Human-Exoskeleton Coupling Simulation for Lifting Tasks with Shoulder, Spine, and Knee-Joint Powered Exoskeletons

In this study, we introduce a two-dimensional (2D) human skeletal model coupled with knee, spine, and shoulder exoskeletons. The primary purpose of this model is to predict the optimal lifting motion and provide torque support from the exoskeleton through the utilization of inverse dynamics optimization. The kinematics and dynamics of the human model are expressed using the Denavit-Hartenberg (DH) representation. The lifting optimization formulation integrates the electromechanical dynamics of the DC motors in the exoskeletons of the knee, spine, and shoulder. The design variables for this study include human joint angle profiles and exoskeleton motor current profiles. The optimization objective is to minimize the squared normalized human joint torques, subject to physical and task-specific lifting constraints. We solve this optimization problem using the gradient-based optimizer SNOPT. Our results include a comparison of predicted human joint angle profiles, joint torque profiles, and ground reaction force (GRF) profiles between lifting tasks with and without exoskeleton assistance. We also explore various combinations of exoskeletons for the knee, spine, and shoulder. By resolving the lifting optimization problems, we designed the optimal torques for the exoskeletons located at the knee, spine, and shoulder. It was found that the support from the exoskeletons substantially lowers the torque levels in human joints. Additionally, we conducted experiments only on the knee exoskeleton. Experimental data indicated that using the knee exoskeleton decreases the muscle activation peaks by 35.00%, 10.03%, 22.12%, 30.14%, 16.77%, and 25.71% for muscles of the erector spinae, latissimus dorsi, vastus medialis, vastus lateralis, rectus femoris, and biceps femoris, respectively.

Modelling the interaction between wearable assistive devices and digital human models-A systematic review

Exoskeletons, orthoses, exosuits, assisting robots and such devices referred to as wearable assistive devices are devices designed to augment or protect the human body by applying and transmitting force. Due to the problems concerning cost- and time-consuming user tests, in addition to the possibility to test different configurations of a device, the avoidance of a prototype and many more advantages, digital human models become more and more popular for evaluating the effects of wearable assistive devices on humans. The key indicator for the efficiency of assistance is the interface between device and human, consisting mainly of the soft biological tissue. However, the soft biological tissue is mostly missing in digital human models due to their rigid body dynamics. Therefore, this systematic review aims to identify interaction modelling approaches between wearable assistive devices and digital human models and especially to study how the soft biological tissue is considered in the simulation. The review revealed four interaction modelling approaches, which differ in their accuracy to recreate the occurring interactions in reality. Furthermore, within these approaches there are some incorporating the appearing relative motion between device and human body due to the soft biological tissue in the simulation. The influence of the soft biological tissue on the force transmission due to energy absorption on the other side is not considered in any publication yet. Therefore, the development of an approach to integrate the viscoelastic behaviour of soft biological tissue in the digital human models could improve the design of the wearable assistive devices and thus increase its efficiency and efficacy.

Evaluation of Optimal Control Approaches for Predicting Active Knee-Ankle-Foot-Orthosis Motion for Individuals With Spinal Cord Injury

Gait restoration of individuals with spinal cord injury can be partially achieved using active orthoses or exoskeletons. To improve the walking ability of each patient as much as possible, it is important to personalize the parameters that define the device actuation. This study investigates whether using an optimal control-based predictive simulation approach to personalize pre-defined knee trajectory parameters for an active knee-ankle-foot orthosis (KAFO) used by spinal cord injured (SCI) subjects could potentially be an alternative to the current trial-and-error approach. We aimed to find the knee angle trajectory that produced an improved orthosis-assisted gait pattern compared to the one with passive support (locked knee). We collected experimental data from a healthy subject assisted by crutches and KAFOs (with locked knee and with knee flexion assistance) and from an SCI subject assisted by crutches and KAFOs (with locked knee). First, we compared different cost functions and chose the one that produced results closest to experimental locked knee walking for the healthy subject (angular coordinates mean RMSE was 5.74°). For this subject, we predicted crutch-orthosis-assisted walking imposing a pre-defined knee angle trajectory for different maximum knee flexion parameter values, and results were evaluated against experimental data using that same pre-defined knee flexion trajectories in the real device. Finally, using the selected cost function, gait cycles for different knee flexion assistance were predicted for an SCI subject. We evaluated changes in four clinically relevant parameters: foot clearance, stride length, cadence, and hip flexion ROM. Simulations for different values of maximum knee flexion showed variations of these parameters that were consistent with experimental data for the healthy subject (e.g., foot clearance increased/decreased similarly in experimental and predicted motions) and were reasonable for the SCI subject (e.g., maximum parameter values were found for moderate knee flexion). Although more research is needed before this method can be applied to choose optimal active orthosis controller parameters for specific subjects, these findings suggest that optimal control prediction of crutch-orthosis-assisted walking using biomechanical models might be used in place of the trial-and-error method to select the best maximum knee flexion angle during gait for a specific SCI subject.

Human-device interface pressure measurement in prosthetic, orthotic and exoskeleton applications: A systematic review

This study aimed to investigate normal and shear load sensor technology that has been characterised and used at the human-device interface in prosthetic, orthotic and exoskeleton applications. In addition to taking a cross-disciplinary view, this study expands on previous reviews by considering recently published papers, clinical translation of sensors, and development of the sensor technology itself. A search of MEDLINE, INSPEC, SCOPUS and Web of Science was performed up to 26 January 2021. A total of 33 studies were assessed for quality and their data extracted. The review found variable quality of published papers, with normal load being most commonly measured, and resistive sensor technology most commonly used. The translation to clinical environments was indicated in most studies, though the study population was not always made up of the target users. Studies could benefit from more direct comparison with clinically relevant load thresholds and by ensuring clinical testing is performed in the most realistic and representative way possible. Additionally, more focus on developing sensors that measure shear loads would enable further insights into conditions at the human-device interface. Finally, all researchers would benefit from better and more widespread anonymous data sharing practices to facilitate further experimentation.

Comparing the risk of low-back injury using model-based optimization: Improved technique versus exoskeleton assistance

Although wearable robotic systems are designed to reduce the risk of low-back injury, it is unclear how effective assistance is, compared to improvements in lifting technique. We use a two-factor block study design to simulate how effective exoskeleton assistance and technical improvements are at reducing the risk of low-back injury when compared to a typical adult lifting a box. The effects of assistance are examined by simulating two different models: a model of just the human participant, and a model of the human participant wearing the SPEXOR exoskeleton. The effects of lifting technique are investigated by formulating two different types of optimal control problems: a least-squares problem which tracks the human participant's lifting technique, and a minimization problem where the model is free to use a different movement. Different lifting techniques are considered using three different cost functions related to risk factors for low-back injury: cumulative low-back load (CLBL), peak low-back load (PLBL), and a combination of both CLBL and PLBL (HYB). The results of our simulations indicate that an exoskeleton alone can make modest reductions in both CLBL and PLBL. In contrast, technical improvements alone are effective at reducing CLBL, but not PLBL. The largest reductions in both CLBL and PLBL occur when both an exoskeleton and technical improvements are used. While all three of the lifting technique cost functions reduce both CLBL and PLBL, the HYB cost function offers the most balanced reduction in both CLBL and PLBL.

OpenSim Moco: Musculoskeletal optimal control

Musculoskeletal simulations are used in many different applications, ranging from the design of wearable robots that interact with humans to the analysis of patients with impaired movement. Here, we introduce OpenSim Moco, a software toolkit for optimizing the motion and control of musculoskeletal models built in the OpenSim modeling and simulation package. OpenSim Moco uses the direct collocation method, which is often faster and can handle more diverse problems than other methods for musculoskeletal simulation. Moco frees researchers from implementing direct collocation themselves-which typically requires extensive technical expertise-and allows them to focus on their scientific questions. The software can handle a wide range of problems that interest biomechanists, including motion tracking, motion prediction, parameter optimization, model fitting, electromyography-driven simulation, and device design. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which enable modeling of kinematic loops (e.g., cycling models) and complex anatomy (e.g., patellar motion). To show the abilities of Moco, we first solved for muscle activity that produced an observed walking motion while minimizing squared muscle excitations and knee joint loading. Next, we predicted how muscle weakness may cause deviations from a normal walking motion. Lastly, we predicted a squat-to-stand motion and optimized the stiffness of an assistive device placed at the knee. We designed Moco to be easy to use, customizable, and extensible, thereby accelerating the use of simulations to understand the movement of humans and other animals.

Simulation of a Passive Knee Exoskeleton for Vertical Jump Using Optimal Control

Research on exoskeletons designed to augment human activities and the attendant exoskeleton industry are both rapidly growing areas of endeavor. However, progress in the field is currently being hindered by a lack of understanding of human-exoskeleton interactions. At present, the main method applied to reach such an understanding is to build and test prototypes or end-effectors (that simulate the devices), but this is a very time-consuming and costly process. In this study, we aimed to address this problem by simulating passive exoskeleton-human interactions during a vertical jump. The simulation is based on theoretical and empirical models. Using the simulation, we performed a numerical optimization procedure to determine the muscle excitations and starting postures that would give the maximum jump height. The simulation used a planar 4-DOF dynamic model. The muscles at the joints were modeled as torque actuators, with a flexor and an extensor for each joint and passive torque representing the tendon and muscle properties. We then simulated jumps with a passive knee exoskeleton with five different values of stiffness with the aim to study their effect on the jump height. The optimal excitation for the maximum jump height was found by using a genetic algorithm (GA). To improve our optimization performance and to test the convergence of the GA, the GA optimization was performed several times. For each exoskeleton condition, the GA found the optimal jump more than 400 times, and out of these solutions the one that achieved the highest jump was chosen. The result revealed an increase in jump height as the spring became stiffer. In addition, it was found that the energy that was stored in the spring of the exoskeleton was not fully converted to jump height.

Pneumatic Supply System Parameter Optimization for Soft Actuators

Soft actuators using pressurized air are being widely used due to their inherent compliance, conformability, and customizability. These actuators are powered and controlled by pneumatic supply systems (PSSs) consisting of components such as compressors, valves, tubing, and reservoirs. Regardless of the choice of actuator, the PSS critically affects overall performance of soft robots because it governs the soft actuator pressure dynamics, and thereby, the general dynamic behavior. While selecting and controlling PSS components for meeting desired soft actuator performance, specifications such as PSS mass, volume, and duration of operation must also be considered. Currently, there is no comprehensive study on PSS optimization for meeting dynamic performance and PSS specifications, due to limited understanding of soft actuator pressure dynamics, large solution space for PSSs, and variability in soft actuators. By considering critical parameters of PSS and soft actuators, we introduce and demonstrate PSS parameter optimization. We propose a normalized model for soft actuator pressure dynamics and quantify the relationship between PSS parameters, soft actuator design parameters, and dynamic performance metrics of rise time, fall time, and actuation frequency. After experimental validation, we applied these results and optimally select and control PSS components to meet desired soft actuator performance for a soft exosuit, while minimizing mass of selected components. The measured pressure response with this prototype agrees well with simulations, with root mean square errors <5.2%. This work is a step toward furthering the scope of soft robotics, as it enables PSS modeling and optimization, for meeting the desired soft actuator performance while also addressing PSS specifications.

A Quick Turn of Foot: Rigid Foot-Ground Contact Models for Human Motion Prediction

Computer simulation can be used to predict human walking motions as a tool of basic science, device design, and for surgical planning. One the challenges of predicting human walking is accurately synthesizing both the movements and ground forces of the stance foot. Though the foot is commonly modeled as a viscoelastic element, rigid foot-ground contact models offer some advantages: fitting is reduced to a geometric problem, and the numerical stiffness of the equations of motion is similar in both swing and stance. In this work, we evaluate two rigid-foot ground contact models: the ellipse-foot (a single-segment foot), and the double-circle foot (a two-segment foot). To evaluate the foot models we use three different comparisons to experimental data: first we compare how accurately the kinematics of the ankle frame fit those of the model when it is forced to track the measured center-of-pressure (CoP) kinematics; second, we compare how each foot affects how accuracy of a sagittal plane gait model that tracks a subjects walking motion; and third, we assess how each model affects a walking motion prediction. For the prediction problem we consider a unique cost function that includes terms related to both muscular effort and foot-ground impacts. Although the ellipse-foot is superior to the double-circle foot in terms of fit and the accuracy of the tracking OCP solution, the predictive simulation reveals that the ellipse-foot is capable of producing large force transients due to its geometry: when the ankle quickly traverses its u-shaped trajectory, the body is accelerated the body upwards, and large ground forces result. In contrast, the two-segment double-circle foot produces ground forces that are of a similar magnitude to the experimental subject because the additional forefoot segment plastically contacts the ground, arresting its motion, similar to a human foot.

Predicting the influence of hip and lumbar flexibility on lifting motions using optimal control

Computational models of the human body coupled with optimization can be used to predict the influence of variables that cannot be experimentally manipulated. Here, we present a study that predicts the motion of the human body while lifting a box, as a function of flexibility of the hip and lumbar joints in the sagittal plane. We modeled the human body in the sagittal plane with joints actuated by pairs of agonist-antagonist muscle torque generators, and a passive hamstring muscle. The characteristics of a stiff, average and flexible person were represented by co-varying the lumbar range-of-motion, lumbar passive extensor-torque and the hamstring passive muscle-force. We used optimal control to solve for motions that simulated lifting a 10 kg box from a 0.3 m height. The solution minimized the total sum of the normalized squared active and passive muscle torques and the normalized passive hamstring muscle forces, over the duration of the motion. The predicted motion of the average lifter agreed well with experimental data in the literature. The change in model flexibility affected the predicted joint angles, with the stiffer models flexing more at the hip and knee, and less at the lumbar joint, to complete the lift. Stiffer models produced similar passive lumbar torque and higher hamstring muscle force components than the more flexible models. The variation between the motion characteristics of the models suggest that flexibility may play an important role in determining lifting technique.

Passive Back Support Exoskeleton Improves Range of Motion Using Flexible Beams

In the EU, lower back pain affects more than 40% of the working population. Mechanical loading of the lower back has been shown to be an important risk factor. Peak mechanical load can be reduced by ergonomic interventions, the use of cranes and, more recently, by the use of exoskeletons. Despite recent advances in the development of exoskeletons for industrial applications, they are not widely adopted by industry yet. Some of the challenges, which have to be overcome are a reduced range of motion, misalignment between the human anatomy and kinematics of the exoskeleton as well as discomfort. A body of research exists on how an exoskeleton can be designed to compensate for misalignment and thereby improve comfort. However, how to design an exoskeleton that achieves a similar range of motion as a human lumbar spine of up to 60° in the sagittal plane, has not been extensively investigated. We addressed this need by developing and testing a novel passive back support exoskeleton, including a mechanism comprised of flexible beams, which run in parallel to the spine, providing a large range of motion and lowering the peak torque requirements around the lumbo-sacral (L5/S1) joint. Furthermore, we ran a pilot study to test the biomechanical (N = 2) and functional (N = 3) impact on subjects while wearing the exoskeleton. The biomechanical testing was once performed with flexible beams as a back interface and once with a rigid structure. An increase of more than 25% range of motion of the trunk in the sagittal plane was observed by using the flexible beams. The pilot functional tests, which are compared to results from a previous study with the Laevo device, suggest, that the novel exoskeleton is perceived as less hindering in almost all tested tasks.