Reference Joint Trajectories Generation of CUHK-EXO Exoskeleton for System Balance in Walking Assistance

Chen, Bing; Zhong, Chun-Hao; Zhao, Xuan; Ma, Hao; Qin, Ling; Liao, Wei-Hsin

doi:10.1109/access.2019.2904296

Cited by 36 publications

(24 citation statements)

References 33 publications

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“…Chen et al considered the influence of a crutch on human-machine balance control and designed crutches with FSR sensors at the bottom [ 19 ]. On the one hand, these sensors are helpful to measure GRF directly and conveniently.…”

Section: Methodsmentioning

confidence: 99%

“…With the above sensors, GRF on the crutch can be calculated by simple force analysis, as shown in Figure 4 b. Compared to the direct measurement method in [ 19 ], this indirect measurement method is: (i) Easily acquires the contacting force data of the palm and forearm, and (ii) has the ability to remove the disturbance of the crutch pitch angle. The disadvantage is that some yaw, pitch, roll angle errors, palm, and forearm contacting force errors may lead to large vertical GRF errors.…”

Section: Methodsmentioning

confidence: 99%

“…With measurements of both human gravity and exoskeleton support force, the CoP is calculated, and the stability condition is judged. Chen et al calculated the CoP of a human-machine-crutch system and controlled an exoskeleton with an offline designed gait [ 19 ]. During walking, the gait is modified online if the calculated CoP exceeds a predefined stable area.…”

Section: Introductionmentioning

confidence: 99%

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Lower Limb Exoskeleton Gait Planning Based on Crutch and Human-Machine Foot Combined Center of Pressure

Yang

Zhang

et al. 2020

Sensors

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With the help of wearable robotics, the lower limb exoskeleton becomes a promising solution for spinal cord injury (SCI) patients to recover lower body locomotion ability. However, fewer exoskeleton gait planning methods can meet the needs of patient in real time, e.g., stride length or step width, etc., which may lead to human-machine incoordination, limit comfort, and increase the risk of falling. This work presents a human-exoskeleton-crutch system with the center of pressure (CoP)-based gait planning method to enable the balance control during the exoskeleton-assisted walking with crutches. The CoP generated by crutches and human-machine feet makes it possible to obtain the overall stability conditions of the system in the process of exoskeleton-assisted quasi-static walking, and therefore, to determine the next stride length and ensure the balance of the next step. Thus, the exoskeleton gait is planned with the guidance of stride length. It is worth emphasizing that the nominal reference gait is adopted as a reference to ensure that the trajectory of the swing ankle mimics the reference one well. This gait planning method enables the patient to adaptively interact with the exoskeleton gait. The online gait planning walking tests with five healthy volunteers proved the method’s feasibility. Experimental results indicate that the algorithm can deal with the sensed signals and plan the landing point of the swing leg to ensure balanced and smooth walking. The results suggest that the method is an effective means to improve human–machine interaction. Additionally, it is meaningful for the further training of independent walking stability control in exoskeletons for SCI patients with less assistance of crutches.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lower Limb Exoskeleton Gait Planning Based on Crutch and Human-Machine Foot Combined Center of Pressure

Yang

Zhang

et al. 2020

Sensors

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“…One is the end-effector-type rehabilitation robot [3]- [9], which has only a human-robot interaction point at the robot end-effector. Another is the exoskeleton-type rehabilitation robot [10]- [15], which can be worn on the human extremity with multiple connected points.…”

Section: Introductionmentioning

confidence: 99%

Integral Fuzzy Sliding Mode Impedance Control of an Upper Extremity Rehabilitation Robot Using Time Delay Estimation

Dawen

Chen

et al. 2019

IEEE Access

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Robot-assisted cooperative rehabilitation training has shown superiority in helping the individuals with motion impairment problems to regain their motor functions. This paper presents the development and evaluation of a new cooperative training control scheme for an end-effector-type rehabilitation robot to provide upper extremity rehabilitation training with desired compliance and intensity. Firstly, the overall mechanical structure and real-time control system of rehabilitation robot are introduced. Secondly, an integral fuzzy sliding mode impedance control strategy combined with time-delay estimation (IFSMIC-TDE) is proposed to induce the active participation of patients and suppress impedance error. The IFSMIC-TDE approach is free from nonlinear robot dynamics, and it is designed to be robust to the external uncertainties and inherent chattering characteristics. After that, the closed-loop system stability with IFSMIC-TDE is proved based on the Lyapunov stability theory. Finally, experimental investigations are conducted to illustrate the effectiveness of the proposed rehabilitation robot and control algorithm. The comparison results indicate that the proposed IFSMIC-TDE can achieve better control performance and less impedance error. Besides, the interaction compliance and training intensity can be qualitatively adjusted via appropriate impedance parameters. INDEX TERMS Upper extremity rehabilitation robot, cooperative training, sliding mode impedance control, fuzzy turner, time delay estimation.

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“…The employment of robot-based treatment has demonstrated a high capacibility to improve the motor function of affected limb of patients. Compared with the manual training, the robotic training is able to deliver high-intensity and high-efficiency training with pertinence control algorithms and, furthermore, monitor the therapy progress of patients with integrated sensing system [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

RBFN-Based Adaptive Backstepping Sliding Mode Control of an Upper-Limb Exoskeleton With Dynamic Uncertainties

Chen

2019

IEEE Access

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In recent decades, robot-assisted rehabilitation therapy has been widely researched and proven to be effective in the motor function recovery of disabled individuals. In this paper, an adaptive backstepping sliding mode control approach combined with neural uncertainty observer is developed for upper-limb exoskeleton, which can help the human operator perform repetitive rehabilitation training. Firstly, a comprehensive overview about the therapeutic exoskeleton hardware and real-time control system is introduced. Then, the neural adaptive backstepping sliding mode controller (NABSMC) is developed based on radial basis function network (RBFN) to improve the trajectory tracking accuracy with external disturbances and dynamics errors. Next, the closed-loop stability of the proposed controller is demonstrated according to the Lyapunov stability theory. Finally, further experimental investigation are conducted on three volunteers to compare the control performance of NABSMC strategy with an optimal backstepping sliding mode control (OBSMC) strategy. The comparison results show that the proposed NABSMC algorithm is capable of achieving higher trajectory tracking accuracy and better step response characteristic during repetitive passive rehabilitation training. INDEX TERMS Upper-limb exoskeleton, rehabilitation training, adaptive backstepping sliding mode control, neural uncertainty observer, Lyapunov stability theory.

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Reference Joint Trajectories Generation of CUHK-EXO Exoskeleton for System Balance in Walking Assistance

Cited by 36 publications

References 33 publications

Lower Limb Exoskeleton Gait Planning Based on Crutch and Human-Machine Foot Combined Center of Pressure

Lower Limb Exoskeleton Gait Planning Based on Crutch and Human-Machine Foot Combined Center of Pressure

Integral Fuzzy Sliding Mode Impedance Control of an Upper Extremity Rehabilitation Robot Using Time Delay Estimation

RBFN-Based Adaptive Backstepping Sliding Mode Control of an Upper-Limb Exoskeleton With Dynamic Uncertainties

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