[Abstract] Soft Exoskeleton for Hand Rehabilitation: An Overview

Abstract:

Robotics based rehabilitation have attracted much attention during the past two decades. Recent intensive research publications in the area indicates that soft exoskeleton is one of the promising technologies for robotic-based rehabilitation. This paper aims to review the up-to-date research work in soft exoskeletons material, manufacturing, sensing and control for hand rehabilitation. Applied materials, its preferred properties, and manufacturing technologies of soft exoskeletons are reviewed. Different position and force sensing technologies as well as recent control techniques applying bio-signals as control signals are reviewed. The major challenges, which are also recommendations for future research work, are highlighted.

Source

[Abstract] Design of an Exoskeletal Prototype to Contribute to the Partial Rehabilitation of the Upper-Limb

Abstract:

Rehabilitation processes are very important to recover the movements of the limbs of the human body, but in many countries where technology is very scarce, rehabilitation procedures are performed traditionally; that is why the objective of this research is to design an exoskeleton prototype that contributes to the rehabilitation of patients with partial mobility of the upper limbs. For the development of the prototype, the VDI 2206 methodology is applied, where the research is divided into mechanical systems and electronic systems. For the design of the mechanical structure, it is considered that the links are adjustable to achieve a greater scope of use, whose parameters are for patients whose height is in the range of 155 cm to 175cm; the development of the robotic structure is designed in SolidWorks software. For the electronic design, EMG sensors and triaxial accelerometers are considered to detect the movements that the patient wants to perform. Also, a circuit with an LCD screen is made so that the specialist can program the routines according to the patient’s requirements. The results obtained after being subjected to a stress analysis simulation are that the structure can support a maximum weight of 76 kg, which is equivalent to a person whose height is 175cm, and the suitable material is 6061 aluminum alloy; the results of the electronic design show the good interaction of the sensors with the controller as it performs the movements of the actuators properly.

Source

[Abstract] Towards Unsupervised Rehabilitation: Development of a Portable Compliant Device for Sensorimotor Hand Rehabilitation

Abstract

Sensorimotor impairments of the hand after stroke can drastically reduce the ability to perform activities of daily living. Recently, there has been an increased interest in minimally supervised and unsupervised rehabilitation to increase therapy dosage and to complement conventional therapy. Several devices have been developed that are simple to use and portable. Yet, they do not incorporate diversified somatosensory feedback, which has been suggested to promote sensorimotor recovery. Here we present the prototype of a portable one-degree-of-freedom hand trainer based on a novel compliant shell mechanism. Our solution is safe, intuitive, and can be used for various hand sizes. Importantly, it also provides rich sensory feedback through haptic rendering. We complement our device with a rehabilitation game, where we leverage interactive tangible game elements with diverse haptic characteristics to provide somatosensory training and foster recovery.

Source

[Abstract] Design of Elbow Exoskeleton with Wireless System Control for Post Stroke Flexion-Extension Rehabilitation – Conference Publication

Abstract

In this paper we proposed a rehabilitation system that assists therapists to restore motor function by repetitive flexion-extension movement using robotic technology. The sys-tem is an elbow exoskeleton that can be controlled based on elbow rotation angle and angular velocity. The system also equipped with wireless communication to give therapists an ability to determine the trajectory of motor movement patterns and speed. For speed control, therapists need to input Pulse Width Modulation (PWM) value by Android application that conducts 100% accuracy to communicate the PWM value to microcontroller through bluetooth connection. INA226 sensor used by the system for measuring current value in order to detect the voluntary movement that occured in subject’s arm. Proportional Integral Derivative control (PID) used in order to achieve a predetermined trajectory value and maintain the Rotation per Minute (RPM) of the DC motor. As a result, the system can reach the target for fulfill 120-degree movement with 83.3% accuracy with passive hand and 100% accuracy with active arm condition. The measure of the sensor is used to analyze the rehabilitation process quantitatively and determine future treatment. This method expected can help patients to get intensive rehabilitation and make it easier for therapists to evaluate and rehabilitate patients.

Source

[Abstract] A Review: Hand Exoskeleton Systems, Clinical Rehabilitation Practices, and Future Prospects

Abstract

Spinal cord injury (SCI) and stroke are pathologies that often result in the loss of/decrease in hand functionality. Hand function is a critical component of everyday life and therefore, a primary focus of clinical SCI/stroke rehabilitation is hand function recovery/improvement. In recent years, there has been a surge in hand exoskeleton research due to the potential for exoskeletons to improve clinical rehabilitation efficiency through automation. However, there is a disconnect between current clinical practice and exoskeleton research, resulting in a minority of hand exoskeletons being tested on individuals with SCI and/or stroke. This review article provides a comprehensive analysis and review of hand exoskeleton studies based on clinical rehabilitation practices to bridge the knowledge gap between clinical application and laboratory research. The key findings from this paper are: 1) current hand exoskeletons can successfully complete simple ADL tasks but lack the precision for fine motor control, 2) most hand exoskeletons exhibit a low number of degrees-of-freedom compared to the human hand, which may limit movement capability, 3) the majority of hand exoskeletons lack sensing capabilities, restricting viable control methods and user interfaces, and 4) inconsistent evaluation methods across studies do not allow for accurate performance assessment for different exoskeletons. The comparative assessments performed by this survey article show that there remain deficits between clinical hand rehabilitation practices and the current state of hand exoskeletons. By delineating these shortcomings, the information presented in this work can help inform future developments in the field of assistive and rehabilitative hand exoskeletons such that the gap between research and application may be closed.

Published in: IEEE Transactions on Medical Robotics and Bionics ( Early Access )

[Abstract] Machine Learning in Robot Assisted Upper Limb Rehabilitation: A Focused Review

Abstract

Robot-assisted rehabilitation, which can provide repetitive, intensive and high-precision physics training, has a positive influence on motor function recovery of stroke patients. Current robots need to be more intelligent and more reliable in clinical practice. Machine learning algorithms (MLAs) are able to learn from data and predict future unknown conditions, which is of benefit to improve the effectiveness of robot-assisted rehabilitation. In this paper, we conduct a focused review on machine learning-based methods for robot-assisted upper limb rehabilitation. Firstly, the current status of upper rehabilitation robots is presented. Then, we outline and analyze the designs and applications of MLAs for upper limb movement intention recognition, human-robot interaction control and quantitative assessment of motor function. Meanwhile, we discuss the future directions of MLAs-based robotic rehabilitation. This review article provides a summary of MLAs for robotic upper limb rehabilitation and contributes to the design and development of future advanced intelligent medical devices.

Source

[Abstract] Robotic Assisted Passive Wrist and Forearm Rehabilitation: Design of an Exoskeleton and Implementation

Abstract

An exoskeleton for human wrist and forearm rehabilitation has been designed and manufactured. Considering the torque values required for daily life activities, a structural analysis study has been presented. It has three degrees of freedom (DOF) which must be fitted to real human wrist and forearm. Anatomical motion ranges of human limbs have been taken into account during design. IMU has been used in order to get the kinematic values of the limbs and to evaluate the performance level of the therapy. Adapting a six DOF Denso robot to rehabilitation has been completed and experiments have been performed.

Source

[Abstract + References] Real-Time Evaluation of Hand Motor Function Recovery in Home Use Finger Rehabilitation Device Using Gaussian Process Regression – IEEE Conference Publication

Abstract:Continuous hand rehabilitation after discharge is important for hemiplegic patients to regain an independent finger movement. However, most patients cannot rehabilitate by themselves without therapists. For this problem, robotic rehabilitation has been investigated to support patients even at home. Most of the programs performed by these robots are focusing on the assistance for voluntary movement. However, the approach to the voluntary movement is not enough for regaining dexterous movement. Voluntary suppression of body parts that should not move is important. However, previous studies focusing on voluntary suppression are few. In this paper, we show a detailed program for voluntary suppression rehabilitation. The program is performed by our robotic finger rehabilitation device aiming at home use. In this program, a patient is requested to flex and extend an index finger independently. During moving, individual pressure sensors monitor the other fingers. If the device detects unnecessary movements such as compensatory movement at some fingers, the patient is notified that unnecessary movements are found there. The detection is based on 3σ range of healthy subject’s finger pressure data which was constructed by using Gaussian Process Regression. Through experiments with hemiplegic patients, we have shown that the frequency of deviation of patients’ data from 3σ range of healthy subjects decreases according to the degree of recovery.

References

1.C. L. Jones, F. Wang, R. Morrison, N. Sarkar and D. G. Kamper, “Design and Development of the Cable Actuated Finger Exoskeleton for Hand Rehabilitation Following Stroke”, IEEE/ASME Transactions on Mechatronics, vol. 19, no. 1, pp. 131-140, 2014.Show Context View Article Full Text: PDF (740KB) Google Scholar 2.D. Leonardis et al., “An EMG-Controlled Robotic Hand Exoskeleon for Bilateral Rehabilitation”, IEEE Transactions on Haptics, vol. 8, no. 2, pp. 140-151, 2015.Show Context View Article Full Text: PDF (1941KB) Google Scholar 3.S. Biggar and W. Yao, “Design and Evaluation of a Soft and Wearable Robotic Glove for Hand Rehabilitation”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 10, pp. 1071-1080, 2016.Show Context View Article Full Text: PDF (1597KB) Google Scholar 4.P. Polygerinos, K. C. Galloway, S. Sanan, M. Herman and C. J. Walsh, “EMG Controlled Soft Robotic Glove for Assistance during Activities of Daily Living”, 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 55-60, 2015.Show Context View Article Full Text: PDF (2640KB) Google Scholar 5.I. Ben Abdallah, Y. Bouteraa and C. Rekik, “Design and Development of 3D Printed Myoelectric Robotic Exoskeleton for Hand Rehabilitation”, International Journal on Smart Sensing and Intelligent Systems, vol. 10, pp. 341-366, 2017.Show Context CrossRef  Google Scholar 6.K. Yamamoto, Y. Furudate, K. Chiba, Y. Ishida and S. Mikami, “Home Robotic Device for Rehabilitation of Finger Movement of Hemiplegia Patients”, 2017 IEEE/SICE International Symposium on System Integration (SII), pp. 300-305, 2017.Show Context View Article Full Text: PDF (1305KB) Google Scholar 7.C. D. Takahashi, L. Der-Yeghiaian, V. Le, R. R. Motiwala and S. C. Cramer, “Robot-based Hand Motor Therapy after Stroke”, Brain, vol. 131, no. Pt 2, pp. 425-437, 2008.Show Context CrossRef  Google Scholar 8.L. Dovat et al., A Technique to Train Finger Coordination and Independence after Stroke, Disability and Rehabilitation:Assistive Technology, vol. 5, no. 4, pp. 279-287, 2010.Show Context Google Scholar 9.Y. Furudate, N. Onuki, K. Chiba, Y. Ishida and S. Mikami, “Automated Evaluation of Hand Motor Function Recovery by Using Finger Pressure Sensing Device for Home Rehabilitation”, 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 207-214, 2018.Show Context View Article Full Text: PDF (500KB) Google Scholar 10.Y. Furudate, N. Onuki, K. Chiba, Y. Ishida and S. Mikami, “Hand Motor Function Evaluation by Integrating Multi-Tasks Using Home Rehabilitation Device”, 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), pp. 272-274, 2020.Show Context View Article Full Text: PDF (2090KB) Google Scholar 

Source

[Abstract] A Smart-Band Operated Wrist Rehabilitation Robot

Abstract

Many people in the world are increasingly suffering from stroke issues. Survivors often tend to suffer from hemiplegia or related conditions, in which some portion of their body may be rendered useless. The wrist is one such part. But this injury can be recovered by conventional rehabilitation processes like physical therapy. In this paper, a device for robot-assisted physical therapy is presented for wrist rehabilitation. It can overcome the lack of availability of physical therapists and reduce the cost incurred in long-term therapy. Also, it can provide accurate regular exercises without missing any step even in the absence of the therapist. These two DOF robotic devices can learn the physical exercise (i.e. wrist-based movements) from the trained therapist through an electronic smart-band. It can also replicate these exercises when the patient wears this device over his/her wrist. Here, an accelerometer sensor and a magnetometer sensor-based smart-band are used for recognizing the wrist motions like flexion, extension, abduction, and adduction. The objective of this preliminary work is to drive accurately all the motor actuators which are attached to the robot and calibrate the feedback sensor to reflect the movement of the smart-band. In the future, this robot can be used as a teleoperated rehabilitation device through an IoT platform.

Source

[Abstract] A Multi-Functional Lower-and Upper-Limb Stroke Rehabilitation Robot

Abstract

It is estimated that about 15 million people a year suffer from stroke worldwide, with 5 million stroke survivors experiencing permanent motor disability requiring therapeutic services. It has been shown that early involvement in rehabilitation therapies has a desirable effect on the long-term recovery of patients. There are, however, several challenges with the current state of delivering rehabilitation services, including limitations on the number of clinics, financial needs, and human resources. Robotic systems have been proposed in the literature to help with these challenges. However, most of the existing robotic systems are expensive, not-portable, and cannot be used for both upper-and lower-limb rehabilitation. This paper presents a 3-DOF robotic device that has been designed to deliver both upper-and lower-limb therapy and incorporates a novel mechanical safety mechanism. The device is capable of teleoperation which makes it particularly suitable for telerehabilitation in the current COVID-19 environment. The rehabilitation robot can deliver therapy in assistive and resistive modes to aid patients at all stages of recovery. In the assistive mode, the robot’s motion provides input to help the patient in completing the therapy task, while in the resistive mode, the robot opposes the motions generated by the patient thereby requiring additional muscle actuation. The robot has been tested by physiotherapists to assess its validity in a clinical setting, and by healthy participants to assess its functionality, safety, and engineering design. The study found that 80physiotherapists agreed the platform has the potential to improve patient outcomes.

Source