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In this study, a 13 degrees of freedom (DOFs) three-dimensional (3D) human arm model and a 10 DOFs 3D robotic arm model are used to validate the grasping force for human-robot lifting motion prediction. The human arm and robotic arm are modeled in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The human-box and robot-box interactions are characterized as a collection of grasping forces. The joint torque squares of human arm and robot arm are minimized subjected to physics and task constraints. The design variables include (1) control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box; and (2) the discretized grasping forces during lifting. Both numerical and experimental human-robot liftings were performed with a 2 kg box. The simulation reports the human arm’s joint angle profiles, joint torque profiles, and grasping force profiles. The comparisons of the joint angle profiles and grasping force profiles between experiment and simulation are presented. The simulated joint angle profiles have similar trends to the experimental data. It is concluded that human and robot share the load during lifting process, and the predicted human grasping force matches the measured experimental grasping force reasonably well.

 

In this study, a hybrid predictive model is used to predict 3D asymmetric lifting motion and assess 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 optimization method. The equations of motion are built by recursive Lagrangian dynamics. The musculoskeletal module consists of a 324-muscle-actuated full-body lumbar spine model. Based on the generated 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. Muscle activation results between simulated and experimental EMG are compared to validate the model. Finally, potential lower back injuries are evaluated for a specific-weight asymmetric lifting task. The shear and compression spine loads are compared to NIOSH recommended limits. At the beginning of the dynamic lifting process, the simulated compressive spine load beyond the NIOSH action limit but less than the permissible limit. This is due to the fatigue factors considered in NIOSH lifting equation.

 

In this paper, an optimization-based dynamic modeling method is used for human-robot lifting motion prediction. The three-dimensional (3D) human arm model has 13 degrees of freedom (DOFs) and the 3D robotic arm (Sawyer robotic arm) has 10 DOFs. The human arm and robotic arm are built in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The interactions between human arm and box, and robot and box are modeled as a set of grasping forces which are treated as unknowns (design variables) in the optimization formulation. The inverse dynamic optimization is used to simulate the lifting motion where the summation of joint torque squares of human arm is minimized subjected to physical and task constraints. The design variables are control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box, and the box grasping forces at each time point. A numerical example is simulated for huma-robot lifting with a 10 Kg box. The human and robotic arms’ joint angle, joint torque, and grasping force profiles are reported. These optimal outputs can be used as references to control the human-robot collaborative lifting task.

 

Lifting heavy weight is one of the main reasons for manual material handling related injuries which can be mitigated by determining the limiting lifting weight of a person. In this study, a 40 degrees of freedom (DOFs) spatial skeletal model was employed to predict the symmetric maximum weight lifting motion. The lifting problem was formulated as a multi-objective optimization (MOO) problem to minimize the dynamic effort and maximize the box weight. An inverse-dynamics-based optimization approach was used to determine the optimal lifting motion and the maximum lifting weight considering dynamic joint strength. The predicted lifting motion, ground reaction forces (GRFs), and maximum box weight were shown to match well with the experimental results. It was found that for the three-dimensional (3D) symmetric lifting the left and right GRFs were not same.

 

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.

 

The ability to predict the decline in muscle strength over the course of an activity (i.e., fatigue) can be a crucial aid to task design, injury prevention, and rehabilitation efforts. Current models of muscle fatigue have been hitherto validated only for isometric contractions, but most real-world tasks are dynamic in nature, involving continuously varying joint velocities. It has previously been proposed that a three-compartment-controller (3CC) model might be used to predict fatigue for such tasks by using it in conjunction with joint- and direction-specific torque-velocity-angle (TVA) surfaces. This allows for the calculation of a time-varying target load parameter that can be used by the 3CC model, but it increases model complexity and has not been validated by experimental data. An alternative approach is proposed where the effect of joint velocity is modeled by a velocity parameter and integrated into the fatigue model equations, removing the dependence on external TVA surfaces. The predictions using both methods are contrasted against experimental data collected from 20 subjects in a series of isokinetic tests involving the knee and shoulder joints, covering a range of velocities encountered in day-to-day tasks. A much lower degree of fatigue is observed for moderate velocities compared to that for very low or very high velocities. Predictions using the integrated velocity parameter are computationally less expensive than using TVA surfaces and are also closer to experimentally obtained values. The modified fatigue model can therefore be applied to dynamic tasks with varying velocities when the task is discretized into several isokinetic tasks. 

Exoskeleton technology has gained great interests in several fields including robotics, medicine, rehabilitation, ergonomics, and military. Especially, upper-limb exoskeletons are developed aiming to increase worker’s physical ability such as stability, force and power production and reduce biomechanical loads, working fatigue, which relieves overexertion risk. Extensive research has been conducted to assess existing and newly proposed exoskeletons, but they still have trade-off and user convenience issues to resolve. Therefore, the primary purpose of this paper is to review classification of the upper-limb exoskeletons and functional assessment, particularly regarding the complex interactions between human and exoskeleton. Secondly the paper is to provide insight in issues associated with the upper-limb exoskeletons. Finally, discussion on future directions for upper limb exoskeleton development and assessment is presented. 

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.