scholarly journals ASSIST AS NEEDED CONTROL STRATEGY FOR UPPER LIMB REHABILITATION ROBOT IN EATING ACTIVITY

2021 ◽  
Vol 22 (1) ◽  
pp. 298-322
Author(s):  
Norsinnira Zainul Azlan ◽  
Nurul Syuhadah Lukman

The slacking behaviour or lack of participation from impaired patients during robotic rehabilitation therapy is one of the factors that slow down their recovery. The implementation of Assist As Needed (AAN) control law in the robotic assisted rehabilitation treatment may alleviate this problem and encourage the patients to be actively involved in the rehabilitation exercises. This paper presents a new Assist As Needed control strategy for an upper limb rehabilitation robot in assisting subjects with various levels of capabilities to regain their original upper limb’s functionality in realizing basic motions in eating activity. The controller consists of Proportional, Integral, Derivative (PID) controller in the feedback loop, with an adjustable gain K that varies according to the user’s level of capability. A Force Sensing Resistor (FSR) is used to identify the user’s upper extremity capability level. The controller regulates the necessary amount of assistance provided by the robot based on the information obtained from the sensor. The automatic adjustment of the robot’s assistance to the subjects leads them to put in their own effort in accomplishing the desired movements. The proposed control strategy is simple, easy to program, and mathematically less complicated. A prototype of the wearable upper limb rehabilitation robot has been built and a Graphical User Interface (GUI) has been developed using MATLAB software to facilitate the rehabilitation process and for progress monitoring. The simulation and experimental results have proven that the proposed control strategy is successful in regulating the necessary amount of robot assistance according to the patients’ level of capability. The proposed controller has effectively driven the upper limb rehabilitation robot to achieve the desired trajectory with zero steady state error, percentage overshoot less than 8% and settling time below 6 seconds, whilst providing the correct amount of robotic assistance in accordance to the subjects’ capability level. ABSTRAK: Reaksi kurang respon dari pesakit kurang keupayaan semasa terapi pemulihan robotik adalah satu faktor melambatkan kadar pemulihan. Pelaksanaan teknik kawalan Bantu Apabila Diperlukan (AAN) dalam rawatan pemulihan dengan bantuan robot dapat membantu dan mendorong pesakit terlibat secara aktif dalam latihan pemulihan. Artikel ini membentangkan strategi kawalan baru, iaitu Bantu Apabila Diperlukan oleh robot pemulihan bagi anggota atas pesakit yang mempunyai pelbagai tahap kemampuan, dalam mengembalikan fungsi asas gerakan tangan seperti aktiviti makan. Teknik kawalan terdiri daripada kawalan Berkadar, Integral, Terbitan (PID) dalam lingkaran tindak balas, dengan pemboleh ubah K mengikut tahap kemampuan pesakit. Alat pengukur Resistan Daya Rasa (FSR) digunakan bagi mengenal pasti tahap kemampuan maksima pesakit dalam menggerakkan tangan. Berdasarkan maklumat yang diperoleh daripada sensor, teknik kawalan akan menghantar maklumat kepada robot bagi membantu pesakit. Bantuan automatik yang dibekalkan robot kepada pesakit akan mendorong pesakit berusaha melakukan gerakan yang diperlukan. Strategi kawalan yang dicadangkan ini adalah ringkas, mudah diprogramkan dan kurang rumit dari segi matematik. Sebuah prototaip robot pemulihan anggota tangan telah dibina dan sebuah platform grafik bagi pengguna (Antara Muka Grafik Pengguna, GUI) telah dibangunkan menggunakan perisian MATLAB bagi memudahkan proses pemulihan dan pemantauan kemajuan pesakit. Hasil simulasi dan eksperimen membuktikan bahawa strategi cadangan kawalan ini berjaya mengatur jumlah bantuan daripada robot bersesuaian dengan tahap kemampuan pesakit. Teknik kawalan yang dicadangkan telah berjaya menggerakkan robot pemulihan tangan bagi mencapai lintasan gerakan yang diinginkan dengan ralat sifar pada keadaan stabil, peratusan ayunan berlebihan kurang daripada 8%, masa penyelesaian bawah 6 saat dan pada masa sama, memberikan maklumat bantuan robot yang tepat, bersesuaian dengan tahap kemampuan pesakit.

ROBOT ◽  
2011 ◽  
Vol 33 (3) ◽  
pp. 307-313 ◽  
Author(s):  
Baoguo XU ◽  
Si PENG ◽  
Aiguo SONG

ROBOT ◽  
2012 ◽  
Vol 34 (5) ◽  
pp. 539 ◽  
Author(s):  
Lizheng PAN ◽  
Aiguo SONG ◽  
Guozheng XU ◽  
Huijun LI ◽  
Baoguo XU

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2146
Author(s):  
Manuel Andrés Vélez-Guerrero ◽  
Mauro Callejas-Cuervo ◽  
Stefano Mazzoleni

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.


2020 ◽  
pp. 1-17
Author(s):  
Qing Sun ◽  
Shuai Guo ◽  
Leigang Zhang

BACKGROUND: The definition of rehabilitation training trajectory is of great significance during rehabilitation training, and the dexterity of human-robot interaction motion provides a basis for selecting the trajectory of interaction motion. OBJECTIVE: Aimed at the kinematic dexterity of human-robot interaction, a velocity manipulability ellipsoid intersection volume (VMEIV) index is proposed for analysis, and the dexterity distribution cloud map is obtained with the human-robot cooperation space. METHOD: Firstly, the motion constraint equation of human-robot interaction is established, and the Jacobian matrix is obtained based on the speed of connecting rod. Then, the Monte Carlo method and the cell body segmentation method are used to obtain the collaborative space of human-robot interaction, and the VMEIV of human-robot interaction is solved in the cooperation space. Finally, taking the upper limb rehabilitation robot as the research object, the dexterity analysis of human-robot interaction is carried out by using the index of the approximate volume of the VMEIV. RESULTS: The results of the simulation and experiment have a certain consistency, which indicates that the VMEIV index is effective as an index of human-robot interaction kinematic dexterity. CONCLUSIONS: The VMEIV index can measure the kinematic dexterity of human-robot interaction, and provide a reference for the training trajectory selection of rehabilitation robot.


2016 ◽  
Vol 16 (02) ◽  
pp. 1650008 ◽  
Author(s):  
PIN-CHENG KUNG ◽  
CHOU-CHING K. LIN ◽  
SHU-MIN CHEN ◽  
MING-SHAUNG JU

Spastic hypertonia causes loss of range of motion (ROM) and contractures in patients with post-stroke hemiparesis. The pronation/supination of the forearm is an essential functional movement in daily activities. We developed a special module for a shoulder-elbow rehabilitation robot for the reduction and biomechanical assessment of pronator/supinator hypertonia of the forearm. The module consisted of a rotational drum driven by an AC servo motor and equipped with an encoder and a custom-made torque sensor. By properly switching the control algorithm between position control and torque control, a hybrid controller able to mimic a therapist’s manual stretching movements was designed. Nine stroke patients were recruited to validate the functions of the module. The results showed that the affected forearms had significant increases in the ROM after five cycles of stretching. Both the passive ROM and the average stiffness were highly correlated to the spasticity of the forearm flexor muscles as measured using the Modified Ashworth Scale (MAS). With the custom-made module and controller, this upper-limb rehabilitation robot may be able to aid physical therapists to reduce hypertonia and quantify biomechanical properties of the muscles for forearm rotation in stroke patients.


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