Robot-Assisted Rehabilitation Therapy: Recovery Mechanisms and Their Implications for Machine Design

Bejarano, Noelia Chia; Maggioni, Serena; Rijcke, Laura De; Cifuentes, Carlos A.; Reinkensmeyer, David J.

doi:10.1007/978-3-319-24901-8_8

Cited by 24 publications

(7 citation statements)

References 117 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although robotic devices allow patients to practice movement without direct therapist supervision [1][2][3] and enhance recovery, 4,5 the mechanisms of benefits, beyond increasing the dose of movement repetitions, remain unclear. [6][7][8] Understanding these mechanisms is essential for improving this approach.…”

Section: Introductionmentioning

confidence: 99%

Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial

Rowe

Chan

Ingemanson

et al. 2017

Neurorehabil Neural Repair

Self Cite

View full text Add to dashboard Cite

Background Robots that physically assist movement are increasingly used in rehabilitation therapy after stroke, yet some studies suggest robotic assistance discourages effort and reduces motor learning. Objective To determine the therapeutic effects of high and low levels of robotic assistance during finger training. Methods We designed a protocol that varied the amount of robotic assistance while controlling the number, amplitude, and exerted effort of training movements. Participants (n = 30) with a chronic stroke and moderate hemiparesis (average Box and Blocks Test 32+/−18 and upper extremity Fugl-Meyer score 46+/−12) actively moved their index and middle fingers to targets to play a musical game similar to GuitarHero three hours/week for three weeks. The participants were randomized to receive high assistance (causing 82% success at hitting targets) or low assistance (55% success). Participants performed ~8,000 movements during nine training sessions. Results Both groups improved significantly at the one-month follow-up on functional and impairment-based motor outcomes, on depression scores, and on self-efficacy of hand function, with no difference between groups in the primary endpoint (change in Box and Blocks). High assistance boosted motivation, as well as secondary motor outcomes (Fugl-Meyer and Lateral Pinch Strength) – particularly for individuals with more severe finger motor deficits. Individuals with impaired finger proprioception at baseline benefited less from the training. Conclusions Robot-assisted training can promote key psychological outcomes known to modulate motor learning and retention. Further, the therapeutic effectiveness of robotic assistance appears to derive at least in part from proprioceptive stimulation, consistent with a Hebbian plasticity model. ClinicalTrials.gov (NCT02048826)

show abstract

Section: Introductionmentioning

confidence: 99%

Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial

Rowe

Chan

Ingemanson

et al. 2017

Neurorehabil Neural Repair

Self Cite

View full text Add to dashboard Cite

show abstract

“…Post-stroke rehabilitation therapy aims to restore the patient’s physical, neurological, and psychological capacities to achieve the highest level of functional independence [ 5 ]. In fact, robotic devices like lower-limb exoskeletons in motor rehabilitation programs have been shown to improve automatic repetitive training and promote new motor skill acquisition after stroke [ 6 , 7 ]. User’s intention in this field is usually detected and predicted through control approaches based on the sensing of human biomechanics (i.e., through inertial sensors, direct contact operation, or external transducers) [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…In fact, robotic devices like lower-limb exoskeletons in motor rehabilitation programs have been shown to improve automatic repetitive training and promote new motor skill acquisition after stroke [ 6 , 7 ]. User’s intention in this field is usually detected and predicted through control approaches based on the sensing of human biomechanics (i.e., through inertial sensors, direct contact operation, or external transducers) [ 7 , 8 ]. Therefore, conventional robotic control systems generally do not include efficient and natural interaction methods between users and exoskeletons [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Stimuli with Stroke Survivors

Barria

Pino

Tovar

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Brain–computer interface (BCI) remains an emerging tool that seeks to improve the patient interaction with the therapeutic mechanisms and to generate neuroplasticity progressively through neuromotor abilities. Motor imagery (MI) analysis is the most used paradigm based on the motor cortex’s electrical activity to detect movement intention. It has been shown that motor imagery mental practice with movement-associated stimuli may offer an effective strategy to facilitate motor recovery in brain injury patients. In this sense, this study aims to present the BCI associated with visual and haptic stimuli to facilitate MI generation and control the T-FLEX ankle exoskeleton. To achieve this, five post-stroke patients (55–63 years) were subjected to three different strategies using T-FLEX: stationary therapy (ST) without motor imagination, motor imagination with visual stimulation (MIV), and motor imagination with visual-haptic inducement (MIVH). The quantitative characterization of both BCI stimuli strategies was made through the motor imagery accuracy rate, the electroencephalographic (EEG) analysis during the MI active periods, the statistical analysis, and a subjective patient’s perception. The preliminary results demonstrated the viability of the BCI-controlled ankle exoskeleton system with the beta rebound, in terms of patient’s performance during MI active periods and satisfaction outcomes. Accuracy differences employing haptic stimulus were detected with an average of 68% compared with the 50.7% over only visual stimulus. However, the power spectral density (PSD) did not present changes in prominent activation of the MI band but presented significant variations in terms of laterality. In this way, visual and haptic stimuli improved the subject’s MI accuracy but did not generate differential brain activity over the affected hemisphere. Hence, long-term sessions with a more extensive sample and a more robust algorithm should be carried out to evaluate the impact of the proposed system on neuronal and motor evolution after stroke.

show abstract

“…Emerging robotic technology in a traditional rehabilitation therapy session would provide high quality treatment at a lower cost and effort. The level of motor recovery of patients can be quantified by defining different rehabilitation exercises for the robot [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Parallel Robot for Lower Limb Rehabilitation Exercises

Rastegarpanah

Saadat

Borboni

2016

Applied Bionics and Biomechanics

View full text Add to dashboard Cite

The aim of this study is to investigate the capability of a 6-DoF parallel robot to perform various rehabilitation exercises. The foot trajectories of twenty healthy participants have been measured by a Vicon system during the performing of four different exercises. Based on the kinematics and dynamics of a parallel robot, a MATLAB program was developed in order to calculate the length of the actuators, the actuators' forces, workspace, and singularity locus of the robot during the performing of the exercises. The calculated length of the actuators and the actuators' forces were used by motion analysis in SolidWorks in order to simulate different foot trajectories by the CAD model of the robot. A physical parallel robot prototype was built in order to simulate and execute the foot trajectories of the participants. Kinect camera was used to track the motion of the leg's model placed on the robot. The results demonstrate the robot's capability to perform a full range of various rehabilitation exercises.

show abstract

Robot-Assisted Rehabilitation Therapy: Recovery Mechanisms and Their Implications for Machine Design

Cited by 24 publications

References 117 publications

Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial

Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial

BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Stimuli with Stroke Survivors

Parallel Robot for Lower Limb Rehabilitation Exercises

Contact Info

Product

Resources

About