&lt;italic&gt;iLeg&lt;/italic&gt;—A Lower Limb Rehabilitation Robot: A Proof of Concept

Zhang, Feng; Hou, Zeng-Guang; Cheng, Long; Wang, Weiqun; Chen, Yixiong; Jin, Hu; Peng, Liang; Wang, Hongbo

doi:10.1109/thms.2016.2562510

Cited by 55 publications

(28 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The exoskeleton type robot needs to be worn on the patient's body, which applies few constraints on the patient's ordinary movement. There are considerable applications of the exoskeleton type robot in the area of the rehabilitation, i.e., the upper limb exoskeleton, lower limb exoskeleton and hand exoskeleton [3][4][5]. The platform type robot is fixed on the stationary platform rather than putting on the patient's body.…”

Section: Introductionmentioning

confidence: 99%

Iterative assist-as-needed control with interaction factor for rehabilitation robots

Cao

Cheng

Yang

et al. 2021

Sci. China Technol. Sci.

Self Cite

View full text Add to dashboard Cite

Rehabilitation robots for stroke patients have drawn considerable attention because they can reduce the economic and labor costs brought by traditional rehabilitation. Control methods for rehabilitation robots have been developed to stimulate the active motion of patients and to improve the effectiveness of rehabilitation care. However, current control methods can only roughly adjust the system's stiffness and may fail in achieving satisfactory performance. To this end, this paper introduces a novel cost function consisting of the tracking error term and the stiffness term. The cost function contains an interaction factor that represents the patient's motion intention to balance the weight of these two terms. When the patients try to actively do training tasks, the weight of stiffness term increases, which leads to the larger allowable tracking error and lower stiffness eventually. An iterative updating law of the stiffness matrix is given to reduce the proposed cost function. Theoretical analysis based on the Lyapunov theory is given to ensure the feasibility of the proposed algorithm. Furthermore, a force estimation is used to improve interaction control performance. Finally, simulation experiments are provided to show the effectiveness of the proposed algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%

Iterative assist-as-needed control with interaction factor for rehabilitation robots

Cao

Cheng

Yang

et al. 2021

Sci. China Technol. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are many excellent achievements to recognize the limb motion intention. Zhang et al developed a sitting/lying lower limb rehabilitation robot, named iLeg [26]. It employs the surface electromyography (EMG) signals from muscle groups to obtain the Cartesian torque/force.…”

Section: Introductionmentioning

confidence: 99%

New Motion Intention Acquisition Method of Lower Limb Rehabilitation Robot Based on Static Torque Sensors

Yan

Wang

Vlădăreanu

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

The rehabilitation robot is an application of robotic technology for people with limb disabilities. This paper investigates a new applicable and effective sitting/lying lower limb rehabilitation robot (the LLR-Ro). In order to improve the patient’s training initiative and accelerate the rehabilitation process, a new motion intention acquisition method based on static torque sensors is proposed. This motion intention acquisition method is established through the dynamics modeling of human–machine coordination, which is built on the basis of Lagrangian equations. Combined with the static torque sensors installed on the mechanism leg joint axis, the LLR-Ro can obtain the active force from the patient’s leg. Based on the variation of the patient’s active force and the kinematic functional relationship of the patient’s leg end point, the patient motion intention is obtained and used in the proposed active rehabilitation training method. The simulation experiment demonstrates the correctness of mechanism leg dynamics equations through ADAMS software and MATLAB software. The calibration experiment of the joint torque sensors’ combining limit range filter with an average value filter provides the hardware support for active rehabilitation training. The consecutive variation of the torque sensors from just the mechanism leg weight, as well as both the mechanism leg and the patient leg weights, obtains the feasibility of lower limb motion intention acquisition.

show abstract

“…Rehabilitation robot has brought the hope to patients with limbs disabled and therapists. At present, the lower limb rehabilitation robot is now a research hotspot, and few restoration robots have reclamation robots have been structured as the ask about achievement [1][2][3][4][5][6]. The commended recuperation robot structure LOKOMAT which was asked about by the Swiss Government Established of Innovation Zurich used suspension device to reinforce or modify human weight, and the calm may do bring down extremity walking walk reclamation getting ready with the help of outside forces [7].…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of a Spatial Four-DOF Lower Limb Rehabilitation Robot

Wang¹

2019

IJATCSE

View full text Add to dashboard Cite

Aiming at the existing problem that the majority of lower limb rehabilitation robots can only realize patient's leg to move in the sagittal plane, cannot achieve the patient's leg to move in coronal plane, a spatial four-DOF multi-pose lower limb rehabilitation robot is proposed. The robot could also realize the sitting training posture, the lying training posture and the standing training posture. The robot includes seat, left mechanical leg and right mechanical leg. Each mechanical leg with series-parallel mechanism includes the hip joint with two DOFs, knee joint with one DOF and ankle joint with one DOF which correspond to the hip joint, knee joint and ankle joint of human. This paper describes the mechanical design and kinematic analysis of the rehabilitation robot. The coordinate system of the lower limb rehabilitation robot is established, and the degree of freedom of the spatial leg mechanism is calculated. The inverse kinematics of the single leg mechanism and the forward kinematics of the lower limb rehabilitation robot are analyzed. The workspace of the leg mechanism was calculated. The kinematics analysis of the leg mechanism lays a theoretical base for the future controlling of the robot prototype.

show abstract

<italic>iLeg</italic>—A Lower Limb Rehabilitation Robot: A Proof of Concept

Cited by 55 publications

References 19 publications

Iterative assist-as-needed control with interaction factor for rehabilitation robots

Iterative assist-as-needed control with interaction factor for rehabilitation robots

New Motion Intention Acquisition Method of Lower Limb Rehabilitation Robot Based on Static Torque Sensors

Design and Analysis of a Spatial Four-DOF Lower Limb Rehabilitation Robot

Contact Info

Product

Resources

About