Posts Tagged rehabilitation robot

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

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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®.

Background

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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[ARTICLE] The Optimal Speed for Cortical Activation of Passive Wrist Movements Performed by a Rehabilitation Robot: A Functional NIRS Study – Full Text

Objectives: To advance development of rehabilitation robots, the conditions to induce appropriate brain activation during rehabilitation performed by robots should be optimized, based on the concept of brain plasticity. In this study, we examined differences in cortical activation according to the speed of passive wrist movements performed by a rehabilitation robot.

Methods: Twenty three normal subjects participated in this study. Passive movements of the right wrist were performed by the wrist rehabilitation robot at three different speeds: 0.25 Hz; slow, 0.5 Hz; moderate and 0.75 Hz; fast. We used functional near-infrared spectroscopy to measure the brain activity accompanying the passive movements performed by a robot. The relative changes in oxy-hemoglobin (HbO) were measured in two regions of interest (ROI): the primary sensory-motor cortex (SM1) and premotor area (PMA).

Results: In the left SM1 the HbO value was significantly higher at 0.5 Hz, compared with movements performed at 0.25 Hz and 0.75 Hz (p < 0.05), while no significant differences were observed in the left PMA (p > 0.05). In the group analysis, the left SM1 was activated during passive movements at three speeds (uncorrected p < 0.05) and the greatest activation in the SM1 was observed at 0.5 Hz.

Conclusions: In conclusion, the contralateral SM1 showed the greatest activation by a moderate speed (0.5 Hz) rather than slow (0.25 Hz) and fast (0.75 Hz) speed. Our results suggest an ideal speed for execution of the wrist rehabilitation robot. Therefore, our results might provide useful data for more effective and empirically-based robot rehabilitation therapy.

Introduction

A number of rehabilitation robots have been developed in the past two decades to aid functional recovery of impaired limbs in patients with brain injury (Volpe et al., 2000Hesse et al., 2005Kahn et al., 2006Lum et al., 2006Masiero et al., 2007Nef et al., 2007Coote et al., 2008Housman et al., 2009Chang et al., 2014). In the field of rehabilitation, high intensive, task-oriented and repetitive execution of movements is effective for functional recovery of impaired upper limbs following brain injury (Bütefisch et al., 1995Kwakkel et al., 2004Schaechter, 2004Levin et al., 2008Murphy and Corbett, 2009Oujamaa et al., 2009). Rehabilitation robots can easily and precisely provide these labor-intensive rehabilitative treatments, and the effect of rehabilitation robots on functional recovery in patients with brain injury has been demonstrated in many studies (Volpe et al., 2000Hesse et al., 2005Lum et al., 2006Masiero et al., 2007Coote et al., 2008Norouzi-Gheidari et al., 2012). Compared to conventional therapy (CT) provided by a therapist, the effectiveness of robot assisted therapy (RT) is questionable (Masiero et al., 2011Norouzi-Gheidari et al., 2012). There is no difference between RT and intensive CT of the same duration/intensity condition, and extra sessions of RT in addition to CT bring better motor recovery of the shoulder and elbow (not for hand and wrist) compared with CT (Norouzi-Gheidari et al., 2012). To make the best use of robot for upper limb rehabilitation, increased efficacy of robotic rehabilitation is necessary. However, research on the optimal conditions to maximize the rehabilitative effect during treatment with a rehabilitation robot has been limited (Reinkensmeyer et al., 2007).

Brain plasticity, the ability of our brain system to reorganize its structure and function, is the basic mechanism underlying functional recovery in patients with brain injury (Schaechter, 2004Murphy and Corbett, 2009). The underlying principle of rehabilitation in terms of brain plasticity is based on the modulation of cortical activation induced by the manipulation of external stimuli (Kaplan, 1988). Little is known about the cortical effects resulting from rehabilitation robot treatment (Li et al., 2013Chang et al., 2014Jang et al., 2015).

Functional neuroimaging techniques, including functional MRI (fMRI), Positron Emission Tomography (PET) and functional Near Infrared Spectroscopy (fNIRS) provide important information about the activation of the brain by external stimuli (Frahm et al., 1993Willer et al., 1993Miyai et al., 2001Fujii and Nakada, 2003Perrey, 2008Kim et al., 2011Leff et al., 2011Gagnon et al., 2012). Of these, fNIRS provides a non-invasive method for measurement of the hemodynamic responses associated with activation of the cerebral cortex based on the intrinsic optical absorption of blood (Arenth et al., 2007Irani et al., 2007Perrey, 2008Ye et al., 2009Leff et al., 2011). Compared with other functional neuroimaging techniques, fNIRS has a unique advantage of less sensitivity to motion artifact and metallic material. Therefore, fNIRS appears suitable for the study of brain response during treatment with rehabilitation robots (Perrey, 2008Mihara et al., 2010Leff et al., 2011Li et al., 2013Chang et al., 2014).

In this study, we hypothesized that there exists optimal conditions for robotic rehabilitation to enhance the rehabilitative effect. The speed of movement performed by rehabilitation robot could be a unique aspect of robot rehabilitation, because varied speed can be provided consistently only with the robot. To confirm our hypothesis, using fNIRS, we examined the optimal speed of passive wrist movements performed by a rehabilitation robot that induces cortical activation through proprioceptive input by passive movements (Radovanovic et al., 2002Francis et al., 2009Lee et al., 2012). As a part of upper limb, the wrist enhances the usefulness of the hand by allowing it to take different orientations with respect to the elbow (van der Lee, 2001). If there exists an optimal speed that offers the greatest cortical activation, it could be applicable for robotic rehabilitation and research for other optimal conditions such as duration.

Subjects and Methods

Subjects

Healthy right-handed subjects (15 males, 8 females; mean age 26.5, range 21–30) with no history of neurological, psychiatric, or physical illness were recruited for this study. Handedness was evaluated using the Edinburg Handedness Inventory (Oldfield, 1971). All subjects were fully informed about the purpose of the research and provided written, informed consent prior to participation in this study. The study protocol was approved by the Institutional Review Board of the Daegu Gyeongbuk Institute of Science and Technology (DGIST). Data from two subjects were excluded because the subjects did not follow the required instructions during the data collection.

Methods

Robot

Regarding flexion and extension only, the human wrist can be simplified as a one degree of freedom (DOF) kinematic model with one revolute joint (Zatsiorsky, 2002). As mentioned above, the wrist rehabilitation robot was designed and manufactured as a simplified kinematic model of the wrist. The robot used for wrist rehabilitation has three parts: hand, wrist joint and forearm, and provides passive movement of flexion and extension (Figure 1). It has a gear driven mechanism using a single motor. The actuation system for the wrist part is composed of DC, a brushless motor with encoder (EC-i 40, Maxon motor), harmonic drive (CSF-11-50, Sam-ik THK, gear ratio 50:1), and force-torque sensor (Mini 45, ATI). In house developed software was used to control the robot. For the real-time control, Linux Fedora 11 and the Real Time Application Interface for Linux (RTAI) Ver 3.8 systems were mounted. Real-time sensing control was achieved using an encoder and Sensoray s626 board, in which time delay control (TDC) was used for precise position control. The robot showed a position error of 0.1°–1° during the experiment.

Enter Figure 1. (A) The wrist rehabilitation robot. Lateral view of the wrist rehabilitation robot, the hand part (dotted line), wrist part (solid line) and forearm part (dashed line). (B) A front view of robot and subjects with the trunk strap and near infrared spectroscopy (NIRS) optodes. (C) Wrist flexion of the robot. (D) Wrist extension of the robot.a caption

 When using the robot for wrist rehabilitation, the hand and forearm must be fixed to the robot in order to perform the passive wrist movement. First, the subjects placed their forearm on the armrest made of foam covered with a soft cloth. They were instructed to place their hand on the support bar under the hand part of the robot before fixing all fingers to the finger holder with velcro straps. The robot performs the passive wrist exercise using a rotary motion of a gear driven by a motor and realizes a full range of motion (ROM) from 80° (flexion) to 75° (extension) when the degree of neutral wrist position is 0°, with the wrist in a flat position, with velocity of the wrist motions up to 2 Hz.[…]

 

Continue —> Frontiers | The Optimal Speed for Cortical Activation of Passive Wrist Movements Performed by a Rehabilitation Robot: A Functional NIRS Study | Frontiers in Human Neuroscience

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[ARTICLE] A novel approach for robot-assisted upper-limb rehabilitation – Full Text

This study presented a novel control approach for rehabilitation robotic system using the hybrid system theory and the subject’s bio-damping and bio-stiffness parameters. Resistance training was selected as a paradigm. The proposed control architecture incorporated the physical therapist’s behavior intervention, the stroke survivor’s muscle strength changes, and the robotic device’s motor control into a unified framework. The main focuses of this research were to (i) automatically monitor the subject’s muscle strength changes using the online identified bio-damping/stiffness parameters; (ii) make decisions on the modification of the desired resistive force so as to coincide with the subject’s muscle strength changes; and (iii) generate accommodating plans when the safety-related issues such as spasticity and the abnormal robotic working states happen during the execution of training tasks. A Barrett WAM compliant manipulator-based resistance training system and two experiments including four scenarios were developed to verify the proposed approach. Experimental results with healthy subjects showed that the hybrid system–based control architecture could administrate the subject’s muscle strength changes and the robotic device’s interventions in an automated and safe manner.

Patients suffering from strokes need to receive intensive rehabilitation training to improve range of motion, movement coordination, and muscle strength. These therapy programs are traditionally conducted by physical therapists. However, the efficacy of therapist-centered rehabilitation therapy often relies on the therapist’s subjective clinical experience. In recent years, there is an increasing interest in using robotic devices to help providing rehabilitation training because these devices can provide a variety of highly repetitive movements and training protocols for stroke patients.1

Human–robot interactive control approaches, that specify how rehabilitation robotic devices adaptively interact with stroke survivors in an automated and safe manner, are one of the major challenges in developing robot-assisted training system. The control strategies explored in some early rehabilitation robotic devices, such as the proportional integral derivative controller for MIME,2 the impedance controller for MIT-Manus,3 and the admittance controller for GENTLE/s,4are mainly concentrated on providing constant assistance while not integrating stroke patients’ feedback into the control loop. It is a common hypothesis in the field of robot-assisted rehabilitation that the control approaches, that can close the loop via the patients and further adapt robotic devices’ assistance to the stroke patients’ progress, will be more efficient.5 This issue is typically addressed using assist-as-needed6,7 or user-cooperative control strategies.8,9 In the study by Hussain et al.,6 an adaptive seamless assist-as-needed control scheme is developed for the robotic gait training, which learns in real time the disability level of human subjects based on the trajectory tracking errors and adapts the robotic assistance accordingly. Riener et al.8 presents a patient-cooperative strategy for robotic gait training, and results with healthy subjects show that subjects train more actively and only get support as much as needed. Nonetheless, the potential issue of these methods is that they focus on low-level robotic motor controllers. The training tasks update, the safety-related issues (e.g. spasticity and twitch) monitoring, the robotic working states (e.g. joint torque, voltage, workload, and end-effector velocity) detection, the therapeutic progress assessment, and the decision-making behaviors are all administered by physical therapists.

In the last few years, hierarchical supervisory control strategies have been developed. These approaches incorporate the training tasks update and the physical therapist’s behavior decision into the low-level robotic motor controller. Denève et al.10 merge a high-level sequential controller into a robotic-assisted upper limb rehabilitation system, by which three different low-level control laws for passive, active, and resistance modes can be switched. In the study by Varol et al.,11 a three-level hierarchical supervisory control architecture is proposed, which consists of the lowest level robotic joint torque controller, the middle-level torque references generator, and the high-level intent recognizer. Fuzzy-based hierarchical supervisory control strategies are also presented in our previous studies.12,13 Especially in the study,13 a high-level safety supervisory controller is designed to monitor spasticity-related issues. Great improvements have been made in the current hierarchical supervisory control architecture for incorporating therapists’ behavior-decision experiences. Unfortunately, these supervisory control methods are statically designed concerning some predefined situations, and the absence of dynamic mechanism makes them incapable of dealing with the extended/unexpected events and the complex training tasks coordination. Besides, few rehabilitation robotic control system design takes the safety-related issues into consideration.

In this article, we present a hybrid system–based control architecture using resistance training as a paradigm, which can incorporate the physical therapist’s behavior intervention, the stroke survivor’s muscle strength progress, and the robotic device’s motor control into a unified framework. In fact, over the years, the hybrid systems framework has been effectively used in many fields to model and analyze their performances, such as the power systems,14 the communication networks,15 and the coordinated control of assistive robotic devices for complex tasks.16

The primary focus of this article is to (i) automatically monitor the impaired limb’s muscle strength progress using the online identified bio-impedance changes; (ii) make decisions on the modification of the desired resistive force so as to coincide with the impaired limb’s muscle strength changes; and (iii) be aware of robotic working state/safety-related issues during the execution of training task and to generate accommodating plans when such events happen. The remainder of this article is organized as follows: “Methods” section presents the experimental setup and protocols, bio-impedance parameters identification, and controller development. “Results” section details the results of the proposed control strategy. Some discussions and conclusions are given in sections “Discussions” and “Conclusions”.

 

Experimental setup

The rehabilitation robotic system for upper limb muscle strength training used in the trials, shown in Figure 1(a), consisted of a Barrett WAM™ manipulator, a three dimensional (3-D) force sensor, and an external PC offered by Barrett Technology.17 The standard WAM™ is a four degree of freedom highly dexterous, back-drivable manipulator. Its human-like kinematics and high back drivability enable inherent force-control, haptic interaction, and rehabilitation application. In order to record the force interaction between the impaired limb and the WAM end-effector, a 3-D force sensor was designed and attached to the end-effector. Figure 1(b) and (c) shows the mechanical structure, appearance, and strain gauges distribution of the 3-D force sensor. Force data measured from the sensor must be transformed from the WAM tool frame into its world frame. The graphical user interface developed using Linux/GDK technology, shown in Figure 1(d), was used to display the actual training trajectory when the patient moved his arm in the XOZ vertical plane (O-XYZ coordinates shown in Figure 1(a)), where no reference trajectories were predefined except for several via points. The external PC, running with the Ubuntu Linux system and the Xenomai real-time module, was responsible for executing the control loop and sending high-level commands to the WAM-aided rehabilitation training system. Real-time communication between the external PC and motor Pucks™ was conducted via a high-speed controller area network bus.

figure

Figure 1. The Barrett WAM™ rehabilitation robotic system for upper limb muscle strength training and its attachments. (a) The Barrett WAM™ rehabilitation robotic system, (b) mechanical structure and appearance of the 3-D force sensor, (c) distribution of 16 strain gauges on the cross beam, and (d) the graphical user interface for muscle training in XOZ plane.

 

Continue —>  A novel approach for robot-assisted upper-limb rehabilitationInternational Journal of Advanced Robotic Systems – Guozheng Xu, Xiang Gao, Sheng Chen, Qiang Wang, Bo Zhu, Jinfei Li, 2017

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[Abstract] Recent Advances on Lower Limb Exoskeleton Rehabilitation Robot

Abstract

Background: Lower limb exoskeleton rehabilitation robot is a bionic robot, which is the product of the combination of medical technology and robot technology, simulating human walking movement. It can be mainly used for rehabilitation training of patients with lower limb dysfunction.

Objective: To provide an overview of recent lower limb exoskeleton rehabilitation robot and introduce their respective characteristics and development.

Method: A recent lower limb exoskeleton rehabilitation robot is divided into passive drive, pneumatic drive, hydraulic drive and motor drive. This paper reviews various representative patents related to lower limb exoskeleton rehabilitation robot. The structural characteristics and applications of the typical lower limb exoskeleton rehabilitation robots are introduced.

Results: The differences between different types of lower limb exoskeleton rehabilitation robots are compared and analyzed, and the structural characteristics are concluded. The main problems in its development are analyzed, the development trend is foreseen, and the current and future research of the patents on lower limb exoskeleton rehabilitation robot is discussed.

Conclusion: There are a lot of patents and articles about the exoskeleton rehabilitation robots, however, if these problems can be solved, such as small size, light weight and high power output are solved at the same time, the consistency with human body will be advanced, with the combination of traditional rehabilitation medicine. It will be possible to maximize the rehabilitation of the lower limbs.

Source: Recent Advances on Lower Limb Exoskeleton Rehabilitation Robot: Ingenta Connect

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[Abstract] A pilot study on the optimal speeds for passive wrist movements by a rehabilitation robot of stroke patients: A functional NIRS study  

Abstract:

The optimal conditions inducing proper brain activation during performance of rehabilitation robots should be examined to enhance the efficiency of robot rehabilitation based on the concept of brain plasticity. In this study, we attempted to investigate differences in cortical activation according to the speeds of passive wrist movements performed by a rehabilitation robot for stroke patients. 9 stroke patients with right hemiparesis participated in this study. Passive movements of the affected wrist were performed by the rehabilitation robot at three different speeds: 0.25 Hz; slow, 0.5Hz; moderate and 0.75 Hz; fast. We used functional near-infrared spectroscopy to measure the brain activity during the passive movements performed by a robot. Group-average activation map and the relative changes in oxy-hemoglobin (ΔOxyHb) in two regions of interest: the primary sensory-motor cortex (SM1); premotor area (PMA) and region of all channels were measured. In the result of group-averaged activation map, the contralateral SM1, PMA and somatosensory association cortex (SAC) showed the greatest significant activation according to the movements at 0.75 Hz, while there is no significantly activated area at 0.5 Hz. Regarding ΔOxyHb, no significant diiference was observed among three speeds regardless of region. In conclusion, the contralateral SM1, PMA and SAC showed the greatest activation by a fast speed (0.75 Hz) rather than slow (0.25 Hz) and moderate (0. 5 Hz) speed. Our results suggest an optimal speed for execution of the wrist rehabilitation robot. Therefore, we believe that our findings might point to several promising applications for future research regarding useful and empirically-based robot rehabilitation therapy.

Source: A pilot study on the optimal speeds for passive wrist movements by a rehabilitation robot of stroke patients: A functional NIRS study – IEEE Xplore Document

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[Abstract+References] Robot assisted rehabilitation of the arm after stroke: prototype design and clinical evaluation

Abstract

Robot assisted rehabilitation training is a promising tool for post-stroke patients’ recovery, and some new challenges are imposed on robot design, control, and clinical evaluation. This paper presents a novel upper limb rehabilitation robot that can provide safe and compliant force feedbacks to the patient for the benefits of its stiff and low-inertia parallel structure, highly backdrivable capstan-cable transmission, and impedance control method in the workspace. The “assist-as-needed” (AAN) clinical training principle is implemented through the “virtual tunnel” force field design, the “assistance threshold” strategy, as well as the virtual environment training games, and preliminary clinical results show its effectiveness for motor relearning for both acute and chronic stroke patients, especially for coordinated movements of shoulder and elbow.

Supplementary material

11432_2017_9076_MOESM1_ESM.pdf (1.7 mb)

Robot assisted rehabilitation of the arm after stroke: prototype design and clinical evaluation
11432_2017_9076_MOESM2_ESM.mp4 (96.2 mb)

Supplementary material, approximately 96.2 MB.
11432_2017_9076_MOESM3_ESM.mp4 (43.6 mb)

Supplementary material, approximately 43.6 MB.

Source: Robot assisted rehabilitation of the arm after stroke: prototype design and clinical evaluation | SpringerLink

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[REVIEW] A structured overview of trends and technologies used in dynamic hand orthoses – Full Text

Abstract

The development of dynamic hand orthoses is a fast-growing field of research and has resulted in many different devices. A large and diverse solution space is formed by the various mechatronic components which are used in these devices. They are the result of making complex design choices within the constraints imposed by the application, the environment and the patient’s individual needs. Several review studies exist that cover the details of specific disciplines which play a part in the developmental cycle. However, a general collection of all endeavors around the world and a structured overview of the solution space which integrates these disciplines is missing. In this study, a total of 165 individual dynamic hand orthoses were collected and their mechatronic components were categorized into a framework with a signal, energy and mechanical domain. Its hierarchical structure allows it to reach out towards the different disciplines while connecting them with common properties. Additionally, available arguments behind design choices were collected and related to the trends in the solution space. As a result, a comprehensive overview of the used mechatronic components in dynamic hand orthoses is presented.

Background

Human hands are complex and versatile instruments. They play an essential role in the interaction between a person and the environment. Many people suffer from hand impairments like spasticity, lack of control or muscle weakness, which may be due to the consequences of stroke, paralysis, injuries or muscular diseases. Such impairments may limit an individual’s independence in performing activities of daily living (ADL) and the ability to socially interact (e.g. non-verbal communication). Devices like hand exoskeletons, rehabilitation robots and assistive devices, here collectively termed as dynamic hand orthoses, aim to overcome these limitations. Their development is a fast-growing field of research and has already resulted in a large variety of devices [1, 2, 3, 4].

Each individual has different demands for a dynamic hand orthoses. Some patients benefit from rehabilitation therapy (e.g. stroke patients [5]) while others would more likely benefit from daily assistance (e.g. Duchenne Muscular Dystrophy [6]). The resulting diversity between the different devices can be illustrated by the elaborate overviews on robotic devices [4], training modalities [3] and intention detection systems [7] they use. Clearly, there are many mechatronic components to choose from and are often the result of making particular design choices within the imposed design constraints. However, not everybody has the resources (i.e. time, accessibility) to investigate all possible design choices within these constraints. Moreover, not always are design choices reported in literature and are therefore hard to retrieve. The full potential of learning from each other’s endeavors is therefore not yet fully exploited, leaving several questions in this field of research unanswered. For example, there is the discussion whether pneumatic or electric actuation is better for some applications.

The goal of this study is to collect a high quantity of dynamic hand orthoses and extract the mechatronic components which are used. Their collective properties are analyzed by using a framework which uses a generic categorization applicable for any mechatronic system: a signal domain (e.g. controllers, sensors), energy domain (e.g. energy sources, actuators) and mechanical domain (e.g. cables, linkages). Additionally, feasible technologies from other, but similar, disciplines are included (e.g. prosthetics, haptics). Trends are then visualized using bar charts and compared to available arguments behind design choices. This not only includes arguments from often-cited success-stories, but also from small-scale projects. Referring to the case of using pneumatic or electric actuation, this approach can answer how often each method is used and what arguments are reported, which may help in scoping further research and making a well-considered choice.

This paper is structured in different sections. The “Scope” section describes the boundaries and limitations of this study and Framework introduces the basis of the framework structure that is proposed. The “Results” section describes the quantitative results which illustrate the trends. How this relates to the functionality of the components, is discussed and summarized in the “Discussion” and “Conclusion” section, respectively.

Continue —> A structured overview of trends and technologies used in dynamic hand orthoses | Journal of NeuroEngineering and Rehabilitation | Full Text

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[ARTICLE] Upper limb rehabilitation robot for physical therapy: design, control, and testing – Full Text PDF

Abstract:

In recent years, the treatment process of patients and disabled people who need rehabilitation and physical medicine has become more effective with the use of new devices and developing technology. The rehabilitation robot is one of the most important developments in new technology. The right therapeutic exercises and the objectivity of the evaluation of the range of motion (ROM; generated torque and muscular activation measurement of the patient) are extremely important factors during treatment.

In this study, a lower limb rehabilitation robot was modified and controlled for upper limbs. The retrofitted robot system is able to do passive, active, and active-assistive therapeutic exercises. On the other hand, it performs active-assistive exercises using muscular activation. A web-based human machine interface, which can support home-care service, was developed to control the robotic system. This interface includes a performance evaluation unit, which is able to use not only ROM and torque, but also muscular activation of patients for the assessment of therapy results. Thanks to this system, assessment and physical therapy can be realized by a single robotic system for both upper and lower limbs using physical parameters such as ROM, generated torque, and muscular activation of the limbs. Satisfactory performance of the system is observed in the tests performed with a healthy subject.

Full Text: PDF

Source: Academic Journals

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[ARTICLE] Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation

Abstract

Robot-assisted rehabilitation and therapy has become more and more frequently used to help the elderly, disabled patients or movement disorders to perform exercise and training. The field of robot-assisted lower limb rehabilitation has rapidly evolved in the last decade. This article presents a review on the most recent progress (from year 2001 to 2014) of mechanisms, training modes and control strategies for lower limb rehabilitation robots. Special attention is paid to the adaptive robot control methods considering hybrid data fusion and patient evaluation in robot-assisted passive and active lower limb rehabilitation. The characteristics and clinical outcomes of different training modes and control algorithms in recent studies are analysed and summarized. Research gaps and future directions are also highlighted in this paper to improve the outcome of robot-assisted rehabilitation.

via Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation.

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[VIDEO] Rehab robot helps restore damaged muscles and nerves

Date: March 1, 2015

Source: Reuters – Innovations Video Online / Powered by NewsLook.com

Summary: A rehabilitation robot prototype to help restore deteriorated nerves and muscles using electromyography and computer games. Ben Gruber reports. Video provided by Reuters

http://www.sciencedaily.com/videos/7d3eca1b12734fb773d7fd9ac3bda6ce.htm

via Rehab robot helps restore damaged muscles and nerves — ScienceDaily.

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