Robot-assisted Rehabilitation System Research Articles

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.

The Route of Motor Recovery in Stroke Patients Driven by Exoskeleton-Robot-Assisted Therapy: A Path-Analysis.

Background: Exoskeleton-robot-assisted therapy is known to positively affect the recovery of arm functions in stroke patients. However, there is a lack of evidence regarding which variables might favor a better outcome and how this can be modulated by other factors. Methods: In this within-subject study, we evaluated the efficacy of a robot-assisted rehabilitation system in the recovery of upper limb functions. We performed a path analysis using a structural equation modeling approach in a large sample of 102 stroke patients (age 63.6 ± 13.1 years; 61% men) in the post-acute phase. They underwent 7 weeks of bilateral arm training assisted by an exoskeleton robot combined with a conventional treatment (consisting of simple physical activity together with occupational therapy). The upper extremity section of the Fugl–Meyer (FM-UE) scale at admission was used as a predictor of outcome, whereas age, gender, side of the lesion, days from the event, pain scale, duration of treatment, and number of sessions as mediators. Results: FM-UE at admission was a direct predictor of outcome, as measured by the motricity index of the contralateral upper limb and trunk control test, without any other mediating factors. Age, gender, days from the event, side of lesion, and pain scales were independently associated with outcomes. Conclusions: To the best of our knowledge, this is the first study assessing the relationship between clinical variables and outcomes induced by robot-assisted rehabilitation with a path-analysis model. We define a new route for motor recovery of stroke patients driven by exoskeleton-robot-assisted therapy, highlighting the role of FM-UE at admission as a useful predictor of outcome, although other variables need to be considered in the time-course of disease.

Quantitative Assessment of Motor Function by an End-Effector Upper Limb Rehabilitation Robot Based on Admittance Control

Various rehabilitation robots have been developed to assist the movement training of the upper limbs of stroke patients, among which some have been used to evaluate the motor recovery. However, how to understand the recovery of motor function from the quantitative assessment following robot-assisted rehabilitation training is still not clear. The objective of this study is to propose a quantitative assessment method of motor function based on the force and trajectory characteristics during robotic training to reflect motor functional recovery. To assist stroke patients who are not able to move voluntarily, an assistive training mode was developed for the robot-assisted rehabilitation system based on admittance control. Then, to validate the relationship between characteristic information and functional recovery, a clinical experiment was conducted, in which nine stroke patients and nine healthy subjects were recruited. The results showed a significant difference in movement range and movement smoothness during trajectory tracking tasks between stroke patients and healthy subjects. The two parameters above have a correlation with the Fugl-Meyer Assessment for Upper Extremity (FMU) of the involved patients. The multiple linear regression analysis showed FMU was positively correlated with parameters (R2=0.91,p&lt;0.005). This finding indicated that the above-mentioned method can achieve quantitative assessment of motor function for stroke patients during robot-assisted rehabilitation training, which can contribute to promoting rehabilitation robots in clinical practice.

Dynamic Difficulty Level Adjustment Based on Score and Physiological Signal Feedback in the Robot-Assisted Rehabilitation System, RehabRoby

In this letter, subjects take part in a fruit picker game on the robot-assisted rehabilitation system RehabRoby. The difficulty level of the game is adjusted using three feedback strategies. We use the scores of the subjects as the first feedback to determine the difficulty level in score feedback adjustment (SFA). We then consider physiological feedback, which is the mean skin conductance of the subjects, in physiological feedback adjustment (PFA). Finally, we use both the score and mean skin conductance to determine the difficulty level in combined performance and physiological feedback adjustment (CFA). Subsequently, we seek to understand the subjects' level of excitement by analyzing the subjective ratings using a survey, and by observing the statistically significant features generated from the collected physiological signals when they were performing the task using the proposed three feedback strategies. Survey results indicate that subjects respond positively to SFA and CFA but respond negatively to PFA because PFA proposes underchallenging levels to prevent a stressful experience, and as a result the subjects become bored. We find the skin conductance response (SCR), mean skin conductance (Mean <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SC</sub> ), heart rate (HR), inter-beat interval (IBI), and mean temperature (Mean <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">temp</sub> ) as significant features between SFA, PFA, and CFA. The statistically significant features indicate that CFA proposes unpredictable difficulty levels, which keep the subjects more active, focused, and excited when compared to SFA and PFA.

Exploring dynamic difficulty adjustment mechanism for rehabilitation tasks using physiological measures and subjective ratings

Task engagement is a key factor in sustaining patients’ participation in rehabilitation. Adaptive task difficulty level adjustment techniques are designed to determine the appropriate exercise difficulty level in where subjects are appropriately challenged and engaged without causing any distress. Such adaptive difficulty adjustment within rehabilitation tasks has the potential to individualise training. In this study, the authors have compared two dynamic difficulty level adjustment algorithms, partially ordered set master (POSM) and increment/decrement one level (IDOL), those change the difficulty levels for each individual adaptively based on his/her performance. These two algorithms are integrated into the robot-assisted rehabilitation system, RehabRoby, and their functionality is explored via a small user study with 20 healthy subjects. The subjects were asked to perform a computer-based fruit picker game using RehabRoby under these two algorithms. The impacts of the adaptation algorithms are evaluated in terms of engagement of the subjects by looking at their physiological signals, performance (score), and survey results. Experiments show that although POSM on the average suggests easier difficulty levels to the subjects than IDOL, the subjects experience a wider range of difficulty levels in POSM that may help them to become more engaged in the game, which can also be observed by lower skin temperatures of the subjects.

Myoelectric control algorithm for robot-assisted therapy: a hardware-in-the-loop simulation study

BackgroundA direct blow to the knee is one way to injure the anterior cruciate ligament (ACL), e.g., during a football or traffic accident. Robot-assisted therapy (RAT) rehabilitation, simulating regular walking, improves walking and balance abilities, and extensor strength after ACL reconstruction. However, there is a need to perform RAT during other phases of ACL injury rehabilitation before attempting an advanced exercise such as walking. This paper aims to propose a myoelectric control (MEC) algorithm for a robot-assisted rehabilitation system, “Nukawa”, to assist knee movement during these types of exercises, i.e., such as in active-assisted extension exercises.MethodsSurface electromyography (sEMG) signal processing algorithm was developed to detect the motion intention of the knee joint. The sEMG signal processing algorithm and the movement control algorithm, reported by the authors in a previous publication, were joined together as a hardware-in-the-loop simulation to create and test the MEC algorithm, instead of using the actual robot.Experiments and resultsAn experimental protocol was conducted with 17 healthy subjects to acquire sEMG signals and their lower limb kinematics during 12 ACL rehabilitation exercises. The proposed motion intention algorithm detected the orientation of the intention 100% of the times for the extension and flexion exercises. Also, it detected in 94% and 59% of the cases the intensity of the movement intention in a comparable way to the maximum voluntary contraction (MVC) during extension exercises and flexion exercises, respectively. The maximum position mean absolute error was 0.1^{circ }, 6.3^{circ }, and 0.3^{circ } for the hip, knee, and ankle joints, respectively.ConclusionsThe MEC algorithm detected the intensity of the movement intention, approximately, in a comparable way to the MVC and the orientation. Moreover, it requires no prior training or additional torque sensors. Also, it controls the speed of the knee joint of Nukawa to assist the knee movement, i.e., such as in active-assisted extension exercises.

Stiffness-based tuning of an adaptive impedance controller for robot-assisted rehabilitation of upper limbs.

In this paper, the tuning procedure of an adaptive impedance control approach, for upper limb rehabilitation therapies assisted by robots, is presented. The main feature of the proposed approach is a custom tuning of the impedance parameters for the controller, based on the stiffness estimation of users (patients), thus achieving a suitable robot-assisted rehabilitation system according with the different conditions of user's mobility. A set of simulation results are presented, in order to verify the suitable performance of the proposed approach in human-robot interaction tasks.

Hybrid Automata System for Emotion Embodiment in Upper Extremity Rehabilitation System

This paper highlights the embodiment of emotions which is structured using hybrid automata framework. Each initialization value, modes, condition for each mode, guard conditions, and transition between modes of the automata model designed for robot-assisted rehabilitation system are elaborated. A structured experimental setup was designed to invoke the emotion of the subject and to test the integration between detected emotion with the rehabilitation platform. The evaluation of the hybrid automata proposed is based on real time application. The result demonstrates the efficacy of hybrid automata approach in the rehabilitation application where emotion of the subject is taken into consideration in deploying suitable upper extremity rehabilitation tasks for Human Machine Interaction (HMI) applications.

A proposal for patient-tailored supervision of movement performance during end-effector-based robot-assisted rehabilitation of the upper extremities.

Millions of people worldwide suffer from stroke each year. One way to assist patients cost-effectively during their rehabilitation process is using end-effector-based robot-assisted rehabilitation. Such systems allow patients to use their own movement strategies to perform a movement task, which encourages them to do self-motivated training but also allow compensation movements if they have problems executing the movement tasks. Therefore, a patient supervision system was developed on the basis of inertial measurement units and a patient-tailored movement interpretation system. Very light and small inertial measurement units were developed to record the patients' movements during a teaching phase in which the desired movement is shown to the patient by a physiotherapist. During a following exercise phase, the patient is training the previously shown movement alone with the help of an end-effector-based robot-assisted rehabilitation system, and the patient's movement is recorded again. The data from the teaching and exercise phases are compared with each other and evaluated by using fuzzy logic tailored to each patient. Experimental tests with one healthy subject and one stroke patient showed the capability of the system to supervise patient movements during the robot-assisted end-effector-based rehabilitation.

Research and Design of the Rehabilitation Platform Based on the Kinect Motion Acquisition

Computer vision have a wide application in the modern information technology industry, the rapid development of content-based image analysis technology has been successfully applied in the analysis of transport vehicles, in various fields of industrial product testing, agricultural pest control, interactive advertising, visual intercom and detection of other medical illnesses occasions. Rehabilitation in the medical field, mainly in artificial robot-assisted rehabilitation and rehabilitation in two ways. for its expensive, high maintenance costs and single actual intelligence, Robot-assisted rehabilitation system is bad in the market promotion and application dismal; artificial rehabilitation is the best choice for the rehabilitation of patients, but in reality the patient needs to be recovered ambitious rehabilitation coach on the supply side. Provide a rehabilitation patients meet rehabilitation purchasing power and ease of use of the system is an important topic in the field of rehabilitation. Aiming at this problem, this paper take computer vision as the core to collect spatial depth chart patients through Kinect device and rebuild the action flow in patients with rehabilitation training. System Settings roles in two sports: Rehabilitation coaches and rehabilitation patients by assessing the actions of its demonstration and training flows back to the doctor the patient rehabilitation progress reports.

Upper-Extremity Rehabilitation Robot RehabRoby: Methodology, Design, Usability and Validation

In this study, an exoskeleton type robot-assisted rehabilitation system, called RehabRoby, is developed for rehabilitation purposes. A control architecture, which contains a high-level controller and a low-level controller, is designed so that RehabRoby can complete the given rehabilitation task in a desired and safe manner. A hybrid system modelling technique is used for the high-level controller. An admittance control with an inner robust position control loop is used for the low-level control of the RehabRoby. Real-time experiments are performed to evaluate the control architecture of the robot-assisted rehabilitation system, RehabRoby. Furthermore, the usability of RehabRoby is evaluated.

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.

Development of mirror image motion system with sEMG for shoulder rehabilitation of post-stroke hemiplegic patients

A robot-assisted bimanual shoulder flexion rehabilitation system using surface electromyography (sEMG) for post-stroke hemiplegic patients is presented. Impedance compensation of the actuators using a disturbance observer (DOB) was applied for back-drivable operation. The sEMG signal processing was utilized to obtain a desired assistive torque. Four modes of motion (passive, mirror image, shared, and voluntary) were suggested as an appropriate training platform for the various patient statuses and levels of individual recovery. Then the performance of the impedance compensation and assistive operation of the system was verified by experiments with healthy participants. The DOB decreased resistive torque by 99% compared to the open loop performance. The shoulder torque was estimated using the sEMG and linear regression (CORR = 0.960 ± 0.011, NRMSE = 7.31 ± 1.32%) and an artificial neural network (ANN) (CORR = 0.986 ± 0.005, NRMSE = 6.96 ± 1.08%) methods for generating system input based on the user’s motion intention. Every mode had less than a 6% NRMSE motion error in experiments without discomforts or resistance during shoulder flexion motion of mirror the arm.

A Framework for the Development of Measurement and Quality Assurance in Software-Based Medical Rehabilitation Systems

The field of computer and robot-assisted rehabilitation system is rooted in the principle that software must be largely errorless, user-friendly, robust, accurate with respect to data, respond in a timely manner, and yet inexpensive, which lead to enhanced patient outcomes. In this digitized age, computerized and robotic rehabilitation systems act as a vital support for disabled individuals. Till today, different types of software for medical rehabilitation systems have been developed and applied to the rehabilitation process successfully, but improvement in quality and measurement of rehabilitation software is continuously in progress. Some ways of the software production have been established but further measurement process has always been a necessity. This paper presents the framework and recommends establishment of software quality measurement in computer- and robot-assisted automated medical rehabilitation system. Also, a brief discussion of rehabilitation technique and their software quality is also included. Lastly, we include its importance in medical technology and quality. To satisfy the end user, vendor satisfaction, software measurement and quality assurance are important components in software-based medical rehabilitation systemsy. © 2012 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Centre of Humanoid Robots and Bio-Sensor (HuRoBs), Faculty of Mechanical Engineering, Universiti Teknologi MARA.

Evaluation of elbow joint proprioception with RehabRoby: a pilot study

The aim of this study was to evaluate the proprioceptive sense of elbow flexion through a robot-assisted rehabilitation system, RehabRoby, and to understand the usability of RehabRoby as a robotic system in physiotherapy. The study included 20 volunteer, healthy students studying either physiotherapy (PT) (5 females and 5 males) or electrical and electronics engineering (EEE) (5 females and 5 males). Using the RehabRoby, they were asked to flex their elbow joints in pronation actively and then against a comfortable resistance to the target angles (20°, 45° and 90°), with eyes open and closed. Angle of movement and applied torque for each target angle and error of movement with respect to the target angle (error of matching) were recorded as absolute values. Participants' socio-demographic and physical features were also evaluated. Physiotherapy students had less matching error at 45° with eyes opened than EEE students. A negative correlation was found between resistive elbow flexion and applied torque while eyes closed at 20° (p<0.05). Biceps brachii strength and being female were significant predictive factors for the least matching error in active elbow flexion at 20° with eyes closed. Error of matching at 45° without vision was lower in the PT group (-0.31) than in the EEE group (0.77). In addition, it was noticed that biceps brachii muscle strength played an important role in the proprioceptive sense of the motion at 20°. The RehabRoby can be considered a usable system for the evaluation of joint proprioception sense. With future validity studies, the RehabRoby may be used to assess, diagnose and improve the proprioceptive sense of patients.

Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG

Stroke is a leading cause of disability worldwide. In this paper, a novel robot-assisted rehabilitation system based on motor imagery electroencephalography (EEG) is developed for regular training of neurological rehabilitation for upper limb stroke patients. Firstly, three-dimensional animation was used to guide the patient image the upper limb movement and EEG signals were acquired by EEG amplifier. Secondly, eigenvectors were extracted by harmonic wavelet transform (HWT) and linear discriminant analysis (LDA) classifier was utilized to classify the pattern of the left and right upper limb motor imagery EEG signals. Finally, PC triggered the upper limb rehabilitation robot to perform motor therapy and gave the virtual feedback. Using this robot-assisted upper limb rehabilitation system, the patient's EEG of upper limb movement imagination is translated to control rehabilitation robot directly. Consequently, the proposed rehabilitation system can fully explore the patient's motivation and attention and directly facilitate upper limb post-stroke rehabilitation therapy. Experimental results on unimpaired participants were presented to demonstrate the feasibility of the rehabilitation system. Combining robot-assisted training with motor imagery-based BCI will make future rehabilitation therapy more effective. Clinical testing is still required for further proving this assumption.

Impact of Visual Error Augmentation When Integrated With Assist-as-Needed Training Method in Robot-Assisted Rehabilitation

The paper investigates the impact of the integration of the visual error augmentation training method with the assist-as-needed training method in robot-assisted rehabilitation training of the upper extremity. A robot-assisted rehabilitation system is developed that integrates an assistive controller, which can provide robotic assistance to the participant as and when needed, with a visual error augmentation mechanism, which amplifies the tracking error to heighten the participant's motivation to improve tracking accuracy. A crossover study is designed to evaluate the impact of the integration of the visual error augmentation method with the assist-as-needed training method. Experimental results on unimpaired participants demonstrate improved performance has been achieved in the integrated training session.

Incorporating verbal feedback into a robot-assisted rehabilitation system

SUMMARYThis paper presents a control architecture, which has the potential to monitor the task and safety issues, to provide assessment of the progress and alter the task parameters, and to incorporate patient's feedback in order to make the necessary modifications to impart effective therapy during the execution of the task in an automated manner. Experimental results are presented to demonstrate the efficacy of the proposed control architecture.

Towards a Smooth Human–Robot Interaction for Rehabilitation Robotic Systems

The goal of this work is to develop a control framework to provide assistance to the subjects in such a manner that the interaction between the subjects and a robot-assisted rehabilitation system is smooth during the rehabilitation therapy. In order to achieve smoothness of interaction, a control framework is designed in such a way that it can automatically adjust the control gains of the robot-assisted rehabilitation system to modify the interaction dynamics between the system and the subject. An artificial neural network (ANN)-based proportional–integral (PI) gain scheduling controller is proposed to automatically determine the appropriate control gains for each individual subject. The human arm model is integrated with the ANN-based PI gain scheduling controller where the ANN uses estimated human arm parameters to select the appropriate PI gains for each subject such that the resultant interaction dynamics between the subject and the robot-assisted rehabilitation system could result in smooth interaction. Experimental results involving unimpaired subjects on a PUMA robot-based rehabilitation system are presented to demonstrate the efficacy of the proposed ANN-based PI gain scheduling controller on unimpaired subjects.