcation phase between in-phase and anti-phase postural coordination modes in visual target tracking. Transition frequency and predicted effects of environmental and intentional constraints are also in line with experimental observations described in existing literature. In particular, these investigations pointed out that postural planning process can be related to the minimization of a dynamic cost criterion with an equilibrium constraint. In conclusion, the optimization technique is well suited for the prediction of postural modes of coordination and seems to offer many opportunities for better comprehension of neuromuscular movement control.

Representing and identifying alternative movement techniques for goal-directed manual tasks

Differences in motion patterns subserving the same movement goal can be identified qualitatively. These alternatives, which may characterize 'movement techniques' (e.g., the stoop and the squat lifting technique), may be associated with significantly different biomechanical constraints and physiological responses. Despite the widely shared understanding of the significance of alternative movement techniques, quantitative representation and identification of movement techniques have received little attention, especially for three-dimensional whole-body motions. In an attempt to systematically differentiate movement techniques, this study introduces a quantitative index termed joint contribution vector (JCV) representing a motion in terms of contributions of individual joint degrees-of-freedom to the achievement of the task goal. Given a set of uncharacterized (unlabeled) motions represented by joint angle trajectories (motion capture data), the JCV and statistical clustering methods enable automated motion classification to uncover a taxonomy of alternative movement techniques. The results of our motion data analyses show that the JCV was able to characterize and discern stoop and squat lifting motions, and also to identify movement techniques for a three-dimensional, whole-body, one-handed load-transfer task. The JCV index would facilitate consideration of alternative movement techniques in a variety of applications, including work method comparison and selection, and human motion modeling and simulation.

Balance control during an arm raising movement in bipedal stance: which biomechanical factor is controlled?

In order to obtain new insight into the control of balance during arm raising movements in bipedal stance, we performed a biomechanical analysis of kinematics and dynamical aspects of arm raising movements by combining experimental work, large-scale models of the body, and techniques simulating human behavior. A comparison between experimental and simulated joint kinematics showed that the minimum torque change model yielded realistic trajectories. We then performed an analysis based on computer simulations. Since keeping the center of pressure (CoP) and the projection of the center of mass (CoM) inside the support area is essential for equilibrium, we modeled an arm raising movement where displacement of one or the other variable is limited. For this optimization model, the effects of adding equilibrium constraints on movement trajectories were investigated. The results show that: (a) the choice of the regulated variable influences the strategy adopted by the system and (b) the system was not able to regulate the CoM for very fast movements without compromising its balance. Consequently, we suggest that the system is able to maintain balance while raising the arm by only controlling the CoP. This may be done mainly by using hip mechanisms and controlling net ankle torque.