scholarly journals Human Intention Recognition for Lower Limb Rehabilitation Exoskeleton Robot

2021 ◽  
Author(s):  
Jing Chen ◽  
Zhiyuan Yu ◽  
Xiaorong Zhu ◽  
Longfei Jia ◽  
Yuping Huang
Keyword(s):  
Lower Limb ◽  
Cooperative Control ◽  
Surface Emg ◽  
Semg Signal ◽  
Exoskeleton Robot ◽  
Intention Recognition ◽  
Increasing Demand ◽  
Hidden Layer ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

Abstract With the increasing aging of the population, the contradiction between the increasing demand for rehabilitation and the insufficient rehabilitation medical resources of the patients with moderate limb disorders in stroke is increasingly prominent. As a new means of rehabilitation, rehabilitation exoskeleton robot has gradually become a research hotspot in recent years. The rapid and accurate recognition of human lower limb movement intentions is very important for the control system of lower limb rehabilitation exoskeleton robots, this paper innovatively proposes an adaptive inertial weighted improved particle swarm optimization LSTM algorithm (IPSO-LSTM), which not only characterizes the mapping relationship between the surface EMG (sEMG) signal and the joint angle of the lower extremity in continuous motion, but also solves the values random setting problems of iterations number, learning rate and hidden layer number. More importantly, the optimization algorithm solves the network over fitting problem and further improves the predict accuracy of the model. Finally, based on the complex system of lower limb rehabilitation exoskeleton robot, the algorithm is applied to the human-machine cooperative control experiment of active rehabilitation training, the experiment verifies that the IPSO-LSTM algorithm model can meet the requirements of real-time and accuracy of active intention recognition.

Human-Like Robust Adaptive PD Based Human Gait Tracking for Exoskeleton Robot

2021 ◽  
Vol 33 (1) ◽  
pp. 88-96
Author(s):  
Aihui Wang ◽  
Ningning Hu ◽  
Jun Yu ◽  
Junlan Lu ◽  
Yifei Ge ◽  
...  
Keyword(s):  
Control Strategy ◽  
Lower Limb ◽  
Structural Parameters ◽  
Human Gait ◽  
Pd Control ◽  
Rehabilitation Training ◽  
Exoskeleton Robot ◽  
Robust Adaptive ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

For patients with dyskinesias caused by central nervous system diseases such as stroke, in the early stage of rehabilitation training, lower limb rehabilitation robots are used to provide passive rehabilitation training. This paper proposed a human-like robust adaptive PD control strategy of the exoskeleton robot based on healthy human gait data. When the error disturbance is bounded, a human-like robust adaptive PD control strategy is designed, which not only enables the rehabilitation exoskeleton robot to quickly track the human gait trajectory obtained through the 3D NOKOV motion capture system, but also can well identify the structural parameters of the system and avoid excessively initial output torque for the robot. MATLAB simulation verifies that the proposed method has a better performance to realize tracking the experimental trajectory of human movement and anti-interference ability under the condition of ensuring global stability for a lower limb rehabilitation exoskeleton robot.

scholarly journals Lower limb rehabilitation exoskeleton robot: A review

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110118
Author(s):  
Jinman Zhou ◽  
Shuo Yang ◽  
Qiang Xue
Keyword(s):  
Lower Limb ◽  
Physical Status ◽  
Positive Role ◽  
Advantages And Disadvantages ◽  
Exoskeleton Robot ◽  
Human Kinematics ◽  
Patient Groups ◽  
Limb Rehabilitation ◽  
Movement Rules ◽  
Lower Limb Rehabilitation

Lower limb rehabilitation exoskeleton robots (LLRERs) play a positive role in lower limb rehabilitation and assistance for patients with lower limb disorders, and they are helpful to improve patients’ physical status. More and more experiments pay more attention to the kinematic and dynamic data characteristics of different patient groups. However, it is not clear whether these devices have broad adaptability and their clinical significance, so it is necessary to summarize and analyze these research results. This paper summarizes the LLRERs prototype and product in recent years, also compares the advantages and disadvantages of the theory and technology used in these research, and compares the functional characteristics of the devices, finally summarizes the aspects of the LLRERs to be improved. These devices apply advanced theories, techniques or structures, as well as human kinematics and dynamics data. However, due to the complexity of human body characteristics and movement rules, the theory or technology applied in the study design of LLRERs remains to be further studied, which can be improved in many aspects, such as improve the human-computer cooperation of equipment or carry out clinical trials. This paper can provide reference for researchers and designers in the future study, as well as understanding and selecting LLRERs for all kinds of therapist and patients.

scholarly journals A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons

2021 ◽  
Vol 15 ◽  
Author(s):  
Can Wang ◽  
Ziming Guo ◽  
Shengcai Duan ◽  
Bailin He ◽  
Ye Yuan ◽  
...  
Keyword(s):  
Real Time ◽  
Lower Limb ◽  
Short Term Memory ◽  
Control Method ◽  
Positive Impact ◽  
Stability Control ◽  
Intention Recognition ◽  
Switching Method ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

Herein, we propose a real-time stable control gait switching method for the exoskeleton rehabilitation robot. Exoskeleton rehabilitation robots have been extensively developed during the past decade and are able to offer valuable motor ability to paraplegics. However, achieving stable states of the human-exoskeleton system while conserving wearer strength remains challenging. The constant switching of gaits during walking may affect the center of gravity, resulting in imbalance of human–exoskeleton system. In this study, it was determined that forming an equilateral triangle with two crutch-supporting points and a supporting leg has a positive impact on walking stability and ergonomic interaction. First, the gaits planning and stability analysis based on human kinematics model and zero moment point method for the lower limb exoskeleton are demonstrated. Second, a neural interface based on surface electromyography (sEMG), which realizes the intention recognition and muscle fatigue estimation, is constructed. Third, the stability of human–exoskeleton system and ergonomic effects are tested through different gaits with planned and unplanned gait switching strategy on the SIAT lower limb rehabilitation exoskeleton. The intention recognition based on long short-term memory (LSTM) model can achieve an accuracy of nearly 99%. The experimental results verified the feasibility and efficiency of the proposed gait switching method for enhancing stability and ergonomic effects of lower limb rehabilitation exoskeleton.

scholarly journals An Adaptive Sliding Mode Variable Admittance Control Method for Lower Limb Rehabilitation Exoskeleton Robot

2020 ◽  
Vol 10 (7) ◽  
pp. 2536
Author(s):  
Yao Tu ◽  
Aibin Zhu ◽  
Jiyuan Song ◽  
Huang Shen ◽  
Zhitao Shen ◽  
...  
Keyword(s):  
Lower Limb ◽  
Sliding Mode ◽  
Active Participation ◽  
Training Intensity ◽  
Active Muscle ◽  
Adaptive Sliding Mode ◽  
Exoskeleton Robot ◽  
Limb Rehabilitation ◽  
Movement Intention ◽  
Lower Limb Rehabilitation

As passive rehabilitation training with fixed trajectory ignores the active participation of patients, in order to increase the active participation of patients and improve the effect of rehabilitation training, this paper proposes an innovative adaptive sliding mode variable admittance (ASMVA) controller for the Lower Limb Rehabilitation Exoskeleton Robot. The ASMVA controller consists of an outer loop with variable admittance controller and an inner loop with an adaptive sliding mode controller. It estimates the wearer’s active muscle strength and movement intention by judging the deviation between the actual and standard interaction force of the wearer’s leg and the exoskeleton, thereby adaptively changing admittance controller parameters to alter training intensity. Three healthy volunteers engaged in further experimental studies, including trajectory tracking experiments with no admittance, fixed admittance, and variable admittance adjustment. The experimental results show that the proposed ASMVA control scheme has high control accuracy. Besides, the ASMVA can not only increase training intensity according to the active muscle strength of the patient during positive movement intention (so as to increase active participation of the patient), but also increase the amount of trajectory adjustment during negative movement intention to ensure the safety of the patient.

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