689 research outputs found
Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits
Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users’ preferences over a high-dimensional gait parameter space. However, existing preference-based learning methods have only explored low-dimensional domains due to computational limitations. To learn user preferences in high dimensions, this work presents LINECOSPAR, a human-in-the-loop preference-based framework that enables optimization over many parameters by iteratively exploring one-dimensional subspaces. Additionally, this work identifies gait attributes that characterize broader preferences across users. In simulations and human trials, we empirically verify that LINECOSPAR is a sample-efficient approach for high-dimensional preference optimization. Our analysis of the experimental data reveals a correspondence between human preferences and objective measures of dynamicity, while also highlighting differences in the utility functions underlying individual users’ gait preferences. This result has implications for exoskeleton gait synthesis, an active field with applications to clinical use and patient rehabilitation
Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking
This manuscript presents control of a high-DOF fully actuated lower-limb
exoskeleton for paraplegic individuals. The key novelty is the ability for the
user to walk without the use of crutches or other external means of
stabilization. We harness the power of modern optimization techniques and
supervised machine learning to develop a smooth feedback control policy that
provides robust velocity regulation and perturbation rejection. Preliminary
evaluation of the stability and robustness of the proposed approach is
demonstrated through the Gazebo simulation environment. In addition,
preliminary experimental results with (complete) paraplegic individuals are
included for the previous version of the controller.Comment: Submitted to IEEE Control System Magazine. This version addresses
reviewers' concerns about the robustness of the algorithm and the motivation
for using such exoskeleton
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PREDICTIVE SIMULATION OF HUMAN MOVEMENT AND APPLICATIONS TO ASSISTIVE DEVICE DESIGN AND CONTROL
Predictive simulation based on dynamic optimization using musculoskeletal models is a powerful approach for studying biomechanics of human gait. Predictive simulation can be used for a variety of applications from designing assistive devices to testing theories of motor controls. However, one of the challenges in formulating the predictive dynamic optimization problem is that the cost function, which represents the underlying goal of the walking task (e.g., minimal energy consumption) is generally unknown and is assumed a priori. While different studies used different cost functions, the qualities of the gaits with those cost functions were often not provided. Therefore, this dissertation evaluates and examines different cost function forms for dynamic simulation of human walking. The problem of the walking cost function determination was cast as a bilevel optimization, which was solved using a nested evolutionary approach. The results showed cost functions based on a weighted combination of muscle-based performance criteria (e.g., metabolic cost, muscle fatigue), gait smoothness, and gait stability led to better walking solutions compared to any cost functions only based on muscle performance criteria. Further evaluations of the walking cost functions were done in the simulation cases of human walking augmented with assistive devices such as prosthesis and exoskeleton. The simulations of augmented walking were comparable to the experimental results, which suggests the potential of using the simulation approach to address problems of finding assistive device design and control
Learning to Assist Different Wearers in Multitasks: Efficient and Individualized Human-In-the-Loop Adaption Framework for Exoskeleton Robots
One of the typical purposes of using lower-limb exoskeleton robots is to
provide assistance to the wearer by supporting their weight and augmenting
their physical capabilities according to a given task and human motion
intentions. The generalizability of robots across different wearers in multiple
tasks is important to ensure that the robot can provide correct and effective
assistance in actual implementation. However, most lower-limb exoskeleton
robots exhibit only limited generalizability. Therefore, this paper proposes a
human-in-the-loop learning and adaptation framework for exoskeleton robots to
improve their performance in various tasks and for different wearers. To suit
different wearers, an individualized walking trajectory is generated online
using dynamic movement primitives and Bayes optimization. To accommodate
various tasks, a task translator is constructed using a neural network to
generalize a trajectory to more complex scenarios. These generalization
techniques are integrated into a unified variable impedance model, which
regulates the exoskeleton to provide assistance while ensuring safety. In
addition, an anomaly detection network is developed to quantitatively evaluate
the wearer's comfort, which is considered in the trajectory learning procedure
and contributes to the relaxation of conflicts in impedance control. The
proposed framework is easy to implement, because it requires proprioceptive
sensors only to perform and deploy data-efficient learning schemes. This makes
the exoskeleton practical for deployment in complex scenarios, accommodating
different walking patterns, habits, tasks, and conflicts. Experiments and
comparative studies on a lower-limb exoskeleton robot are performed to
demonstrate the effectiveness of the proposed framework.Comment: 16 pages journal articl
Two-link lower limb exoskeleton model control enhancement using computed torque
Robotic technology has recently been used to help stroke patients with gait and balance rehabilitation. Rehabilitation robots such as gait trainers are designed to assist patients in systematic, repetitive training sessions to speed up their recovery from injuries. Several control algorithms are commonly used on exoskeletons, such as proportional, integral and derivative (PID) as linear control. However, linear control has several disadvantages when applied to the exoskeleton, which has the problem of uncertainties such as load and stiffness variations of the patient’s lower limb. To improve the lower limb exoskeleton for the gait trainer, the computed torque controller (CTC) is introduced as a control approach in this study. When the dynamic properties of the system are only partially known, the computed torque controller is an essential nonlinear controller. A mathematical model forms the foundation of this controller. The suggested control approach’s effectiveness is evaluated using a model or scaled-down variation of the method. The performance of the suggested calculated torque control technique is then evaluated and contrasted with that of the PID controller. Because of this, the PID controller’s steady-state error in the downward direction can reach 5.6%, but the CTC can lower it to 2.125%
ExoRecovery: Push Recovery with a Lower-Limb Exoskeleton based on Stepping Strategy
Balance loss is a significant challenge in lower-limb exoskeleton
applications, as it can lead to potential falls, thereby impacting user safety
and confidence. We introduce a control framework for omnidirectional recovery
step planning by online optimization of step duration and position in response
to external forces. We map the step duration and position to a human-like foot
trajectory, which is then translated into joint trajectories using inverse
kinematics. These trajectories are executed via an impedance controller,
promoting cooperation between the exoskeleton and the user.
Moreover, our framework is based on the concept of the divergent component of
motion, also known as the Extrapolated Center of Mass, which has been
established as a consistent dynamic for describing human movement. This
real-time online optimization framework enhances the adaptability of
exoskeleton users under unforeseen forces thereby improving the overall user
stability and safety. To validate the effectiveness of our approach,
simulations, and experiments were conducted. Our push recovery experiments
employing the exoskeleton in zero-torque mode (without assistance) exhibit an
alignment with the exoskeleton's recovery assistance mode, that shows the
consistency of the control framework with human intention. To the best of our
knowledge, this is the first cooperative push recovery framework for the
lower-limb human exoskeleton that relies on the simultaneous adaptation of
intra-stride parameters in both frontal and sagittal directions. The proposed
control scheme has been validated with human subject experiments.Comment: Submitted for a conference. 8 pages including references, 8 figure
Effect of gait speed on trajectory prediction using deep learning models for exoskeleton applications
Gait speed is an important biomechanical determinant of gait patterns, with joint kinematics being influenced by it. This study aims to explore the effectiveness of fully connected neural networks (FCNNs), with a potential application for exoskeleton control, in predicting gait trajectories at varying speeds (specifically, hip, knee, and ankle angles in the sagittal plane for both limbs). This study is based on a dataset from 22 healthy adults walking at 28 different speeds ranging from 0.5 to 1.85 m/s. Four FCNNs (a generalised-speed model, a low-speed model, a high-speed model, and a low-high-speed model) are evaluated to assess their predictive performance on gait speeds included in the training speed range and on speeds that have been excluded from it. The evaluation involves short-term (one-step-ahead) predictions and long-term (200-time-step) recursive predictions. The results show that the performance of the low- and high-speed models, measured using the mean absolute error (MAE), decreased by approximately 43.7% to 90.7% when tested on the excluded speeds. Meanwhile, when tested on the excluded medium speeds, the performance of the low-high-speed model improved by 2.8% for short-term predictions and 9.8% for long-term predictions. These findings suggest that FCNNs are capable of interpolating to speeds within the maximum and minimum training speed ranges, even if not explicitly trained on those speeds. However, their predictive performance decreases for gaits at speeds beyond or below the maximum and minimum training speed ranges
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