Posts Tagged Exoskeleton

[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.

© Shutterstock

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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[ARTICLE] Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks – Full Text

Abstract

Background

To assist people with disabilities, exoskeletons must be provided with human-robot interfaces and smart algorithms capable to identify the user’s movement intentions. Surface electromyographic (sEMG) signals could be suitable for this purpose, but their applicability in shared control schemes for real-time operation of assistive devices in daily-life activities is limited due to high inter-subject variability, which requires custom calibrations and training. Here, we developed a machine-learning-based algorithm for detecting the user’s motion intention based on electromyographic signals, and discussed its applicability for controlling an upper-limb exoskeleton for people with severe arm disabilities.

Methods

Ten healthy participants, sitting in front of a screen while wearing the exoskeleton, were asked to perform several reaching movements toward three LEDs, presented in a random order. EMG signals from seven upper-limb muscles were recorded. Data were analyzed offline and used to develop an algorithm that identifies the onset of the movement across two different events: moving from a resting position toward the LED (Go-forward), and going back to resting position (Gobackward). A set of subject-independent time-domain EMG features was selected according to information theory and their probability distributions corresponding to rest and movement phases were modeled by means of a two-component Gaussian Mixture Model (GMM). The detection of movement onset by two types of detectors was tested: the first type based on features extracted from single muscles, whereas the second from multiple muscles. Their performances in terms of sensitivity, specificity and latency were assessed for the two events with a leave one-subject out test method.

Results

The onset of movement was detected with a maximum sensitivity of 89.3% for Go-forward and 60.9% for Go-backward events. Best performances in terms of specificity were 96.2 and 94.3% respectively. For both events the algorithm was able to detect the onset before the actual movement, while computational load was compatible with real-time applications.

Conclusions

The detection performances and the low computational load make the proposed algorithm promising for the control of upper-limb exoskeletons in real-time applications. Fast initial calibration makes it also suitable for helping people with severe arm disabilities in performing assisted functional tasks.

Background

Exoskeletons are wearable robots exhibiting a close physical and cognitive interaction with the human users. Over the last years, several exoskeletons have been developed for different purposes, such as augmenting human strength [1], rehabilitating neurologically impaired individuals [2] or assisting people affected by many neuro-musculoskeletal disorders in activities of daily life [3]. For all these applications, the design of cognitive Human-Robot Interfaces (cHRIs) is paramount [4]; indeed, understanding the users’ intention allows to control the device with the final goal to facilitate the execution of the intended movement. The flow of information from the human user to the robot control unit is particularly crucial when exoskeletons are used to assist people with compromised movement capabilities (e.g. post-stroke or spinal-cord-injured people), by amplifying their movements with the goal to restore functions.

In recent years, different approaches have been pursued to design cHRIs, based on invasive and non-invasive approaches. Implantable electrodes, placed directly into the brain or other electrically excitable tissues, record signals directly from the peripheral or central nervous system or muscles, with high resolution and high precision [5]. Non-invasive approaches exploit different bio-signals: some examples are electroencephalography (EEG) [6], electrooculography (EOG) [7], and brain-machine interfaces (BMI) combining the two of them [8910]. In addition, a well-consolidated non-invasive approach is based on surface electromyography (sEMG) [11], which has been successfully used for controlling robotic prostheses and exoskeletons due to their inherent intuitiveness and effectiveness [121314]. Compared to EEG signals, sEMG signals are easy to be acquired and processed and provide effective information on the movement that the person is executing or about to start executing. Despite the above-mentioned advantages, the use of surface EMG signals still has several drawbacks, mainly related to their time-varying nature and the high inter-subject variability, due to differences in the activity level of the muscles and in their activation patterns [1115], which requires custom calibrations and specific training for each user [16]. For these reasons, notwithstanding the intuitiveness of EMG interfaces, it is still under discussion their efficacy and usability in shared human-machine control schemes for upper-limb exoskeletons. Furthermore, the need for significant signal processing can limit the use of EMG signals in on-line applications, for which fast detection is paramount. In this scenario, machine learning methods have been employed to recognize the EMG onset in real time, using different classifiers such as Support Vector Machines, Linear Discriminant Analysis, Hidden Markov Models, Neural Networks, Fuzzy Logic and others [151617]. In this process, a set of features is previously selected in time, frequency, or time-frequency domains [18]. Time-domain features extract information associated to signal amplitude in non-fatiguing contractions; when fatigue effects are predominant, frequency-domain features are more representative; finally, time-frequency domain features better elicit transient effects of muscular contractions. Before feeding the features into the classifier, dimensionality reduction is usually performed, to increase classification performances while reducing complexity [19]. The most common strategies for reduction are: i) feature projection, to map the set of features into a new set with reduced dimensionality (e.g., linear mapping through Principal Component Analysis); ii) feature selection, in which a subset of features is selected according to specific criteria, aimed at optimizing a chosen objective function. All the above-mentioned classification approaches ensure good performance under controlled laboratory conditions. Nevertheless, in order to be used effectively in real-life scenarios, smart algorithms must be developed, which are able to adapt to changes in the environmental conditions and intra-subject variability (e.g. changes of background noise level of the EMG signals), as well as to the inter-subject variability [20].

In this paper, we exploited a cHRI combining sEMG and an upper-limb robotic exoskeleton, to fast detect the users’ motion intention. We implemented offline an unsupervised machine-learning algorithm, using a set of subject-independent time-domain EMG features, selected according to information theory. The probability distributions of rest and movement phases of the set of features were modelled by means of a two-component Gaussian Mixture Model (GMM). The algorithm simulates an online application and implements a sequential method to adapt GMM parameters during the testing phase, in order to deal with changes of background noise levels during the experiment, or fluctuations in EMG peak amplitudes due to muscle adaptation or fatigue. Features were extracted from two different signal sources, namely onset detectors, which were tested offline and their performance in terms of sensitivity (or true positive rate), specificity (or true negative rate) and latency (delay on onset detection) were assessed for two different events, i.e. two transitions from rest to movement phases at different initial conditions. The two events were selected in order to replicate a possible application scenario of the proposed system. Based on the results we obtained, we discussed the applicability of the algorithm to the control of an upper-limb exoskeleton used as an assistive device for people with severe arm disabilities.

Materials and methods

Experimental setup

The experimental setup includes: (i) an upper-limb powered exoskeleton (NESM), (ii) a visual interface, and (iii) a commercial EMG recording system (TeleMyo 2400R, Noraxon Inc., AZ, US).

NESM upper-limb exoskeleton

NESM (Fig. 1a) is a shoulder-elbow powered exoskeleton designed for the mobilization of the right upper limb [2122], developed at The BioRobotics Institute of Scuola Superiore Sant’Anna (Italy). The exoskeleton mechanical structure hangs from a standing structure and comprises four active and eight passive degrees of freedom (DOFs), along with different mechanisms for size regulations to improve comfort and wearability of the device.
Fig. 1

Fig. 1a Experimental setup, comprising NESM, EMG electrodes and the visual interface; b Location of the electrodes for EMG acquisition; c Timing and sequence of action performed by the user during a single trial

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Continue —-> Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks | Journal of NeuroEngineering and Rehabilitation | Full Text

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[ARTICLE] Design of a robot-assisted exoskeleton for passive wrist and forearm rehabilitation – Full Text

Abstract

This paper presents a new exoskeleton design for wrist and forearm rehabilitation. The contribution of this study is to offer a methodology which shows how to adapt a serial manipulator that reduces the number of actuators used on exoskeleton design for the rehabilitation. The system offered is a combination of end-effector- and exoskeleton-based devices. The passive exoskeleton is attached to the end effector of the manipulator, which provides motion for the purpose of rehabilitation process. The Denso VP 6-Axis Articulated Robot is used to control motion of the exoskeleton during the rehabilitation process. The exoskeleton is designed to be used for both wrist and forearm motions. The desired moving capabilities of the exoskeleton are flexion–extension (FE) and adduction–abduction (AA) motions for the wrist and pronation–supination (PS) motion for the forearm. The anatomical structure of a human limb is taken as a constraint during the design. The joints on the exoskeleton can be locked or unlocked manually in order to restrict or enable the movements. The parts of the exoskeleton include mechanical stoppers to prevent the excessive motion. One passive degree of freedom (DOF) is added in order to prevent misalignment problems between the axes of FE and AA motions. Kinematic feedback of the experiments is performed by using a wireless motion tracker assembled on the exoskeleton. The results proved that motion transmission from robot to exoskeleton is satisfactorily achieved. Instead of different exoskeletons in which each axis is driven and controlled separately, one serial robot with adaptable passive exoskeletons is adequate to facilitate rehabilitation exercises.

 

Introduction

Deficiencies in the upper extremities restrain a person’s ability to go about daily life, consequently limiting one’s independence. Therefore, robots are used to perform task-oriented repetitive movements in order to improve motor recovery, muscle strength and movement coordination. Stroke is one of the primary reasons for a decrease in motor function of the upper limbs of human beings. It restricts the daily, social and household activities of the patients. Therefore, rehabilitation therapy is required to recover some of the movement lost (Bayona et al., 2005; Bonita and Beaglehole, 1988; Cramer and Riley, 2008). This is accomplished by a long-term intensive and repetitive rehabilitation period. Traditional therapies not only require great effort but also require the manual assistance of physiotherapists. The one-to-one contact of the therapists with their patients leaves the therapists exhausted. Moreover, therapists have limited abilities with regard to speed, senses, strength, and repeatability.

Robot-aided therapy is a developing part of post-stroke rehabilitation care (Reinkensmeyer et al., 2004). Robotic rehabilitation systems ensure compact therapy which can be applied in repetitive, controllable and accurate manner (Kahn et al., 2006; Marchal-Crespo and Reinkensmeyer, 2009). Robotic devices can provide limitless repeatability for patients thus decreasing the effort that therapists have to make (Kwakkel et al., 2008; Lum et al., 2002). Additionally, patient performance evaluation can easily be monitored and assessed by the therapists to adjust the rest of the required therapy (Celik et al., 2010; Ponomarenko et al., 2014).

The types of exercises are grouped into two branches: active and passive exercises. The subjects move their limbs actively and apply torque and/or force in active exercises. Passive exercises are in contrast to active exercises, in which the subjects remain passive during the exercise while an active device moves the limb. Continuous passive motion (CPM) is generated in this way (Maciejasz et al., 2014).

There is a broad range of robotic systems presented for upper-extremity rehabilitation. The mechanical structure of the rehabilitation robots can be mainly grouped into two parts: “end-effector-based” and “exoskeletons”. MIT-MANUS (Krebs et al., 1998) and MIME (Lum et al., 2002) are included in the first part. End-effector-type robots cover a large workspace without having the capability to apply torques to specific joints of the arm. Having simpler control structure than exoskeletons is an advantage of end-effector-type devices. The most distal part of the robot is in contact with the patient limb. The segments of the upper extremities can be regarded as a mechanical chain. Therefore, motion in the end effector of the robot will automatically move other segments of the patient. They may cause redundant configurations of the patient’s upper extremities and may risk injury. Exoskeletons are the external structural mechanisms that have joints and links that can collaborate with the human body. They transmit motion exerted by the links to the human joints, thus making them suitable for the human anatomy. Exoskeletons must be able to carry out movements within the natural limitations of a human wrist for an ergonomic design. Mechanical and control issues are more complex than end-effector-type devices. The 5 degrees of freedom (DOF) MAHI (Gupta and O’Malley, 2006), 6 DOF ARMin (Nef et al., 2008) and 7 DOF CADEN-7 (Perry et al., 2007) are some examples of exoskeletons used in upper-extremity rehabilitation. LIMPACT (Otten et al., 2015), MIT-Manus (Krebs et al., 1998) and MIME (Lum et al., 2005) are prime examples of systems designed for assisting upper-limb proximal joints (the shoulder and the elbow). On the other hand, CR-2 Haptic (Khor et al., 2014) has one rotational DOF. There are manual reconfigurations for any specific wrist movement. Systems called Universal Haptic Drive (Oblak et al., 2010), Bi-Manu-Track (Lum et al., 1993) and Supinator Extender (Allington et al., 2011) have 2 DOF. The closest configuration resembling a human wrist and a rehabilitation robot can be employed by a 3 DOF system with three revolute joints. This configuration type enhances the functionality of devices providing rehabilitation services as it allows independence for specific motions of the wrist. RiceWrist (Gupta et al., 2008) and CRAMER (Spencer et al., 2008) use parallel mechanisms for wrist and forearm rehabilitation. RiceWrist-S (Pehlivan et al., 2012) is a 3 DOF exoskeleton system which is the developed version of RiceWrist (Gupta et al., 2008). A three-axis gimbal called WristGimbal (Martinez et al., 2013) offers flexibility to adjust rotation centers of the axes in order to match the wrist center of the patient. A 3 DOF self-aligning exoskeleton given in Beekhuis et al. (2013) compensates for misalignment of the wrist and forearm. Parallelogram linkages are used for this purpose. Nu-Wrist (Omarkulov et al., 2016) is a novel self-aligning 3 DOF system allowing passive adaptation in the wrist joint.

This paper presents the design of an exoskeleton for human wrist and forearm rehabilitation. Specific wrist and forearm therapies are performed. An issue with the angular displacement limit of a robot axis was experienced. The solution method obtained by changing the design is given herein. Adapting a 6 DOF Denso robot for wrist and forearm rehabilitation is proposed. The novelty of the study is the use of an exoskeleton driven by a serial robot, which is a method that has not yet been tackled in the literature. The proposed system hybridized the end-effector-type and exoskeleton-type rehabilitation systems in order to utilize advantages and to avoid disadvantages. Precise movement transmission from robot to patient limb can be provided by using an exoskeleton which plays a guide role in the exercises. This adaptation makes the system feasible to apply torques to specific joints of the wrist and allow independent, concurrent and precise movement control. This technique offers flexibility to the users. If the user wants wrist and forearm rehabilitation, a 3-D model of the exoskeleton is designed, manufactured with 3-D printing technology and interfaced with the robot. The exoskeleton may be designed for ankle, shoulder and/or elbow applications. Therefore, a serial robot can be used as a motion provider for different types of rehabilitation. Instead of different exoskeletons having a motor for each axis, the combination of a serial robot and passive exoskeleton is enough to perform the rehabilitation exercises.

Wrist and forearm motion and exoskeleton design

A human uses the distal parts of his/her arm (i.e., wrist, forearm) in coordination with proximal parts (i.e., elbow, shoulder) in order to carry out movements required in daily life, e.g., wrist and forearm motions such as eating, writing, opening a door, driving an automobile and so on. The wrist joint has got 2 DOF; flexion and extension (FE) and radial–ulnar deviation. Radial–ulnar deviations can also be called adduction and abduction (AA), respectively. Flexion is the bending of the wrist so that the palm approaches the anterior surface of the forearm. The extension is the reverse of flexion. Abduction (radial deviation) is the bending of the wrist towards to the thumb side. The reverse of this motion is called adduction (ulnar deviation). Pronation and supination (PS) are the movements for the forearm. Pronation is applied to a hand such that the palm turns backward or downward. Supination is the rotation of the forearm such that the palm of the hand faces anteriorly to the anatomic position (Omarkulov et al., 2016). These motions are given in Fig. 1.

https://www.mech-sci.net/10/107/2019/ms-10-107-2019-f01

Figure 1DOF of wrist and forearm (Omarkulov et al., 2016).

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Continue —> MS – Design of a robot-assisted exoskeleton for passive wrist and forearm rehabilitation

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[VIDEO] Robot Arm Platform for VR, Rehabilitation, and More – YouTube

Δημοσιεύτηκε στις 6 Δεκ 2018

LinkDyn’s robot arm, and the actuator technology it’s built on, has the potential to fundamentally change the way we interact with robots. This technology which features seamless, safe, and smooth interaction with humans and changing physical environments, will drive the next big push in robotics toward humans and robots interacting closely and safely. The robotic arm provides just one example of how this ability to interact easily and safely with robots can empower the human workforce by exploiting the radical benefits of VR training while losing no drawbacks compared to conventional training. This same technology, with slight adjustments to the VR contents and software, can also serve to help patients suffering from physical or neurological injuries to rehabilitate more effectively than ever. The device was built on LinkDyn’s patent-pending actuator platform, which takes cues from human muscle design and cutting edge human-robot research to deliver the highest performing force control in a compact form factor. This fundamental technology is designed from the ground up for seamless human-machine interaction, and enables a massive leap forward in wearable exoskeletons, interactive robots, haptic feedback, and more compared to conventional robotic technology. https://linkdyn.com

 

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[VIDEO] Watch a robotic exoskeleton help a stroke patient walk – YouTube

Although it’s a far cry from the exosuits of science fiction, researchers have developed a robotic exoskeleton that can help stroke victims regain use of their legs. Nine out of 10 stroke patients are afflicted with partial paralysis, leaving some with an abnormal gait. The exosuit works by pulling cords attached to a shoe insole, providing torque to the ankle and correcting the abnormal walking motion. With the suit providing assistance to their joints, the stroke victims are able to maintain their balance, and walk similarly to the way they had prior to their paralysis, the team reports today in Science Translational Medicine. The exosuit is an adaptation of a previous design developed for the Defense Advanced Research Projects Agency Warrior Web program, a Department of Defense plan to develop assistive exosuits for military applications. Although similar mechanical devices have been built in the past to assist in gait therapy, these were bulky and had to be kept tethered to a power source. This new suit is light enough that with a decent battery, it could be used to help patients walk over terrain as well, not just on a treadmill. The researchers say that although the technology needs long-term testing, it could start to decrease the time it takes for stroke patients to recover in the near future.

via Watch a robotic exoskeleton help a stroke patient walk | Science | AAAS

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[Abstract + References] Cartesian Sliding Mode Control of an Upper Extremity Exoskeleton Robot for Rehabilitation

Abstract

Rehabilitation robots play an important role in rehabilitation treatment. Unlike conventional rehabilitation approach, the rehabilitation robotics provides an intensive rehabilitation motion with different modes (passive, active and active-assisted) based on the ability of the exoskeleton robot to perform assistive motion for a long period. However, this technology is still an emerging field. In this chapter, we present a Cartesian adaptive control based on a robust proportional sliding mode combined with time delay estimation for controlling a redundant exoskeleton robot called ETS-MARSE subject to uncertain nonlinear dynamics and external forces. The main objective of this research is to allow the exoskeleton robot to perform both rehabilitation modes, passive and active assistive motions with real subjects. The stability of the closed loop system is solved systematically, ensuring asymptotic convergence of the output tracking errors. Experimental results confirm the efficiency of the proposed control to provide an excellent performance despite the presence of dynamic uncertainties and external disturbances.

References

  1. Baek, J., Jin, M., & Han, S. (2016). A new adaptive sliding-mode control scheme for application to robot manipulators. IEEE Transactions on Industrial Electronics, 63(6), 3628–3637.CrossRefGoogle Scholar
  2. Brahim, B., Maarouf, S., Luna, C. O., Abdelkrim, B., & Rahman, M. (2016a). Adaptive iterative observer based on integral backstepping control for upper extremity exoskelton robot. In 8th International Conference on Modelling, Identification and Control (ICMIC) (pp. 886–891). Piscataway: IEEE.Google Scholar
  3. Brahim, B., Rahman, M. H., Saad, M., & Luna, C. O. (2016b). Iterative estimator-based nonlinear backstepping control of a robotic exoskeleton. World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 10(8), 1313–1319.Google Scholar
  4. Brahmi, B., Saad, M., Lam, J. T. A. T., Luna, C. O., Archambault, P. S., & Rahman, M. H. (2018a). Adaptive control of a 7-DOF exoskeleton robot with uncertainties on kinematics and dynamics. European Journal of Control, 42, 77–87.MathSciNetzbMATHCrossRefGoogle Scholar
  5. Brahmi, B., Saad, M., Luna, C. O., Rahman, M. H., & Brahmi, A. (2018b). Adaptive tracking control of an exoskeleton robot with uncertain dynamics based on estimated time delay control. IEEE/ASME Transactions on Mechatronics, 23, 575–585.CrossRefGoogle Scholar
  6. Chen, P., Chen, C.-W., & Chiang, W.-L. (2009). GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems. Expert Systems with Applications, 36(3), 5872–5879.CrossRefGoogle Scholar
  7. Craig, J. J. (2005). Introduction to robotics: Mechanics and control. Upper Saddle River: Pearson/Prentice Hall.Google Scholar
  8. De Morand, A. (2014). Pratique de la rééducation neurologique. Issy-les-Moulineaux: Elsevier Masson.Google Scholar
  9. Fridman, L. (1999). The problem of chattering: An averaging approach. In Variable structure systems, sliding mode and nonlinear control(pp. 363–386). London: Springer.CrossRefGoogle Scholar
  10. Khalil, H. K., & Grizzle, J. (1996). Nonlinear systems. New Jersey: Prentice Hall.Google Scholar
  11. Khan, A. M., Yun, D.-W., Ali, M. A., Zuhaib, K. M., Yuan, C., & Iqbal, J. (2016). Passivity based adaptive control for upper extremity assist exoskeleton. International Journal of Control, Automation and Systems, 14(1), 291–300.CrossRefGoogle Scholar
  12. Lewis, F. L., Dawson, D. M., & Abdallah, C. T. (2003). Robot manipulator control: Theory and practice. Boca Raton: CRC Press.CrossRefGoogle Scholar
  13. Nakamura, Y., & Hanafusa, H. (1986). Inverse kinematic solutions with singularity robustness for robot manipulator control. Journal of Dynamic Systems, Measurement, and Control, 108(3), 163–171.zbMATHCrossRefGoogle Scholar
  14. Ochoa-Luna, C., Habibur Rahman, M., Saad, M., Archambault, P. S., & Bruce Ferrer, S. (2015). Admittance-based upper limb robotic active and active-assistive movements. International Journal of Advanced Robotic Systems, 12(9), 117.CrossRefGoogle Scholar
  15. Park, C.-W., & Cho, Y.-W. (2007). Robust fuzzy feedback linearisation controllers for Takagi-Sugeno fuzzy models with parametric uncertainties. IET Control Theory & Applications, 1(5), 1242–1254.MathSciNetCrossRefGoogle Scholar
  16. Rahman, M. H., Saad, M., Kenné, J.-P., & Archambault, P. S. (2013). Control of an exoskeleton robot arm with sliding mode exponential reaching law. International Journal of Control, Automation and Systems, 11(1), 92–104.CrossRefGoogle Scholar
  17. Rahman, M. H., Rahman, M. J., Cristobal, O., Saad, M., Kenné, J.-P., & Archambault, P. S. (2015). Development of a whole arm wearable robotic exoskeleton for rehabilitation and to assist upper limb movements. Robotica, 33(1), 19–39.CrossRefGoogle Scholar
  18. Reyes-Sierra, M., & Coello, C. A. C. (2005). A study of fitness inheritance and approximation techniques for multi-objective particle swarm optimization. In IEEE Congress on Evolutionary Computation, Edinburgh (pp. 65–72).Google Scholar
  19. Shieh, H.-J., & Hsu, C.-H. (2008). An adaptive approximator-based backstepping control approach for piezoactuator-driven stages. IEEE Transactions on Industrial Electronics, 55(4), 1729–1738.CrossRefGoogle Scholar
  20. Slotine, J.-J. E., & Li, W. (1991). Applied nonlinear control. Englewood Cliffs: Prentice-Hall.zbMATHGoogle Scholar
  21. Wampler, C. W. (1986). Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods. IEEE Transactions on Systems, Man, and Cybernetics, 16(1), 93–101.zbMATHCrossRefGoogle Scholar
  22. Wang, L., Chai, T., & Zhai, L. (2009). Neural-network-based terminal sliding-mode control of robotic manipulators including actuator dynamics. IEEE Transactions on Industrial Electronics, 56(9), 3296–3304.CrossRefGoogle Scholar
  23. Xie, S. (2016). Advanced robotics for medical rehabilitation. Cham: Springer.CrossRefGoogle Scholar
  24. Yim, J., & Park, J. H. (1999). Nonlinear H  control of robotic manipulator. In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, SMC’99. Proceedings 1999 (pp. 866–871). Piscataway: IEEE.Google Scholar
  25. Youcef-Toumi, K., & Ito, O. (1990). A time delay controller for systems with unknown dynamics. Journal of Dynamic Systems, Measurement, and Control, 112(1), 133–142.zbMATHCrossRefGoogle Scholar
  26. Young, K. D., Utkin, V. I., & Ozguner, U. (1999). A control engineer’s guide to sliding mode control. IEEE Transactions on Control Systems Technology, 7(3), 328–342.CrossRefGoogle Scholar

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[Webthesis] DESIGN AND CONTROL OF A ROBOTIC EXOSKELETON FOR WRIST REHABILITATION – Full Text

Abstract

Control of a exoskeleton with different sensors using a microcontroller and Matlab: This project will be used the exoskeleton for wrist rehabilitation and evaluation designed in the RoboticsLab. This device is actuated with SMA (Shape Memory Alloys) wires and it has two DOF. The objectives of the work will be: to integrate position and pressure sensors into the exoskeleton; to use the information of these sensors to control in position and / or strength the exoskeleton in repetitive tasks for the flexion-extension movement of the wrist; collect data on the execution of the task that could be used by the doctor to evaluate the patient’s progression.

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