A generalized control framework of assistive controllers and its application to lower limb exoskeletons

Oh, Sehoon; Baek, Eunyoung; Song, Seok-ki; Mohammed, Samer; Jeon, Doyoung; Kong, Kyoungchul

doi:10.1016/j.robot.2014.10.001

Cited by 59 publications

(24 citation statements)

References 30 publications

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“…S. Oh [20] thought that a single control method is not the best option for all of the motion phases during the gait cycle. H. Kazerooni [21] adopted a hybrid control strategy for different gait phases to control the Berkeley Lower Extremity Exoskeleton (BLEEX).…”

Section: Introductionmentioning

confidence: 99%

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Liu

Wang

et al. 2016

Sensors

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Gait phase is widely used for gait trajectory generation, gait control and gait evaluation on lower-limb exoskeletons. So far, a variety of methods have been developed to identify the gait phase for lower-limb exoskeletons. Angular sensors on lower-limb exoskeletons are essential for joint closed-loop controlling; however, other types of sensors, such as plantar pressure, attitude or inertial measurement unit, are not indispensable.Therefore, to make full use of existing sensors, we propose a novel gait phase recognition method for lower-limb exoskeletons using only joint angular sensors. The method consists of two procedures. Firstly, the gait deviation distances during walking are calculated and classified by Fisher’s linear discriminant method, and one gait cycle is divided into eight gait phases. The validity of the classification results is also verified based on large gait samples. Secondly, we build a gait phase recognition model based on multilayer perceptron and train it with the phase-labeled gait data. The experimental result of cross-validation shows that the model has a 94.45% average correct rate of set (CRS) and an 87.22% average correct rate of phase (CRP) on the testing set, and it can predict the gait phase accurately. The novel method avoids installing additional sensors on the exoskeleton or human body and simplifies the sensory system of the lower-limb exoskeleton.

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Section: Introductionmentioning

confidence: 99%

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Liu

Wang

et al. 2016

Sensors

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“…Foot-pressure patterns are also used in the BLEEX system to detect the phase of the motion [22]. Oh et al (2015) proposed a generalized control framework incorporating various assistive control methods. The framework consists of a feedforward disturbance compensation control, reference tracking feedback control, reference tracking feedforward control, and a model-based torque control.…”

Section: Introductionmentioning

confidence: 99%

“…The framework consists of a feedforward disturbance compensation control, reference tracking feedback control, reference tracking feedforward control, and a model-based torque control. The proposed control framework is designed taking into consideration the linearity of each control algorithm to enable continuous and smooth switching between assistive control algorithms [23]. Vijaykumar developed a control strategy that provides the necessary assistance required and is related to balance recovery and postural stability [24].…”

Section: Introductionmentioning

confidence: 99%

A Human-Robot Interaction Based Coordination Control Method for Assistive Walking Devices and an Assessment of Its Stability

Zhang

Wenliang

et al. 2018

Mathematical Problems in Engineering

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A biologically inspired motion control method is introduced to ameliorate the flexibility and multijoint autonomy of assistive walking devices based on human-robot interactions (HRIs). A new HRI-based coordination control system consisting of a hip central pattern generator (CPG) control, a knee hierarchical impedance control, and a hip-knee linkage control is also investigated. Simulations and walking experiments are carried out which demonstrate that (i) the self-oscillation and external communication characteristics of the CPG are capable of realizing ideal master/slave hip joint trajectories. In addition, symmetrical inhibition in the CPG unit is essential for maintaining the antiphase motion of the left and right hip joints. (ii) High and low hierarchical impedance control laws allow appropriate knee joint torque to be calculated to maintain posture during the support and swing phases as walking proceeds. (iii) A hip-knee joint linkage mechanism which incorporates a hip joint CPG control and knee joint impedance control allows natural and relevant hip-knee trajectories to be realized. The stability of the HRI-based coordination control method is also confirmed using Lyapunov stability theory.

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“…Liu et al [13] proposed an approach of gait phase recognition for a lower limb exoskeleton with only joint angular sensors. Oh et al [14] considered that a single control method is not the best option for all motion phases during the gait cycle. It is difficult to use a fixed model to describe the process of walking and obtain a fixed output setting due to individual differences in human walking and the changes in road conditions.…”

Section: Introductionmentioning

confidence: 99%

Design and Voluntary Motion Intention Estimation of a Novel Wearable Full-Body Flexible Exoskeleton Robot

Chen

Wang

Liu

et al. 2017

Mobile Information Systems

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The wearable full-body exoskeleton robot developed in this study is one application of mobile cyberphysical system (CPS), which is a complex mobile system integrating mechanics, electronics, computer science, and artificial intelligence. Steel wire was used as the flexible transmission medium and a group of special wire-locking structures was designed. Additionally, we designed passive joints for partial joints of the exoskeleton. Finally, we proposed a novel gait phase recognition method for full-body exoskeletons using only joint angular sensors, plantar pressure sensors, and inclination sensors. The method consists of four procedures. Firstly, we classified the three types of main motion patterns: normal walking on the ground, stair-climbing and stair-descending, and sit-to-stand movement. Secondly, we segregated the experimental data into one gait cycle. Thirdly, we divided one gait cycle into eight gait phases. Finally, we built a gait phase recognition model based on -Nearest Neighbor perception and trained it with the phase-labeled gait data. The experimental result shows that the model has a 98.52% average correct rate of classification of the main motion patterns on the testing set and a 95.32% average correct rate of phase recognition on the testing set. So the exoskeleton robot can achieve human motion intention in real time and coordinate its movement with the wearer.

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A generalized control framework of assistive controllers and its application to lower limb exoskeletons

Cited by 59 publications

References 30 publications

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

A Human-Robot Interaction Based Coordination Control Method for Assistive Walking Devices and an Assessment of Its Stability

Design and Voluntary Motion Intention Estimation of a Novel Wearable Full-Body Flexible Exoskeleton Robot

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