Robotic Rehabilitation System Research Articles

Robotic psychophysics system for assessment, diagnosis and rehabilitation of the neurological causes of falls in the elderly.

Falls are the leading causes of unintentional injuries in the elderly and thus a pose a major hazard to our ageing society. We present the FOHEPO (FOot HEight POsitioning) system to measure, diagnose and eventually rehabilitate ageing-related neurological causes of falls. We hypothesise that both perceptual and motor variability is likely to increase with age and may lead to imprecise perception and movements causing trip overs, the major triggers of falls. Our robotic experimental system automatically measures and tracks different sources of noise in the nervous system: visual perception noise of obstacle height, proprioceptive noise of localising raising one's foot to a desired height, noise in the visual feedback of the foot movements. We developed age-appropriate psychophysical measurement protocols shorter than standard protocols for perceptual and motor accuracy. These quantify individual subjects perceptual and movement accuracy thresholds through their psychometric curves. Therefore, these platform measurements will enable us to estimate fall probabilities quantitatively, i.e. the chance that a foot will clip an obstacle because subjects did not add a sufficient safety factor when clearing it. Potentially, we can use our FOHEPO system in a game-ified setting to rehabilitate elderly users to move with larger safety factors so as to reduce their risks of trip-over.

EEG classification of different imaginary movements within the same limb.

The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises.

Influence of complementing a robotic upper limb rehabilitation system with video games on the engagement of the participants: a study focusing on muscle activities.

Efficacious stroke rehabilitation depends not only on patients' medical treatment but also on their motivation and engagement during rehabilitation exercises. Although traditional rehabilitation exercises are often mundane, technology-assisted upper-limb robotic training can provide engaging and task-oriented training in a natural environment. The factors that influence engagement, however, are not fully understood. This paper therefore studies the relationship between engagement and muscle activities as well as the influencing factors of engagement. To this end, an experiment was conducted using a robotic upper limb rehabilitation system with healthy individuals in three training exercises: (a) a traditional exercise, which is typically used for training the grasping function, (b) a tracking exercise, currently used in robot-assisted stroke patient rehabilitation for fine motor movement, and (c) a video game exercise, which is a proliferating approach of robot-assisted rehabilitation enabling high-level active engagement of stroke patients. These exercises differ not only in the characteristics of the motion that they use but also in their method of triggering engagement. To measure the level of engagement, we used facial expressions, motion analysis of the arm movements, and electromyography. The results show that (a) the video game exercise could engage the participants for a longer period than the other two exercises, (b) the engagement level decreased when the participants became too familiar with the exercises, and (c) analysis of normalized root mean square in electromyographic data indicated that muscle activities were more intense when the participants are engaged. This study shows that several sub-factors on engagement, such as versatility of feedback, cognitive tasks, and competitiveness, may influence engagement more than the others. To maintain a high level of engagement, the rehabilitation system needs to be adaptive, providing different exercises to engage the participants.

Robotic Exoskeleton for Rehabilitation of the Upper Limb

This paper deals with robotic exoskeletons and their using in rehabilitation of the upper limbs. First part describes application of robotic devices in rehabilitation. In the second part there is described an architecture of rehabilitation robotic system and its main subsystems. Third part of the proposed paper is dedicated to robotic exoskeletons. This section deals with internal and external force action in exoskeletons. In the last part of the paper, there is shown design of robotic exoskeleton for the upper limb.

RETRACTED ARTICLE: Cascade controller design and stability analysis in FES-aided upper arm stroke rehabilitation robotic system

A three-dimensional stroke rehabilitation system is studied which combines the functional electrical stimulation (FES) with a robotic support to provide assistance to stroke patients who are required to perform upper extremity trajectory-tracking exercises with their residual voluntary efforts. When not enough voluntary efforts can be supplied, FES-based assistance is provided by applying electrical stimulation to the actuated muscles. In order to realize more rapid and accurate control of trajectory tracking, a new cascade control scheme is developed for the combined muscle and supported arm system. The stability of the cascade-controlled system and the internal stability of the unactuated dynamics are rigorously studied. The parameter optimal iterative learning control is then employed to further improve the trajectory-tracking accuracy of the cascade-based robotic system. Performance evaluation results confirm the effectiveness of the proposed method for upper arm trajectory-tracking-oriented stroke rehabilitation.

Toward minimum effort reaching trajectories formation in robot-based rehabilitation after stroke: an innovative guidance scheme proposition.

In therapy after stroke, intensive and repetitive training is crucial for relearning of motor abilities and functionalities. Besides conventional therapy, rehabilitation robotic systems are used in the treatment, where a minimum jerk model is used widely in the formation of the point-to-point movement trajectory, which is not necessarily the appropriate choice for hemiparetic upper extremity training. This paper examines and analyzes the influence of a selected form of a flexor muscle tightness on trajectory formation and proposes a guidance scheme that yields minimum effort in robot-assisted arm reaching movement. A dynamic upper extremity model with the three clinically most relevant muscles that develop tightness because of spasticity after stroke was used in the optimization study that examined key features of trajectory formation. The results showed that the trajectories and their velocity profiles become significantly different in the presence of muscle tightness. On the basis of dynamic optimization results, we experimentally examined selected trajectories in a selected individual with chronic hemiparesis. The experimental results confirmed the importance of appropriate selection of reaching trajectories' velocity profiles. The results of optimization and experimentation showed the importance of the velocity profile of the robot-based guidance scheme, which has an influence on the necessary invested 'effort' between starting/ending points to overcome muscle tightness. This study showed that muscle tightness exerts a significant influence on trajectory formation as well as on its velocity profile. The proposed innovative guidance scheme might be useful for practical arm reaching robot-supported movement training in the clinical environment. We suggested that for each hemiparetic patient, the initial identification session before training should experimentally determine the 'optimal' parameters for the rehabilitation guidance scheme that would optimally match the particular muscle tightness constraints.

Comprehensive Review on Main Technology of Exoskeletal Robot System for Upper Limb Rehabilitation

Major function of exoskeletal robot system for upper limbs rehabilitation is to assist patient to carry out upper limbs’ rehabilitation training. Main technology of exoskeletal robot system for upper limbs rehabilitation includes design of mechanical structure of exoskeletal robot, design of control system of exoskeletal robot and implemention of data and information transmission between exoskeletal robot and upper limbs of human body. Reviewing and analyzing the specific technical development and deficiency in field of exoskeletal robot system for upper limbs rehabilitation will be important way in improving and upgrading the technology in future. Current development trend of main technology is to advance degree of comfort, accuracy and effect in rehabilitation training. Further development trend of main technology henceforth is people-oriented, strengthening effect, improving efficiency, optimizing structure and reducing energy consumption that follow science-technology ethical claims.

Comprehensive Example Analysis on Main Technology of Exoskeletal Robot System for Upper Limb Rehabilitation

Many medical treatments need to confront problem of upper limbs’ rehabilitation. Major function of exoskeletal robot system for upper limbs rehabilitation is to assist patient to carry out upper limbs’ rehabilitation training. Main technology of exoskeletal robot system for upper limbs rehabilitation includes design of mechanical structure of exoskeletal robot, design of control system of exoskeletal robot and implemention of data and information transmission between exoskeletal robot and upper limbs of human body. Currently implemention of data and information transmission rely mainly on methods of acquiring sEMG signal and force feedback. Reviewing and analyzing the specific technical examples will be important way in improving and upgrading the technology in future. Current development trend of main technology is to advance degree of comfort, accuracy and effect in rehabilitation training. Further development trend of main technology henceforth is people-oriented, strengthening effect, improving efficiency, optimizing structure and reducing energy consumption that follow claims of medical treatment.

Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke.

Stroke can be a source of significant upper extremity dysfunction and affect the quality of life (QoL) in survivors. In this context, novel rehabilitation approaches employing robotic rehabilitation devices combined with brain-machine interfaces can greatly help in expediting functional recovery in these individuals by actively engaging the user during therapy. However, optimal training conditions and parameters for these novel therapeutic systems are still unknown. Here, we present preliminary findings demonstrating successful movement intent detection from scalp electroencephalography (EEG) during robotic rehabilitation using the MAHI Exo-II in an individual with hemiparesis following stroke. These findings have strong clinical implications for the development of closed-loop brain-machine interfaces to robotic rehabilitation systems.

Changes of Achilles tendon properties via 12-week PNF based robotic rehabilitation of ankle joints with spasticity and/or contracture.

Ankle joint with spasticity and/or contracture can severely affect mobility and independence of stroke survivors. Due to that, the Achilles tendon(AT) is affected. In this paper, we aim to study changes of AT properties via proprioceptive neuromuscular facilitation (PNF) treatment. A robotic ankle-foot rehabilitation system has been proposed, which consists of a robotic ankle-foot platform and a graphic user interface. In this pilot study, two post-stroke patients participated and carried out a 12-week PNF treatment with the robotic system. The treatment is evaluated quantitatively in AT properties. The evaluation shows that after the PNF treatment, the average decrease of AT length is 4.1 mm (6.5%) and the recovery ratio is 30.4%, while the thickness has no change. The results indicate that the PNF based robotic rehabilitation for ankle joints with spasticity and/or contracture is effective to improve the ankle spasticity/contracture.

Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke.

The objective of this study was to investigate the efficacy of an Electroencephalography (EEG)-based Motor Imagery (MI) Brain-Computer Interface (BCI) coupled with a Haptic Knob (HK) robot for arm rehabilitation in stroke patients. In this three-arm, single-blind, randomized controlled trial; 21 chronic hemiplegic stroke patients (Fugl-Meyer Motor Assessment (FMMA) score 10–50), recruited after pre-screening for MI BCI ability, were randomly allocated to BCI-HK, HK or Standard Arm Therapy (SAT) groups. All groups received 18 sessions of intervention over 6 weeks, 3 sessions per week, 90 min per session. The BCI-HK group received 1 h of BCI coupled with HK intervention, and the HK group received 1 h of HK intervention per session. Both BCI-HK and HK groups received 120 trials of robot-assisted hand grasping and knob manipulation followed by 30 min of therapist-assisted arm mobilization. The SAT group received 1.5 h of therapist-assisted arm mobilization and forearm pronation-supination movements incorporating wrist control and grasp-release functions. In all, 14 males, 7 females, mean age 54.2 years, mean stroke duration 385.1 days, with baseline FMMA score 27.0 were recruited. The primary outcome measure was upper extremity FMMA scores measured mid-intervention at week 3, end-intervention at week 6, and follow-up at weeks 12 and 24. Seven, 8 and 7 subjects underwent BCI-HK, HK and SAT interventions respectively. FMMA score improved in all groups, but no intergroup differences were found at any time points. Significantly larger motor gains were observed in the BCI-HK group compared to the SAT group at weeks 3, 12, and 24, but motor gains in the HK group did not differ from the SAT group at any time point. In conclusion, BCI-HK is effective, safe, and may have the potential for enhancing motor recovery in chronic stroke when combined with therapist-assisted arm mobilization.

Biologically Inspired Soft Robot for Thumb Rehabilitation1

More than 130,000 people have strokes each year in the United States [1]. Of these victims, 76% are left with disabilities that cost the nation over $54 billion in lost work and medical fees. One prominent disability is upper extremity hemiplegia, which occurs among approximately 50% of stroke sufferers [2]. Robotic technology has the potential to provide an automated platform for controlled rehabilitation and assisted, task-oriented therapy. Several systems have been designed to assist in patient articulation of an impaired hand using rigid robotic components. While these products have been successful in articulating the pure bending motions of the four fingers, the limited capabilities of rigid technologies fail to reproduce the complicated motion path [5] of the thumb during opposition grasp (see Figure 1). This is the most important articulation for normal hand function and specifically for picking up everyday objects. To date, robotic systems for thumb rehabilitation have not been widely investigated [3] apart from some recent work [4] that used a multi-joint rigid robot.

A Portable Powered Knee-Ankle- Foot Orthosis1

A Portable Powered Knee-Ankle- Foot Orthosis1

Recent trends for practical rehabilitation robotics, current challenges and the future

This paper presents and studies various selected literature primarily from conference proceedings, journals and clinical tests of the robotic, mechatronics, neurology and biomedical engineering of rehabilitation robotic systems. The present paper focuses of three main categories: types of rehabilitation robots, key technologies with current issues and future challenges. Literature on fundamental research with some examples from commercialized robots and new robot development projects related to rehabilitation are introduced. Most of the commercialized robots presented in this paper are well known especially to robotics engineers and scholars in the robotic field, but are less known to humanities scholars. The field of rehabilitation robot research is expanding; in light of this, some of the current issues and future challenges in rehabilitation robot engineering are recalled, examined and clarified with future directions. This paper is concluded with some recommendations with respect to rehabilitation robots.

Modeling and Simulating a Robotic System for Upper-Limb Rehabilitation

In this paper, we present a virtual prototype for a Upper-Limb Rehabilitation Robotic System is review, where the conflict of stability for a non-linear system its implemented, using as a model a double inverted pendulum and its response in Matlab - SimMechanics. It demonstrates the ability and ease of using new computerized tools where simulation and modeling of a system becomes an important utility to understand control concepts and carry them out in a controlled environment, it also can be of great help ensuring the complete characterization of a system, in which it can also result in experimental results. A soft trajectory follower PID controller is implemented to provide individual rehabilitation movements in a 2 DoF robotic system.

An Adaptive Home-Use Robotic Rehabilitation System for the Upper Body.

Robotic rehabilitation systems have been developed to treat musculoskeletal conditions, but limited availability prevents most patients from using them. The objective of this paper was to create a home-use robotic rehabilitation system. Data were obtained in real time from a Microsoft n}{}{rm Kinect}^{rm TM}n and a wireless surface electromyograph system. Results from the n}{}{rm Kinect}^{rm TM}n sensor were compared to a standard motion capture system. A subject completed visual follow exercise tasks in a 3-D visual environment. Data from two training exercises were used to generate a neural network, which was then used to simulate the subject's individual performance. The subjects completed both the exercise task output from the neural network (custom), and the unmodified task (standard). In addition, a wearable arm robot prototype was built. Basic system identification was completed, and a control algorithm for the robot based on pressure control was designed and tested. The subjects had greater root-mean-square error for position and velocity variables during the custom exercise tasks. These results suggest that the custom task was difficult to complete, possibly because the neural network was unconstrained. Finally, the robot prototype was able to mimic changes in a subject's elbow angle in real time, demonstrating the feasibility of the robotic rehabilitation system.

Research and Development of Compact Wrist Rehabilitation Robot System

Compact rehabilitation robot system which can support movement of the wrist of patients has been developed. The robot system can detect and analyze the patient's intention to move the wrist by such a biological signal as muscle potential, then, assist the wrist exercise of patients. Also, both-wrist rehabilitation robot system by mirror effect has been successfully developed for practical use in the hospital and at home.

A novel exoskeleton robotic system for hand rehabilitation – Conceptualization to prototyping

This research presents a novel hand exoskeleton rehabilitation device to facilitate tendon therapy exercises. The exoskeleton is designed to assist fingers flexion and extension motions in a natural manner. The proposed multi-Degree Of Freedom (DOF) system consists of a direct-driven, optimized and underactuated serial linkage mechanism having capability to exert extremely high force levels perpendicularly on the finger phalanges. Kinematic and dynamic models of the proposed device have been derived. The device design is based on the results of multi-objective optimization algorithm and series of experiments conducted to study capabilities of the human hand. To permit a user-friendly interaction with the device, the control is based on minimum jerk trajectory generation. Using this control system, the transient response and steady state behavior of the proposed device are analyzed after designing and fabricating a two-fingered prototype. The pilot study shows that the proposed rehabilitation system is capable of flexing and extending the fingers with accurate trajectories.

Robotic Rehabilitation System for Human Upper Limbs Using Guide Control and Manipulability Ellipsoid Prediction

Abstract In this paper, we propose a robotic assistive control system for rehabilitation of the human arm. The admittance control produced low stiffness in the moving direction and high stiffness in the orthogonal direction. Using the admittance control for guide control, subjects can easily move their arms in accordance with the virtual guide line. The manipulability ellipsoid is obtained by image processing and singular value decomposition of the Jacobian matrix. The major axis of the ellipse indicates the easiest direction for the operation. The proposed guide control using admittance control and the manipulability ellipsoid was confirmed to be very effective.

SEMG Based Movement Quantitative Estimation of Joins Using SVM Method

The sEMG based movement recognition developed rapidly in recent years, which focus on intention estimation that velocity and angle of movement joint are not concerned. This paper proposed a quantitative analysis method of sEMG, with ability to estimate motion of human joints, which can be used to control rehabilitation robot system control by patient's own intention. The quantitative model of the relationship between sEMG signals and movement joint was established utilizing error Back Propagation artificial Neural Network and support vector machine with a Gaussian kernel, where the features of sEMG were taken as input. Considering of the actual demands of rehabilitation, the 1-DOF, 2-DOFs and 3-DOFs movement experiments were supposed to collect the information of joint angle and sEMG signals for model training. The result shows the angle prediction curve outputted by model of SVM has more than 90% consistency with the actual movement, while the model of BPNN gets a more imprecise output with complexity of movement arising. Initial online experiments on rehabilitation robot controlled by a healthy subject demonstrate that sEMG based movement control using the proposed method is feasible.