On-line walking speed control in HUman-powered exoskeleton systems

Ahmed, Abusabah I. A.; Cheng, Hong; Lin, Xichuan; Elhassan, Zkieldeen M. E.; Omer, Mohamed I.

doi:10.1109/iccccee.2017.7866085

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…In this section for the efficiency augmentation of VAC algorithm considering walking speed transitions, based on the sensory system signals we apply Neural Networks (NNs), to identify the pilot intentions for walking speed changes. Dynamic Threshold Neural networks in which the input signal propagate forward to generate the output successfully applied for classification or prediction tasks [15], [16]. The suitable model for this application is the Feed-forward Single-hidden layer DTNNs, which is often trained by the back propagation algorithm.…”

Section: Dynamic Threshold Neural Networkmentioning

confidence: 99%

A Novel Walking Speed Control Strategy for Power Augmentation Exoskeletons based on Neural Networks

Ahmed

Musa²

2020

FJES

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This paper addresses a novel sensing technique to minimize the interaction force of walking speed transitions during the navigation of a coupled human-exoskeleton power augmentation system. The proposed technique is able to classify the intended walking speed based on Dual Reaction Force (DRF) Sensor. The human brain as a complex information processing device is quite difficult to be simulated, especially when considering the processing speed and the speed of sensed signals through the human nervous system throughout the human body. We developed the DRF sensors for preemptive identification of pilot intentions for walking speed transitions to augment the response of the exoskeleton to shadow pilot's movements. The Dynamic Threshold Neural Networks (DTNNs) is used to classify the input signals and make a decision on the transition of system's walking speed according to the pilot's intentions and walking speed limitation. The actions for walking speed transitions are simulated in MATLAB/Simulink, and Variable Admittance Controller (VAC) and applied. To show the efficiency of the proposed walking speed control strategy, comparison is conducted with ordinary VAC algorithm technique.

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Section: Dynamic Threshold Neural Networkmentioning

confidence: 99%

A Novel Walking Speed Control Strategy for Power Augmentation Exoskeletons based on Neural Networks

Ahmed

Musa²

2020

FJES

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“…The controller is based on a Proxy-Based Sliding Control (PSMC) method. Ahmed et al [21], [22] have employed a recursive least square estimator (RLSE) for their variable admittance controller (VAC), which can adjust the position controller's stiffness and damping values. Hu et al [23] have employed an impedance control for their exoskeleton robotic iLeg intended for rehabilitation applications.…”

Section: The Position Controllermentioning

confidence: 99%

“…The DMPs model provides a time-normalized and flexible trajectory planning framework. Relatedly, Ahmed et al [21], [22] have developed a control algorithm that applies adaptive central pattern generators (ACPGs) to generate gait trajectories.…”

Section: Gait Trajectoriesmentioning

confidence: 99%

“…As a result, a feedback synchronization algorithm was designed that compensates during the desired walking phase by assessing the error between the desired and the actual stepping period. Similarly, Ahmed et al [21], [22] have employed ground reaction force sensors to adjust the walking speed. The Mina [53], [54] uses a musical instrument digital interface (MIDI) to adjust the recorded walking speed in a range of 50%-130%.…”

Section: The Synchronized Modelmentioning

confidence: 99%

“…Index of articles Gait trajectory based [14], [15], [16], [17], [18]- [20], [21], [22], [23], [24], [25], [26], position assistance [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43]- [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58] Posture estimation based [59], [60], [61], [62], [63], [64], [65] position assistance I...…”

Section: Control Strategymentioning

confidence: 99%

See 2 more Smart Citations

A Review on Human–exoskeleton Coordination Towards Lower Limb Robotic Exoskeleton Systems

Ma¹,

Wang²,

Yi³

et al. 2019

International Journal of Robotics and Automation

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The study of lower extremity exoskeleton robots has been pursued for many years and is now receiving significant attention. A number of review articles have focused on describing the mechanical design of exoskeleton robots, their control methods, human-exoskeleton robot interfaces, and so on. None, however, have focused on the coordination of control between humans and these kinds of robots. In this paper, we examine the research regarding human-exoskeleton robot coordinated control to capture the current state-of-the-art. One hundred and fifty six relevant articles were collected and screened from the Web of science, and classified into six categories, based on human neurobiology and exoskeleton robots' assistive effects. These categories were further subdivided according to their perspective on human-exoskeleton robots coordinated control. The paper extensively reviews various control methods we uncovered and how they are coordinated between wearer and exoskeleton robot.

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Gait Learning for Lower Limb Rehabilitation Exoskeleton using Dynamical Movement Primitives based on General Regression Neural Networks

Wang

2020

2020 Chinese Automation Congress (CAC)

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On-line walking speed control in HUman-powered exoskeleton systems

Cited by 5 publications

References 26 publications

A Novel Walking Speed Control Strategy for Power Augmentation Exoskeletons based on Neural Networks

A Novel Walking Speed Control Strategy for Power Augmentation Exoskeletons based on Neural Networks

A Review on Human–exoskeleton Coordination Towards Lower Limb Robotic Exoskeleton Systems

Gait Learning for Lower Limb Rehabilitation Exoskeleton using Dynamical Movement Primitives based on General Regression Neural Networks

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