Archive for category Rehabilitation robotics

[ARTICLE] Brain–computer interface and assist-as-needed model for upper limb robotic arm – Full Text

Post-stroke paralysis, whereby subjects loose voluntary control over muscle actuation, is one of the main causes of disability. Repetitive physical therapy can reinstate lost motions and strengths through neuroplasticity. However, manually delivered therapies are becoming ineffective due to scarcity of therapists, subjectivity in the treatment, and lack of patient motivation. Robot-assisted physical therapy is being researched these days to impart an evidence-based systematic treatment. Recently, intelligent controllers and brain–computer interface are proposed for rehabilitation robots to encourage patient participation which is the key to quick recovery. In the present work, a brain–computer interface and assist-as-needed training paradigm have been proposed for an upper limb rehabilitation robot. The brain–computer interface system is implemented with the use of electroencephalography sensor; moreover, backdrivability in the actuator has been achieved with the use of assist-as-needed control approach, which allows subjects to move the robot actively using their limited motions and strengths. The robot only assists for the remaining course of trajectory which subjects are unable to perform themselves. The robot intervention point is obtained from the patient’s intent which is captured through brain–computer interface. Problems encountered during the practical implementation of brain–computer interface and achievement of backdrivability in the actuator have been discussed and resolved.

The recovery of upper limb motions and strengths in patients with damaged neuromuscular system via robotic rehabilitation devices is a promising way of enhancing existing treatments and their efficacies. Various reasons may cause limb dysfunctions, including stroke, spinal cord injuries, or even ligament rupture. According to the World Health Organization, about 15 million people globally suffer from Cerebro-Vascular Accidents (CVAs) each year and up to 65% of these need limb recovery procedures.1 Only in the last 15 years, the number of CVA or stroke patients is increased by 40%, which is the result of a more intense pace of living, deterioration of ecology, and increased aging population.2 Considering these statistics, development of new and efficient ways of rehabilitation is just as important as implementation of improved prevention strategies.

For the last 20 years, robotics-based therapy was steadily paving its way for becoming an essential practice in rehabilitation medicine.3,4 According to the systematic review of Kwakkel et al.5 on the upper limb recovery using robot-aided therapy, repetitive, meaningful, labor-intensive treatment programs implemented with robotic devices provide positive impact for the restoration of functional abilities in human limbs. In medical terminology, a device that provides support, and aligns or improves the function of movable limbs is known as orthosis, and robotic devices intended to provide such treatment are called robotic orthoses.6 Particularly, two key directions gained major attention in the medical engineering research: robot-assisted therapy and functional electrical simulation (FES) therapy. The FES therapy describes a technique that stimulates weakened or paralyzed muscles on a human limb by applying electric charges externally. The goal of FES therapy is to reactivate the neural connections between a muscle and human’s sensorimotor system to enable patients’ ability to control their limbs without assistance.7 In the study by Popovic and others, the functional electrical therapy (FET) was applied with the use of surface electrodes and it was used to stimulate arm fingers of patients, this therapy has demonstrated positive therapeutic effects.8 It was revealed that daily 30-min therapy for 1-month period allowed improvement in movement range, speed, and increased strength in muscles. There are also side effects of FES-based treatment such as pain and irritation on the affected area, autonomic dysreflexia, increased spasticity, broken bones, and mild electric shocks from faulty equipment. However, the robot-assisted rehabilitation is non-invasive and free from above risks, and it is preferred for the rehabilitation of stroke survivors.

The important advantage of robotic devices is that they can reduce the burden on health care workers who traditionally had to conduct labor-intensive training sessions for patients. Equipped with sensors, intelligent controllers, and haptic and visual interfaces, robotic orthosis can have a potential to put the recovery process to a new level by collecting relevant data about various health parameters (pulse rate, body temperature, etc.) and adjusting the training modes accordingly. Besides the positive impacts of robot-based rehabilitation, the reliability of robot-based assistance is still questionable and adversely it may worsen the recovery progress made before, and that depends on the type of assistance control robot employs.9 Assist-as-needed (AAN) control type has become one of the prominent strategies recently which has been recommended positively from clinical trials.10 In order to stabilize the system, AAN-based approach has become subject to be researched by scientists. In the work done by Wolbrecht, AAN control is obtained from the adaptive control by incorporating novel force to address and decrease the system’s parametric errors.11 There are also other works which propose AAN type of control for their systems;1214 however, there are no works which have incorporated both BCI (brain–computer interface)- and AAN-based control approach into the system.

Owing to the recent advances in biosensors, especially in their robustness and signal processing, robot controllers equipped with bio-sensing are able to achieve intelligence with less complex algorithms. One of the most recent applications of BCI is in the domain of orthoses.1517 Newer instances of orthoses combine latest advances in control theory and brain activity. Berlin Technical University in cooperation with Korean University created an exoskeleton to maneuver lower limbs. A feature of this work is the use of non-invasive electroencephalography (EEG). The study involved 11 healthy men aged 25 to 32 years.18 First upper limb exoskeleton controlled by BCI was proposed by AA Frolov et al.19 Authors concluded that BCI inclusion improves the movements of the paretic hand in post-stroke patients irrespective of severity and localization of the disease. In addition, it was shown that duration of the training also increases effectiveness of rehabilitation.

Based on the letters on the screen, it was possible to determine native language of the patient in the work done by Vasileva.20 In this work, non-invasive EEG had been used. However, it was noted that non-invasive devices have less accuracy than professional medical EEG equipment. To improve signal detection, Agapov et al.21 have developed advanced algorithm of processing visually evoked potentials. To visualize stimuli, “eSpeller” software was developed.

Motivated by the above-mentioned successes and advances, in the present work, possible use of BCI is investigated in the rehabilitation robots for the treatment of stroke survivors. The aim of this work is to develop EEG-based mechatronic system that can receive electrical brain signals, detect emotions and gestures of the patient, and intelligently control robotic arm. In addition, to ensure smooth and compliant movement of the rehabilitation robot and improve treatment efficacy, AAN control paradigm is also considered. This research used EEG package and a controller to develop BCI system and realize AAN-based control. Developed system can help patients to control robot with their thoughts and enhance their participation in the rehabilitation process. Methodology of the current work is explained in the “Methodology” section, and in the subsequent sections, results are discussed before drawing conclusions from this research work.

EEG sensor

In order to register the brain activity, 16 EEG electrodes distributed around the patient’s head have been used. To provide more information which is related to motor imaginary signals, the frequency characteristics were extracted from the data by converting them from the time domain to the frequency domain. Furthermore, to distinguish between movement intentions and rest positions, bandpass filter in the range of 5 to 40 Hz was used.22,23 Since EEG data set recording can be very large, the powerful surface Laplacian technique was applied to lower the risk of influence from the neighboring neurons on the crucial cerebral cortex neurons.24 Finally, only dominant frequency of 13 to 30 Hz, also known as beta wave frequency, was featured according to Gropper et al.25 This band distinction was benchmarker as a sensible area of resting brain activity.

Abiding by the previous works associated with EEG signal processing in Iáñez et al.26 and Hortal et al.,27 the feature selection was reduced to the group of 29 features, which later were used for the further classification and predictive model construction.

After receiving data using an EEG, algorithm needs to determine the desired effect for the user. Input data for this algorithm are EEG signals recorded during the demonstration of stimuli. In most of the currently existing studies on this subject, the problem of classifying signals is divided into three large subtasks:

  • Preprocessing the signal (in order to remove noise components);
  • Formation of a feature space;
  • Classification of objects in the constructed feature space.

It should be noted that the greatest influence on the final quality of the classification is made by the extent to which the task of forming the feature space was successfully accomplished. The general scheme of operation of BCI is depicted in Figure 1.


                        figure

Figure 1. Block diagram of BCI interface.

 

[…]

Continue —>  Brain–computer interface and assist-as-needed model for upper limb robotic arm – Akim Kapsalyamov, Shahid Hussain, Askhat Sharipov, Prashant Jamwal, 2019

                        figure

Figure 4. (a) ELA actuated upper limb rehabilitation robot, (b) upper limb rehabilitation robot in use, and (c) robotic orthosis in use with EEG sensor.

 

, , , , , , , ,

Leave a comment

[Abstract] An Elbow Exoskeleton for Upper Limb Rehabilitation With Series Elastic Actuator and Cable-Driven Differential

Abstract

Movement impairments resulting from neurologic injuries, such as stroke, can be treated with robotic exoskeletons that assist with movement retraining. Exoskeleton designs benefit from low impedance and accurate torque control. We designed a two-degrees-of-freedom tethered exoskeleton that can provide independent torque control on elbow flexion/extension and forearm supination/pronation. Two identical series elastic actuators (SEAs) are used to actuate the exoskeleton. The two SEAs are coupled through a novel cable-driven differential. The exoskeleton is compact and lightweight, with a mass of 0.9 kg. Applied rms torque errors were less than 0.19 Nm. Benchtop tests demonstrated a torque rise time of approximately 0.1 s, a torque control bandwidth of 3.7 Hz, and an impedance of less than 0.03 Nm/° at 1 Hz. The controller can simulate a stable maximum wall stiffness of 0.45 Nm/°. The overall performance is adequate for robotic therapy applications and the novelty of the design is discussed.

via An Elbow Exoskeleton for Upper Limb Rehabilitation With Series Elastic Actuator and Cable-Driven Differential – IEEE Journals & Magazine

, , , , , , , , , , , ,

Leave a comment

[Abstract] Robotic Techniques Used for Increasing Improvement Rate In The Rehabilitation Process Of Upper Limb Stroke Patients – Full Text PDF

Abstract

The rate of stroke patients in Pakistan is increasing, resulting in the decrease mobility of the patients. The movement of upper limb stoke patient is decreased due to the weakness and loss of joint control in his upper body. To improve the coordination of movement of the upper limb stroke patients, many methods e.g. passive and active modes for improving the disrupted mobility are introduced. The objectives of this paper are to first review the studies on upper limb stroke patients and the techniques used for increasing the improvement rate through physical therapy by exoskeleton and evaluation of the performance of the patient using methods such as quantification and graphical representations so that it can be shown to the patient for his motivation to improve further. The paper introduces a mechanical design of exoskeleton with 1 degree of freedom for elbow and 2 degrees of freedom for shoulder movement for rehabilitation of joints of stoke patients. It also mentions the safety that will be taken in the process so that the exoskeleton is safe to use when it is in contact with human. The model of the exoskeleton has the characteristic of being modular and easily operable and use admittance control strategy. Control strategy of the exoskeleton is discussed to maintain the position and orientation of the device and also is able to cater the gravitational attraction which plays an important part in the movement of the actuators. The mathematical model of motion attained due to the degrees of freedom of the exoskeleton is then evaluated and the lastly areas where the future work of exoskeleton can be done are discussed.

Full Text PDF

via Robotic Techniques Used for Increasing Improvement Rate In The Rehabilitation Process Of Upper Limb Stroke Patients | Sukkur IBA Journal of Computing and Mathematical Sciences

, , , , , ,

Leave a comment

[Abstract] Modeling and analysis of hydraulic piston actuation of McKibben fluidic artificial muscles for hand rehabilitation

Soft robotic actuators are well-suited for interactions with the human body, particularly in rehabilitation applications. The fluidic artificial muscle (FAM), specifically the McKibben FAM, is a type of soft robotic actuator that can be driven either pneumatically or hydraulically, and has potential for use in rehabilitation devices. The force applied by a FAM is well-described by a variety of models, the most common of which is based on the virtual work principle. However, the use of a piston assembly as a hydraulic power source for activation of FAMs has not previously been modeled in detail. This article presents a FAM designed to address the specific needs of a hand rehabilitation device. A syringe pump test bed is used to find and validate a novel volume–strain relationship. The volume–strain relationship remains constant with the coupled piston–FAM system, regardless of load. This confirms a bivariate approach to FAM control which is particularly beneficial in the exoskeleton application as the load varies throughout use. A novel, fixed-end cylindrical model is found to predict the strain of the FAM, given a volume input, regardless of load. For the FAMs tested in this work, the fixed-end cylindrical model improves strain prediction seven-fold when compared with traditional models.

via Modeling and analysis of hydraulic piston actuation of McKibben fluidic artificial muscles for hand rehabilitation – Anderson S Camp, Edward M Chapman, Paola Jaramillo Cienfuegos,

, , , , , , , , , ,

Leave a comment

[Abstract] Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger

Abstract

Purpose

The purpose of this paper is to introduce a new design for a finger and wrist rehabilitation robot. Furthermore, a fuzzy sliding mode controller has been designed to control the system.

Design/methodology/approach

Following an introduction regarding the hand rehabilitation, this paper discusses the conceptual and detailed design of a novel wrist and finger rehabilitation robot. The robot provides the possibility of rehabilitating each phalanx individually which is very important in the finger rehabilitation process. Moreover, due to the model uncertainties, disturbances and chattering in the system, a fuzzy sliding mode controller design method is proposed for the robot.

Findings

With the novel design for moving the DOFs of the system, the rehabilitation for the wrist and all phalanges of fingers is done with only two actuators which are combined in one device. These features make the system a good choice for home rehabilitation. To control the robot, a fuzzy sliding mode controller has been designed for the system. The fuzzy controller does not affect the coefficient of the sliding mode controller and uses the overall error of the system to make a control signal. Thus, the dependence of the controller to the model decreases and the system is more robust. The stability of the system is proved by the Lyapunov theorem.

Originality/value

The paper provides a novel design of a hand rehabilitation robot and a controller which is used to compensate the effects of the uncertain parameters and chattering phenomenon.

via Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger | Emerald Insight

, , , , , , ,

Leave a comment

[WEB SITE] Gait Rehabilitation Improvement Approach for Stroke Survivors Receives Research Funding

Published on 

RehabRobot

 

For stroke survivors whose ability to walk has been impaired by neurological damage, rehabilitation using robotics has proven to be an effective therapy to improve their gait. However, one of the major issues with this type of rehabilitation is that following training with a robotic device, motor improvements are not maintained in the patient’s daily life.

Gelsy Torres-Oviedo, assistant professor of bioengineering at the University of Pittsburgh Swanson School of Engineering, was awarded a $805,670 CAREER Award by the National Science Foundation to apply a novel approach to improve locomotor learning in stroke patients. She is the fifth Swanson School CAREER Award recipient in 2019, tying the school’s record from 2017.

“Our bodies adjust their movement to adapt to changes in the environment, but the very first thing that we need to do is sense that environment,” Torres-Oviedo, who directs the Sensorimotor Learning Laboratory at Pitt, explains in a media release.

“We then use this sensory information as input to our motor system, which drives our movement.”

The challenge with measuring sensation in people is that it is an internal variable; therefore, Torres-Oviedo’s group will use mathematical tools and perception experiments to estimate what individuals feel.

“We think that some stroke survivors have difficulty perceiving their asymmetric movement, and these proposed studies will help us characterize this deficit and indicate if split-belt walking – in which the legs move at different speeds – can correct it,” she says.

In the first part of this study, the lab will track how patients with brain lesions perceive asymmetries in their gait. They will then measure how their perception is adjusted once their movements are adapted in the split-belt environment.

In the second part of this study, the lab will use these data and a unique method to manipulate how people perceive their movement and create the illusion of error-free performance during split-belt walking, the release, from the University of Pittsburgh, continues.

They will use a human-in-the-loop (HITL) method, which is a closed-loop approach in which the behavioral output is feedback to tune the input to the motor system – in this case, the speed difference. This strategy creates an individualized outcome for each subject, which is a more effective method for training purposes.

“The idea is that if we understand how each patient adjusts their perceived movements, we can create the illusion of error-free performance where they think that they’re walking normally even though their movements are changing,” Torres-Oviedo explains.

“If they never perceive that they are doing something different, the hope is that changes in their movements can be carried over to the patient’s daily life.”

This research aims to enhance the generalization of movements from devices like treadmills and exoskeletons to daily activities.

“If Professor Torres-Oviedo and her group are successful in their work, it could have a profound effect on gait rehabilitation for stroke survivors,” notes Sanjeev G. Shroff, Distinguished Professor and the Gerald E. McGinnis Chair of Bioengineering.

Torres-Oviedo will also use this project as a way to increase the participation of students from underrepresented minorities (URM) in science and engineering. She will recruit, mentor, and prepare URM students from K-12 and college to pursue advanced education, with the ultimate goal of broadening the professional opportunities for this population, the release concludes.

[Source(s): University of Pittsburgh, EurekAlert]

 

via Gait Rehabilitation Improvement Approach for Stroke Survivors Receives Research Funding – Rehab Managment

, , , , ,

Leave a comment

[WEB PAGE] Ekso Bionics Unveils the EksoNR Neurorehabilitation Device

EksoNR, the latest exoskeleton from Ekso Bionics, features EksoView, a new touchscreen controller that allows therapists to intuitively adapt assistance to challenge patients using real-time feedback. (Photo courtesy of Ekso Bionics Holdings Inc)

EksoNR, the latest exoskeleton from Ekso Bionics, features EksoView, a new touchscreen controller that allows therapists to intuitively adapt assistance to challenge patients using real-time feedback. (Photo courtesy of Ekso Bionics Holdings Inc)

Published on 

The EksoNR is a next-generation EksoGT exoskeleton device developed by Ekso Bionics Holdings Inc to aid the neurorehabilitation of patients recovering from stroke and spinal cord injury, and to help them learn to walk again with a more natural gait.

Among the EksoNR’s new features and enhancements is EksoView, a new touchscreen controller that allows therapists to intuitively adapt assistance to challenge patients using real-time feedback and perform outcome measures during use.

Held in the palm of a therapists’ hand, EksoView provides visualization of various exercises beyond gait training, such as balancing, squatting from sit-to-stand positioning, lifting one leg, or standing in place, to actively engage patients and enhance the use of these beneficial features.

Another feature is the optimized SmartAssist software, developed to enable EksoNR to have a smoother and more natural gait path when transitioning between steps.

SmartAssist also gives gait symmetry and posture feedback and allows therapists to track patient progress with the upgraded EksoPulse, a cloud-based analytics solution. EksoPulse now uses rehabilitation data to generate insightful metrics and graphs for therapists and administrators to monitor patient progress and outcomes, Ekso Bionics notes in a media release.

“Ekso Bionics is committed to developing the latest exoskeleton advances for rehabilitation. We continue to innovate to ensure physical therapists have access to the latest tools to deliver better patient outcomes and superior care in neurorehabilitation,” says Jack Peurach, chief executive officer and president of Ekso Bionics, in the release.

“EksoNR is a full neurorehabilitation tool that is effective, intuitive, and differentiating. There is an increasing demand for adoption, as our technology sets rehabilitation centers apart,” he adds.

EksoNR is cleared by the US Federal Drug Administration for stroke and spinal cord injury rehabilitation. The device is also CE-marked and available in Europe.

Ekso Bionics will begin taking orders for EksoNR immediately. Existing customers will have the option to upgrade, the release continues.

[Source: Ekso Bionics]

 

via Ekso Bionics Unveils the EksoNR Neurorehabilitation Device – Rehab Managment

, , , , , , , ,

Leave a comment

[Abstract + References] Design and Development of a Robotic Platform Based on Virtual Reality Scenarios and Wearable Sensors for Upper Limb Rehabilitation and Visuomotor Coordination – Conference paper

Abstract

The work reintegration following shoulder biomechanical overload illness is a multidimensional process, especially for those tasks requiring strength, movement control and arm dexterity. Currently different robotic devices used for upper limb rehabilitation are available on the market, but these devices are not based on activities focused on the work reintegration. Furthermore, the rehabilitation programmes aimed to the work reintegration are insufficiently focused on the recovery of the necessary skills for the re-employment.

In this study the details of the design of an innovative robotic platform integrated with wearable sensors and virtual reality scenarios for upper limbs motor rehabilitation and visuomotor coordination is presented. The design of control strategy will also be introduced. The robotic platform is based on a robotic arm characterized by seven degrees of freedom and by an adaptive control, wearable sensorized insoles, virtual reality (VR) scenarios and the Leap Motion device to track the hand gestures during the rehabilitation training. Future works will address the application of deep learning techniques for the analysis of the acquired big amount of data in order to automatically adapt both the difficulty level of the VR serious games and amount of motor assistance provided by the robot.

References

  1. 1.
    MacEachen, E., et al.: Systematic review of the qualitative literature on return to work after injury. Scand. J. Work Environ. Health 32(4), 257–269 (2006)CrossRefGoogle Scholar
  2. 2.
    Franche, R.-L., Krause, N.: Readiness for return to work following injury or illness: conceptualizing the interpersonal impact of healthcare, workplace, and insurance factor. J. Occup. Rehabil. 12(4), 233–256 (2002)CrossRefGoogle Scholar
  3. 3.
    Hou, W.H., Chi, C.C., Lo, H.L.D., Kuo, K.N., Chuang, H.Y.: Vocational rehabilitation for enhancing return-to-work in workers with traumatic upper limb injuries (2013)Google Scholar
  4. 4.
    Shi, Q., Sinden, K., Macdermid, J.C., Walton, D., Grewal, R.: A systematic review of prognostic factors for return to work following work-related traumatic hand injury. J. Hand Ther. 27(1), 55–62 (2014)CrossRefGoogle Scholar
  5. 5.
    Fadyl, J., McPherson, K.: Return to work after injury: a review of evidence regarding expectations and injury perceptions, and their influence on outcome. J. Occup. Rehabil. 18(4), 362–374 (2008)CrossRefGoogle Scholar
  6. 6.
    Krebs, H.I.: Twenty + years of robotics for upper-extremity rehabilitation following a stroke. In: Rehabilitation Robotics (2018)CrossRefGoogle Scholar
  7. 7.
    Buongiorno, D., Sotgiu, E., Leonardis, D., Marcheschi, S., Solazzi, M., Frisoli, A.: WRES: a novel 3 DoF WRist exoskeleton with tendon-driven differential transmission for neuro-rehabilitation and teleoperation. IEEE Robot. Autom. Lett. 3(3), 2152–2159 (2018)CrossRefGoogle Scholar
  8. 8.
    Krebs, H.I., et al.: Robotic applications in neuromotor rehabilitation. Robotica 21(1), 3–11 (2003)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lee, S.-S., Park, S.-A., Kwon, O.-Y., Song, J.-E., Son, K.-C.: Measuring range of motion and muscle activation of flower arrangement tasks and application for improving upper limb function. Korean J. Hortic. Sci. Technol. 30(4), 449–462 (2012)CrossRefGoogle Scholar
  10. 10.
    Spreeuwers, D., et al.: Work-related upper extremity disorders: one-year follow-up in an occupational diseases registry. Int. Arch. Occup. Environ. Health 84(7), 789–796 (2011)CrossRefGoogle Scholar
  11. 11.
    Mehrholz, J., Pohl, M., Platz, T., Kugler, J., Elsner, B.: Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke (2018)Google Scholar
  12. 12.
    Lederer, V., Rivard, M., Mechakra-Tahiri, S.D.: Gender differences in personal and work-related determinants of return-to-work following long-term disability: a 5-year cohort study. J. Occup. Rehabil. 22(4), 522–531 (2012)CrossRefGoogle Scholar
  13. 13.
    Siciliano, B., Lorenzo, S., Villani, L., Orilo, G.: Robotics: Modelling, Planning and Control, 2nd edn. Springer, London (2010).  https://doi.org/10.1007/978-1-84628-642-1CrossRefGoogle Scholar
  14. 14.
    Balasubramanian, S., Melendez-Calderon, A., Roby-Brami, A., Burdet, E.: On the analysis of movement smoothness. J. Neuroeng. Rehabil. 12, 112 (2015).  https://doi.org/10.1186/s12984-015-0090-9CrossRefGoogle Scholar
  15. 15.
    Berger, D.J., d’Avella, A.: Effective force control by muscle synergies. Front. Comput. Neurosci. 8, 46 (2014).  https://doi.org/10.3389/fncom.2014.00046CrossRefGoogle Scholar
  16. 16.
    Holzbaur, K.R.S., Murray, W.M., Delp, S.L.: A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control. Ann. Biomed. Eng. 33(6), 829–840 (2005).  https://doi.org/10.1007/s10439-005-3320-7CrossRefGoogle Scholar
  17. 17.
    Delp, S.L., et al.: OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54(11), 1940–1950 (2007)CrossRefGoogle Scholar
  18. 18.
    Buongiorno, D., et al.: Evaluation of a pose-shared synergy-based isometric model for hand force estimation: towards myocontrol. In: Biosystems and Biorobotics (2017)Google Scholar
  19. 19.
    Buongiorno, D., Barsotti, M., Barone, F., Bevilacqua, V., Frisoli, A.: A linear approach to optimize an EMG-driven neuromusculoskeletal model for movement intention detection in myo-control: a case study on shoulder and elbow joints. Front. Neurorobot. 12, 74 (2018)CrossRefGoogle Scholar

via Design and Development of a Robotic Platform Based on Virtual Reality Scenarios and Wearable Sensors for Upper Limb Rehabilitation and Visuomotor Coordination | SpringerLink

, , , , , , , , , ,

Leave a comment

[WEB SITE] ReStore™ Exo-Suit – ReWalk – More Than Walking

ReStore Soft Exo-Suit – A Revolution in Post-Stroke Gait Training
What is the ReStore?

The ReStore is a powered, lightweight soft exo-suit intended for use in the rehabilitation of persons with lower limb disability due to stroke. It will be a first of its kind gait training solution.

Functional

The ReStore soft design combines natural movements with plantarflexion and dorsiflexion assistance that adaptively synchronize with the patient’s own gait to facilitate functional gait training.

Versatile

Individualized levels of assistance and compatibility with supplemental support aids ensure that ReStore has broad applications for patients across the gait rehabilitation spectrum.

 

Data-Driven

Real time feedback and adjustable levels of assistance enable the therapist to optimize sessions and track each patient’s progress.

How Does ReStore Compare to Other Stroke Rehabilitation Methods?

Click here to contact us for more information and to discuss bringing ReStore to your clinic. Click here to download the ReStore brochure.

via ReStore™ Exo-Suit – ReWalk – More Than Walking

, , , , ,

Leave a comment

[Abstract + References] Design of Finger Exoskeleton Rehabilitation Robot Using the Flexible Joint and the MYO Armband

Abstract

High-risk diseases such as stroke can do great harm to human hands. Hand rehabilitation for stroke patients is a complex and necessary task. To achieve this goal, this paper introduces a hand exoskeleton equipment with flexible joints and EMG-base motion prediction. Experiment of the equipment includes kinematics analysis, EMG signal detection by MYO armband and motion prediction base on BP neural network. The result shows that the device can not only assists patient bending or extending fingers, but also perform six kinds of rehabilitation exercises with 92% accuracy for target motion recognition.

References

  1. 1.
    Ates, S., Haarman, C.J.W., Stienen, A.H.A.: SCRIPT passive orthosis: design of interactive hand and wrist exoskeleton for rehabilitation at home after stroke. Auton. Robots 41(3), 711–723 (2017)CrossRefGoogle Scholar
  2. 2.
    Wolf, S.L., Blanton, S., Baer, H., et al.: Repetitive task practice: a critical review of constraint-induced movement therapy in stroke. Neurologist 8(6), 325–338 (2002)Google Scholar
  3. 3.
    Diez, J.A., Catalan, J.M., Lledo, L.D., et al.: Multimodal robotic system for upper-limb rehabilitation in physical environment. Adv. Mech. Eng. 8(9), 8/9/1687814016670282 (2016)CrossRefGoogle Scholar
  4. 4.
    Sarac, M., Solazzi, M., Sotgiu, E., et al.: Design and kinematic optimization of a novel underactuated robotic hand exoskeleton. Meccanica 52, 749–761 (2017)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Hansen, C., Gosselin, F., Ben Mansour, K., et al.: Design-validation of a hand exoskeleton using musculoskeletal modeling. Appl. Ergon. 68, 283–288 (2018)CrossRefGoogle Scholar
  6. 6.
    Kim, S.J., Kim, Y., Lee, H., Ghasemlou, P., Kim, J.: Development of an MR-compatible hand exoskeleton that is capable of providing interactive robotic rehabilitation during fMRI imaging. Med. Biol. Eng. Comput. 56, 261–272 (2018)CrossRefGoogle Scholar
  7. 7.
    Dicicco, M., Lucas, L., Matsuoka, Y.: Comparison of control strategies for an EMG controlled orthotic exoskeleton for the hand. In: Proceedings of IEEE International Conference on Robotics and Automation, New Orleans, LA, USA, pp. 1622–1627. IEEE (2004)Google Scholar
  8. 8.
    Bouzit, M., Burdea, G., Popescu, G., Boian, R.: The Rutgers Master II new design force feedback glove. IEEE/ASME Trans. Mechatron 7, 256–263 (2002)CrossRefGoogle Scholar
  9. 9.
    Yisheng, T.M.: Robotic Glove for Hand Rehabilitation [OL] (2019). http://www.siyizn.com. Accessed 27 Mar 2019
  10. 10.
    Adamovich, S., Merians, A., Boian, R., Tremaine, M., et al.: A virtual reality-based exercise system for hand rehabilitation post stroke. Teleoperators Virtual Environ. 14(2), 161–174 (2005)CrossRefGoogle Scholar
  11. 11.
    Wang, J., Li, J., Zhang, Y., Wang, S.: Design of an exoskeleton for index finger rehabilitation. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, pp. 5957–5960. IEEE (2009)Google Scholar
  12. 12.
    Conti, R., et al.: Kinematic synthesis and testing of a new portable hand exoskeleton. Meccanica 52, 2873–2897 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Randazzo, L., Iturrate, I., Perdikis, S., et al.: mano: A wearable hand exoskeleton for activities of daily living and neurorehabilitation. IEEE Robot. Autom. Lett. 3(1), 500–507 (2018)CrossRefGoogle Scholar
  14. 14.
    Bataller, A., Cabrera, J.A., Clavijo, M., Castillo, J.J.: Evolutionary synthesis of mechanisms applied to the design of an exoskeleton for finger rehabilitation. Mech. Mach. Theory 105, 31–43 (2016)CrossRefGoogle Scholar
  15. 15.
    Park, Y., Jo, I., Lee, J., et al.: A dual-cable hand exoskeleton system for virtual reality. Mechatronics 49, 177–186 (2018)CrossRefGoogle Scholar
  16. 16.
    Tadano, K., Akai, M., Kadota, K., Kawashima, K.: Development of grip amplified glove using bi-articular mechanism with pneumatic artificial rubber muscle. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2363–2368 (2010)Google Scholar
  17. 17.
    Biggar, S., Yao, W.: Design and evaluation of a soft and wearable robotic glove for hand rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 24(10), 1 (2016)CrossRefGoogle Scholar
  18. 18.
    Hu, X., Tong, K., Wei, X., Rong, W., Susanto, E., Ho, S.: The effects of post-stroke upper-limb training with an electromyography (EMG)-driven hand robot. J. Electromyogr. Kinesiol. 23(5), 1065–1074 (2013)CrossRefGoogle Scholar
  19. 19.
    Jones, C.L., Wang, F., Morrison, R., et al.: Design and development of the cable actuated finger exoskeleton for hand rehabilitation following stroke. IEEE/ASME Trans. Mechatron. 19(1), 131–140 (2014)CrossRefGoogle Scholar

via Design of Finger Exoskeleton Rehabilitation Robot Using the Flexible Joint and the MYO Armband | SpringerLink

, , , , ,

Leave a comment

%d bloggers like this: