A New Trajectory Determination Method for Robot-Assisted Ankle Ligament Rehabilitation

Liu, Zixiao; Zhong, B.; Zhong, Wenjuan; Guo, Kaiqi; Zhang, Mingming

doi:10.1109/embc.2019.8857542

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Exoskeletons have enormous potential to enhance movement and to contribute to neuromuscular rehabilitation in persons with motor disorders such as stroke, cerebral palsy, and spinal cord injury (Sartori et al, 2016 ; Li et al, 2019 , 2021 ; Liu et al, 2019 ; Zhang et al, 2019 ). Exoskeleton-assisted rehabilitation training involves the use of control algorithms aimed at improving muscle strength, neuroplasticity, and movement enhancement in users (Fujii et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification

Zhang,

Zhu

et al. 2023

Front. Neurosci.

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IntroductionResearch interest in exoskeleton assistance strategies that incorporate the user's torque capacity is growing rapidly. However, the predicted torque capacity from users often includes uncertainty from various sources, which can have a significant impact on the safety of the exoskeleton-user interface.MethodsTo address this challenge, this paper proposes an adaptive control framework for a knee exoskeleton that uses muscle electromyography (EMG) signals and joint kinematics. The framework predicted the user's knee flexion/extension torque with confidence bounds to quantify the uncertainty based on a neuromusculoskeletal (NMS) solver-informed Bayesian Neural Network (NMS-BNN). The predicted torque, with a specified confidence level, controlled the assistive torque provided by the exoskeleton through a TCP/IP stream. The performance of the NMS-BNN model was also compared to that of the Gaussian process (NMS-GP) model.ResultsOur findings showed that both the NMS-BNN and NMS-GP models accurately predicted knee joint torque with low error, surpassing traditional NMS models. High uncertainties were observed at the beginning of each movement, and at terminal stance and terminal swing in self-selected speed walking in both NMS-BNN and NMS-GP models. The knee exoskeleton provided the desired assistive torque with a low error, although lower torque was observed during terminal stance of fast walking compared to self-selected walking speed.DiscussionThe framework developed in this study was able to predict knee flexion/extension torque with quantifiable uncertainty and to provide adaptive assistive torque to the user. This holds significant potential for the development of exoskeletons that provide assistance as needed, with a focus on the safety of the exoskeleton-user interface.

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Section: Introductionmentioning

confidence: 99%

Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification

Zhang,

Zhu

et al. 2023

Front. Neurosci.

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“…P HYSICAL changes as a result of neurological disorders, for example, muscle weakness, poor coordination, and loss of sensation, affect daily activities, such as locomotion in a large number of people [1]. The rehabilitation of locomotion has always been a key priority for patients with movement disorders [2]. Devices have been developed to restore motor function and facilitate locomotion rehabilitation in individuals with spinal cord injury (SCI) and stroke, for instance.…”

Section: Introductionmentioning

confidence: 99%

Ankle Joint Torque Estimation Using an EMG-Driven Neuromusculoskeletal Model and an Artificial Neural Network Model

Zhang

et al. 2021

IEEE Trans. Automat. Sci. Eng.

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In recent decades, there has been an increasing interest in the use of robotic powered exoskeletons to assist patients with movement disorders in rehabilitation and daily life. Providing assistive torque that compensates for the user's remaining muscle contributions is a growing and challenging field within exoskeleton control. In this article, ankle joint torques were estimated using electromyography (EMG)-driven neuromusculoskeletal (NMS) model and an artificial neural network (ANN) model in seven movement tasks, including fast walking, slow walking, self-selected speed walking, and isokinetic dorsi/plantar flexion at 60 • /s and 90 • /s. In each method, EMG signals and ankle joint angles were used as input, the models were trained with data from 3-D motion analysis, and ankle joint torques were predicted. Six cases using different motion trials as calibration (for the NMS model)/training (for the ANN) were devised, and the agreement between the predicted and measured ankle joint torques was computed. We found that the NMS model could overall better predict ankle joint torques from EMG and angle data than the ANN model with some exceptions; the ANN predicted ankle joint torques with better agreement when trained with data from the same movement. The NMS model predicted ankle joint torque best when calibrated with trials during which EMG reached maximum levels, whereas the ANN predicted well when trained with many trials and types of movements. In addition, the ANN prediction may become less reliable when Manuscript

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The Human–Machine Interaction Methods and Strategies for Upper and Lower Extremity Rehabilitation Robots: A Review

Zongxing,

Jie,

Ligang

et al. 2024

IEEE Sensors J.

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A New Trajectory Determination Method for Robot-Assisted Ankle Ligament Rehabilitation

Cited by 3 publications

References 14 publications

Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification

Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification

Ankle Joint Torque Estimation Using an EMG-Driven Neuromusculoskeletal Model and an Artificial Neural Network Model

The Human–Machine Interaction Methods and Strategies for Upper and Lower Extremity Rehabilitation Robots: A Review

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