Posts Tagged rehabilitation robot

[Abstract] State of the Art Robotic Devices for Wrist Rehabilitation: Design and Control Aspects

Abstract

Robot assisted physical therapy of the upper limb is becoming popular among the rehabilitation community. The wrist is the second most complicated joint in the upper limb after shoulder in terms of degrees of freedom. Several robotic devices have been developed during the past three decades for wrist joint rehabilitation.

Intensive physical therapy and repetitive selfpractice, with objective measurement of performance could be provided by using these wrist rehabilitation robots at a low cost.
There has been an increasing trend in the development of wrist rehabilitation robots to provide safe and customized therapy according to the disability level of patients. The mechanical design and control paradigms are two active fields of research undergoing rapid developments in the field of robot assisted wrist rehabilitation. The mechanical design of these robots could be divided into the categories of end-effector based robots and wearable robotic orthoses.

The control for these wrist rehabilitation robots could also be divided into the conventional trajectory tracking control mode and the Assist-as-Needed control mode for providing customized robotic assistance. This paper presents a review of the mechanical design and control aspects of wrist rehabilitation robots. Experimental evaluations of these robots with healthy and neurologically impaired are also discussed along with the future directions of research in the design and control domains of wrist rehabilitation robots.

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[ARTICLE] Design and Analysis of a Wearable Upper Limb Rehabilitation Robot with Characteristics of Tension Mechanism – Full Text HTML

Abstract

Nowadays, patients with mild and moderate upper limb paralysis caused by cerebral apoplexy are uncomfortable with autonomous rehabilitation. In this paper, according to the “rope + toothed belt” generalized rope drive design scheme, we design a utility model for a wearable upper limb rehabilitation robot with a tension mechanism. Owing to study of the human upper extremity anatomy, movement mechanisms, and the ranges of motion, it can determine the range of motion angles of the human arm joints, and design the shoulder joint, elbow joint, and wrist joint separately under the principle of ensuring the minimum driving torque. Then, the kinematics, workspace and dynamics analysis of each structure are performed. Finally, the control system of the rehabilitation robot is designed. The experimental results show that the structure is convenient to wear on the human body, and the robot’s freedom of movement matches well with the freedom of movement of the human body. It can effectively support and traction the front and rear arms of the affected limb, and accurately transmit the applied traction force to the upper limb of the joints. The rationality of the wearable upper limb rehabilitation robot design is verified, which can help patients achieve rehabilitation training and provide an effective rehabilitation equipment for patients with hemiplegia caused by stroke.

1. Introduction

The number of young patients with functional impairment of the upper limbs caused by stroke has increased rapidly, as influenced by accelerated pace of life, poor lifestyles and environmental factors [1,2]. Limb movement disorder, which is caused by hemiplegia after stroke, not only reduces the quality of life of patients, but also brings great pain to their physiology and psychology. Effective rehabilitation training can improve the defect of patients’ nerve function and maintain the degree of joint activity; it also prevents joint spasms and enhances the final rehabilitation degree of patients’ motor functions significantly [3]. The traditional rehabilitation training is one-to-one auxiliary exercise for patients by therapists. This method is difficult to develop an effective treatment plan, and it is tough to control accurately [4]. With the development of rehabilitation robot technology and rehabilitation medicine, the rehabilitation robot has become a novel motor nerve rehabilitation treatment technology. It is of great significance to take advantage of rehabilitation robot technology for rehabilitation training to the recovery of limb function of stroke patients [5]. The traditional methods of treatment, which are based on the therapist’s clinical experience, have the problems of large staff consumption, long rehabilitation cycles, limited rehabilitation effects, and so on. The research and application of rehabilitation robot system is expected to alleviate the contradiction between supply and demand of rehabilitation medical resources effectively, and improve the quality of life of stroke patients [6,7].[…]

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Figure 1. Shoulder joint freedom of motion. (a) Flexion/extension; (b) abduction/adduction; (c) internal rotation/external rotation.

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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).

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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.

Subjects

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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