PNEUMATIC PLANAR REHABILITATION ROBOT FOR POST-STROKE PATIENTS

2014 ◽  
Vol 26 (02) ◽  
pp. 1450025 ◽  
Author(s):  
Franciso J. Badesa ◽  
Ana Llinares ◽  
Ricardo Morales ◽  
Nicolas Garcia-Aracil ◽  
Jose M. Sabater ◽  
...  
Keyword(s):  
Cerebrovascular Accident ◽  
Rehabilitation Robot ◽  
Design Development ◽  
Stroke Patients ◽  
Robot Assisted ◽  
Upper Limb Rehabilitation ◽  
Post Stroke ◽  
Planar Robot ◽  
Limb Rehabilitation ◽  
And Control

Cerebrovascular accident or stroke in aging population is the primary cause of disability and the second leading cause of death in many countries, including Spain. Arm impairment is common and the recovery is partly dependent on the intensity and frequency of rehabilitation intervention. However, physical therapy resources are often limited, so methods of supplementing traditional physiotherapy, such as robot assisted therapy, are essential. This paper describes design, development and control aspects of a planar robot driven by pneumatic swivel modules for upper-limb rehabilitation of post-stroke patients. Moreover, first experimental results with one post-stroke patient are presented to show the benefits of using the proposed system.

CONTROL OF FOREARM MODULE IN UPPER-LIMB REHABILITATION ROBOT FOR REDUCTION AND BIOMECHANICAL ASSESSMENT OF PRONATOR HYPERTONIA OF STROKE PATIENTS

2016 ◽  
Vol 16 (02) ◽  
pp. 1650008 ◽  
Author(s):  
PIN-CHENG KUNG ◽  
CHOU-CHING K. LIN ◽  
SHU-MIN CHEN ◽  
MING-SHAUNG JU
Keyword(s):  
Upper Limb ◽  
Position Control ◽  
Biomechanical Properties ◽  
Torque Control ◽  
Rehabilitation Robot ◽  
Stroke Patients ◽  
Upper Limb Rehabilitation ◽  
Biomechanical Assessment ◽  
Custom Made ◽  
Limb Rehabilitation

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.

scholarly journals The Effect of an Upper Limb Rehabilitation Robot on Hemispatial Neglect in Stroke Patients

2016 ◽  
Vol 40 (4) ◽  
pp. 611 ◽  
Author(s):  
Yoon Sik Choi ◽  
Kyeong Woo Lee ◽  
Jong Hwa Lee ◽  
Sang Beom Kim ◽  
Gyu Tae Park ◽  
...  
Keyword(s):  
Upper Limb ◽  
Rehabilitation Robot ◽  
Hemispatial Neglect ◽  
Stroke Patients ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

scholarly journals Design and Analysis of Cloud Upper Limb Rehabilitation System Based on Motion Tracking for Post-Stroke Patients

2019 ◽  
Vol 9 (8) ◽  
pp. 1620 ◽  
Author(s):  
Bai ◽  
Song ◽  
Li
Keyword(s):  
Upper Limb ◽  
Motion Tracking ◽  
Stroke Patients ◽  
Mean Velocity ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Post Stroke ◽  
Reachable Workspace ◽  
Rehabilitation System ◽  
Limb Rehabilitation

In order to improve the convenience and practicability of home rehabilitation training for post-stroke patients, this paper presents a cloud-based upper limb rehabilitation system based on motion tracking. A 3-dimensional reachable workspace virtual game (3D-RWVG) was developed to achieve meaningful home rehabilitation training. Five movements were selected as the criteria for rehabilitation assessment. Analysis was undertaken of the upper limb performance parameters: relative surface area (RSA), mean velocity (MV), logarithm of dimensionless jerk (LJ) and logarithm of curvature (LC). A two-headed convolutional neural network (TCNN) model was established for the assessment. The experiment was carried out in the hospital. The results show that the RSA, MV, LC and LJ could reflect the upper limb motor function intuitively from the graphs. The accuracy of the TCNN models is 92.6%, 80%, 89.5%, 85.1% and 87.5%, respectively. A therapist could check patient training and assessment information through the cloud database and make a diagnosis. The system can realize home rehabilitation training and assessment without the supervision of a therapist, and has the potential to become an effective home rehabilitation system.

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