Inclusive and seamless control framework for safe robot-mediated therapy for upper limbs rehabilitation

Mancisidor, Aitziber; Cabanes, Itziar; Bengoa, Pablo; Brull, Asier; Jung, Je Hyung

doi:10.1016/j.mechatronics.2019.02.002

Cited by 13 publications

(11 citation statements)

References 30 publications

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“…The PID control deviation is obtained by subtracting the given parameter value   rt and the actual output value   ct of the electrical equipment to be debugged, and its formula is given in expression (8).…”

Section: Multi-motor Drive Controlmentioning

confidence: 99%

Multi-motor Drive Control Method of Upper-Retort-Robot Based on Machine Vision

Xiao¹,

Kang²,

He³

et al. 2021

Preprint

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A research on the multi-motor drive control method of the upper-retort-robot based on machine vision is proposed in this paper for wine brewing automation to suffice the demand of military areas situated in cold regions as wine is recommended to keep the body temperature of soldiers normal in highly cold regions of China. Based on machine vision, the target is converted into an image signal by an image pickup device and is sent to the image processing system. The pixel distribution, brightness, color and other information are converted into digital signals and the target features are extracted to control the actions of the field equipment. The Monte-Carlo method is exploited to randomly generate joint variables within the variation range of each joint. The positive aspects of kinematics model are utilized and the working space of the upper-retort-robot is calculated using multi-motor drive control method. The multi-motor drive compensates the harmonic ripple torque, and establishes the fault-tolerant automatic control of the system to maintain quality of the liquor. The experimental results show that the robot arm can reach at any position in the barrel within the defined range. The robot will work in an automated mode to control the quality of the liquor. The transmission performance of the robot can meet the requirements of the automated quality control of the liquor during processing of wine from grapes. The results are obtained for robot transmission performance and robot dexterity which proves the robustness and viability of the proposed multi-motor drive control method (MMDCM).

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Section: Multi-motor Drive Controlmentioning

confidence: 99%

Multi-motor Drive Control Method of Upper-Retort-Robot Based on Machine Vision

Xiao¹,

Kang²,

He³

et al. 2021

Preprint

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“…In [16], dynamic and kinematic modelling of the multipurpose rehabilitation robot, the Universal Haptic Pantograph, was performed. Validation of the model was carried out through experiments with healthy subjects reflecting its performance.…”

Section: Introductionmentioning

confidence: 99%

Modelling a 1-Dof Finger Extensor Machine for Hand Rehabilitation

Shahdad

Azlan

Ahmad

2021

IIUMEJ

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It is essential to have an accurate representation of a robotic rehabilitation device in the form of a system model in order to design a robust controller for it. This paper presents mathematical modelling and validation through simulation and experimentation of the 1-DOF Finger Extensor rehabilitation machine. The machine’s design is based on an iris mechanism, built specifically for training open and close movements of the hand. The goal of this research is to provide an accurate model for the Finger Extensor by taking into consideration various factors affecting its dynamics and to present an experimental validation of the devised model. Dynamic system modelling of the machine is performed using Lagrangian formulation and the involved physical parameters are obtained experimentally. To validate the developed model and demonstrate its effectiveness, hardware-in-the-loop experiments are conducted in the Simulink-MATLAB environment. Mean absolute error between the simulated and experimental response is 1.38° and the relative error is 1.13%. The results obtained are found to be within the human motion resolution limits of 5 mm or 5º and exhibit suitability of the model for application in robotic rehabilitation systems. The model accurately replicates the actual behavior of the machine and is suitable for use in controller design. ABSTRAK: Gambaran tepat mengenai model sistem peranti rehabilitasi robotik adalah sangat penting bagi pembangunan sesebuah reka bentuk alat kawalan tahan lasak. Kajian mengenai model matematik dan pengesahan melalui simulasi dan eksperimentasi mesin pemulihan 1-DOF ‘Finger Extensor’. Mesin ini direka bentuk berdasarkan mekanisme iris, dibangunkan khusus bagi melatih gerakan buka dan tutup tangan. Tujuan kajian ini adalah bagi menyediakan model Finger Extensor yang tepat dengan mengambil kira faktor mempengaruhi dinamik dan pengesahan model eksperimen yang dirancang. Model sistem dinamik mesin ini diuji menggunakan formula Lagrangian dan parameter fizikal yang terlibat diperoleh melalui eksperimen. Model ini disahkan dan diuji keberkesanannya menggunakan eksperimen Perkakasan-dalam-gelung melalui MATLAB-Simulink. Purata ralat mutlak antara dapatan simulasi dan respon eksperimen adalah 1.38° dan ralat relatif 1.13%. Dapatan kajian adalah dalam had resolusi gerakan tangan manusia iaitu 5 mm atau 5º dan didapati model ini sesuai bagi aplikasi sistem rehabilitasi robotik. Model ini tepat dalam mereplikasi kelakuan sebenar mesin dan sesuai digunakan bagi reka bentuk kawalan.

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“…They have been demonstrated to provide repeated and progressive rehabilitation training required for the effective recovery of patients with limb disabilities. To meet the requirement of assistance at different rehabilitation stages, active or passive rehabilitation robots of multiple degrees-of-freedom (DoFs) have been developed to assist the motion of human upper limbs [1][2][3][4][5][6][7][8][9][10][11][12][13] or lower limbs [14][15][16][17][18]. Compared with end-effector type robots [2,[11][12][13]18], exoskeleton type robots [1,[3][4][5][6][7][8][9][10][14][15][16][17] can provide direct assistance and recovery evaluation at each human joint.…”

Section: Introductionmentioning

confidence: 99%

“… Previous AAN controllers focused on assist-as-needed rehabilitation and had rarely shown the ability to achieve passive, active, or resistive rehabilitation. Although a controller can be developed for each rehabilitation stage and switching between controllers is possible [2], using a single controller for the entire rehabilitation process can certainly provide the simplicity of software design and ease of user operation. The developed AAN controller could be used for all the rehabilitation stages.…”

Section: Introductionmentioning

confidence: 99%

A Spatial-Motion Assist-as-Needed Controller for the Passive, Active, and Resistive Robot-Aided Rehabilitation of the Wrist

Lin

Lai

et al. 2020

IEEE Access

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Demand for robot-assisted therapy has increased at every stage of the neurorehabilitation recovery. This paper presents a controller that is suitable for the assist-as-needed (AAN) training of the wrist when performing the spatial motion. A compact wrist exoskeleton robot is presented to realize the AAN controller. This wrist robot includes series elastic actuators with high torque-to-weight ratios to provide accurate force control required for the AAN controller. In addition to assist-as-needed rehabilitation, the parameters of the AAN controller can be adjusted to deliver passive, active, or resistive rehabilitation. Experimental results demonstrate that the proposed AAN controller can provide the total solution to cover each stage of wrist spatial-motion rehabilitation.

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Inclusive and seamless control framework for safe robot-mediated therapy for upper limbs rehabilitation

Cited by 13 publications

References 30 publications

Multi-motor Drive Control Method of Upper-Retort-Robot Based on Machine Vision

Multi-motor Drive Control Method of Upper-Retort-Robot Based on Machine Vision

Modelling a 1-Dof Finger Extensor Machine for Hand Rehabilitation

A Spatial-Motion Assist-as-Needed Controller for the Passive, Active, and Resistive Robot-Aided Rehabilitation of the Wrist

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