Posts Tagged UL

[Abstract] Virtual reality therapy for upper limb rehabilitation in patients with stroke: a meta-analysis of randomized clinical trials

Background: Stroke is a major cause of life-long disability in adults, associated with poor quality of life. Virtual reality (VR)-based therapy systems are known to be helpful in improving motor functions following stroke, but recent clinical findings have not been included in the previous publications of meta-analysis studies.

Aims: This meta-analysis was based on the available literature to evaluate the therapeutic potential of VR as compared to dose-matched conventional therapies (CT) in patients with stroke.

Methods: We retrieved relevant articles in EMBASE, MEDLINE, PubMed, and Web of Science published between 2010 and February 2019. Peer-reviewed randomized controlled trials that compared VR with CT were included.

Results: A total of 27 studies met the inclusion criteria. The analysis indicated that the VR group showed statistically significant improvement in the recovery of UL function (Fugl-Meyer Upper Extremity [FM-UE]: n = 20 studies, Mean Difference [MD] = 3.84, P = .01), activity (Box and Block Test [BBT]: n = 13, MD = 3.82, P = .04), and participation (Motor Activity Log [MAL]: n = 6, MD = 0.8, P = .0001) versus the control group.

Conclusion: VR appears to be a promising therapeutic technology for UL motor rehabilitation in patients with stroke.


via Virtual reality therapy for upper limb rehabilitation in patients with stroke: a meta-analysis of randomized clinical trials: Brain Injury: Vol 0, No 0

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[Abstract] The Role of Robotic Path Assistance and Weight Support in Facilitating 3D Movements in Individuals With Poststroke Hemiparesis

Background. High-intensity repetitive training is challenging to provide poststroke. Robotic approaches can facilitate such training by unweighting the limb and/or by improving trajectory control, but the extent to which these types of assistance are necessary is not known.

Objective. The purpose of this study was to examine the extent to which robotic path assistance and/or weight support facilitate repetitive 3D movements in high functioning and low functioning subjects with poststroke arm motor impairment relative to healthy controls.

Methods. Seven healthy controls and 18 subjects with chronic poststroke right-sided hemiparesis performed 300 repetitions of a 3D circle-drawing task using a 3D Cable-driven Arm Exoskeleton (CAREX) robot. Subjects performed 100 repetitions each with path assistance alone, weight support alone, and path assistance plus weight support in a random order over a single session. Kinematic data from the task were used to compute the normalized error and speed as well as the speed-error relationship.

Results. Low functioning stroke subjects (Fugl-Meyer Scale score = 16.6 ± 6.5) showed the lowest error with path assistance plus weight support, whereas high functioning stroke subjects (Fugl-Meyer Scale score = 59.6 ± 6.8) moved faster with path assistance alone. When both speed and error were considered together, low functioning subjects significantly reduced their error and increased their speed but showed no difference across the robotic conditions.

Conclusions. Robotic assistance can facilitate repetitive task performance in individuals with severe arm motor impairment, but path assistance provides little advantage over weight support alone. Future studies focusing on antigravity arm movement control are warranted poststroke.


via The Role of Robotic Path Assistance and Weight Support in Facilitating 3D Movements in Individuals With Poststroke Hemiparesis – Preeti Raghavan, Seda Bilaloglu, Syed Zain Ali, Xin Jin, Viswanath Aluru, Megan C. Buckley, Alvin Tang, Arash Yousefi, Jennifer Stone, Sunil K. Agrawal, Ying Lu, 2020

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[Abstract] Game-Based Virtual Reality Interventions to Improve Upper Limb Motor Function and Quality of Life After Stroke: Systematic Review and Meta-analysis

Stroke is the main cause of disability in adulthood. Recent advances in virtual reality (VR) technologies have led to its increased use in the rehabilitation of stroke patients. A systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to determine the effectiveness of game-based reality on upper limb (UL) motor function and quality of life after stroke. In March 2018, a search of the following databases was performed: PubMed, PEDro, Web of Science, Scopus, The Cochrane Library, and Medline at EBSCO. The selection criteria were all RCTs published in English or Spanish during the past 10 years. The PEDro scale was used to evaluate the methodological quality of the studies. A total of 20 clinical trials were included in the systemic review, of which 15 contributed information to the meta-analysis. Favorable results were found for VR interventions on UL motor function (Fugl-Meyer Assessment for upper extremity, standardized mean difference [SMD] = 1.53, 95% CI [0.51–2.54]) and quality of life (functional independence measure, SMD = 0.77, 95% CI [0.05–1.49]). The results demonstrate the potential benefits of VR interventions on the recovery of UL motor function and on quality of life after stroke.


via Game-Based Virtual Reality Interventions to Improve Upper Limb Motor Function and Quality of Life After Stroke: Systematic Review and Meta-analysis | Games for Health Journal

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[ARTICLE] Cost Analysis of a Home-Based Virtual Reality Rehabilitation to Improve Upper Limb Function in Stroke Survivors – Full Text PDF


Loss of arm function occurs in up to 85% of stroke survivors. Home-based telerehabilitation is a viable approach for upper limb training post-stroke when rehabilitation services are not available. Method: A costing analysis of a telerehabilitation program was conducted under several scenarios, alongside a single-blind two-arm randomized controlled trial with participants randomly allocated to control (N=25) or intervention group (N=26). Detailed analysis of the cost for two different scenarios for providing telerehabilitation were conducted. The fixed costs of the telerehabilitation are an important determinant of the total costs of the program. The detailed breakdown of the costs allows for costs of future proposed telerehabilitation programs to be easily estimated. The costs analysis found that a program supplying all required technology costs between CAD$475 per patient and CAD$482 per patient, while a program supplying only a camera would have total costs between CAD$242 per patient and $245 per patient. The findings of this study support the potential implementation of telerehabilitation for stroke survivors for improving accessibility to rehabilitation services. This cost-analysis study will facilitate the implementation and future research on cost-effectiveness of such interventions.

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via Cost Analysis of a Home-Based Virtual Reality Rehabilitation to Improve Upper Limb Function in Stroke Survivors | Veras | Global Journal of Health Science | CCSE

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[ARTICLE] Connected Elbow Exoskeleton System for Rehabilitation Training Based on Virtual Reality and Context-Aware. – Full Text


Traditional physiotherapy rehabilitation systems are evolving into more advanced systems based on exoskeleton systems and Virtual Reality (VR) environments that enhance and improve rehabilitation techniques and physical exercise. In addition, due to current connected systems and paradigms such as the Internet of Things (IoT) or Ambient Intelligent (AmI) systems, it is possible to design and develop advanced, effective, and low-cost medical tools that patients may have in their homes. This article presents a low-cost exoskeleton for the elbow that is connected to a Context-Aware architecture and thanks to a VR system the patient can perform rehabilitation exercises in an interactive way. The integration of virtual reality technology in rehabilitation exercises provides an intensive, repetitive and task-oriented capacity to improve patient motivation and reduce work on medical professionals. One of the system highlights is the intelligent ability to generate new exercises, monitor the exercises performed by users in search of progress or possible problems and the dynamic modification of the exercises characteristics. The platform also allows the incorporation of commercial medical sensors capable of collecting valuable information for greater accuracy in the diagnosis and evolution of patients. A case study with real patients with promising results has been carried out.

1. Introduction

Currently, there are more than 890 million people with chronic diseases worldwide [1]. It is estimated that 25% of these patients could benefit immediately from solutions for monitoring their health from home [2]; another 50% would benefit from the integration in their mobile phones or other devices of existing medical resources [3,4]. In Europe, there is a large group of people suffering from some type of chronic diseases, such as diabetes or cardiovascular diseases. To these data, we must add all those patients who suffer from some type of mobility problem or have suffered an accident or limb fracture. Considering the advances made in technologies such as the Internet of Things, e-health, or the mbient Assisted Living, monitoring health and improving patients from their own homes is now an unquestionable reality. Ambient Intelligence (AmI) is a paradigm that seeks to build intelligent models capable of adapting to the user’s environment, responding in an appropriate way to the needs detected and with the ability to sensor both physical and human assets [5]. In recent years, numerous scientific works related to this important area have proliferated. A large part of these works is focused on the use of different medical sensors for remote monitoring, disease monitoring or early warning of new pathologies. There are fewer studies that investigate how the technologies listed above can help users to rehabilitate certain minor pathologies in the comfort of their home without going to medical centers. This work tries to contribute a new practical case of AmI technology applied to medical domestic environments for the active rehabilitation of a patient.
One of these cases is physiotherapeutic rehabilitation patients who, through current technological advances, could benefit from adapted, effective and easy-to-use rehabilitation systems from their own home. The exoskeleton-based rehabilitation systems available today for upper limbs are complex systems, difficult to install on the arm, and difficult to use. In addition, they have a heavy size that prevents the arm from moving naturally, making rehabilitation tasks difficult. Another added problem lies in the price of these devices, which causes that only the institutions and health centers can acquire them. The user does not have the possibility of carrying out rehabilitation exercises from the comfort of their home, they are also forced to go periodically to the physiotherapy center, which has the necessary equipment. Traditional rehabilitation systems also have a lack of motivation problem for patients, the vast majority of the exercises are based on repetition and this makes the sessions become monotonous and not very stimulating.
The main objective of this work is to design a rehabilitation device based on an exoskeleton for the elbow of a degree of freedom (one DOF) connected to a virtual reality system called EXOMedical. This Virtual Reality (VR) system is specially designed for working immersively with the user, controlling the motor of the exoskeleton, and measuring the force exerted by the patient. In addition, a Context-Aware architecture [6] has been designed to control all the data generated by the exoskeleton, detecting user progress and identifying possible problems in advance. In addition, it will be able to generate new exercises adapted to progress and patient ability. This architecture will also have the assistance of the medical professional in charge of rehabilitation, which can continuously monitor its evolution, allowing personalized follow-up on each patient.
Currently, the rehabilitation process is carried out in a hospital or in specialized physiotherapy centers with the help and supervision of a professional in that area. The professional must perform the work of evaluation or triage as well as the monitoring and performance of the corresponding therapy. Due to the high number of patients, it may lead to the need for a large number of professionals to carry out all these activities and attend them correctly. In the same way, it is difficult to quantify the improvement or evolution of patients since there is currently no way to quantify them and the subjective assessment of an expert must be used. The developed system allows a quantitative measure of the evolution and capabilities of the patient over the Context-Aware architecture that has historical data on progress individually and in detail of the entire recovery. These data will serve the system to analytically measure the patient’s recovery, as well as the generation of new exercises adapted to that evolution. Although these processes are normally carried out in physiotherapy centers, the user can use it in a more comfortable place, such as in their own home, transmitting the data of their evolution to the platform, generating a more favorable and collaborative attitude on the patient part. As for chronic patients, this device allows an increasement in their quality of life by assisting in weightlifting. For example, a person who has suffered a car accident in which his arm has been damaged, or someone who has suffered a work accident or a stroke, can perform daily tasks such as moving weights of up to 15 kg without letting the effort fall on your body.
This article is structured as follows: Section 2 reviews the current state of the art; Section 3 describes the proposed system in detail; Section 4 introduces the case study with real patients; Finally, conclusions drawn from the work are outlined in Section 5.

2. Background

In the current literature, it is possible to find articles that address the use of robotic exoskeleton systems for the recovery of patients with mobility problems. The vast majority of these works are focused on the recovery of injuries and problems of the upper limbs. There are other works that focus on the study of injuries from other areas such as the ankle [7,8,9]. Other works focused on one of the most important joints of the lower extremities such as the knee [10,11,12,13,14,15]. In all these works, the use of robotic exoskeleton systems is another part of the traditional physiotherapeutic rehabilitation that is advised by an expert and is performed in a traditional clinical setting.
In the works that address the recovery of the joints in the upper extremities, there are different projects that seek to provide significant progress [16]. These works combine, like this article, the use of a robotic exoskeleton, with virtual reality technology and environments that allow patients to develop the exercises in a more dynamic and interactive way. It is possible to identify, within the upper extremities, those works focused on the rehabilitation of the hand (wrist and fingers), the rehabilitation of the elbow and the integral rehabilitation of the entire upper limb. In the work [17], the authors present a study based on the generation of virtual reality-based interfaces for the rehabilitation of arms and hands, in which the use of optoelectronic sensors such as Leap Motion, Microsoft Kinect and Oculus VR is necessary. These devices are capable of capturing user movements and translating them into digital environments that motivate the user to participate in dynamic rehabilitation sessions. In the case of the studies [18,19], the authors focus their work on the treatment of hand-related problems. The authors of [20] propose a system based on fuzzy logic, virtual environments, and an active hand orthosis operated through servomotors that allow exerting a certain force with which users can perform different strengthening exercises. The combination of flexibility sensors, servomotors, and optoelectric control of the Leap Motion sensor generates an accurate simulation of hand movements in the virtual environment.
The article that addresses the integral physical rehabilitation of the arm, highlights numerous works that use virtual reality techniques as an additional recovery tool. The authors of the paper [21] propose the use of an exoskeleton device with 5 DOF for the integral rehabilitation of the arm using virtual environments during a session with a medical professional. In the article [22], the authors propose an active rehabilitation training system based on virtual reality technology specially designed for patients with upper limb hemiparesis. These authors develop several virtual games to increase the interest of patients when performing the exercises. In the same area, the authors of [23] use an exoskeleton of 5 DOF but making use of immersive environments with virtual reality as well as the work of the authors of [24].
Another researching lines in upper limb rehabilitation devices are stroke patients who require specific rehabilitation to recover effective arm movement. The authors of the work [25], in their pilot study with a stroke patient working with an exoskeleton for the arm, determined that its use was very advantageous with respect to patients following a more traditional recovery. Similarly, the study [26] highlights this type of therapy based on the use of exoskeletons and virtual environments as potentially more beneficial than traditional therapies as a result of a large study with real patients. The authors of [27] implement a modular and reconfigurable exoskeleton, which seeks to reduce costs and size by adopting different therapeutic end effectors for different training movements using a single robot in stroke patients.
As for the works that address the design of Context-Aware architectures and frameworks, it is possible to find a large number of works based on telemonitoring and diseasing monitoring systems [28,29,30,31]. It is less frequent to find works that present this type of architecture focused on rehabilitation exercises for home monitoring, in combination with telemonitoring and e-health. These works include the work of the authors of [32] whose objective is to integrate software architectures with person-computer interfaces to develop context-sensitive systems for telerehabilitation of people from their homes. Another outstanding work is that of the authors of [33] where a Context-Aware framework is proposed and validated for the use of animatronic biofeedback, as a way to potentially increase the compliance of older users with physical rehabilitation exercises performed in home. In this context, animatronic biofeedback involves the use of preprogrammed actions in a robot that are activated in response to certain changes detected in the biomechanical or electrophysiological signals of the users. Another paper presented is [34] where the authors present a model-based approach to the development of telerehabilitation systems through context-aware systems.
It is possible to summarize that there is no work in the current literature that comprehensively addresses the design of a Context-Aware system in combination with a low-cost exoskeleton integrated with a virtual reality environment for performing rehabilitation exercises. The current systems based on exoskeletons are supervised and unconnected systems, without the possibility of sharing the data obtained or generating new exercises dynamically as the device proposed in this work.

3. Proposed System

This section presents the system proposed in this work. Firstly, it is described in a connected hardware device that will serve as a rehabilitation exoskeleton for system patients. Next, the architecture based on Context-Aware technology for monitoring and control of the rehabilitation system is described. Finally, the virtual reality system that will connect to the hardware device and Context-Aware architecture and whose purpose is to provide an immersive user interface to make physical exercises is analyzed.

3.1. One DOF Exoskeleton for Elbow

The set of elements that make up the EXOMedical device are described below. This exoskeleton for the elbow consists of two parts that are attached to the outside of the arm and forearm with a series of velcro straps, as shown in Figure 1. The device has a rotation axis or DOF corresponding to the axis of rotation of the elbow joint.
Sensors 20 00858 g001 550

Figure 1. Designed device deployed on a user’s arm. The device is fixed through the three velcro straps.


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[ARTICLE] Comparisons between end-effector and exoskeleton rehabilitation robots regarding upper extremity function among chronic stroke patients with moderate-to-severe upper limb impairment – Full Text


End-effector (EE) and exoskeleton (Exo) robots have not been directly compared previously. The present study aimed to directly compare EE and Exo robots in chronic stroke patients with moderate-to-severe upper limb impairment. This single-blinded, randomised controlled trial included 38 patients with stroke who were admitted to the rehabilitation hospital. The patients were equally divided into EE and Exo groups. Baseline characteristics, including sex, age, stroke type, brain lesion side (left/right), stroke duration, Fugl–Meyer Assessment (FMA)–Upper Extremity score, and Wolf Motor Function Test (WMFT) score, were assessed. Additionally, impairment level (FMA, motor status score), activity (WMFT), and participation (stroke impact scale [SIS]) were evaluated. There were no significant differences in baseline characteristics between the groups. After the intervention, improvements were significantly better in the EE group with regard to activity and participation (WMFT–Functional ability rating scale, WMFT–Time, and SIS–Participation). There was no intervention-related adverse event. The EE robot intervention is better than the Exo robot intervention with regard to activity and participation among chronic stroke patients with moderate-to-severe upper limb impairment. Further research is needed to confirm this novel finding.


Upper extremity dysfunction is a common complication after stroke, and it has been reported to affect approximately 85% of stroke survivors in the early stage1 and 50% in the chronic stage2. Impaired upper extremity function limits performance of activities of daily living (ADLs) and decreases social participation3. Novel therapeutic techniques have been introduced to promote upper extremity function, and one such technique is robotic rehabilitation.

Rehabilitation robots are capable of reducing the burden on therapists by substituting human intervention and providing ideal therapies that fulfil the following main principles of stroke rehabilitation: repetition, high intensity, and task specificity4. Thus, robotic intervention has been highlighted as a promising therapy. A recent multicentre randomised controlled trial showed better improvements in FMA scores with robot-assisted training on comparing robot-assisted training with usual care, but showed no significant difference in scores on comparing robot-assisted training with enhanced upper limb therapy. These findings indicate that robot-assisted training can reduce the burden for therapists but is not a definite superior option5. Many systematic reviews and meta-analyses on rehabilitation robots have been published in the last two decades. In 2012, Norouzi-Gheidari et al. summarised 10 trials that compared robotic therapy with dose-matched conventional therapy and reported no significant differences in Fugl–Meyer Assessment (FMA) of the upper extremity and Functional Independence Measure scores between the therapies6. However, with an increase in the number of randomised controlled trials, a recent review involving 38 trials reported a significant difference in the FMA–Upper Extremity score between robotic therapy and conventional therapy, with a better score for robotic therapy7.

Many rehabilitation robots for the upper extremity have been released and are available for clinical use. These robots have shown positive clinical results. Thus, healthcare professionals and patients have multiple choices among many kinds of robots; however, there is limited evidence to guide their choices. Physicians tend to prescribe ‘robot intervention’ rather than specify a particular robot, unlike medication prescription, when selecting robotic rehabilitation. So far, different rehabilitation robots have been considered broadly as rehabilitation robots per se, despite some differences in effectiveness.

Rehabilitation robots are generally categorised into end-effector (EE) and exoskeleton (Exo) types according to their mechanical structures8. EE robots are connected to patients at one distal point, and their joints do not match with human joints. Force generated at the distal interface changes the positions of other joints simultaneously, making isolated movement of a single joint difficult8,9. Exo robots resemble human limbs as they are connected to patients at multiple points and their joint axes match with human joint axes. Training of specific muscles by controlling joint movements at calculated torques is possible8,9. Recent systematic reviews have performed indirect comparisons by subgroup analysis and have demonstrated contradictory results for EE and Exo robots. Veerbeek et al. reported significant favourable results with regard to FMA–Upper Extremity for EE robots but not for Exo robots7. On the other hand, Bertani et al. reported significant favourable results with regard to arm function for Exo robots but not for EE robots; however, the risk of bias should be considered owing to the smaller sample size of Exo robots when compared with that of EE robots10. Although these indirect comparisons are helpful, they are limited by the heterogeneity in clinical studies, including design, population, outcomes, and intervention protocols.

Many new robotic devices have been developed; however, there are no guidelines or standard requirements with regard to the most appropriate robot subtype, extent of degrees of freedom, and approach (functionality based or impairment based) for favourable outcomes. To our knowledge, no head-to-head clinical trial comparing different types of rehabilitation robots has been performed. Such a comparison may help in the decision making of healthcare professionals with regard to rehabilitation robots and may ultimately offer more optimal rehabilitation for patients. In particular, there is a great need for a direct comparison study to clarify effects according to the types of robots, as robots are expensive.

Therefore, we performed a randomised controlled trial to directly compare EE and Exo robots in a selected population of chronic stroke patients with moderate-to-severe upper limb impairment. The InMotion2 (Interactive Motion Technologies, Watertown, MA, USA) and Armeo Power (Hocoma, Volketswil, Switzerland) robots were selected as representative EE and Exo robots, respectively, among commercially available robots for their proven efficacy and safety, as well as accessibility around hospitals11,12,13,14.


Study design

This single-blinded, randomised controlled trial was conducted at a single rehabilitation hospital. Participants were randomly allocated to an EE group and Exo group (1:1 ratio) by using concealed envelopes with a card representing the group assignment. Occupational therapists who carried out assessments were blinded to group allocation. The study was approved by the Ethics Committee of the National Rehabilitation Center, Korea and was carried out in accordance with the approved guidelines. Written informed consent was provided by all participants. The study was registered at (NCT03104881).


For enrolment, the study considered 92 patients with stroke who were admitted to the rehabilitation hospital between March 2015 and August 2016. The inclusion criteria were as follows: (1) unilateral hemiplegic upper extremity dysfunction secondary to a unilateral ischaemic or haemorrhagic brain lesion; (2) stroke duration > 3 months; (3) FMA–Upper Extremity score of 8–30 for the affected upper extremity; and (4) ability to follow simple instructions. The exclusion criteria were as follows: (1) age < 20 years or > 80 years; (2) previous ischaemic or haemorrhagic stroke; (3) shoulder or elbow spasticity with a modified Ashworth scale (MAS) score ≥ 2; (4) severe upper extremity pain that could interfere with rehabilitation therapy; (5) neurological disorders other than stroke that can cause motor deficits, such as Parkinson’s disease, spinal cord injury, traumatic brain lesion, brain tumour, and peripheral neuropathy; and (4) uncontrolled severe medical conditions. Of the 92 patients, 53 did not meet the inclusion criteria or declined to participate. Thus, 39 patients were finally enrolled.


All participants received robot-assisted therapy with InMotion2 (EE group) or Armeo Power (Exo group) (30 minutes of active therapy 5 days a week for 4 weeks [total 20 sessions]) along with conventional occupational therapy (30 minutes of therapy [total 20 sessions]). Both robot-assisted therapies were managed by the same experienced research physical therapist. The therapy period was quantified by considering the active intervention time and not the time for preparations, such as attaching the robot to the patient and aligning the axis of the robot to that of the patient. Conventional occupational therapy involved range of motion exercises, strengthening exercises for the affected upper extremity, and basic ADL training. Overall, the same dosing parameters, including frequency and duration, were applied in the EE and Exo groups.

EE group

The EE robot InMotion2 was used in the EE group. In the seated position, each participant held the handle attached to an arm support and performed goal-directed reaching movements in the gravity-compensated horizontal plane with two degrees of freedom, including the shoulder and elbow joints. From the starting point in the centre, the participant was instructed to move the handle toward eight targets positioned 45 degrees apart in circular arrangements, and the position of the handle was marked on the screen for real-time visual feedback (Fig. 1A). Reaching movements were supported through an assist-as-needed control system when targets could not be reached independently.

Figure 1

Two types of rehabilitation robots used for the robot-assisted therapy (A) InMotion2 for the EE group and (B) Armeo Power for the Exo group. EE, end-effector; Exo, exoskeleton.


via Comparisons between end-effector and exoskeleton rehabilitation robots regarding upper extremity function among chronic stroke patients with moderate-to-severe upper limb impairment | Scientific Reports

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[Abstract] Improving Healthcare Access: A Preliminary Design of a Low-Cost Arm Rehabilitation Device


A low-cost continuous passive motion (CPM) machine, the Gannon Exoskeleton for Arm Rehabilitation (GEAR), was designed. The focus of the machine is on the rehabilitation of primary functional movements of the arm. The device developed integrates two mechanisms consisting of a four-bar linkage and a sliding rod prismatic joint mechanism that can be mounted to a normal chair. When seated, the patient is connected to the device via a padded cuff strapped on the elbow. A set of springs have been used to maintain the system stability and help the lifting of the arm. A preliminary analysis via analytical methods is used to determine the initial value of the springs to be used in the mechanism given the desired gravity compensatory force. Subsequently, a multibody simulation was performed with the software simwise 4D by Design Simulation Technologies (DST). The simulation was used to optimize the stiffness of the springs in the mechanism to provide assistance to raising of the patient’s arm. Furthermore, the software can provide a finite element analysis of the stress induced by the springs on the mechanism and the external load of the arm. Finally, a physical prototype of the mechanism was fabricated using polyvinyl chloride (PVC) pipes and commercial metal springs, and the reaching space was measured using motion capture. We believed that the GEAR has the potential to provide effective passive movement to individuals with no access to postoperative or poststroke rehabilitation therapy.


via Improving Healthcare Access: A Preliminary Design of a Low-Cost Arm Rehabilitation Device | Journal of Medical Devices | ASME Digital Collection

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[Abstract] Cable driven exoskeleton for upper-limb rehabilitation: A design review


One of the primary reasons for long-term disabilities in the world is strokes. The causes of these cerebrovascular diseases are various, i.e., high blood pressure, heart disease, etc. For those who survive strokes, this affectation causes lose in mobility of extremities, requiring the intervention of long session with a therapeutic professional to recover the movement of the impair limb. Hence, the investment to threat this condition is usually high. Those devices permit the user a mean to conduct the therapies without the constant supervision of a professional. Furthermore, exoskeletons are capable of maintaining a detailed recording of the forces and movements developed for the patients throughout the session. However, the construction of an exoskeleton is not cheap principally for the actuation systems, especially if the exoskeleton requires the actuator to be placed at the joints of the user; thus, the actuator at a joint would have to withstand the load of the actuator of the following joint and so on.

Researchers have addressed this drawback by applying cable transmission systems that allow the exoskeleton to place their actuator at a base, reducing the weight of their design and decreasing their cost. Thus, this paper reviews the principal models of cable-driven exoskeleton for stroke rehabilitation focusing on the upper-limb. The analysis departs from the study of the anatomy of the arm in all its extension, including the shoulder, elbow, wrist, fingers, and the thumb. Besides, it also includes the mechanical consideration the researchers have to take in mind to design a proper exoskeleton. Then, the article presents a compendium of the different transmission systems found in the literature, addressing their advantages, disadvantages and their requirements for the design. Lastly, the paper reviews the cable-driven exoskeleton for stroke rehabilitation of the upper limb. Again, for this analysis, it is included the design consideration of each prototype focusing on their advantages in terms of anatomical mechanics.


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[Abstract] An Upper Limb Rehabilitation Training and Evaluation System for Stroke Patients


This system combines information technology and rehabilitation medicine. It adopts Motor Imagery (MI) intervention and mental rotation training mode in order to change the traditional inefficient mode of clinical stroke rehabilitation. We developed multi-functional side recognition rehabilitation and evaluation peripheral to evaluate the rehabilitation effect of stroke patients accurately and quantitatively. The healing effect, which reveals the degree of recovery to the patients, will no longer depend on the personal experience of the rehabilitation therapist. Based on the psychological hint and a client designed with Unity 3D, it makes the treatment less boring to stimulate the patients’ initiative during the training. This system confirms that the MI Intervention can to a certain degree improve function of limb motor and sensory feedback by analyzing 38 volunteer patients’ data in Huashan Hospital and Shanghai Jing’an District Central Hospital. Precise and quantitative evaluation results are given for the further treatment.

via An Upper Limb Rehabilitation Training and Evaluation System for Stroke Patients | ZHAO | DEStech Transactions on Computer Science and Engineering

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[Abstract + References] A Mechatronic Mirror-Image Motion Device for Symmetric Upper-Limb Rehabilitation


This paper presents an upper-limb rehabilitation device that provides symmetric bilateral movements with motion measurements using inertial sensors. Mirror therapy is one of widely used methods for rehabilitation of impaired side movements because voluntary movement of the unimpaired side facilitates reorganizational changes in the motor cortex. The developed upper-limb exoskeleton was equipped with two brushless DC motors that helped generate three axes of upper-limb movements corresponding to other arm movements that were measured using inertial sensors. In this study, inertial sensors were used to estimate the joint angles for three target upper-limb movements: elbow flexion and extension (flex/ext), wrist flex/ext, and forearm pronation and supination (pro/sup). Elbow flex/ext was performed by the actuator that was directly attached to the elbow joint. The actuation of the forearm pro/sup and wrist flex/ext shared one motor using a developed cable-driven mechanism, and two types of motion were selectively performed. We assessed the feasibility of the proposed mirror-image device with the accuracy and precision of the motion estimation and the actuation of joint movements. An individual could perform most upper-limb movements for activities of daily living using the proposed device.


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