Slope Gradient Adaptive Gait Planning for Walking Assistance Lower Limb Exoskeletons

Zou, Chaobin; Huang, Rui; Qiu, Jing; Chen, Qiming; Cheng, Hong

doi:10.1109/tase.2020.3037973

Cited by 14 publications

(5 citation statements)

References 22 publications

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“…Gait-related assistance and rehabilitation with exoskeletons is a very common application of DMPs, and there are numerous examples (Abu-Dakka et al, 2015; Amatya et al, 2020; Escarabajal et al, 2023; Huang et al, 2016a; Hwang et al, 2019, 2021; Schaal, 2006; Yuan et al, 2020; Zou et al, 2021). In Abu-Dakka et al, 2015, 2020), a parallel robot was used for ankle rehabilitation, where the movements were generated by DMPs (Figure 20).…”

Section: Dmps In Application Scenariosmentioning

confidence: 99%

Dynamic movement primitives in robotics: A tutorial survey

Saveriano,

Abu-Dakka,

Kramberger

et al. 2023

The International Journal of Robotics Research

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Biological systems, including human beings, have the innate ability to perform complex tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to comprehend and formally define this innate characteristic. The idea, supported by several experimental findings, that biological systems are able to combine and adapt basic units of motion into complex tasks finally leads to the formulation of the motor primitives’ theory. In this respect, Dynamic Movement Primitives (DMPs) represent an elegant mathematical formulation of the motor primitives as stable dynamical systems and are well suited to generate motor commands for artificial systems like robots. In the last decades, DMPs have inspired researchers in different robotic fields including imitation and reinforcement learning, optimal control, physical interaction, and human–robot co-working, resulting in a considerable amount of published papers. The goal of this tutorial survey is two-fold. On one side, we present the existing DMP formulations in rigorous mathematical terms and discuss the advantages and limitations of each approach as well as practical implementation details. In the tutorial vein, we also search for existing implementations of presented approaches and release several others. On the other side, we provide a systematic and comprehensive review of existing literature and categorize state-of-the-art work on DMP. The paper concludes with a discussion on the limitations of DMPs and an outline of possible research directions.

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Section: Dmps In Application Scenariosmentioning

confidence: 99%

Dynamic movement primitives in robotics: A tutorial survey

Saveriano,

Abu-Dakka,

Kramberger

et al. 2023

The International Journal of Robotics Research

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“…In light of the above, this study aims to investigate gait training using LLEs specifically for post-stroke patients presenting primarily with weakness and no spasticity. To construct the online gait learning framework, Dynamic Movement Primitives (DMP) are adopted [23], given their efficacy in motion planning for robot arms [24] and gait planning for exoskeleton robots [25][26][27]. Leveraging DMP, joint angles from the healthy leg can be dynamically learned in real-time for each individual step, empowering patients to guide the movement of the paretic leg and exert control over gait patterns.…”

Section: Passivementioning

confidence: 99%

Online gait learning with Assist-As-Needed control strategy for post-stroke rehabilitation exoskeletons

Zou,

Zeng,

Huang

et al. 2023

Robotica

Self Cite

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Lower limb exoskeletons (LLEs) have demonstrated their potential in delivering quantified repetitive gait training for individuals afflicted with gait impairments. A critical concern in robotic gait training pertains to fostering active patient engagement, and a viable solution entails harnessing the patient’s intrinsic effort to govern the control of LLEs. To address these challenges, this study presents an innovative online gait learning approach with an appropriate control strategy for rehabilitation exoskeletons based on dynamic movement primitives (DMP) and an Assist-As-Needed (AAN) control strategy, denoted as DMP-AAN. Specifically tailored for post-stroke patients, this approach aims to acquire the gait trajectory from the unaffected leg and subsequently generate the reference gait trajectory for the affected leg, leveraging the acquired model and the patient’s personal exertion. Compared to conventional AAN methodologies, the proposed DMP-AAN approach exhibits adaptability to diverse scenarios encompassing varying gait patterns. Experimental validation has been performed using the lower limb rehabilitation exoskeleton HemiGo. The findings highlight the ability to generate suitable control efforts for LLEs with reduced human-robot interactive force, thereby enabling highly patient-controlled gait training sessions to be achieved.

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“…inertial measurement unit (IMU), multi-axis force/torque, from humans or robotic prosthesis are utilized [4][5][6], and based on these signals, versatile algorithms including model-based computational methods, e.g. fuzzy logic [7], a slope gradient estimator [8], and model-free computational methods, traditional machine learning, e.g. support vector machines (SVMs) [9,10], and big-data-driven neural network, e.g., convolutional neural network (CNN) [6,11], are proposed to obtain more accurate recognition.…”

Section: Introductionmentioning

confidence: 99%

“…Model-based computational methods are proposed to address the issue on accurate locomotion mode recognition [7,8]. With one inertial measurement unit (IMU) in the backpack and encoders to measure lower-limb joint angles, a slope gradient estimator based on the sensor data fusion is proposed to construct an adaptive gait planning approach for sloped terrains.…”

Section: Introductionmentioning

confidence: 99%

“…With one inertial measurement unit (IMU) in the backpack and encoders to measure lower-limb joint angles, a slope gradient estimator based on the sensor data fusion is proposed to construct an adaptive gait planning approach for sloped terrains. The performance of the approach is limited by the sensor accuracy, tracking errors of the controllers and kinematics computation [8]. The fuzzy logic-based method could apply fuzzy sets and fuzzy rules for reasoning about transitional boundaries or qualitative knowledge experiences for describing systems which is suitable for terrain identification for robotic prostheses, and the average identification accuracy could be 98.74%, and the average identification delay could be 9.06% of one gait cycle [7].…”

Section: Introductionmentioning

confidence: 99%

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Small-Data-Driven Temporal Convolutional Capsule Network for Locomotion Mode Recognition of Robotic Prostheses

Feng

Xue

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Locomotion mode recognition has been shown to substantially contribute to the precise control of robotic lowerlimb prostheses under different walking conditions. In this study, we proposed a temporal convolutional capsule network (TCCN) which integrates the spatial-temporal-based, dilationconvolution-based, dynamic routing and vector-based features for recognizing locomotion mode recognition with small data rather than big-data-based neural networks for robotic prostheses. TCCN proposed in this study has four characteristics, which extracts the (1) spatial-temporal information in the data and then makes (2) dilated convolution to deal with small data, and uses (3) dynamic routing, which produces some similarity to the human brain to processes the data as a (4) vector, which is different from other scalar-based networks, such as convolutional neural network (CNN). By comparison with a traditional machine learning, e.g., support vector machine(SVM) and big-data-driven neural networks, e.g., CNN, recurrent neural network(RNN), temporal convolutional network(TCN) and capsule network(CN). The accuracy of TCCN is 4.1% higher than CNN under 5fold cross-validation of three-locomotion-mode and 5.2% higher under the 5-fold cross-validation of five-locomotion modes. The main confusion we found appears in the transition state. The results indicate that TCCN may handle small data balancing global and local information which is closer to the way how the human brain works, and the capsule layer allows for better processing of vector information and retains not only magnitude information, but also direction information.

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Slope Gradient Adaptive Gait Planning for Walking Assistance Lower Limb Exoskeletons

Cited by 14 publications

References 22 publications

Dynamic movement primitives in robotics: A tutorial survey

Dynamic movement primitives in robotics: A tutorial survey

Online gait learning with Assist-As-Needed control strategy for post-stroke rehabilitation exoskeletons

Small-Data-Driven Temporal Convolutional Capsule Network for Locomotion Mode Recognition of Robotic Prostheses

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