Assistive Control System for Upper Limb Rehabilitation Robot

Chen, Sung-Hua; Lien, Wei-Ming; Wang, Weiwen; Lee, Guan-De; Hsu, Li-Chun; Lee, Kai-Wen; Lin, Shu-Yung; Lin, C.-H.; Fu, Li-Chen; Lai, Jin‐Shin; Luh, Jer-Junn; Chen, Wen-Shiang

doi:10.1109/tnsre.2016.2532478

Cited by 82 publications

(40 citation statements)

References 19 publications

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“…Power assistive devices have been developed in recent years [1][2][3] for various purposes: in the welfare field for nursing [4,5], rehabilitation [6][7][8][9][10], and gait assistance [11][12][13][14][15][16][17][18][19]; in the industrial field for assembling and load-carrying [20][21][22][23][24]; and in the military field [25]. For example, Sankai [4] developed a robot suit HAL (Hybrid Assistive Limb) driven by electric motors, and it detects the walking intention by bioelectrical sensors attached to the users.…”

Section: Introductionmentioning

confidence: 99%

A Motion Control of Soft Gait Assistive Suit by Gait Phase Detection Using Pressure Information

et al. 2019

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Power assistive devices have been developed in recent years. To detect the wearer’s motion, conventional devices require users to wear sensors. However, wearing many sensors increases the wearing time, and usability of the device will become worse. We developed a soft gait assistive suit actuated by pneumatic artificial rubber muscles (PARMs) and proposed its control method. The proposed suit is easy to wear because the attachment unit does not have any electrical sensors that need to be attached to the trainee’s body. A target application is forward walking exercise on a treadmill. The control unit detects the pre-swing phase in the gait cycle using the pressure information in the calf back PARMs. After the detection, the suit assists the trainee’s leg motion. The assist force is generated by the controlled PARM pressure, and the pressure input time is changed appropriately considering the gait cycle time. We conducted walking experiments; (1) verifies the proposed control method works correctly, and (2) verifies whether the gait assistive suit is effective for decreasing muscular activity. Finally, we confirmed that the accurate phase detection can be achieved by using the proposed control method, and the suit can reduce muscular activity of the trainee’s leg.

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Section: Introductionmentioning

confidence: 99%

A Motion Control of Soft Gait Assistive Suit by Gait Phase Detection Using Pressure Information

et al. 2019

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“…In this paper, the ankle rehabilitation robot only installs the force sensors in each pneumatic muscle, the interaction force between the robot and the external environment can be reflected by the force in joint space. Therefore, we can track the output force of each pneumatic muscle through impedance control of each driven branch to realize the force control [12]. Equation (1) is a commonly used target impedance model, which describes the relationship between the interaction force and the position [13].…”

Section: Robot Platformmentioning

confidence: 99%

Impedance Control of a Pneumatic Muscles-Driven Ankle Rehabilitation Robot

Zhang

et al. 2017

Intelligent Robotics and Applications

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Abstract. Pneumatic muscle is a new type of flexible actuator with advantages in terms of light weight, large output power/weight ratio, good security, low price and clean. In this paper, an ankle rehabilitation robot with two degrees of freedom driven by pneumatic muscle is studied. The force control method with an impedance controller in outer loop and a position inner loop is proposed. The demand of rehabilitation torque is ensured through tracking forces of three pneumatic muscle actuators. In the simulation, the constant force and variable force are tracked with error less than 10N. In the experiment, the force control method also achieved satisfactory results, which provides a good support for the application of the robot in the ankle rehabilitation.

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“…[6][7][8][9] The direct force control based on pHRI identifies motion intention by measuring the force and torque between the human and the robot, in order to realize control of the exoskeleton. 2,[10][11][12][13] Because there are no constraints between the human arm and the exoskeleton arm, it is easy and fast to put on and take off the robot when the wearer operates the exoskeleton using the controller, and the wearer will not feel uncomfortable. The control signals are provided by the human-robot interaction sensor, so external noise has less influence on the system.…”

Section: Introductionmentioning

confidence: 99%

Multi-connection load compensation and load information calculation for an upper-limb exoskeleton based on a six-axis force/torque sensor

Wang

Song

Zhou

et al. 2019

International Journal of Advanced Robotic Systems

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In this article, a method of multi-connection load compensation and load information calculation for an upper-limb exoskeleton is proposed based on a six-axis force/torque sensor installed between the exoskeleton and the end effector. The proposed load compensation method uses a mounted sensor to measure the force and torque between the exoskeleton and load of different connections and adds a compensator to the controller to compensate the component caused by the load in the human-robot interaction force, so that the human-robot interaction force is only used to operate the exoskeleton. Therefore, the operator can manipulate the exoskeleton with the same interaction force to lift loads of different weights with a passive or fixed connection, and the human-robot interaction force is minimized. Moreover, the proposed load information calculation method can calculate the weight of the load and the position of its center of gravity relative to the exoskeleton and end effector accurately, which is necessary for acquiring the upper-limb exoskeleton center of gravity and stability control of whole-body exoskeleton. In order to verify the effectiveness of the proposed method, we performed load handling and operational stability experiments. The experimental results showed that the proposed method realized the expected function.

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Assistive Control System for Upper Limb Rehabilitation Robot

Cited by 82 publications

References 19 publications

A Motion Control of Soft Gait Assistive Suit by Gait Phase Detection Using Pressure Information

A Motion Control of Soft Gait Assistive Suit by Gait Phase Detection Using Pressure Information

Impedance Control of a Pneumatic Muscles-Driven Ankle Rehabilitation Robot

Multi-connection load compensation and load information calculation for an upper-limb exoskeleton based on a six-axis force/torque sensor

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