Design and control strategy of 3-prismatic-revolute-spherical ankle rehabilitation robot

Chen, Guoqiang; Mao, Zhuangzhuang; Zhou, Hongpeng; Yang, Pengcheng

doi:10.1080/14484846.2021.1939490

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…The robot adopts the impedance control principle and can realize the smooth and compliant movement of the end point of the mechanism. The robot can collect and store the rehabilitation data of patients and provide help for the formulation of rehabilitation programs [5]. Yuvaraj and others designed a three degree of freedom arm guide for upper limb rehabilitation robot.…”

Section: Literature Reviewmentioning

confidence: 99%

Motion Rehabilitation Robot Control Based on Human Posture Information

He¹

2022

Journal of Sensors

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In order to meet the needs of postoperative rehabilitation training of lower limbs, a motion rehabilitation robot control system based on human posture information is proposed in this paper. The functions of active/passive training mode control, movement posture and EMG signal acquisition, WiFi communication, safety protection, etc. of the lower limb rehabilitation robot are realized. The recognition and analysis of the training process are realized by using random forest machine learning algorithm and linear regression algorithm. The experimental results show that in the first row of the confusion matrix of the random forest algorithm, 7316 data are correctly identified as speed a and only one data is incorrectly identified as speed B , which is superior to other algorithms. In conclusion, the developed control and monitoring system of lower limb rehabilitation robot can be portable controlled by Android and can realize intelligent analysis of the training process through the monitoring signals in the training process. At the same time, the random forest algorithm has more advantages than the linear regression algorithm in motion recognition, which is of positive significance to the automatic monitoring and intelligent control of the training process.

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Section: Literature Reviewmentioning

confidence: 99%

Motion Rehabilitation Robot Control Based on Human Posture Information

He¹

2022

Journal of Sensors

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“…Therefore, variable domain fuzzy adaptive PD control is adopted in the system. On the premise that the rule form remains unchanged, the domain shrinks with the decrease of error, thus improving the control accuracy [14].…”

Section: Variable Domain Fuzzymentioning

confidence: 99%

A Training Method for a Sensor-Based Exercise Rehabilitation Robot

et al. 2022

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In order to solve the problem that the traditional mirror therapy did not take into account the real-time recovery of the affected limb and the training effect was limited, a training method of sports rehabilitation robot based on sensor was proposed. A mirror active rehabilitation training system was proposed, which was composed of four steps including trajectory acquisition of the limb inertial measurement unit (IMU), fuzzy adaptive proportion differentiation (PD) control in closed-loop variable domain, muscle force estimation of the surface electromyographic signal (sEMG) of the affected limb, and power compensation of the outer ring of the affected limb. The experimental results showed that the sagittal forward flexion angle of the healthy shoulder increased from 0° to 128° at a relatively uniform speed, and the sagittal forward flexion angle of the shoulder was basically consistent with that of the healthy limb after the adaptive power compensation of the affected limb. The calculated trajectory tracking error of the healthy limb controlled by the fuzzy adaptive PD controller in the variable domain was 0.21 ± 1.35 ° . The horizontal backward extension angle of the healthy shoulder joint increased from 0° to 43°, and the following trajectory of the affected limb was roughly consistent with the movement trajectory of the healthy limb. The calculated tracking error of the healthy limb trajectory was 0.39 ± 1.45 ° . It was concluded that the control system could provide the real-time power compensation according to the recovery of the affected limb, give full play to the training initiative of the affected limb, and make the affected limb achieve a better rehabilitation training effect.

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“…One of the first robots was a Stewart platform [64], which is a rigid robot with 6 DoF. Other examples are a 3-RUS/RRR [65], a redundantly actuated mechanism [66], a 3-prismatic-revolute-spherical ankle rehabilitation robot [67], a 3-PRS Parallel Robot [68], a 2-UPS/RRR ankle rehabilitation [69], a nonredundant parallel robot [70], a2-dof turntable [71], and a 2PSS platform [72]. Another approach is the use of pneumatic actuators configured as a 2-SPS Mechanism [73] and pneumatic artificial muscles [74][75][76].…”

Section: Related Workmentioning

confidence: 99%

Using Screw Theory for the Kinematics and Statics Design of the Turmell-Bot: a Cable-Driven, Reconfigurable and Compliant Ankle Rehabilitation Parallel Robot

Vargas-Riaño,

Agudelo-Varela,

Valera Fernández

2023

Preprint

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The ankle is a complex joint with a high injury incidence. Rehabilitation Robotics applied to the ankle is a very active research field. We present cable-driven reconfigurable robot kinematics and statics. We studied how the tendons pull mid-foot bones around the talocrural and subtalar axes. Likewise, we propose a hybrid serial-parallel mechanism analogous to the ankle. Then, using screw theory, we synthesized a cable-driven robot with the human ankle in the closed-loop kinematics. We incorporate a draw-wire sensor to measure the axes’ pose and compute the product of exponentials. We reconfigure the cables to balance the tension and pressure forces using the axis projection on the base and platform planes. Likewise, we also computed the workspace to show that the reconfigurable design fits several sizes. The data used is from anthropometry and statistics. Finally, we validated the robot’s statics with MuJoCo for various cable length groups corresponding to the axes’ range of motion. We suggested a platform adjusting system and an alignment method. The design is lightweight, and the cable-driven robot has advantages over rigid parallel robots, such as Stewart platforms. We will use compliant actuators.

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Design and control strategy of 3-prismatic-revolute-spherical ankle rehabilitation robot

Cited by 4 publications

References 22 publications

Motion Rehabilitation Robot Control Based on Human Posture Information

Motion Rehabilitation Robot Control Based on Human Posture Information

A Training Method for a Sensor-Based Exercise Rehabilitation Robot

Using Screw Theory for the Kinematics and Statics Design of the Turmell-Bot: a Cable-Driven, Reconfigurable and Compliant Ankle Rehabilitation Parallel Robot

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