Design on intelligent perception system for lower limb rehabilitation exoskeleton robot

Ma, Weiguang; Zhang, Xiaodong; Yin, Guofu

doi:10.1109/urai.2016.7625785

Cited by 28 publications

(12 citation statements)

References 10 publications

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“…As we all know, the research of lower limb exoskeleton robot is certainly inseparable from human participation. That is, when people wear exoskeleton robots to move, it is essential to collect, identify, and feedback human static and dynamic biological information [ 23 ]. Especially for the rehabilitation exoskeleton robot, it is obviously incomplete and unscientific to only consider the data of healthy people, because the user is a patient with limb dysfunction.…”

Section: Acquisition and Analysis Of Interaction Informationmentioning

confidence: 99%

“…If plantar pressure refers to plantar pressure detection, the interaction force mainly emphasizes the relationship between force and reaction between human and exoskeleton. In order to obtain the interaction force between the body and the exoskeleton, Ma et al [ 23 ] proposed to install a piezoelectric film sensor on the outer bandage of the lower limb rehabilitation robot. Similarly, Kyoungchul et al [ 41 ] suggested placing the pressure-sensitive sensor on the thigh bracket; these sensors are employed to measure muscle movement and analyse the ratio between pressure and knee angle.…”

Section: Acquisition and Analysis Of Interaction Informationmentioning

confidence: 99%

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Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion

Chen

Zhu

et al. 2022

Applied Bionics and Biomechanics

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In recent decades, although the research on gait recognition of lower limb exoskeleton robot has been widely developed, there are still limitations in rehabilitation training and clinical practice. The emergence of interactive information fusion technology provides a new research idea for the solution of this problem, and it is also the development trend in the future. In order to better explore the issue, this paper summarizes gait recognition based on interactive information fusion of lower limb exoskeleton robots. This review introduces the current research status, methods, and directions for information acquisition, interaction, fusion, and gait recognition of exoskeleton robots. The content involves the research progress of information acquisition methods, sensor placements, target groups, lower limb sports biomechanics, interactive information fusion, and gait recognition model. Finally, the current challenges, possible solutions, and promising prospects are analysed and discussed, which provides a useful reference resource for the study of interactive information fusion and gait recognition of rehabilitation exoskeleton robots.

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Section: Acquisition and Analysis Of Interaction Informationmentioning

confidence: 99%

Section: Acquisition and Analysis Of Interaction Informationmentioning

confidence: 99%

Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion

Chen

Zhu

et al. 2022

Applied Bionics and Biomechanics

Self Cite

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“…Accurate motion prediction makes exoskeleton react to users' need properly and provide efficient movement. A practical motion prediction algorithm is supposed to predict user's intention of motion based on the sensor information, such as joint angular of lower-limb [4], EMG signals of muscles [5], neural activation signals [6], human motion sequences [7,8], and multisenor information [9].…”

Section: Introductionmentioning

confidence: 99%

Motion Prediction of Human Wearing Powered Exoskeleton

Jin

Guo

et al. 2020

Mathematical Problems in Engineering

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With the development of powered exoskeleton in recent years, one important limitation is the capability of collaborating with human. Human-machine interaction requires the exoskeleton to accurately predict the human motion of the upcoming movement. Many recent works implement neural network algorithms such as recurrent neural networks (RNN) in motion prediction. However, they are still insufficient in efficiency and accuracy. In this paper, a Gaussian process latent variable model (GPLVM) is employed to transform the high-dimensional data into low-dimensional data. Combining with the nonlinear autoregressive (NAR) neural network, the GPLVM-NAR method is proposed to predict human motions. Experiments with volunteers wearing powered exoskeleton performing different types of motion are conducted. Results validate that the proposed method can forecast the future human motion with relative error of 2%∼5% and average calculation time of 120 s∼155 s, depending on the type of different motions.

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“…They have a wide variety of applications in numerous fields. A powered exoskeleton in the field of rehabilitation [5][6][7][8] has low output power for safety assistance. On the other hand, the assisting power used to transport heavy baggage tends to be high [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Several approaches have been proposed to control powered exoskeletons. One is based on myoelectric signals measured by electromyography (EMG) sensors [8,[14][15][16]. It estimates human intentions by measuring the action potential of the muscles, and the powered exoskeleton assists human action according to the intention.…”

Section: Introductionmentioning

confidence: 99%

Power Assist Control Based on Human Motion Estimation Using Motion Sensors for Powered Exoskeleton without Binding Legs

et al. 2019

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In this study, we propose a novel power assist control method for a powered exoskeleton without binding its legs. The proposed method uses motion sensors on the wearer’s torso and legs to estimate his/her motion to enable the powered exoskeleton to assist with the estimated motion. It can detect the start of walking motion quickly because it does not prevent the motion of the wearer’s knees at the beginning of the walk. A nine-axis motion sensor on the wearer’s body is designed to work robustly in very hot and humid spaces, where an electromyograph is not reliable due to the wearer’s sweat. Moreover, the sensor avoids repeated impact during the walk because it is attached to the body of the wearer. Our powered exoskeleton recognizes the motion of the wearer based on a database and accordingly predicts the motion of the powered exoskeleton that supports the wearer. Experiments were conducted to prove the validity of the proposed method.

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Design on intelligent perception system for lower limb rehabilitation exoskeleton robot

Cited by 28 publications

References 10 publications

Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion

Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion

Motion Prediction of Human Wearing Powered Exoskeleton

Power Assist Control Based on Human Motion Estimation Using Motion Sensors for Powered Exoskeleton without Binding Legs

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