An official website of the United States government
Abstract This study introduces a hybrid model that utilizes a model-based optimization method to generate training data and an artificial neural network (ANN)-based learning method to offer real-time exoskeleton support in lifting activities. For the model-based optimization method, the torque of the knee exoskeleton and the optimal lifting motion are predicted utilizing a two-dimensional (2D) human–exoskeleton model. The control points for exoskeleton motor current profiles and human joint angle profiles from cubic B-spline interpolation represent the design variables. Minimizing the square of the normalized human joint torque is considered as the cost function. Subsequently, the lifting optimization problem is tackled using a sequential quadratic programming (SQP) algorithm in sparse nonlinear optimizer (SNOPT). For the learning-based approach, the learning-based control model is trained using the general regression neural network (GRNN). The anthropometric parameters of the human subjects and lifting boundary postures are used as input parameters, while the control points for exoskeleton torque are treated as output parameters. Once trained, the learning-based control model can provide exoskeleton assistive torque in real time for lifting tasks. Two test subjects’ joint angles and ground reaction forces (GRFs) comparisons are presented between the experimental and simulation results. Furthermore, the utilization of exoskeletons significantly reduces activations of the four knee extensor and flexor muscles compared to lifting without the exoskeletons for both subjects. Overall, the learning-based control method can generate assistive torque profiles in real time and faster than the model-based optimal control approach.
more »
« less
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.
more »
« less
- Award ID(s):
- 2014281
- PAR ID:
- 10634605
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Biomimetics
- Volume:
- 9
- Issue:
- 8
- ISSN:
- 2313-7673
- Page Range / eLocation ID:
- 454
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract In this study, the fatigue progression and optimal motion trajectory during repetitive lifting task is predicted by using a 10 degrees of freedom (DOFs) two-dimensional (2D) digital human model and a three-compartment controller (3CC) fatigue model. The numerical analysis is further validated by conducting an experiment under similar conditions. The human is modeled using Denavit-Hartenberg (DH) representation. The task is mathematically formulated as a nonlinear optimization problem where the dynamic effort of the joints is minimized subjected to physical and task specific constraints. A sequential quadratic programming method is used for the optimization process. The design variables include control points of (1) quartic B-splines of the joint angle profiles; and (2) the three compartment sizes profiles for the six physical joints of interest — spine, shoulder, elbow, hip, knee, and ankle. Both numerical and experimental liftings are performed with a 15.2 kg box as external load. The simulation reports the human joint torque profiles and the progression of joint fatigue. The joint torque profiles show periodic trends. A maximum of 17 cycles are predicted before the repetitive lifting task fails, which also reasonably agrees with that of the experimental results (16 cycles). This formulation is also a generalized one, hence it can be used for other repetitive motion studies as well.more » « less
-
Abstract This paper predicts the optimal motion for a repetitive lifting task considering muscle fatigue. The Denavit–Hartenberg (DH) representation is employed to characterize the two-dimensional (2D) digital human model with 10 degrees-of-freedom (DOFs). Two joint-based muscle fatigue models, i.e., a three-compartment controller (3CC) muscle fatigue model (validated for isometric tasks) and a four-compartment controller with augmented recovery (4CCr) muscle fatigue model (validated for dynamic tasks), are utilized to account for the fatigue effect due to the repetitive motion. The lifting problem is formulated mathematically as an optimization problem, with the objective of minimizing dynamic effort and joint acceleration subjected to both physical and task-specific constraints. The design variables include joint angle profiles, discretized by quartic B-splines, and the control points of the profiles of the fatigue compartments associated with major body joints (spinal, shoulder, elbow, hip, and knee joints). The outcomes of the simulation encompass profiles of joint angles, joint torques, and the advancement of joint fatigue. It is notable that the profiles of joint angles and torques exhibit distinct periodic patterns. Numerical simulations and experiments with a 20 kg box reveal that the maximum predicted lifting cycles are 11 for the 3CC fatigue model and 13 for the 4CCr fatigue model while the experimental result is 13 cycles. The results indicate that the 4CCr muscle fatigue model provides enhanced accuracy over the 3CC model for predicting task duration (number of cycles) of repetitive lifting.more » « less
-
null (Ed.)In this study, the three-dimensional (3D) asymmetric maximum weight lifting is predicted using an inverse-dynamics-based optimization method considering dynamic joint torque limits. The dynamic joint torque limits are functions of joint angles and angular velocities, and imposed on the hip, knee, ankle, wrist, elbow, shoulder, and lumbar spine joints. The 3D model has 40 degrees of freedom (DOFs) including 34 physical revolute joints and 6 global joints. A multi-objective optimization (MOO) problem is solved by simultaneously maximizing box weight and minimizing the sum of joint torque squares. A total of 12 male subjects were recruited to conduct maximum weight box lifting using squat-lifting strategy. Finally, the predicted lifting motion, ground reaction forces, and maximum lifting weight are validated with the experimental data. The prediction results agree well with the experimental data and the model’s predictive capability is demonstrated. This is the first study that uses MOO to predict maximum lifting weight and 3D asymmetric lifting motion while considering dynamic joint torque limits. The proposed method has the potential to prevent individuals’ risk of injury for lifting.more » « less
-
Robotic lower limb exoskeletons have been shown to successfully provide joint torques to assist human subjects during walking. Assisting the wearer during gait perturbations to prevent falls still poses a challenge due to specific requirements of the device, and complex bipedal dynamics of recovery. In this study, we present a hip exoskeleton device with pneumatically actuated abduction/adduction motion to provide hip torque for assisting with lateral balance. The device was designed to be wearable, allow integration with previously developed wearable gait perturbation detection system and knee exoskeleton, and produce fast actuation to provide assistive joint torque during gait perturbations. We present the results of the experimental benchtop tests of the device. The maximum torque output and rate of torque development were characterized using a load cell. The maximum angular displacement, with added weights to simulate the leg inertia, was recorded using an inertial measurement unit sensor. Lastly, a preliminary test on a human subject demonstrated that the device, when exerting instantaneous hip abduction torque during swing walking gait, can effectively modify foot placement in the lateral direction. This work contributes towards developing exoskeleton control strategies for assistance during gait perturbations to prevent falls.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/biomimetics9080454
