Robot-assisted Stroke Rehabilitation Research Articles

Botulinum toxin treatment may improve myoelectric pattern recognition in robot-assisted stroke rehabilitation.

Botulinum toxin treatment may improve myoelectric pattern recognition in robot-assisted stroke rehabilitation.

Error-Related Negativity-Based Robot-Assisted Stroke Rehabilitation System: Design and Proof-of-Concept.

Conventional rehabilitation systems typically execute a fixed set of programs that most motor-impaired stroke patients undergo. In these systems, the brain, which is embodied in the body, is often left out. Including the brains of stroke patients in the control loop of a rehabilitation system can be worthwhile as the system can be tailored to each participant and, thus, be more effective. Here, we propose a novel brain-computer interface (BCI)-based robot-assisted stroke rehabilitation system (RASRS), which takes inputs from the patient's intrinsic feedback mechanism to adapt the assistance level of the RASRS. The proposed system will utilize the patients' consciousness about their performance decoded through their error-related negativity signals. As a proof-of-concept, we experimented on 12 healthy people in which we recorded their electroencephalogram (EEG) signals while performing a standard rehabilitation exercise. We set the performance requirements beforehand and observed participants' neural responses when they failed/met the set requirements and found a statistically significant (p < 0.05) difference in their neural responses in the two conditions. The feasibility of the proposed BCI-based RASRS was demonstrated through a use-case description with a timing diagram and meeting the crucial requirements for developing the proposed rehabilitation system. The use of a patient's intrinsic feedback mechanism will have significant implications for the development of human-in-the-loop stroke rehabilitation systems.

Denoising Algorithm for Event-Related Desynchronization-Based Motor Intention Recognition in Robot-assisted Stroke Rehabilitation Training with Brain-Machine Interaction

BackgroundRehabilitation robots integrated with brain-machine interaction (BMI) can facilitate stroke patients’ recovery by closing the loop between motor intention and actual movement. The main challenge is to identify the patient’s motor intention based on large training datasets with noise contamination in the Electroencephalogram (EEG) signal. New methodTo address this problem, this paper proposed a self-adaptively denoised Event-Related Desynchronization (ERD)-based motor intention recognition algorithm (DeERD) in order to enable BMI training with a small sample of calibration data. This study recruited 8 stroke patients. Each patient was required to execute paralyzed upper-limb motor attempt for 20 trials and remain in resting state for 20 trials randomly. ERD-based motor intention recognition algorithm, Common spatial filter algorithm (CSP) and Directed Transfer Function analysis (DTF) were used to extract features for classification respectively and compared with the proposed DeERD analysis. ResultsDeERD can filter the noise and extract the average lines as the principal trends. With denoising processing, Accuracy (ACC) was up to 70% for all 8 patients and they could be included in this BMI system effectively. Comparison with existing methodsThe proposed DeERD model generated statistically significant increase in True Positive Rate (TPR) and in ACC than the DTF model. TPR and ACC standard deviation of DeERD was smaller than that of CSP. ConclusionsThe proposed DeERD model can eliminate the principal noise and extract the principal trend of the time-frequency analysis. It provides a practical method to recruit more stroke patients into BMI training with fewer calibration trainings.

Patient-Specific Robot-Assisted Stroke Rehabilitation Guided by EEG - A Feasibility Study.

Multi-session robot-assisted stroke rehabilitation program requires patients to perform repetitive tasks. It is challenging for the patient to maintain concentration during training sessions. A novel intervention strategy using Electroencephalography (EEG) signals is proposed to maintain concentration during training by enhancing the engagement of stroke patients using robot-assisted multi-session rehabilitation. The approach is illustrated by applying it to one stroke patient undergoing 12 training sessions of hand motor training on the AMADEO rehabilitation device. AMADEO offers four modes of training programs of increased intensity comprising passive training, passive training with biofeedback, assistive training as well as active 2D training games. The EEG signals are measured over eight electrode sites: FC4, C4, CP4, FC3, C3, CP3, Cz, and CPz during each training day to extract movement-related cortical potential (MRCP) signals. Moreover, functional hand recovery parameters are determined using the AMADEO assessment tool. The patient's level of engagement is determined by the negative amplitude of the MRCP signal. The rehabilitation program is switched to a more intense training mode when a consistent decrease is observed in the negative amplitude of MRCP signals from the monitored electrodes. Using this approach, the rehabilitation program becomes patient-specific and adaptive. In addition, it is shown that each training mode exhibits a different recovery level of the affected hand and maximum recovery is achieved when MRCP signals indicate that the patient is actively participating in the training.

A domestic robotic rehabilitation device for assessment of wrist function for outpatients.

Available robot-assisted stroke rehabilitation systems are often limited in their utilization in the home environment, due to several barriers such as high cost, absence of therapists, tedious training tasks, or encumbering interfaces. This paper presents a low-cost robotic rehabilitation and assessment device for restoring wrist function, offering wrist exercises incorporating pronation-supination and flexion-extension movements. Furthermore, the device is designed for the assessment of joint stiffness of the wrist, and range of motion in two degrees of freedom. Methods: Mechanical/electrical design of the device as well as the control system is described. A preliminary evaluation focused on the measurement of the torsional stiffness of the limb is presented. It is evaluated by reconstructing the known stiffness values of torsional springs by measuring the motor current required to displace them. The device demonstrates the ability to determine the stiffness of an object with low-cost hardware. Use case scenarios of the device for training and assessment of the wrist are presented, allowing for a range of motion of and , for pronation-supination and flexion-extension respectively. The device shows potential to help objectively quantify the stiffness of the wrist movement, which consecutively could be used to represent and quantify the degree of impairment of patients after stroke in a more objective manner. Further clinical study is necessary to examine this.

Neural correlates of motor recovery after robot-assisted stroke rehabilitation: a case series study

ABSTRACTRobot-assisted bilateral arm therapy (RBAT) has shown promising results in stroke rehabilitation; however, connectivity mapping of the sensorimotor networks after RBAT remains unclear. We used fMRI before and after RBAT and a dose-matched control intervention (DMCI) to explore the connectivity changes in 6 subacute stroke patients. Sensorimotor functions improved in the RBAT and DMCI groups after treatment. Enhanced activation changes were observed in bilateral primary motor cortex (M1) and bilateral supplementary motor area (SMA) after RBAT. Dynamic causal model analysis revealed that interhemispheric connections were enhanced in RBAT patients. These preliminary findings suggest that intracortical and intercortical coupling might underlie poststroke RBAT.

Predicting Clinically Significant Changes in Motor and Functional Outcomes After Robot-Assisted Stroke Rehabilitation

ObjectiveTo investigate the predictors of minimal clinically important changes on outcome measures after robot-assisted therapy (RT). DesignObservational cohort study. SettingOutpatient rehabilitation clinics. ParticipantsA cohort of outpatients with stroke (N=55). InterventionsPatients with stroke received RT for 90 to 105min/d, 5d/wk, for 4 weeks. Main Outcome MeasuresOutcome measures, including the Fugl-Meyer Assessment (FMA) and Motor Activity Log (MAL), were measured before and after the intervention. Potential predictors include age, sex, side of lesion, time since stroke onset, finger extension, Box and Block Test (BBT) score, and FMA distal score. ResultsStatistical analysis showed that the BBT score (odds ratio[OR]=1.06; P=.04) was a significant predictor of clinically important changes in the FMA. Being a woman (OR=3.9; P=.05) and BBT score (OR=1.07; P=.02) were the 2 significant predictors of clinically significant changes in the MAL amount of use subscale. The BBT score was the significant predictor of an increased probability of achieving clinically important changes in the MAL quality of movement subscale (OR=1.07; P=.02). The R2 values for the 3 logistic regression models were low (.114−.272). ConclusionsThe results revealed that patients with stroke who had greater manual dexterity measured by the BBT appear to have a higher probability of achieving clinically significant motor and functional outcomes after RT. Further studies are needed to evaluate other potential predictors to improve the models and validate the findings.

Kinematic Data Analysis for Post-Stroke Patients Following Bilateral Versus Unilateral Rehabilitation With an Upper Limb Wearable Robotic System

Robot-assisted stroke rehabilitation has become popular as one approach to helping patients recover function post-stroke. Robotic rehabilitation requires four important elements to match the robot to the patient: realistic biomechanical robotic elements, an assistive control scheme enabled through the human-robot interface, a task oriented rehabilitation program based on the principles of plasticity, and objective assessment tools to monitor change. This paper reports on a randomized clinical trial utilizing a complete robot-assisted rehabilitation system for the recovery of upper limb function in patients post-stroke. In this study, a seven degree-of-freedom (DOF) upper limb exoskeleton robot (UL-EXO7) is applied in a rehabilitation clinical trial for patients stable post-stroke (greater than six months). Patients had a Fugl-Meyer Score between 16-39, were mentally alert (> 19 on the VA Mini Mental Status Exam) and were between 27 and 70 years of age. Patients were randomly assigned to three groups: bilateral robotic training, unilateral robotic training, and usual care. This study is concerned with the changes in kinematics in the two robotic groups. Both patient groups played eight therapeutic video games over 12 sessions (90 min, two times a week). In each session, patients intensively played the different combination of video games that directly interacted with UL-EXO7 under the supervision of research assistant. At each session, all of the joint angle data was recorded for the evaluation of therapeutic effects. A new assessment metric is reported along with conventional metrics. The experimental result shows that both groups of patients showed consistent improvement with respect to the proposed and conventional metrics.

ROBOT-ASSISTED STROKE REHABILITATION: JOINT TORQUE/FORCE CONVERSION FROM EMG USING GA PROCESS

This project focuses on the development of robot-assisted stroke rehabilitation by implementing electromyography (EMG) as the interface between robot and user communication. The key issue in the implementation of EMG in this application is the conversion of EMG signal into torque data. This article presents a methodology of EMG signal to estimated joint torque conversion by using genetic algorithm (GA). The basic principle of GA, formulation, and implementation to the problem are discussed in this article. Experimentation with real-life EMG data has been carried out to assess the feasibility of the methodology in robot-assisted stroke rehabilitation problem. Preliminary investigations show that the methodology can be used in EMG to joint torque conversion algorithm.

ROBOT ASSISTED STROKE REHABILITATION: JOINT TORQUE/FORCE CONVERSION FROM EMG USING SA PROCESS

This paper focuses on the implementation of robot-assisted stroke rehabilitation using electromyography (EMG) as the interface between the robot and subjects. The key issue in implementing EMG for this application is the conversion process of EMG signal into torque/force, which is used as a input to the control system. This paper presents a methodology of EMG signal conversion into estimated joint torque by using simulated annealing (SA) technique. Basic principle of SA, formulation, and implementation to the problem are discussed in this paper. Experimental studies with real life EMG data have been carried out for five subjects. These studies are used to evaluate the feasibility of the methodology proposed for robot-assisted stroke rehabilitation problem. Experimental investigations and results are discussed at the end of the paper.

AI Techniques to Estimate Muscle Force and Joint Torque for Upper Extremity

This paper is motivated by works done in the area of robot-assisted stroke rehabilitation. To map the EMG to joint torque, evolutionary techniques with suitable mathematical models are proposed. These models have unknown adjustable parameters, and the values of these parameters are obtained using nonlinear regression methods such as GA and SA. Five subjects took part in the experiments and they were asked to perform non-fatiguing and variable force maximal voluntary contractions and sub-maximal voluntary contractions. The recorded EMG signals data of various joints are entered to the model, to estimate the best fit values for the unknown parameters. Once these values of the parameters are obtained they are applied into the model and thus the joint torque is estimated. Predictions made by the proposed techniques are well correlated with experimental data.