Posts Tagged rehabilitation robot

[Abstract] A 5-Degrees-of-Freedom Lightweight Elbow-Wrist Exoskeleton for Forearm Fine-Motion Rehabilitation


Exoskeleton robots have been demonstrated to effectively assist the rehabilitation of patients with upper or lower limb disabilities. To make exoskeletons more accessible to patients, they need to be lightweight and compact without major performance tradeoffs. Existing upper-limb exoskeletons focus on assistance with coarse-motion of the upper arm while forearm fine-motion rehabilitation is often ignored. This paper presents an elbow-wrist exoskeleton with five degrees-of-freedom (DoFs). Using geared bearings, slider crank mechanisms, and a spherical mechanism for the wrist and elbow modules, this exoskeleton can provide 5-DoF rotary motion forearm assistance. The optimized exoskeleton dimensions allow sufficient rotation output while the motors are placed parallel to the forearm and elbow joint. Thus compactness and less inertia loading can be achieved. Linear and rotary series elastic actuators (SEAs) with high torque-to-weight ratios are proposed to accurately measure and control interaction force and impedance between exoskeleton and forearm. The resulting 3-kg exoskeleton can be used alone or easily in combination with other exoskeleton robots to provide various robot-aided upper limb rehabilitation.

via A 5-Degrees-of-Freedom Lightweight Elbow-Wrist Exoskeleton for Forearm Fine-Motion Rehabilitation – IEEE Journals & Magazine

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[Abstract] A novel backstepping adaptive impedance control for an upper limb rehabilitation robot


Stroke contributes to hemiplegia, which severely reduces people’s ability to perform activities of daily living. Due to the insufficiency of medical resources, there is an urgent need for home-based rehabilitation robot. In this paper, we design a home-based upper limb rehabilitation robot, based on the principle that three axes intersect at one point. A three-dimensional force sensor is equipped at the end of the manipulator to measure the interaction forces between the affected upper limb and the robot during rehabilitation training. The virtual rehabilitation training environment is designed to improve the enthusiasm of patients. A backstepping adaptive fuzzy based impedance control method is proposed for the home-based upper limb rehabilitation robot to prevent secondary injury of the affected limb. The adaptive law is introduced, and the backstepping adaptive fuzzy based impedance controller is proved in details. Experiments results demonstrate the effectiveness of the proposed control method.


via A novel backstepping adaptive impedance control for an upper limb rehabilitation robot – ScienceDirect

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[Abstract + References] Design of Finger Exoskeleton Rehabilitation Robot Using the Flexible Joint and the MYO Armband


High-risk diseases such as stroke can do great harm to human hands. Hand rehabilitation for stroke patients is a complex and necessary task. To achieve this goal, this paper introduces a hand exoskeleton equipment with flexible joints and EMG-base motion prediction. Experiment of the equipment includes kinematics analysis, EMG signal detection by MYO armband and motion prediction base on BP neural network. The result shows that the device can not only assists patient bending or extending fingers, but also perform six kinds of rehabilitation exercises with 92% accuracy for target motion recognition.


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[Abstract + References] Preliminary Design of Soft Exo-Suit for Arm Rehabilitation – Conference paper


Every year, millions of people experience a stroke but only a few of them fully recover. Recovery requires a working staff, which is time consuming and inefficient. Therefore, over the past few years rehabilitation robots like Exoskeletons have been used in the recuperation process for patients. In this paper we have designed an Exosuit which takes into considerations of the rigid Exo-Skeleton and its limitations for patients suffering from loss of function of the arm. This paper concentrates on enabling a stroke affected person to perform flexion-extension at elbow joint. Validation of the developed model on general population is still needed.


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[Abstract + References] A compact wrist rehabilitation robot with accurate force/stiffness control and misalignment adaptation


Robots have been demonstrated to assist the rehabilitation of patients with upper or lower limb disabilities. To make exoskeleton robots more friendly and accessible to patients, they need to be lightweight and compact without major performance tradeoffs. Existing upper-limb exoskeleton robots focus on the assistance of the coarse-motion of the upper arm while the fine-motion rehabilitation of the forearm is often ignored. This paper presents a wrist robot with three degrees-of-freedom. Using a geared bearing, slider crank mechanisms, and a spherical mechanism, this robot can provide the complete motion assistance for the forearm. The optimized robot dimensions allow large torque and rotation output while the motors are placed parallel to the forearm. Thus lightweight, compactness, and better inertia properties can be achieved. Linear and rotary series elastic actuators (SEAs) with high torque-to-weight ratios are proposed to accurately measure and control the interaction force and impedance between the robot and the wrist. The resulting 1.5-kg robot can be used alone or easily in combination with other robots to provide various robot-aided upper limb rehabilitation.


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[Abstract] A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation


Previous studies on robotic rehabilitation have shown that subjects’ active participation and effort involved in rehabilitation training can promote the performance of therapies. In order to improve the voluntary effort of participants during the rehabilitation training, assist-as-needed (AAN) control strategies regulating the robotic assistance according to subjects’ performance and conditions have been developed. Unfortunately, the heterogeneity of patients’ motor function capability in task space is not taken into account during the implementation of these controllers. In this paper, a new scheme called greedy AAN (GAAN) controller is designed for the upper limb rehabilitation training of neurologically impaired subjects. The proposed GAAN control paradigm includes a baseline controller and a Gaussian RBF network that is utilized to model the functional capability of subjects and to provide corresponding a task challenge for them. In order to avoid subjects’ slacking and encourage their active engagement, the weight vectors of RBF networks evaluating subjects’ impairment level are updated based on a greedy strategy that makes the networks progressively learn the maximum forces over time provided by subjects. Simultaneously, a challenge level modification algorithm is employed to adjust the task challenge according to the task performance of subjects. Experiments on 12 subjects with neurological impairment are conducted to validate the performance and feasibility of the GAAN controller. The results show that the proposed GAAN controller has significant potential to promote the subjects’ voluntary engagement during training exercises.

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[ARTICLE] Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation – Full Text

In the therapist-centered rehabilitation program, the experienced therapists can observe emotional changes of stroke patients and make corresponding decisions on their intervention strategies. Likewise, robotic-assisted stroke rehabilitation systems will be more appreciated if they can also perceive emotional states of the stroke patients and enhance their engagements by exploring emotion-based dynamic difficulty adjustments. Nevertheless, few research have addressed this issue. A two-phase pilot study with anxiety as the target emotion state was conducted in this article. In phase I, the motor performances and the physiological responses to the stroke subject’s anxiety with high, medium, and low intensities were statistically analyzed, and anxiety models with three intensities were offline developed using support vector machine–based classifiers. In phase II, anxiety-based closed-loop robot-aided training task adaptation and its impacts on patient–robot interaction engagements were explored. As a comparison, a performance-based robotic behavior adaptation was also implemented. Experimental results with 12 recruited stroke patients conducted on the Barrett WAMTM manipulator verified that the rehabilitation robot can implicitly recognize the anxiety intensities of the stroke survivors and the anxiety-based real-time robotic behavior adaptation shows more engagements in the human–robot interactions.

Task-oriented repetitive rehabilitation training is becoming the state-of-the-art therapy approach for poststroke patients. These therapy tasks are traditionally implemented by physical therapists. In recent years, there is an increasing interest in using robotic devices to help providing motor rehabilitation therapy.1Compared with the therapist-centered therapy, robot-assisted stroke rehabilitation can not only provide a variety of highly repetitive movements and training protocols for stroke patients, but also offer objective measurements of stroke patients’ functional improvements.

Stroke patients’ active engagements in rehabilitation training have been shown to be a very positive factor to the success of rehabilitation.2 Early rehabilitation robots are able to provide active assistance to stroke patients, but do not take into account individual properties, spontaneous intentions, or voluntary efforts of that particular person. These problems were addressed by integrating the patients into the sensorimotor control loop. By recognizing the patients’ active motor abilities or movement intentions, the human-in-the-loop rehabilitation robotic systems are able to optimize participation and support the patients only as little as needed.3 However, stroke patients’ active involvements in the existing rehabilitation robotic systems are mostly considered from biomechanical and bioelectrical viewpoints, where the patients’ active force/position signals4,5 or electrical activities of the brain and the muscles6,7 were recorded.

In the therapist-centered program, the therapists who work with the stroke patients can not only perceive the patients’ active motor involvements, but also continuously monitor the patients’ emotion changes in order to make appropriate decisions on their intervention strategies. The stroke patients are particularly vulnerable to anxiety and frustration, which requires to plan tasks at an appropriate level of difficulty. Likewise, robotic-assisted stroke rehabilitation training systems will be more appreciated if they can perceive the stroke patients’ emotion changes and make emotion-based dynamic difficulty adjustment. Offering insights into the patients’ emotional changes and adapting emotion-based behavioral interventions are known as another critical factor to successful stroke rehabilitation.8 Nonetheless, very few research on robot-assisted stroke rehabilitation are specifically addressed how to automatically recognize and respond to the emotion changes of the stroke survivors. One possible reason is that there are some difficulties in perceiving the stroke patient’s emotion states.

There are several modalities such as facial expression,9 vocal intonation,10 body gesture,11 and physiology12 that can be utilized to recognize the emotion states of individuals in human–robot/computer interaction. Nevertheless, the patients with chronic stroke are often characterized by dull facial expression, severe aphasia, and limb motor dysfunction. These vulnerabilities place limits on observational, conversational, and limb methodologies to recognize the stroke survivors’ emotional states. Physiology-based measurements are far more robust against these difficulties because they are noninvasive and further the psycho-physiological signals can be continuously available without the stroke patient’s active cooperation. Besides, evidences show that the transition from one emotion state to another is accompanied by dynamic shifts in indicators of autonomous and central nervous system activity.13,14

In this article, anxiety, which can be easily evoked by training tasks with different difficulties in clinical rehabilitation therapy, was chosen as the target emotion state. The primary focuses of the current research were firstly to offline evaluate anxiety with high, medium, and low intensities and then to carry out real-time anxiety-based robot-aided rehabilitation training task adaptation.

The block diagram of the anxiety detection and subsequent anxiety-based robot-aided training task adaptation system are shown in Figure 1. It consists of two consecutive phases: offline anxiety modeling (phase I) and online anxiety-based robot-aided training task adaptation (phase II). In phase I, the features, from the physiological and the motor performances recordings of the stroke subjects under anxiety with high, medium, and low intensities, were firstly extracted and then subjected to analysis of variance (ANOVA)-based statistical analyses to obtain the features with significant differences among three anxiety intensities. Anxiety with three intensities was further offline evaluated using support vector machine (SVM)–based anxiety classifier, in which the features with significant differences were adopted as inputs while the self-reported questionnaires as outputs. In phase II, robot-aided rehabilitation training tasks were online adapted to the recognized intensities of anxiety from the stroke subjects. Further, to demonstrate the effect of introducing anxiety of stroke patients into robot-assisted stroke rehabilitation, the impacts of anxiety-based robot-aided behavior adaptation on the stroke patient’s engagements were explored using the performance-based robot-aided training task adaptation as a comparison. The details on the enrolled subjects are given in the “Subjects” section while the experimental system setup is depicted in the “Experimental setup” section. “Phase I: Offline anxiety modeling” section demonstrates the offline modeling of the anxiety with high, medium, and low intensities (phase I). The online anxiety-based robot-aided rehabilitation training task adaptation and its impacts on the stroke patient’s engagements are shown in the “Phase II: Online anxiety-based robot-aided rehabilitation training task adaptation” section (phase II).


Figure 1. Overview of the anxiety-based robot-aided training task adaptation system. SVM: support vector machine; EMG: electromyogram; ECG: electrocardiogram; SC: skin conductance; ANOVA: analysis of variance.


The stroke patients, with upper extremity motor impairments and similar Brunnstrom Recovery Scale (BRS) evaluation scores, were recruited as representative of hemiparesis participants. Participants were excluded from the study if they had severe neurological disorder, senile dementia, or cognitive intact.15 Twelve stroke participants (mean age: 53.6 years, 7 males, 5 females, mean stroke time: 12.6 months, 6 right-sided hemiplegia, 6 left-sided hemiplegia, 10 stage-4 and 2 stage-3 BRS scores of upper extremity, and 9 stage-4 and 3 stage-3 BRS scores of hand) were recruited, and all were received motor rehabilitation therapy at the Rehabilitation Medicine Center of the Nanjing Tongren Hospital of China. Before the tasks began, ethical approval was obtained from the Medical Ethics Committee of the Nanjing Tongren Hospital of China, and all subjects were informed about the procedure and that they would be video-recorded and photo-taken during the experiment. All subjects gave written informed consent concerning the use of their video footage and questionnaire data for further analysis. Of the 12 stroke participants, one stroke subject (upper extremity and hand BRS scores were both stage-3) was not able to complete phase I experiments, and the rest also took part in phase II closed-loop experiments.


Continue —> Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation – Guozheng Xu, Xiang Gao, Lizheng Pan, Sheng Chen, Qiang Wang, Bo Zhu, Jinfei Li, 2018

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[WEB SITE] PaRRo Portable Arm Robot Designed for Rehab

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University of Michigan researchers have designed a low-cost, portable arm rehabilitation robot, which they suggest can be used at home and facilitate motor recovery in patients with cerebral palsy, stroke, or spinal cord injury.

The development of the rehab robot, named PaRRo, is described in a study published in the journal IEEE Transactions on Biomedical Engineering.

PaRRo was designed to provide task-specific training, according to the researchers, in a news story from Cerebral Palsy News Today.

It features an effector at the end of a robotic arm, which is engineered to be maneuvered by the patient. The effector is connected to a system of brakes that offer resistance to the arm’s movement, training muscle strength and improving arm resistance.

The amount of resistance can be controlled by each patient, meaning that the arm exercise intensities can be adapted to each patient’s motor skills.

However, the news story continues, the rehab robot is passive, which means it does not have any computer control, nor does it actively operate by taking over from the user.

In their research, the team performed simulations to calculate the robot’s resistive force and workspace. They then constructed a prototype based on these results, which was tested in a healthy male volunteer with no neurological or orthopedic impairments.

Nine surface electrodes were placed in different muscles and recorded the muscle activity via electromyography.

Both the force generated by the robot and the force produced by the user matched those predicted by the simulations when the device was moved across different directions.

Electromyography results also revealed the robot was capable of generating resistive forces adjustable to the subject’s motor abilities, the news story explains.

“These results indicate that PaRRo is a feasible low-cost approach to provide functional resistance training to the muscles of the upper-extremity,” according to the researchers, in the study.

“The proposed robotic device could provide a technological breakthrough that will make rehabilitation robots accessible for small outpatient rehabilitation centers and in-home therapy,” they add.

[Source: Cerebral Palsy News Today]


via PaRRo Portable Arm Robot Designed for Rehab – Rehab Managment

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[ARTICLE] Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies – Full Text



The application of rehabilitation robots has grown during the last decade. While meta-analyses have shown beneficial effects of robotic interventions for some patient groups, the evidence is less in others. We established the Advanced Robotic Therapy Integrated Centers (ARTIC) network with the goal of advancing the science and clinical practice of rehabilitation robotics. The investigators hope to exploit variations in practice to learn about current clinical application and outcomes. The aim of this paper is to introduce the ARTIC network to the clinical and research community, present the initial data set and its characteristics and compare the outcome data collected so far with data from prior studies.


ARTIC is a pragmatic observational study of clinical care. The database includes patients with various neurological and gait deficits who used the driven gait orthosis Lokomat® as part of their treatment. Patient characteristics, diagnosis-specific information, and indicators of impairment severity are collected. Core clinical assessments include the 10-Meter Walk Test and the Goal Attainment Scaling. Data from each Lokomat® training session are automatically collected.


At time of analysis, the database contained data collected from 595 patients (cerebral palsy: n = 208; stroke: n = 129; spinal cord injury: n = 93; traumatic brain injury: n = 39; and various other diagnoses: n = 126). At onset, average walking speeds were slow. The training intensity increased from the first to the final therapy session and most patients achieved their goals.


The characteristics of the patients matched epidemiological data for the target populations. When patient characteristics differed from epidemiological data, this was mainly due to the selection criteria used to assess eligibility for Lokomat® training. While patients included in randomized controlled interventional trials have to fulfill many inclusion and exclusion criteria, the only selection criteria applying to patients in the ARTIC database are those required for use of the Lokomat®. We suggest that the ARTIC network offers an opportunity to investigate the clinical application and effectiveness of rehabilitation technologies for various diagnoses. Due to the standardization of assessments and the use of a common technology, this network could serve as a basis for researchers interested in specific interventional studies expanding beyond the Lokomat®.


The number of technological devices that therapists can utilize to treat people with neurological impairments has grown substantially during the last decade. Alongside this growth in clinical use, research involving robotic therapy has grown rapidly. A search in Pubmed with the terms “robot” OR “robotic*” AND “rehabilitation” revealed 2225 hits (March 2017) with research markedly increasing after 2010. Despite this increase in research activity and clinical use, the effectiveness of robot-assisted interventions in neurorehabilitation is still in debate. While in some patient populations, for example adults with stroke, meta-analyses have shown that robotic interventions for the lower and upper extremity can be beneficial [12], current evidence is much less convincing in other patient groups, such as spinal cord injury (SCI), traumatic brain injury (TBI), multiple sclerosis (MS) and cerebral palsy (CP).

When comparing the effectiveness of robot-assisted gait training (RAGT) to conventional interventions of similar dosage in adult patients after SCI, it appears that neither intervention is superior [34]. In other populations, such as MS, a small number of pilot studies have been conducted, and a review [5] concluded that the evidence for the effectiveness remained inconclusive. In adult patients with TBI, to our knowledge, there is only one randomized controlled trial that investigated the effectiveness of RAGT [6]. While RAGT improved gait symmetry compared to manually assisted body-weight supported treadmill training, improvements in other gait parameters were not different between the interventions. In children with CP, the body of evidence is similarly small, as only two randomized trials were found [78]. To the authors’ knowledge, there are no randomized controlled trials in children with other diagnoses. Studies comparing effectiveness between different patient groups are lacking.

One important factor leading to the lack of conclusive research is the relatively small number of available centers and participating patients and consequently the small statistical power of attempted studies. Multicenter collaborations are needed to achieve adequate number of participants. Several of the limitations in the evidence of the application of RAGT arise from patient selection criteria and use of different, poorly described and/or low-dosed training protocols. For example, when systematically reviewing the literature in children, we found no paper describing a training protocol on how to apply a robot for rehabilitation of gait [9]. Most of the systematic reviews mentioned that it is extremely difficult to pool results from studies due to the large variability in treatment duration and frequency, contents of the training and inclusion criteria of the patients. For children with CP, an expert team was created to formulate goals, inclusion criteria, training parameters and recommendations on including RAGT in the clinical setting, to assist therapists who train children with CP with the Lokomat® (Hocoma AG, Volketswil, Switzerland) [9]. Such information could be used as a first step in defining training protocols, but this information is missing for most other patient groups.

While randomized controlled trials are usually considered the “gold standard” in building solid evidence in the field of medicine, it is often difficult for rehabilitation specialists working in the clinical environment to interpret the findings with respect to the population of patients they treat on a daily basis. Randomized controlled trials require a specialized team, a controlled setting and a strict selection of patients according to well defined inclusion and exclusion criteria. These criteria often select individuals most likely to benefit based on specific parameters and lack of co-morbidities. These narrow criteria may impact the ecological validity, as results only apply to a minority of patients. This was recently investigated by Dörenkamp et al. [10] who reported that the majority of patients in primary care (40% at the age of 50 years and at least two-thirds of the octogenarian population [11]) simultaneously suffered from multiple medical problems. Further, improvements in function might be less comparable to results described in randomized controlled trials and the treatment regimens used may not be applicable to patients with multiple comorbidities.

To overcome these issues, we established the Advanced Robotic Therapy Integrated Centers (ARTIC) network to collect data from patients using RAGT in a wide variety of clinical settings. ARTIC hopes to develop guidelines for usage as well as to answer scientific questions concerning the use of RAGT. While the ARTIC network includes a general patient population, other research networks focus on a specific disorder or diagnostic group (see, for example [1213]). ARTIC focuses on a common technological intervention – currently the driven gait orthosis Lokomat® – and aims to gather evidence for the efficient and effective use of robotic therapy. Variation in practice among ARTIC members together with collection of common data and outcome measurements will enable the group to draw strong, generalizable conclusions. Further goals include establishing standardized treatment protocols and increasing medical and governmental acceptance of robotic therapy. The aims of this paper are to introduce the ARTIC network to the clinical and research community, present initial data on the characteristics of included patients and compare these to those known from existing epidemiological data and interventional studies.[…]


Continue —> Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1 Lokomat® system (of different generations) with (a) adult leg orthoses and (b) pediatric leg orthoses. Patients walk on a treadmill belt, are weight supported, and the exoskeleton device guides the legs through a physiological walking pattern

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