Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke

McConnell, Alistair C.; Moioli, Renan Cipriano; Brasil, Fabrício Lima; Vallejo, Marta; Corne, David; Vargas, Patrícia A.; Stokes, Adam A.

doi:10.2340/16501977-2229

Cited by 54 publications

(36 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, these devices allow to increase of the amount and intensity of the therapy, to standardize the treatment, providing a complex but controlled multisensory stimulation [9,10] and helping the patient to complete the required task while preventing inappropriate movements [11]. Even if most of the robots focuses on the more proximal joints (shoulder and elbow) [12], some devices have been specifically developed to target the hand, using either end-effector [13][14][15] or exoskeleton [16,17] design, with encouraging results in terms of motor recovery [12,[18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Reliability, validity and discriminant ability of a robotic device for finger training in patients with subacute stroke

Germanotta

Gower

Papadopoulou

et al. 2020

J NeuroEngineering Rehabil

111

116

View full text Add to dashboard Cite

Background: The majority of stroke survivors experiences significant hand impairments, as weakness and spasticity, with a severe impact on the activity of daily living. To objectively evaluate hand deficits, quantitative measures are needed. The aim of this study is to assess the reliability, the validity and the discriminant ability of the instrumental measures provided by a robotic device for hand rehabilitation, in a sample of patients with subacute stroke. Material and methods: In this study, 120 patients with stroke and 40 controls were enrolled. Clinical evaluation included finger flexion and extension strength (using the Medical Research Council, MRC), finger spasticity (using the Modified Ashworth Scale, MAS) and motor control and dexterity during ADL performance (by means of the Frenchay Arm Test, FAT). Robotic evaluations included finger flexion and extension strength, muscle tone at rest, and instrumented MAS and Modified Tardieu Scale. Subjects were evaluated twice, one day apart, to assess the test-retest reliability of the robotic measures, using the Intraclass Correlation Coefficient (ICC). To estimate the response stability, the standard errors of measurement and the minimum detectable change (MDC) were also calculated. Validity was assessed by analyzing the correlations between the robotic metrics and the clinical scales, using the Spearman's Correlation Coefficient (r). Finally, we investigated the ability of the robotic measures to distinguish between patients with stroke and healthy subjects, by means of Mann-Whitney U tests. Results: All the investigated measures were able to discriminate patients with stroke from healthy subjects (p < 0.001). Test-retest reliability was found to be excellent for finger strength (in both flexion and extension) and muscle tone, with ICCs higher than 0.9. MDCs were equal to 10.6 N for finger flexion, 3.4 N for finger extension, and 14.3 N for muscle tone. Conversely, test-retest reliability of the spasticity measures was poor. Finally, finger strength (in both flexion and extension) was correlated with the clinical scales (r of about 0.7 with MRC, and about 0.5 with FAT). Discussion: Finger strength (in both flexion and extension) and muscle tone, as provided by a robotic device for hand rehabilitation, are reliable and sensitive measures. Moreover, finger strength is strongly correlated with clinical scales. Changes higher than the obtained MDC in these robotic measures could be considered as clinically relevant and used to assess the effect of a rehabilitation treatment in patients with subacute stroke.

show abstract

Section: Introductionmentioning

confidence: 99%

Reliability, validity and discriminant ability of a robotic device for finger training in patients with subacute stroke

Germanotta

Gower

Papadopoulou

et al. 2020

J NeuroEngineering Rehabil

111

116

View full text Add to dashboard Cite

show abstract

“…It is well known that users' requirements, e.g., weight, comfort, aesthetics, and cost are directly related to the success or failure of hand rehabilitation devices [12]. Some researches [13] suggest that the maximum weight of the exoskeleton over the hand should not exceed 500 g. In a review by [14], all exoskeletons surveyed weighed less than 285 g over the hand.…”

Section: Hand Exoskeletonsmentioning

confidence: 99%

A HERO for Stroke Patients: a new hand exoskeleton 3D printed on textiles for rehabilitation

Araújo¹,

Silva²,

Netto³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Stroke survivors can be affected by motor deficits in the hand. Robotic equipment associated with brain-machine interfaces (BMI) may aid the motor rehabilitation of these patients. BMIs involving orthotic control by motor imagery practices have been successful in restoring stroke patients' movements. However, there is still little acceptance of the robotic devices available, either by patients and clinicians, mainly because of the high costs involved. Motivated by this context, the present work aims to design and construct the Hand Exoskeleton for Rehabilitation Objectives (HERO) to recover extension and flexion movements of the fingers. Methods: 3D printing technique in association with textiles was used to produce a lightweight and wearable device. 3D-printed actuators have also been designed to reduce equipment costs. The actuator transforms the torque of DC motors into linear force transmitted by Bowden cables to move the fingers passively. The exoskeleton was controlled by neuroelectric signal --- electroencephalography (EEG). Concept tests were performed to evaluate control performance. A healthy volunteer was submitted to a training block with the exoskeleton, according to the Graz-BCI protocol. Ergonomy was evaluated with a 2D tracking software. Results: The outcome of the applied manufacturing technique was aesthetically pleasing. HERO's glove can be compared to ordinary clothing. The weight over the hand was around 102 g. The volunteer was able to control the exoskeleton with 91.5\% accuracy. Conclusions: HERO's project resulted in a lightweight, simple, portable, ergonomic, and low-cost device. Its use is not restricted to a clinical setting. Thus, users will be able to execute motor training with the HERO at hospitals, rehabilitation clinics, and home, increasing the rehabilitation intervention time. This may favor motor rehabilitation and improve the life quality of stroke survivors.

show abstract

“…Cervera et al [36] performed a metaanalysis on the clinical effectiveness of BCI-based stroke therapy among 9 randomised clinical trials (RCT). McConnell et al [37] reviewed and provided insights from a total of 110 robotic devices with brain-machine interfaces for hand rehabilitation post-stroke. These reviews, in general, have reported that such systems provide improvements in both functional and clinical outcomes in pilot studies or trials involving small sample sizes.…”

Section: Introductionmentioning

confidence: 99%

Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

Baniqued

Stanyer

Awais

et al. 2019

Preprint

View full text Add to dashboard Cite

Electroencephalography-based brain-computer interfaces (BCI) that allow the control of robotic devices to support stroke patients during upper limb rehabilitation are increasingly popular. Hand rehabilitation is focused on improving dexterity and fine motor control and is a core approach for helping stroke survivors regain activities of daily living. This systematic review examines recent developments in BCI-robotic systems for hand rehabilitation and identifies evidence-based clinical studies on stroke patients. A search for January 2010-October 2019 articles using Ovid MEDLINE, Embase, PEDro, PsycINFO, IEEE Xplore and Cochrane Library databases was performed. The selection criteria included BCI-hand robotic systems for rehabilitation in various development stages involving tests on healthy human subjects or stroke survivors. Data fields include those related to study design, participant characteristics, technical specifications of the system, and clinical outcome measures. 30 studies were identified as eligible for qualitative review and among these, 11 studies involved testing a BCI-hand robot on chronic and subacute stroke patients. Statistically significant improvements in motor assessment scores relative to controls were observed for two BCI-hand robot interventions. The degree of robot control for the majority of studies was limited to triggering the device to perform grasping or pinching movements using motor imagery. Most employed a combination of kinaesthetic and visual response via the robotic device and display screen, respectively, to match feedback to motor imagery. Most studies on BCI-robotic systems for hand rehabilitation report systems at prototype or pre-clinical stages of development. Some studies report statistically significant improvements in functional recovery after stroke, but there is a need to develop a standard protocol for assessing technical and clinical outcomes so that the necessary evidence base on efficiency and efficacy can be developed.

show abstract

Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke

Abstract: This review provides novel insights into the use of robotics in physiotherapy practice, and may help system designers to develop new devices.

Cited by 54 publications

References 54 publications

Reliability, validity and discriminant ability of a robotic device for finger training in patients with subacute stroke

Reliability, validity and discriminant ability of a robotic device for finger training in patients with subacute stroke

A HERO for Stroke Patients: a new hand exoskeleton 3D printed on textiles for rehabilitation

Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

Contact Info

Product

Resources

About