A Novel Weight-Bearing Lower Limb Exoskeleton Based on Motion Intention Prediction and Locomotion State Identification

Hua, Yang; Fan, Jizhuang; Liu, Gangfeng; Zhang, Xuehe; Lai, Mingzhu; Li, Mo; Zheng, Tianjiao; Zhang, Guoan; Zhao, Jie; Zhu, Yanhe

doi:10.1109/access.2019.2904709

Cited by 32 publications

(36 citation statements)

References 40 publications

(42 reference statements)

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“…The best performance was for the semidependent model. An adaptive neural-fuzzy inference system (ANFIS), which is a combination of Takagi-Sugeno fuzzy inference system and neural networks, is implemented by Hua et al [74] for detecting two phases: stance and swing. Their objective was to develop a lower limb exoskeleton that withstands lifting of heavy loads.…”

Section: ) Neural Networkmentioning

confidence: 99%

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Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

2021

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This project has received funding from the Interreg 2 Seas programme 2014-2020 co-funded by the European Regional Development Fund under subsidy contract No 2S05-038 (M.O.T.I.O.N project). Rania Kolaghassi acknowledges the support of studentship through M.O.T.I.O.N, and Mohamad Kenan Al-Hares acknowledges the support of M.O.T.I.O.N. Data used in this work is stored in Kent Academic Repository (https://kar.kent.ac.uk/).

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Section: ) Neural Networkmentioning

confidence: 99%

“…Deep neural network (DNN) and convolutional neural network (CNN) are implemented by Hua et al [74], for detection of 6 locomotion modes. They have experimented with several machine learning algorithms, including DT, DA, KNN, SVM, EM which were pre-processed with kPCA to reduce the dimensions of the input features.…”

Section: ) Deep Neural Networkmentioning

confidence: 99%

Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

2021

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“…Owing to their great potential in improving human mobility, ankle exoskeletons have aroused increasing research interest over the last few decades. (1)(2)(3)(4) To achieve this potential, it is paramount to set suitable control parameters for an ankle exoskeleton since they have a profound impact on its performance. (5)(6)(7) Traditionally, the control parameters have either been hand-tuned or set on the basis of average measured biomechanical properties of a given population group.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive Particle Swarm Optimization with Human-in-the-loop for Ankle Exoskeleton Control

Wang¹,

Tang²,

Pang³

et al. 2021

Sensors and Materials

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Ankle exoskeletons have recently aroused increasing research interest owing to their potential in enhancing human locomotion. Nevertheless, inter-subject variability makes the control of human-exoskeleton interaction complicated. To handle this problem, we designed a human-inthe-loop (HIL) approach to optimization control for an ankle exoskeleton during walking based on an improved self-adaptive particle swarm optimization (ISAPSO) algorithm and the iterative learning control (ILC) algorithm. As part of the development, a self-adaptive updating strategy was first proposed to tune the three key parameters of each particle to obtain a better trade-off between the global and local search abilities of ISAPSO. Moreover, since the performance of the proposed ISAPSO heavily relies on its convergence property, we provided a convergenceguaranteed parameter setting rule for the proposed optimizer after analytically investigating its convergence. Finally, the developed HIL optimization approach was verified via experimental tests on eight subjects. The experimental results revealed that the proposed method reduced the soleus muscle activities of the eight subjects by 23.46 ± 10.21, 47.04 ± 13.54, 28.52 ± 8.14, and 8.58 ± 3.82% compared with those for the static assistance condition, zero-torque model, normal walking condition, and the case optimized by standard particle swarm optimization, respectively. Thus, the proposed method can be regarded as an alternative in the field of exoskeleton HIL optimization control.

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“…For example, Chu [33] employed the particle swarm optimization (PSO) algorithm to obtain the input layer weights of an ELM during the model training process, thereby providing more accurate ELM tool wear estimation performance. Nevertheless, the initial weight values adopted in the PSO process can cause the optimization result to become trapped in a local optimum, thereby obtaining a suboptimal solution [34,35]. Cao [36] introduced a genetic algorithm (GA) to optimize the connection parameters between each network layer, and thereby improved the reliability and generalization ability of an ELM tool wear estimation method.…”

Section: Introductionmentioning

confidence: 99%

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

Lei

Zhu

Zhou

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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Tools are the most vulnerable components in milling processes conducted using numerical control milling machines, and their wear condition directly influences work-product quality and operational safety. As such, tool wear estimation is an essential component of NC milling operations. This study addresses this issue by proposing an extreme learning machine (ELM) method enhanced by a hybrid genetic algorithm and particle swarm optimization (GAPSO) approach for conducting tool wear estimation based on workpiece vibration signals. Here, a few feature parameters in the time, frequency, and time-frequency (Ensemble empirical mode decomposition, EEMD) domains of the workpiece vibration signals are extracted as the input of the ELM model. Then, the initialized weights and thresholds of the ELM model are optimized based on the GAPSO approach with training dataset. Finally, tool wear is estimated using the optimized ELM model with testing dataset. The effectiveness of the proposed method is verified by its application to vibration signals collected from two milling tool wear experiments (an open-access benchmark dataset and a milling tool wear experiment) by comparison to the ELM, GA-ELM, and PSO-ELM methods. The results indicate that the estimation accuracy and optimization efficiency of the proposed method outperforms that of other three methods.

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A Novel Weight-Bearing Lower Limb Exoskeleton Based on Motion Intention Prediction and Locomotion State Identification

Cited by 32 publications

References 40 publications

Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

Self-adaptive Particle Swarm Optimization with Human-in-the-loop for Ankle Exoskeleton Control

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

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