Posts Tagged Exoskeleton

[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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    Li, B., Yuan, B., Chen, J., Zuo, Y., Yang, Y.: Mechanical design and human-machine coupling dynamic analysis of a lower extremity exoskeleton. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds.) ICIRA 2017. LNCS (LNAI), vol. 10462, pp. 593–604. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-65289-4_56CrossRefGoogle Scholar
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[ARTICLE] Mechanical Design of Exoskeleton for Hand Therapeutic Rehabilitation – Full Text PDF

ABSTRACT 

In this study an exoskeleton is designed for hand in therapeutic rehabilitation. The mechanical design is manufactured in consideration of anthropometrical measurements of the hand studied from literature. Kinematic model of the hand exoskeleton was obtained by results of position, velocity and torque-moment analysis.
The exoskeleton has a single degree of freedom (DOF) for the PIP and MCP joints. Basic four-bar linkage mechanisms are used in the exoskeleton. With this design, while movements (flexion and extension) occurs in both joints at the same time, angular displacement come out as in healthy hands. Linkage lengths aroptimized to achieve the targeted angular dynamics. The manipulation of the exoskeleton is actuated by a linear
servo motor.

Continue —>  Mechanical Design of Exoskeleton for Hand Therapeutic Rehabilitation

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Fig 2 Hand Rehabilitation System

 

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[Abstract + References] An Exoskeleton Design Robotic Assisted Rehabilitation: Wrist & Forearm – Conference paper

Abstract

Robotic systems are being used in physiotherapy for medical purposes. Providing physical training (therapy) is one of the main applications of fields of rehabilitation robotics. Upper-extremity rehabilitation involves shoulder, elbow, wrist and fingers’ actions that stimulate patients’ independence and quality of life. An exoskeleton for human wrist and forearm rehabilitation is designed and manufactured. It has three degrees of freedom which must be fitted to real human wrist and forearm. Anatomical motion range of human limbs is taken into account during design. A six DOF Denso robot is adapted. An exoskeleton driven by a serial robot has not been come across in the literature. It is feasible to apply torques to specific joints of the wrist by this way. Studies are still continuing in the subject.

References

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    Lum, P. S., Burgar, C. G., Shor, P. C., Majmundar, M., Van Der Loos, M.: Robot-ass. movement training compared with conventional therapy techniques for the rehab. of upper limb motor function after stroke. Arc. of Phy. Med. and Rehab. 83(7), 952–959 (2002).Google Scholar
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    Gupta, A., O’Malley, M. K.: Design of a haptic arm exoskeleton for training and rehabilitation’’, IEEE/ASME Trans. on Mechatronics 11(3), 280–289 (2006).CrossRefGoogle Scholar
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    Nef, T., Mihelj, M., Kiefer, G., Perndl, C., Muller, R., Riener R.:Armin-Exoskeleton for arm therapy in stroke patients’’, In: 10th Int Conf. on Rehab Robotics, pp: 68–74 (2008).Google Scholar
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    Otten, A., Voort, C., Stienen, A., Aarts, R., Van Asseldonk, E., Van der Kooij, H.: LIMPACT: a hydraulically powered self-aligning upper limb exoskeleton, IEEE/ASME Trans. on Mechatronics 20 (5), 2285–2298 (2015).CrossRefGoogle Scholar
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Fig. 1. Wrist and forearm motions [17]

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[Abstract] Control and Dynamic Manipulability of a Dual-Arm/Hand Robotic Exoskeleton System (EXO-UL8) for Rehabilitation Training in Virtual Reality

Author & Article Info

Every year there are about 800,000 new stroke patients in the US, and many of them suffer from upper limb neuromuscular disabilities including but not limited to: weakness, spasticity and abnormal synergy. Patients usually have the potential to rehabilitate (to some extent) based on neuroplasticity, and physical therapy intervention helps accelerate the recovery. However, many patients could not afford the expensive physical therapy after the onset of stroke, and miss the opportunity to get recovered. Robot-assisted rehabilitation thus might be the solution, with the following unparalleled advantages:

  1. 24/7 capability of human arm gravity compensation;
  2. multi-joint movement coordination/correction, which could not be easily done by human physical therapists;
  3. dual-arm training, either coupled in joint space or task space;
  4. quantitative platform for giving instructions, providing assistance, exerting resistance, and collecting real-time data in kinematics, dynamics and biomechanics;
  5. potential training protocol personalization; etc.

However, in the rehabilitation robotics field, there are still many open problems. I am especially interested in:

  1. compliant control, in high-dimensional multi-joint coordination condition;
  2. assist-as-needed (AAN) control, in quantitative model-based approach and model-free approach;
  3. dual-arm training, in both symmetric and asymmetric modes;
  4. system integration, e.g., virtual reality (VR) serious games and graphical user interfaces (GUIs) design and development.

Our dual-arm/hand robotic exoskeleton system, EXO-UL8, is in its 4th generation, with seven (7) arm degrees-of-freedom (DOFs) and one (1) DOF hand gripper enabling hand opening and closing on each side. While developing features on this research platform, I contributed to the robotics research field in the following aspects:

(1) I designed and developed a series of eighteen (18) serious VR games and GUIs that could be used for interactive post-stroke rehabilitation training. The VR environment, together with the exoskeleton robot, provides patients and physical therapists a quantitative rehabilitation training platform with capability in real-time human performance data collection and analysis.

(2) To provide better compliant control, my colleagues and I proposed and implemented two new admittance controllers, based on the work done by previous research group alumni. Both the hyper parameter-based and Kalman Filter-based admittance controllers have satisfactory heuristic performance, and the latter is more promising in future adaptation. Unlike many other upper-limb exoskeletons, our current system utilizes force and torque (F/T) sensors and position encoders only, no surface electromyography (sEMG) signals are used. It brings convenience to practical use, as well as technical challenges.

(3) To provide better AAN control, which is still not well understood in the academia, I worked out a redundant version of modified dynamic manipulability ellipsoid (DME) model to propose an Arm Postural Stability Index (APSI) to quantify the difficulty heterogeneity of the 3D Cartesian workspace. The theoretical framework could be used to teach the exoskeleton where and when to provide assistance, and to guide the virtual reality where to add new minimal challenges to stroke patients. To the best of my knowledge, it is also for the first time that human arm redundancy resolution was investigated when arm gravity is considered.

(4) For the first time, my colleagues and I have done a pilot study on asymmetric dual-arm training using the exoskeleton system on one (1) post-stroke patient. The exoskeleton on the healthy side could trigger assistance for that on the affected side, and validates that the current mechanism/control is eligible for asymmetric dual-arm training.

(5) Other works of mine include: activities of daily living (ADLs) data visualization for VR game difficulty design; human arm synergy modeling; dual-arm manipulation taxonomy classification (on-going work).

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[ARTICLE] Robotics for rehabilitation of hand movement in stroke survivors – Full Text

This article aims to give an overall review of research status in hand rehabilitation robotic technology, evaluating a number of devices. The main scope is to explore the current state of art to help and support designers and clinicians make better choices among varied devices and components. The review also focuses on both mechanical design, usability and training paradigms since these parts are interconnected for an effective hand recovery. In order to study the rehabilitation robotic technology status, the devices have been divided in two categories: end-effector robots and exoskeleton devices. The end-effector robots are more flexible than exoskeleton devices in fitting the different size of hands, reducing the setup time and increasing the usability for new patients. They suffer from the control of distal joints and haptic aspects of object manipulation. In this way, exoskeleton devices may represent a new opportunity. Nevertheless their design is complex and a deep investigation of hand biomechanics and physical human–robot interaction is required. The main hand exoskeletons have been developed in the last decade and the results are promising demonstrated by the growth of the commercialized devices. Finally, a discussion on the complexity to define which design is better and more effective than the other one is summarized for future investigations.

Over the past years, rehabilitation engineering has played a crucial role in improving the hand and finger function after stroke. The applications of robotics and mechatronic devices have rapidly expanded from the industrial environment to human assistance in rehabilitation and functional improvements. Rehabilitation engineering has increased the quality lives of individuals with disabilities, offering dedicated training that performs better than conventional methods.

In this way, there are many challenges and opportunities to integrate engineering concepts into hand rehabilitation, and increasing population wellbeing and wealth as well as reducing healthcare costs. This motivates researchers to study, design, and develop novel rehabilitative and assistive technologies and devices to help people to motor functions. Specifically, the current challenge is to transfer the research results and new knowledge to stakeholders creating a general awareness of the importance of rehabilitation engineering.

This review aims to present and discuss the main robotic technologies for hand recovery rehabilitation in stroke survivors, evaluating and comparing previous and current works and researches. This study explores the current state of art to help and support designers and clinicians make better choices among varied devices and components. The review also focuses on both mechanical design (e.g. concept), usability (e.g. setup, lightness, portability) and training paradigms (e.g. hand, hand/wrist or entire arm) since these parts are interconnected for an effective hand recovery. An overview of the main advantages and drawbacks in applying robotics to hand motor impairments is provided in order to give a general view of the relationship between hand rehabilitation devices, rehabilitation theories and results. The challenge is to restore the hand movements such as opening, closing, grasping and releasing movements. Second, a discussion on the application and new challenges of rehabilitation robotic devices is summarized for future investigations. In particular, the main challenges are to develop safe devices with less complex designs, increasing potential for portability and efficacy. In fact, future development for patient treatment should include the device portability to increase the potential applications. The preliminary results have highlighted the robot-assisted therapy currently works hand in hand rather than a replacement of traditional therapy. Therapies and rehabilitation strategies should be not only more effective but also more cost-efficient.

Stroke is one of the leading causes of long-term disability, affecting approximately 14% of world’s population.1,2 33% of survivors reports very limited or no functional use of the upper limb.3 Rehabilitation activities based on repeated exercises have been identified suitable in recovering some degree of motion, in particular, a simple flexion and extension of fingers has demonstrated improvements in hand functionality.4,5 In this way, medical devices and robot-assisted strategies may provide a number of advantages guaranteeing the range of motion (ROM) and avoiding inappropriate movements. Nevertheless, only a limited part of the proposed devices by the literature has been clinically tested, highlighting as the design complexity and development costs may negatively impact the system implementation. The previous and current robots and devices are often too complex to be used by patients limiting any testing on the real users.

Note that the hand functional improvement may be the result of a set of compensatory strategies based on an initial support assisted by the physiotherapist. Usually, these approaches may be suggested during the first months after stroke, when the impairment reduction may be preferred to extensive functional training. In this phase of impairment, the patients show a loss of control and a decreased tactile sensation and proprioception, reducing the physical independence and social integration. The patient’s motivation associated with verbal encouragement may significantly impact the therapy efficacy.

Over the last decades, a set of studies has evaluated the influence of the robot-assisted therapies on arm motor improvement and impairment reduction using randomized clinical controlled trials.612 The obtained results have not shown a complete consensus; nevertheless, the therapy assisted by robotics seems to obtain results beyond what is done by conventional methods.1317 In particular, researchers have been slow to investigate the hand function due to the complexity of this limb.11,12,1820

In any case, a number of studies observed that the rehabilitation training can improve the hand motor in terms of pull, push, and grip strengths, confirming that robotic training is at least as effective as conventional training.13,2124 A significant part of the obtained outcomes have been also proved by Fugl-Meyer Assessment (FMA) and Functional Independence Measure (FIM) tests, performed after the robot treatment.2527

Despite these promising results, the literature review shows also researches that did not observed significant difference between conventional and robotic training groups, highlighting as the conventional therapies are more effective in decreasing levels of impairment and disability.2,8,28,29 Mazzoleni et al.29 and Colombo et al.30 have underlined that there are other significant factors that may impact the efficacy of the training outcome, such as recovery stage, intensity, or duration of the rehabilitation therapy. This point needs to be considered to evaluate and compare different therapy treatments. In the light of these considerations, there are not evident conclusions that sustain the robot-therapy efficacy, suggesting further investigations.31,32

 

Robot-based methods may be used independently by patients in different levels of impairment. Robots permit to obtain a quantifiable measure of subjective performance, repeating treatment protocols without the need of continuous involvement of therapists saving a significant amount of the human labor that may lead to high cost.8,10 In fact, traditional therapist–based methods require several sessions of rehabilitation training, inducing impractical and unaffordable therapies for many patients. Robotic therapy techniques guarantee a safe, intensive, and task-oriented rehabilitation at relatively moderate costs.14,1533 They may apply forces with precision, improving accuracy and reducing variance. These actions are potentially effective to strengthen muscle, ROM, and motor coordination. Advanced robots provide also tactile feedback that may correct the impaired movements. In addition, robot-assisted therapies may be quantified easily and collect a number of parameters useful to track the patient’s status (e.g. spasticity or level of voluntary control).34

A further advantage of robotic rehabilitation consists of the possibility to be combined with other technologies (e.g. virtual reality (VR), brain computer interface (BCI) technology or haptic stimuli).3537 This combination allows to motivate the patients to perform the rehabilitation tasks without the constant supervision, guaranteeing repetitive movements and informative feedback. On the other hand, robot-assisted therapy permits the therapist to conduct rehabilitation tasks for two or more patients at the same time, improving the service efficiency.

Finally, it has been noted that robotics may improve the accessibility to rehabilitation. In fact, a patient prefers to use the unaffected limb in daily activities, damaging the recovery of the impaired limb.38 The possibility to perform rehabilitation in remote locations (e.g. home) using robotics devices may involve better the patient in the recovery process.

Despite these noted advantages, a number of limits and constraints of rehabilitation robot-based cannot be ignored. First, there is a significant gap between the outcomes of rehabilitation robots and people’s expectations. This element may negatively impact patient’s motivations during the therapy. In particular, the personalization is still difficult due to the design complexity of devices. Another further issue is the determination of the most efficient dosage of rehabilitation training.

Although the literature has demonstrated the main advantages and benefits of robot applications, more studies involving a large participant size are required to confirm whether robotic-assisted therapy performs better than conventional methods, evaluating and comparing the treatment dosage. In particular, a lack of robust methods to evaluate the efficacy of the robot-assisted therapy making difficult to define which design is better and more effective than the other one. A deep investigation is needed to explore whether the obtained results on the patient can be maintained in the long term and how the potential improvements can be transformed into the motor skills in performing the activities of daily living (ADL). The user’s safety needs to be guaranteed during the training, avoiding the nonlinear movement of the patient. Further limits are noted on the current robotic devices regarding their design, often complex and unconvinced for the user, or the high costs for the treatment access.3941 The ratio between the price and performance is rather dissatisfactory due to the high cost of development combined with a relatively benefit for patients and clinics.4244 These drawbacks need to be considered in the overall evaluation of robotic application. They represent an open challenge to improve the integration of engineering concepts into hand rehabilitation, increasing population wealth, as well as reducing healthcare costs. These issues justify the low penetration of robotics in the market and the requirements of new investigations. Only a limited number of stroke patients (5%–15%) who requires assistive devices and technologies may access to this service. On the other hand, the studies and researches on rehabilitation robots are becoming strategic for the society due to the fact that the costs of excluding people with disabilities are high and borne by community.45

A primary categorization of rehabilitation robotic technologies is based on the design concepts of the device: end-effector or exoskeleton.

An end-effector device (also called endpoint control) recreates dynamic environments corresponding to ADL, determining the movements at the joint level. Usually, the patient’s joint rotation is distally executed using a support (e.g. a table or a tripod) to facilitate the training and avoiding muscle fatigue. It means that the more proximal joints are not directly controlled by the robot. End-effector devices may be dedicated to hand rehabilitation or to be integrated in more complex structures for the arm recovery.

The second main logic to design a rehabilitation robotic device is the exoskeleton. An exoskeleton, from Greek “exo” = outer and “skeletos” = skeleton, is a wearable robot attached to the user’s limbs, in order to enhance their movements. It focuses on the anatomy of the subject’s hand following the limb segments, each degree of freedom is aligned with the corresponding human joint. Figure 1 illustrates a number of examples. An exoskeleton should be compliant with the user’s movements and delivers at least part of the power required by the movements. In order to guarantee the natural motor of the hand joints, their design is more complex than end-effector devices. For example, a set of components (e.g. rings, hinges, external linkages, or structures) is embedded to accomplish the alignment between the forearm axial rotation of the forearm located along an axis between the ulna and the radius50,51 to support in forearm pronation and supination.

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Figure 1. Examples of rehabilitation robotic devices: (a) Gloreha,46 (b) CyberGrasp,47 (c) Hand of Hope,48 and (d) Reha-Digit.49

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Continue —> Robotics for rehabilitation of hand movement in stroke survivors – Francesco Aggogeri, Tadeusz Mikolajczyk, James O’Kane, 2019

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[ARTICLE] An Attention-Controlled Hand Exoskeleton for the Rehabilitation of Finger Extension and Flexion Using a Rigid-Soft Combined Mechanism – Full Text

Hand rehabilitation exoskeletons are in need of improving key features such as simplicity, compactness, bi-directional actuation, low cost, portability, safe human-robotic interaction, and intuitive control. This article presents a brain-controlled hand exoskeleton based on a multi-segment mechanism driven by a steel spring. Active rehabilitation training is realized using a threshold of the attention value measured by an electroencephalography (EEG) sensor as a brain-controlled switch for the hand exoskeleton. We present a prototype implementation of this rigid-soft combined multi-segment mechanism with active training and provide a preliminary evaluation. The experimental results showed that the proposed mechanism could generate enough range of motion with a single input by distributing an actuated linear motion into the rotational motions of finger joints during finger flexion/extension. The average attention value in the experiment of concentration with visual guidance was significantly higher than that in the experiment without visual guidance. The feasibility of the attention-based control with visual guidance was proven with an overall exoskeleton actuation success rate of 95.54% (14 human subjects). In the exoskeleton actuation experiment using the general threshold, it performed just as good as using the customized thresholds; therefore, a general threshold of the attention value can be set for a certain group of users in hand exoskeleton activation.

Introduction

Hand function is essential for our daily life (Heo et al., 2012). In fact, only partial loss of the ability to move our fingers can inhibit activities of daily living (ADL), and even reduce our quality of life (Takahashi et al., 2008). Research on robotic training of the wrist and hand has shown that improvements in finger or wrist level function can be generalized across the arm (Lambercy et al., 2011). Finger muscle weakness is believed to be the main cause of loss of hand function after strokes, especially for finger extension (Cruz et al., 2005Kamper et al., 2006). Hand rehabilitation requires repetitive task exercises, where a task is divided into several movements and patients are asked to practice those movements to improve their hand strength, range of motion, and motion accuracy (Takahashi et al., 2008Ueki et al., 2012). High costs of traditional treatments often prevent patients from spending enough time on the necessary rehabilitation (Maciejasz et al., 2014). In recent years, robotic technologies have been applied in motion rehabilitation to provide training assistance and quantitative assessments of recovery. Studies show that intense repetitive movements with robotic assistance can significantly improve the hand motor functions of patients (Takahashi et al., 2008Ueki et al., 2008Kutner et al., 2010Carmeli et al., 2011Wolf et al., 2006).

Patients should be actively involved in training to achieve better rehabilitation results (Teo and Chew, 2014Li et al., 2018). Motor rehabilitation has implemented Brain Computer Interface (BCI) methods as one of the means to detect human movement intent and get patients to be actively involved in the motor training process (Teo and Chew, 2014Li et al., 2018). Motor imagery-based BCIs (Jiang et al., 2015Pichiorri et al., 2015Kraus et al., 2016Vourvopoulos and Bermúdez I Badia, 2016), movement-related cortical potentials-based BCIs (Xu et al., 2014Bhagat et al., 2016), and steady-state motion visual evoked potential-based BCIs (Zhang et al., 2015) have been used to control rehabilitation robots. However, the high cost and complexity of the preparation in utilizing these methods mean that most current BCI devices are more suitable for research purposes than clinical practices. This is attributable to the fact that the ease of use and device cost are two main factors to consider during the selection of human movement intent detection based on BCIs for practical use (van Dokkum et al., 2015Li et al., 2018). Therefore, non-invasive, easy-to-install BCIs that are convenient to use with acceptable accuracy should be introduced to hand rehabilitation robot control.

Owing to the versatility and complexity of human hands, developing hand exoskeleton robots for rehabilitation assistance in hand movements is challenging (Heo et al., 2012Arata et al., 2013). In recent years, hand exoskeleton devices have drawn much research attention, and the results of current research look promising (Heo et al., 2012). Hand exoskeleton devices mainly use linkage, wire, or hydraulically/pneumatically driven mechanisms (Polygerinos et al., 2015a). The rigid mechanical design of linkage-based mechanisms provides robustness and reliability of power transmission, and has been widely applied in hand exoskeletons (Tong et al., 2010Ito et al., 2011Arata et al., 2013Cui et al., 2015Polygerinos et al., 2015a). However, the safety problem of misalignment between the human finger joints and the exoskeleton joints may occur during rehabilitation movements (Heo et al., 2012Cui et al., 2015). Compensation approaches used in current studies make the mechanism more complicated (Nakagawara et al., 2005Fang et al., 2009Ho et al., 2011). Pneumatic and hydraulic soft hand exoskeletons, which are made of flexible materials, are proposed to assist hand opening or closing (Ang and Yeow, 2017Polygerinos et al., 2015aYap et al., 2015b). However, despite bi-directional assistance—namely finger flexion and extension—being essential for hand rehabilitation (Iqbal et al., 2014), a large group of current soft hand exoskeleton devices only provide finger flexion assistance (Connelly et al., 2010Polygerinos et al., 20132015aYap et al., 2015ab). Wire-driven mechanisms can also be complex to transmit bi-directional movements since wires can only transmit forces along one direction (In et al., 2015Borboni et al., 2016). In order to transmit bi-directional movements, a tendon-driven hand exoskeleton was proposed, where the tendon works as a tendon during the extension movement and as compressed flexible beam constrained into rectilinear slides mounted on the distal sections of the glove during flexion (Borboni et al., 2016). Arata et al. (2013) attempted to avoid wire extension and other associated issues by proposing a hand exoskeleton with a three-layered sliding spring mechanism. Hand rehabilitation exoskeleton devices are still seeking to achieve key features such as low complexity, compactness, bi-directional actuation, low cost, portability, safe human-robotic interaction, and intuitive control.

In this article, we describe the design and characterization of a novel multi-segment mechanism driven by one layer of a steel spring that can assist both extension and flexion of the finger. Thanks to the inherent features of this multi-segment mechanism, joint misalignment between the device and the human finger is no longer a problem, enhancing the simplicity and flexibility of the device. Moreover, its compliance makes the hand exoskeleton safe for human-robotic interaction. This mechanism can generate enough range of motion with a single input by distributing an actuated linear motion to the rotational motions of finger joints. Active rehabilitation training is realized by using a threshold of the attention value measured by a commercialized electroencephalography (EEG) sensor as a brain-controlled switch for the hand exoskeleton. Features of this hand exoskeleton include active involvement of patients, low complexity, compactness, bi-directional actuation, low cost, portability, and safe human-robotic interaction. The main contributions of this article include: (1) prototyping and evaluation of a hand exoskeleton with a rigid-soft combined multi-segment mechanism driven by one layer of a steel spring with a sufficient output force capacity; (2) using attention-based BCI control to increase patients’ participation in exoskeleton-assisted hand rehabilitation; and (3) determining the threshold of attention value for our attention-based hand rehabilitation robot control.

Exoskeleton Design

Design Requirements

The target users are stroke survivors during flaccid paralysis period who need continuous passive motion training of their hands. They should also be able to focus their attention on motion rehabilitation training for at least a short period of time. For the purpose of hand rehabilitation, an exoskeleton should have minimal ADL interference and have the ability to generate adequate forces to perform hand flexion and extension with a range of motion that is similar or slightly lower than the motion range of a natural finger.

To achieve minimal ADL interference, the device is to be confined to the back of the finger and the width of the device should not exceed the finger width. Here, the width and height constraints of the exoskeleton on the back of the finger are both 20 mm. Low weight of the rehabilitation systems is a key requirement to allow practical use by a wide stroke population (Nycz et al., 2016). Therefore, the target weight of the exoskeleton should be as light as possible to make the patient feel more comfortable to wear it. The typical weight of other hand exoskeletons is in the range of 0.7 kg–5 kg (CyberGlove Systems Inc., 2016Delph et al., 2013Polygerinos et al., 2015aRehab-Robotics Company Ltd., 2019). In this article, the target weight of the exoskeleton is less than 0.5 kg.

There are 15 joints in the human hand. The thumb joint consists of an interphalangeal joint (IPJ), a metacarpophalangeal joint (MPJ), and a carpometacarpal joint (CMJ). Each of the other four fingers has three joints including a metacarpophalangeal joint (MCPJ), a proximal interphalangeal joint (PIPJ), and a distal interphalangeal joint (DIPJ). The hand exoskeleton must have three bending degrees of freedom (DOF) to exercise the three joints of the finger. For some rehabilitation applications, it is unnecessary for each of the MCPJ, PIPJ, and DIPJ of the human finger to have independent motion as long as the whole range of motion of the finger is covered. Tripod grasping requires the MPJ and IPJ of the thumb to bend around 51° and 27°; MCPJ, PIPJ, and DIPJ of the index finger to bend around 46°, 48°, and 12°; and for the middle finger to bend around 46°, 54°, and 12° (In et al., 2015). For the execution speed of rehabilitation exercises, physiotherapists suggest a lower speed than 20 s for a flexion/extension cycle of a finger joint (Borboni et al., 2016). It has to be stressed that hyperextension of all these joints should always be carefully avoided.

The exerted force to the finger should be able to enable continuous passive motion training. In addition, the output force should help the patient to generate grasping forces required to manipulate objects in ADL. Pinch forces required to complete functional tasks are typically below 20 N (Smaby et al., 2004). Polygerinos et al. (2015b) estimated each robot finger should exert a distal tip force of about 7.3 N to achieve a palmar grasp—namely four fingers against the palm of the hand—to pick up objects less than 1.5 kg. Existing devices can provide a maximum transmission output force between 7 N and 35 N (Kokubun et al., 2013In et al., 2015Polygerinos et al., 2015bBorboni et al., 2016Nycz et al., 2016).

The design should allow some customization to hand size and adaptability to different patient statuses and different stages of rehabilitation.

Rigid-Soft Combined Mechanism

Based on our established design requirements, a hand exoskeleton was designed and constructed (see Figure 1). In our design, each finger was driven by one actuator for finger extension and flexion, resulting in a highly compact device. A multi-segment mechanism with a spring layer was proposed. It has respectable adaptability, thus avoiding joint misalignment problems. A three-dimensional model of a single finger actuator is shown in Figure 1A. This finger actuator contained a linear motor, a steel strap, and a multi-segment mechanism. As shown in Figure 1B, the spring layer bended and slid because of the linear motion input provided by the linear actuator. The structure then became like a circular sector. When the structure was attached to a finger, it supported the finger flexion/extension motion. Five finger actuators were attached to a fabric glove via Velcro straps and five linear motors were attached to a rigid part which was fixed to the forearm by a Velcro strap. Each steel strap was attached to a motor by a small rigid 3D-printed part. It should be noted that the current structure is not applicable to thumb adduction/abduction.

Figure 1. Design of the hand exoskeleton: (A) CAD drawing of the index finger acuator; (B) bending motion generated by the proposed mutli-segment mechanism with a spring layer; (C) segment thicknesses (unit: mm); and (D) overview of the hand exoskeleton prototype.

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Continue —>  Frontiers | An Attention-Controlled Hand Exoskeleton for the Rehabilitation of Finger Extension and Flexion Using a Rigid-Soft Combined Mechanism | Frontiers in Neurorobotics

 

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[ARTICLE] Mechanics and energetics of post-stroke walking aided by a powered ankle exoskeleton with speed-adaptive myoelectric control – Full Text

Abstract

Background

Ankle exoskeletons offer a promising opportunity to offset mechanical deficits after stroke by applying the needed torque at the paretic ankle. Because joint torque is related to gait speed, it is important to consider the user’s gait speed when determining the magnitude of assistive joint torque. We developed and tested a novel exoskeleton controller for delivering propulsive assistance which modulates exoskeleton torque magnitude based on both soleus muscle activity and walking speed. The purpose of this research is to assess the impact of the resulting exoskeleton assistance on post-stroke walking performance across a range of walking speeds.

Methods

Six participants with stroke walked with and without assistance applied to a powered ankle exoskeleton on the paretic limb. Walking speed started at 60% of their comfortable overground speed and was increased each minute (n00, n01, n02, etc.). We measured lower limb joint and limb powers, metabolic cost of transport, paretic and non-paretic limb propulsion, and trailing limb angle.

Results

Exoskeleton assistance increased with walking speed, verifying the speed-adaptive nature of the controller. Both paretic ankle joint power and total limb power increased significantly with exoskeleton assistance at six walking speeds (n00, n01, n02, n03, n04, n05). Despite these joint- and limb-level benefits associated with exoskeleton assistance, no subject averaged metabolic benefits were evident when compared to the unassisted condition. Both paretic trailing limb angle and integrated anterior paretic ground reaction forces were reduced with assistance applied as compared to no assistance at four speeds (n00, n01, n02, n03).

Conclusions

Our results suggest that despite appropriate scaling of ankle assistance by the exoskeleton controller, suboptimal limb posture limited the conversion of exoskeleton assistance into forward propulsion. Future studies could include biofeedback or verbal cues to guide users into limb configurations that encourage the conversion of mechanical power at the ankle to forward propulsion.

Trial registration

N/A.

Background

Walking after a stroke is more metabolically expensive, leading to rapid exhaustion, limited mobility, and reduced physical activity [1]. Hemiparetic walking is slow and asymmetric compared to unimpaired gait. Preferred walking speeds following stroke range between < 0.2 m s− 1 and ~ 0.8 m s− 1 [2] compared to ~ 1.4 m s− 1 in unimpaired adults, and large interlimb asymmetry has been documented in ankle joint power output [34]. The ankle plantarflexors are responsible for up to 50% of the total positive work needed to maintain forward gait [56]; therefore, weakness of the paretic plantarflexors is especially debilitating, and as a result, the paretic ankle is often a specific target of stroke rehabilitation [78910]. In recent years, ankle exoskeletons have emerged as a technology capable of improving ankle power output by applying torque at the ankle joint during walking in clinical populations [78] and healthy controls [11121314]. Myoelectric exoskeletons offer a user-controlled approach to stroke rehabilitation by measuring and adapting to changes in the user’s soleus electromyography (EMG) when generating torque profiles applied at the ankle [15]. For example, a proportional myoelectric ankle exoskeleton was shown to increase the paretic plantarflexion moment for persons post-stroke walking at 75% of their comfortable overground (OVG) speed [8]; despite these improvements, assistance did not reduce the metabolic cost of walking or improve percent paretic propulsion. The authors suggested exoskeleton performance could be limited because the walking speed was restricted to a pace at which exoskeleton assistance was not needed.

Exoskeleton design for improved function following a stroke would benefit from understanding the interaction among exoskeleton assistance, changes in walking speed, and measured walking performance. Increases in walking speed post-stroke are associated with improvements in forward propulsion and propulsion symmetry [16], trailing limb posture [1718], step length symmetries [1719], and greater walking economies [1719]. This suggests that assistive technologies need to account for variability in walking speeds to further improve post-stroke walking outcomes. However, research to date has evaluated exoskeleton performance at only one walking speed, typically set to either the participant’s comfortable OVG speed or a speed below this value [78]. At constant speeds, ankle exoskeletons have been shown to improve total ankle power in both healthy controls [11] and persons post-stroke [8], suggesting the joint powers and joint power symmetries could be improved by exoskeleton technology. Additionally, an exosuit applying assistance to the ankle was able to improve paretic propulsion and metabolic cost in persons post-stroke walking at their comfortable OVG speed [7]. Assessing the impact of exoskeleton assistance on walking performance across a range of speeds is the next logical step toward developing exoskeleton intervention strategies targeted at improving walking performance and quality of life for millions of persons post-stroke.

In order to assess the impact of exoskeleton assistance across a range of walking speeds in persons post-stroke, we developed a novel, speed-adaptive exoskeleton controller that automatically modulates the magnitude of ankle torque with changes in walking speed and soleus EMG. We hypothesized that: 1) Our novel speed-adaptive controller will scale exoskeleton assistance with increases in walking speed as intended. 2) Exoskeleton assistance will lead to increases in total average net paretic ankle power and limb power at all walking speeds. 3) Exoskeleton assistance will lead to metabolic benefits associated with improved paretic average net ankle and limb powers.

Methods

Exoskeleton hardware

We implemented an exoskeleton emulator comprised of a powerful off-board actuation and control system, a flexible Bowden cable transmission, and a lightweight exoskeleton end effector [20]. The exoskeleton end effector includes shank and foot carbon fiber components custom fitted to participants and hinged at the ankle. The desired exoskeleton torque profile was applied by a benchtop motor (Baldor Electric Co, USA) to the carbon-fiber ankle exoskeleton through a Bowden-cable transmission system. An inline tensile load cell (DCE-2500 N, LCM Systems, Newport, UK) was used to confirm the force transmitted by the exoskeleton emulator during exoskeleton assistance.

Speed-adaptive proportional myoelectric exoskeleton controller

Our exoskeleton controller alters the timing and magnitude of assistance with the user’s soleus EMG signal and walking speed (Fig. 1). The exoskeleton torque is determined from Eq. 1, in which participant mass (mparticipant) is constant across speeds, treadmill speed (V) is measured in real-time, the speed gain (Gspeed) is constant for all subjects and across speeds, the adaptive gain (Gadp) is constant for a gait cycle and calculated anew for each gait cycle, and the force-gated and normalized EMG (EMGGRFgated) is a continuously changing variable.

τexo (t)=mparticipant×V×Gspeed×Gadp×EMGGRFgatedτexo (t)=mparticipant×V×Gspeed×Gadp×EMGGRFgated
(1)
Fig. 1
Fig. 1

Novel speed-adaptive myoelectric exoskeleton controller measures and adapts to users’ soleus EMG signal as well as their walking speed in order to generate the exoskeleton torque profile. Raw soleus EMG signal is filtered and rectified to create an EMG envelope, and the created EMG envelope is then gated by anterior GRFs to ensure assistance is only applied during forward propulsion. The adaptive EMG gain is calculated as a moving average of peak force-gated EMG from the last five paretic gait cycles. The pre-speed gain control signal is the product of the force-gated EMG and the adaptive EMG gain. The speed gain is determined using real-time walking speed and computed as 25% of the maximum biological plantarflexion torque at that given walking speed. Exoskeleton torque is the result of multiplying the speed gain with the pre-speed gain control signal

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Continue —> Mechanics and energetics of post-stroke walking aided by a powered ankle exoskeleton with speed-adaptive myoelectric control | Journal of NeuroEngineering and Rehabilitation | Full Text

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[VIDEO] Indego Therapy – Alice’s Story — Anatomical Concepts (UK)

MEET ALICE

After her brain injury, there seemed little hope for recovery. With the right therapy, tools and attitude she has defied all odds.
Her stepfather, Bob, and therapists at More Rehab tell us her story, her rehabilitation journey so far, and the particular benefits of walking therapy with the Indego exoskeleton.

We’re sure you agree that she is an extraordinary woman!
We also hope that you can see that it is a combination of great therapy, excellent technology, incredible support and hard work that creates results. Here at Anatomical Concepts we focus on the Technology, and we partner with great therapists (just like More Rehab) who we know will give a high standard of support, training and encouragement.

You can learn a lot more about Indego here or complete the form below and we’ll be in touch!

via Indego Therapy – Alice’s Story — Anatomical Concepts (UK)

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[Abstract] Attention-controlled assistive wrist rehabilitation using a low-cost EEG Sensor

Abstract

It is essential to make sure patients be actively involved in motor training using robot-assisted rehabilitation to achieve better rehabilitation outcomes. This paper introduces an attention-controlled wrist rehabilitation method using a low-cost EEG sensor. Active rehabilitation training is realized using a threshold of the attention level measured by the low-cost EEG sensor as a switch for a flexible wrist exoskeleton assisting wrist ?exion/extension and radial/ulnar deviation. We present a prototype implementation of this active training method and provide a preliminary evaluation. The feasibility of the attention-based control was proven with the overall actuation success rate of 95%. The experimental results also proved that the visual guidance was helpful for the users to concentrate on the wrist rehabilitation training; two types of visual guidance, namely looking at the hand motion shown on a video and looking at the user’s own hand, had no significant performance difference; a general threshold of a certain group of users can be utilized in the wrist robot control rather than a customized threshold to simplify the procedure.

via Attention-controlled assistive wrist rehabilitation using a low-cost EEG Sensor – IEEE Journals & Magazine

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[NEWS] Wearable robots usher in next generation of mobility therapies|CORDIS|European Commission

Wearable robots that can anticipate and react to users’ movement in real time could dramatically improve mobility assistance and rehabilitation tools.

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Wearable robots are programmable body-worn devices, or exoskeletons, that are designed to mechanically interact with the user. Their purpose is to assist or even substitute human motor function for people who have severe difficulty moving or walking.

The BIOMOT project, completed in September 2016, has helped to advance this emerging field by demonstrating that personalised computational models of the human body can effectively be used to control wearable exoskeletons. The project has identified ways of achieving improved flexibility and autonomous performance, which could assist in the use of wearable robots as mobility assistance and rehabilitation tools.

‘An increasing number of researchers in the field of neurorehabilitation are interested in the potential of these robotic technologies for clinical rehabilitation following neurological diseases,’ explains BIOMOT project coordinator Dr. Juan Moreno from the Spanish Council for Scientific Research (CSIC). ‘One reason is that these systems can be optimised to deliver diverse therapeutic interventions at specific points of recuperation or care.’

However, a number of factors have limited the widespread market adoption of wearable robots. Moreno and his team identified a need for wearable equipment to be more compact and lightweight, and better able anticipate and detect the intended movements of the wearer. In addition, robots needed to become more versatile and adaptable in order to aid people in a variety of different situations; walking on uneven ground, for example, or approaching an obstacle.

In order to address these challenges, the project developed robots with real-time adaptability and flexibility by increasing the symbiosis between the robot and the user through dynamic sensorimotor interactions. A hierarchical approach to these interactions was taken, allowing the project team to apply different layers for different purposes. This means in effect that an exoskeleton can be personalised to an individual user.

‘Thanks to this framework, the BIOMOT exoskeleton can rely on mechanical and bioelectric measurements to adapt to a changing user or task condition,’ says Moreno. ‘This leads to improved robotic interventions.’

Following theoretical and practical work, the project team then tested these prototype exoskeletons with volunteers. A key technical challenge was how to combine a robust and open architecture with a novel wearable robotic system that can gather signals from human activity. ‘Nonetheless, we succeeded in investigating for the first time the potential of automatically controlling human-robot interactions in order to enhance user compliance to a motor task,’ says Moreno. ‘Our research with healthy humans showed such positive and promising results that we are keen to continue validation with both stroke and spinal cord injury patients.’

Indeed, Moreno is confident that the success of the project will open up potential new research avenues. For example, the results will help scientists to develop computational models for rehabilitation therapies, and better understand human movement in more detail.

‘In the project we also defined novel techniques to evaluate and benchmark performances of wearable exoskeletons,’ says Moreno. ‘Further innovation projects are planned by consortium members to follow up on this research, and to exploit developments in the field of human motion capture, human-machine interaction and adaptive control.’

For further information, please see:
project website

via Wearable robots usher in next generation of mobility therapies | News | CORDIS | European Commission

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