Adaptive CPG-Based Gait Planning With Learning-Based Torque Estimation and Control for Exoskeletons

Sharifi, Mojtaba; Mehr, Javad K.; Mushahwar, Vivian K.; Tavakoli, Mahdi

doi:10.1109/lra.2021.3105996

Cited by 38 publications

(13 citation statements)

References 29 publications

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“…Li et al [16] used an extreme learning machine approach for feature classification and gait recognition of human EMG signals to plan the wearer's lower limb motion trajectory. Sharifi et al [39] used an approach based on an autoregressive network (NARX) model, as shown in Figure 6, and combined it with a supervised learning algorithm to identify the limb motion posture of the exoskeleton robot and the wearer to estimate the active joint on of the torque. Fig.…”

Section: Machine Learning Based Methodsmentioning

confidence: 99%

Review of Human-exoskeleton Control Strategy for Lower Limb Rehabilitation Exoskeleton

Long

Guo

Chi

et al. 2023

J. Phys.: Conf. Ser.

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The research on exoskeleton robots has been widely carried out for many years around the world, especially the development of new-style lower limb exoskeletons for rehabilitation and assistance is one of the key research directions. The focus on the control system of lower limb exoskeletons for rehabilitation is discussed. Based on the public literature in recent years, it is summarized from three aspects, i.e., movement mode switching, human gait recognition and human-exoskeleton interaction control. Finally, the technical issues of the current lower limb rehabilitation exoskeleton control strategy are discussed. The future development prospects and research directions of the lower limb rehabilitation exoskeleton are prospected, and some suggestions on how to achieve a more efficient and accurate control are given.

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Section: Machine Learning Based Methodsmentioning

confidence: 99%

Review of Human-exoskeleton Control Strategy for Lower Limb Rehabilitation Exoskeleton

Long

Guo

Chi

et al. 2023

J. Phys.: Conf. Ser.

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show abstract

“…Adaptable central pattern generators (CPGs) can provide flexibility in the exoskeleton gait trajectory by understanding the user's intentions based on interaction torque (Section 2). [31,32] Further, a divergent component of motion (DCM) can help to predict the loss of postural stability and to prevent it by adjusting the position of the upper body (Section 3). [33] We also proposed a method that detects potentially unsafe actions in the interaction between the exoskeleton and the user during task execution--the method results in a safer collaboration between humans and assistive devices (Section 4).…”

Section: Limitations Of Commercially Available Lower Limb Assistive D...mentioning

confidence: 99%

“…In this section, we discuss the adaptable central pattern generators (CPGs) method, which we introduced to address this problem. [31] CPGs function as connected modules that are capable of generating oscillatory movements with organized patterns in response to nonperiodical inputs. [37] Because CPGs are capable of producing time-continuous rhythmic motions synchronized with adjacent joints, they are an appropriate method for motion planning for exoskeletons.…”

Section: Personalized Locomotion Planningmentioning

confidence: 99%

“…In this section, we discuss the adaptable central pattern generators (CPGs) method, which we introduced to address this problem. [ 31 ]…”

Section: Personalized Locomotion Planningmentioning

confidence: 99%

“…[ 37 ] Because CPGs are capable of producing time‐continuous rhythmic motions synchronized with adjacent joints, they are an appropriate method for motion planning for exoskeletons. Our adaptable CPG method [ 31 ] is capable of adjusting the walking pattern based on the user's intention, which includes two parts: Learning‐based pHRI estimation, and CPG‐based gait motion amplitude and frequency adaptation on a user‐specific and individualized basis.…”

Section: Personalized Locomotion Planningmentioning

confidence: 99%

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Artificial‐Intelligence‐Powered Lower Limb Assistive Devices: Future of Home Care Technologies

Mehr

Akbari

Faridi

et al. 2023

Advanced Intelligent Systems

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Healthcare systems are burdened by mobility impairments resulting from aging and neurological conditions. One of the recent advances in robotics is lower limb assistive/rehabilitative devices that can make independent living possible. Nonetheless, some limitations need to be addressed before robotics can be used in home‐based applications. This paper describes the current state of the art in intelligent motion planning and control of lower limb assistive devices, which have addressed some of these challenges. Adaptable central pattern generators and the divergent component of motion are introduced as methods for personalized motion planning based on physical human–robot interaction (pHRI). Uncertainty analysis for neural networks is introduced to increase safety in motion planning based on pHRI. For the case that a user cannot apply physical interaction, a reinforcement‐learning‐based approach is introduced to switch between different modes of walking based on the user's input via a push button embedded in a walker. Moreover, a smart walker is introduced as a device that can be synchronized with the lower limb exoskeleton to assist users with their daily activities. Also, a roadmap for future steps that can make lower limb assistive/rehabilitative devices a good fit for home use is introduced.

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Learning robotic motion with mirror therapy framework for hemiparesis rehabilitation

et al. 2023

Information Processing & Management

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Adaptive CPG-Based Gait Planning With Learning-Based Torque Estimation and Control for Exoskeletons

Cited by 38 publications

References 29 publications

Review of Human-exoskeleton Control Strategy for Lower Limb Rehabilitation Exoskeleton

Review of Human-exoskeleton Control Strategy for Lower Limb Rehabilitation Exoskeleton

Artificial‐Intelligence‐Powered Lower Limb Assistive Devices: Future of Home Care Technologies

Learning robotic motion with mirror therapy framework for hemiparesis rehabilitation

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