[ARTICLE] An Overview of Artificial Intelligence-based Soft Upper Limb Exoskeleton for Rehabilitation: A Descriptive Review – Full Text PDF

Abstract: The upper limb robotic exoskeleton is an electromechanical device which

use to recover a patient’s motor dysfunction in the rehabilitation field. It can provide

repetitive, comprehensive, focused, positive, and precise training to regain the joints

and muscles’ capability. It has been shown that existing robotic exoskeletons are

generally used rigid motors and mechanical structures. Soft robotic devices can be a

correct substitute for rigid ones. Soft exosuits are flexible, portable, comfortable,

user-friendly, low-cost, and travel-friendly. Somehow, they need expertise or

therapist to assist those devices. Also, they cannot be adaptable to different patients

with non-identical physical parameters and various rehabilitation needs. For that

reason, nowadays we need intelligent exoskeletons during rehabilitation which have

to learn from patient’s previous data and act according to it with patient’s intention.

There also has a big gap between theoretical and practical applications for using

those exoskeletons. Most of the intelligent exoskeletons are prototype in manner. To

solve this problem, the robotic exoskeleton should be made both criteria as

ergonomic and portable. The exoskeletons have to the power of decision-making to

avoid the presence of expertise. In this growing field, the present trend is to make

the exoskeleton intelligent and make it more reliable to use in clinical practice.

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Examples of soft exoskeletons for shoulder joints.

[Conference paper] Brief Overview Upper Limb Rehabilitation Robots/Devices – Full Text

Abstract

The rehabilitation approach has changed with the appearance of robots. As a results the rehabilitation costs significantly decrease but also time for both the patient [1], who does not have to commute for long time to the office and medical professionals. Nowadays medicine, computer science, electronics, and engineering, in general, are strongly connected. A group of specialists is working on newer and newer solutions to improve both diagnosis and therapy. This article provides an overview of basic rehabilitation robotic solutions used in the rehabilitation of upper limb functions.

The literature used is based on PubMed and Scopus databases included articles published between 1999 and 2021. Eligibility criteria included upper limb exoskeletons for rehabilitation of both the wrist, elbow, and shoulder joints.

This paper provides an overview of an important research subject and highlights the current knowledge in the field. Despite extensive attempts to develop rehabilitation systems, exoskeletons are primarily uncommercialised despite a large number of prototypes.

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1 Introduction

We live in an aging society, with an increasing number of diseases, in particular CNS diseases, and with other diseases leading to impaired efficiency, such as atherosclerosis, diabetes, osteoarthritis, etc. Taking into account the CNS, at least 450 million people worldwide suffer from neurodegenerative diseases (around 50 million people suffer from neurodegenerative diseases). Other brain diseases include i.e. stroke (15 million), traumatic brain injury (TBI), and brain tumors, which affect about 1.5 million people. In total, it is about 0.5 billion people affected by brain diseases. According to WHO, the third on the list of civilization diseases leading to disability is stroke. An indispensable element in the process of treating strokes is rehabilitation, the effectiveness of which is very strongly dependent on the time of its implementation after the occurrence of a stroke. Treatment of these diseases and conditions, which largely contribute to motor impairment, is lengthy (situation-dependent), costly and involves many people, including healthcare professionals and family members.

Currently, drug treatment in the form of injections, suppositories, tablets and ointments is the most common. This treatment often leads only to a reduction of pain and only partial recovery. Moreover, the effects of such treatment are not permanent. It is impossible to imagine modern medical treatment without rehabilitation and physiotherapy, whose aim is to restore full and permanent functional capacity. It must be admitted that sometimes it is necessary to carry out surgical – orthopedic treatment, which, however, in order to achieve full success requires the effect of postoperative rehabilitation.

Performing therapeutic exercises requires great commitment from the physiotherapist and is very time consuming. To achieve the expected effect the exercises have to be repeated many times individually (patient – therapist). Group exercises are more beneficial in organisational and economic terms, but unfortunately they are not equivalent and do not lead to the expected effects of therapy. The constant repetition of the therapeutic movement sequences leads to the therapist’s weariness, which may result in less accurate execution of the exercise or shortening the duration of the exercises. The solution to the problem may be the use of robots to relieve the physiotherapist from monotonous and exhausting physical work, at the same time allowing for the implementation of trainings with many patients by one physiotherapist. In addition, a rehabilitator using a robot obtains a diagnostic tool, because the robot’s sensors can, for example, measure ranges of mobility in a given joint, or the strength of selected muscles.

In this review the literature used is based on PubMed and Scopus databases including articles published between 1999 and 2021. A search was used based on the following keywords: “upper limb”, “robot” “rehabilitation”. The total number of results was 1700, including 156 reviews. The review was narrowed down to full text of publications available without charge and review papers and systematic reviews, which numbered 94. Next, the database was searched using the keywords “(exoskeleton) AND (upper limb)”, and the area was also narrowed down to full text of publications, the number of which was 20. From among the available articles, only those focused on the presentation of exoskeletons that can be rehabilitated in all 3 joints (wrist and hand joints, elbow joint, shoulder and clavicle joint) were selected.

As it turns out, the information found in the searched database would not allow the presentation that would be 100% satisfactory to the readers, because key technical information is often not described in publications. Taking into account, for example, one of the main parameters that distinguishes selected robots, i.e. degrees of freedom, it was necessary to additionally search manufacturers’ websites or additional materials found on the Internet, which made it difficult to create the review.[…]

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Fig. 1. ARM-100. Own source (www.itam.lukasiewicz.gov.pl)

[Abstract] Compatibility evaluation of a 4-DOF ergonomic exoskeleton for upper limb rehabilitation

Highlights

• A novel ergonomic exoskeleton is presented for 4-DOF upper limb rehabilitation.
• A corresponding prototype is developed based on the proposed exoskeleton.
• The interaction forces and displacements at the physical connection were sampled.
• The exoskeleton’s compatibility is quantitatively evaluated under two modes.
• An ergonomically designed exoskeleton can markedly reduce the interaction loads.

Abstract

Due to their advantages of high durability, low labor intensiveness and high repeatability, upper limb exoskeletons have become promising tools in stroke rehabilitation. The act of decreasing the undesired interactional loads caused by exoskeleton incompatibility remains an enormous challenge in the design of ergonomic exoskeletons. In this article, a novel 4-DOF upper limb exoskeleton that is kinematically compatible with the upper limb is proposed. A prototype of the proposed exoskeleton was developed. Subsequently, the interaction forces, torques and displacements at the physical human-exoskeleton connection interfaces were detected under static and dynamic modes to quantitatively evaluate the compatibility of the exoskeleton. The results indicated that the proposed exoskeleton can significantly decrease the undesired interactional load at the connective interfaces, and its ergonomic design was found to be effective; thus, this exoskeleton may be used for the rehabilitation of human upper limbs

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[Abstract] Developments and clinical evaluations of robotic exoskeleton technology for human upper-limb rehabilitation

Abstract

The development of upper limb and lower extremity robotic exoskeletons has emerged as a way to improve the quality of life as well as act as a primary rehabilitation device for individuals suffering from stroke or spinal cord injury. This paper contains extractions from the database of robotic exoskeleton for human upper limb rehabilitation and prime factors behind the burden of stroke. Various studies on stroke-induced deficiency from different countries were included in the review. The data were extracted from both clinical tests and surveys. Though there have been splendid advancements in this field, they still present enormous challenges. This paper provides the current developments, progress and research challenges in exoskeleton technology along with future research directions associated with the field of exoskeletons and orthosis. Robot-assisted training (RT) was found to be more effective than conventional training (CT) sessions. The present research articles in this field have many weaknesses as they do not cover the systematic review including the clinical studies and various surveys that lay a foundation for the requirement of robotic assistive devices. This review paper also discusses various exoskeleton devices that have been clinically evaluated.

 

via Developments and clinical evaluations of robotic exoskeleton technology for human upper-limb rehabilitation: Advanced Robotics: Vol 0, No 0

[Abstract] Exoskeleton design and adaptive compliance control for hand rehabilitation

Abstract

An adaptive robotic system has been developed to be used for hand rehabilitation. Previously developed exoskeletons are either very complex in terms of mechanism, hardware and software, or simple but have limited functionality only for a specific rehabilitation task. Some of these studies use simple position controllers considering only to improve the trajectory tracking performance of the exoskeleton which is inadequate in terms of safety and health of the patient. Some of them focus only on either passive or active rehabilitation, but not both together. Some others use EMG signals to assist the patient, but this time active rehabilitation is impossible unless different designs and control strategies are not developed. The proposed mechanical structure is extremely simple. The middle and the proximal phalanxes are used as a link of consecutively connected two 4-bar mechanisms, respectively. The PIP and MCP joints are actuated by a single electro mechanical cylinder to produce complex flexion and extension movements. It is simpler than similar ones from aspect with the mechanical structure and the biodynamic fit of the hand, making it practicable in terms of production and personal usage. Simple design lets to implement adaptive compliance controller for all active and passive rehabilitation tasks, instead of developing complex and different strategies for different rehabilitation tasks. Furthermore, using the Luenberger observer for unmeasured velocity state variable, an on-line estimation method is used to estimate the dynamic parameters of the system. This makes possible to estimate the force exerted by the patient as well, without a force sensor.

 

via Exoskeleton design and adaptive compliance control for hand rehabilitation – Gazi Akgun, Ahmet Emre Cetin, Erkan Kaplanoglu,

[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 (Go–backward). 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 [8, 9, 10]. 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 [12, 13, 14]. 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 [11, 15], 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 [15, 16, 17]. 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 [21, 22], 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. 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

[…]

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

[ARTICLE] Development, Dynamic Modeling, and Multi-Modal Control of a Therapeutic Exoskeleton for Upper Limb Rehabilitation Training – Full Text

Abstract

Robot-assisted training is a promising technology in clinical rehabilitation providing effective treatment to the patients with motor disability. In this paper, a multi-modal control strategy for a therapeutic upper limb exoskeleton is proposed to assist the disabled persons perform patient-passive training and patient-cooperative training. A comprehensive overview of the exoskeleton with seven actuated degrees of freedom is introduced. The dynamic modeling and parameters identification strategies of the human-robot interaction system are analyzed. Moreover, an adaptive sliding mode controller with disturbance observer (ASMCDO) is developed to ensure the position control accuracy in patient-passive training. A cascade-proportional-integral-derivative (CPID)-based impedance controller with graphical game-like interface is designed to improve interaction compliance and motivate the active participation of patients in patient-cooperative training. Three typical experiments are conducted to verify the feasibility of the proposed control strategy, including the trajectory tracking experiments, the trajectory tracking experiments with impedance adjustment, and the intention-based training experiments. The experimental results suggest that the tracking error of ASMCDO controller is smaller than that of terminal sliding mode controller. By optimally changing the impedance parameters of CPID-based impedance controller, the training intensity can be adjusted to meet the requirement of different patients.

1. Introduction

Over the past decade, the increasing stroke patient population has brought great economic and medical pressures to the whole society. Surviving stroke patients usually have a lower quality of life dues to physical disability and cognitive impairment. Studies on clinical stroke treatment indicate that appropriate rehabilitation training has positive therapeutic effects for avoiding muscle atrophy and recovering musculoskeletal motor functions. However, the conventional one-on-one manual-assisted movement training conducted by physiotherapists suffers from many inherent limitations, such as high labor intensity, high cost, long time consumption, lack of repeatability, low participation levels of patient, and high dependence on personnel with specialized skills [1,2]. In recent years, robot-assisted rehabilitation therapies have gained growing interest from academic researchers and the healthcare industry around the world due to their unique advantages and promising application perspectives. Compared with the traditional manual rehabilitation treatment, the combination of robotic technologies and clinical experience can significantly improve the performance and quality of training. Robot-assisted therapy is capable of delivering high-intensity, long-endurance, goal-directed, and low-cost rehabilitation treatment. Moreover, the functional motivations of patient can be activated to enhance active participation and recover cognitive functions. The physical parameters and therapy data can be recorded and analyzed via sensing system, and that can provide objective basis to optimize training strategy and accelerate recovery process [3,4].

Many therapeutic robot system have been developed to assist stroke patients with motor dysfunctions perform the desired rehabilitation training. The existing rehabilitation robotic devices can be categorized into two types, i.e., end-effector-based robots and exoskeleton-based robots. End-effector-based robot has only a connection between its distal end and the impaired extremity of patient. However, the movement of end-effector cannot uniquely identify the configuration of human limb due to the kinematic redundancy. Miller et al. developed a lightweight and potable end-effector-based therapeutic robot, which is integrated with a wrist and finger force sensor module named WFES, for the upper limb rehabilitation training of hemiplegic stroke patients [5]. Pedro et al. developed a parallel kinematic mechanism (PKM) with two translational and two rotational degrees of freedom (DOFs) for knee diagnosis and rehabilitation tasks [6]. Kang et al. proposed a modular and reconfigurable wrist robot called CR2-Haptic for post-stroke subjects to train forearm and wrist movements [7]. Besides, many other end-effector-based therapeutic robot have been investigated and can be referred to [8,9,10,11,12,13]. Comparatively, the exoskeleton-based rehabilitation robots are developed with more complex structures imitating the anatomical human skeleton and guaranteeing the alignment between the joints axis of robot and impaired limb. ChARMin is a powered exoskeleton integrated with audiovisual game-like interface. It can provide intensive pediatric arm rehabilitation training for the children and adolescents with affected motor functions [14]. Simon et al. proposed a spherical shoulder exoskeleton with a double parallelogram linkage to eliminate singularities and achieve good manipulability properties [15]. Crea et al. developed a semi-autonomous whole-arm exoskeleton for the stroke patients performing activities of daily living (ADL) utilizing hybrid electroencephalography and electrooculography feedback signals [16]. Many other representative exoskeleton-based therapeutic robot have also been designed, such as CAREX-7 [17], RUPERT [18], ULEL [19], ArmeoPower [20], Indego [21], and ETS-MARSE [22].

The effectiveness of robot-assisted rehabilitation training depends on the control strategies applied in the therapeutic robot system. Currently, many kinds of control strategies have been developed according to the requirements of patients with various impairment severities in different therapy periods. The existing control schemes can be basically divided into two categories based on the interaction between therapeutic robots and patients, i.e., patient-passive training control and patient-cooperative training control. During the acute period of hemiplegia, the impaired extremity is fully paralyzed without any muscle contraction. The patient-passive training can imitate the manual therapeutic actions of a physiotherapist. It is especially well suited for the patients with severe paralysis to passively execute repetitive reaching missions along predefined training trajectories. However, it is a challenge to guarantee the position control accuracy during rehabilitation training due to the highly nonlinear properties and unexpected uncertainties of human-robot interaction. Different kinds of control algorithms have been developed to improve control performance of patient-passive training, including neural proportional-integral-derivative (PID) control [23], neural proportional-integral (PI) control [24], adaptive nonsingular terminal sliding mode control (SMC) [25], disturbance observer-based fuzzy control [26], neural-fuzzy adaptive control [27], adaptive backlash compensation control [28], and so on. Comparatively, the patient-cooperation training is applicable for the patients at the comparative recovery period, who have regained parts of motor functions. Clinical studies show that integrating the voluntary efforts of patients into rehabilitation training benefits to accelerate recovery progress and promote psychological confidence. Thus, patient-cooperation training should be able to regulate the human-robot interaction in accordance with the motion intentions and hemiplegia degrees of patients. Many patient-cooperation control strategies have been proposed, such as minimal assist-as-needed controller [29], myoelectric pattern recognition controller [30], electromyography (EMG)-based model predictive controllers [31], subject-adaptive controller [32], and fuzzy adaptive admittance controller [33].

Taking the above into consideration, the contribution of this paper is to develop an upper limb exoskeleton to assist the patient with motor disabilities perform multi-modal rehabilitation training. Firstly, the overall mechanical structure and the MATLAB/xPC-based real-time control system of the proposed therapeutic robot are introduced. Secondly, the dynamic modeling of the human-robot system is researched, and the dynamics parameters are obtained via virtual prototype and calibration experiments. After that, a multi-modal control strategy integrated with an adaptive sliding mode controller and a cascade-proportional-integral-derivative (CPID)-based impedance controller is proposed. The controller is combined with an audiovisual therapy interface and is able to realize patient-passive and patient-cooperation training based on the motor ability of patient. Finally, the effectiveness and feasibility of the developed rehabilitation exoskeleton system and control scheme are verified through three experiments conducted by several volunteers.[…]

Continue —> Sensors | Free Full-Text | Development, Dynamic Modeling, and Multi-Modal Control of a Therapeutic Exoskeleton for Upper Limb Rehabilitation Training | HTML

Figure 1. Architecture and major components of the upper extremity rehabilitation exoskeleton. (a) Virtual prototype model. (b) Real-life picture of exoskeleton.

[ARTICLE] Patient-Active Control of a Powered Exoskeleton Targeting Upper Limb Rehabilitation Training – Full Text

Robot-assisted therapy affords effective advantages to the rehabilitation training of patients with motion impairment problems. To meet the challenge of integrating the active participation of a patient in robotic training, this study presents an admittance-based patient-active control scheme for real-time intention-driven control of a powered upper limb exoskeleton. A comprehensive overview is proposed to introduce the major mechanical structure and the real-time control system of the developed therapeutic robot, which provides seven actuated degrees of freedom and achieves the natural ranges of human arm movement. Moreover, the dynamic characteristics of the human-exoskeleton system are studied via a Lagrangian method. The patient-active control strategy consisting of an admittance module and a virtual environment module is developed to regulate the robot configurations and interaction forces during rehabilitation training. An audiovisual game-like interface is integrated into the therapeutic system to encourage the voluntary efforts of the patient and recover the neural plasticity of the brain. Further experimental investigation, involving a position tracking experiment, a free arm training experiment, and a virtual airplane-game operation experiment, is conducted with three healthy subjects and eight hemiplegic patients with different motor abilities. Experimental results validate the feasibility of the proposed scheme in providing patient-active rehabilitation training.

Introduction

Stroke is a severe neurological disease caused by the blockages or rupture of cerebral blood vessels, leading to significant physical disability and cognitive impairment (1, 2). The recent statistics from the World Health Organization indicate that worldwide 15 million people annually suffer from the effect of stroke, and more than 5 million stroke patients survive and, however, require a prolonged physical therapy to recover motor function. Recent trends predict increased stroke incidence at younger ages in the upcoming years (3, 4). Approximately four-fifths of all survived stroke patients suffer from the problems of hemiparesis or hemiplegia and, as a result, have difficulties in performing activities of daily living (ADL). Stroke causes tremendous mental and economic pressure on the patients and their families (5). Medical research has proved that, owing to the neural plasticity of the human brain, appropriate rehabilitation trainings are beneficial for stroke survivors to recover musculoskeletal motor abilities. Repetitive and task-oriented functional activities have substantial positive effects on improving motor coordination and avoiding muscle atrophy (6, 7). Traditional stroke rehabilitation therapy involves many medical disciplines, such as orthopedics, physical medicine, and neurophysiology (8, 9). Physiotherapists and medical personnel are required to provide for months one-on-one interactions to patients that are labor intensive, time consuming, patient-passive, and costly. Besides, the effectiveness of traditional therapeutic trainings is limited by the personal experiences and skills of therapists (10, 11).

In recent decades, robot-assisted rehabilitation therapies have attracted increasing attention because of their unique advantages and promising applications (12, 13). Compared with the traditional manual repetitive therapy, the use of robotic technologies helps improve the performance and efficiency of therapeutic training (14). Robot-assisted therapy can deliver high-intensive, long-endurance, and goal-directed rehabilitation treatments and reduce expense. Besides, the physical parameters and the training performance of patients can be monitored and evaluated via built-in sensing systems that facilitate the improvement of the rehabilitation strategy (15, 16). Many therapeutic robots have been developed to improve the motor functions of the upper extremity of disabled stroke patients exhibiting permanent sensorimotor arm impairments (17). The existing robots used for upper limb training can be basically classified into two types: end-point robots and exoskeleton robots. End-point robots work by applying external forces to the distal end of impaired limbs, and some examples are MIME (18), HipBot (19), GENTLE/s (20), and TA-WREX (21). Comparatively, exoskeleton robots have complex structures similar to anatomy of the human skeleton; some examples of such robots are NMES (22), HES (23), NEUROExos (24), CAREX-7 (25), IntelliArm (26), BONES (27), and RUPERT (28). The joints of the exoskeleton need to be aligned with the human anatomical joints for effective transfer of interactive forces.

The control strategies applied in therapeutic robots are important to ensure the effectiveness of rehabilitation training. So far, according to the training requirement of patients with different impairment severities, many control schemes have been developed to perform therapy and accelerate recovery. Early rehabilitation robot systems implemented patient-passive control algorithms to imitate the manual therapeutic actions of therapists. These training schemes are suitable for patients with severe paralysis to passively execute repetitive reaching tasks along predefined trajectories. Primary clinical results indicate that patient-passive training contributes to motivating muscle contraction and preventing deterioration of arm functions. The control of the human–robot interaction system is a great challenge due to its highly nonlinear characteristics. Many control algorithms have been proposed to enhance the tracking accuracy of passive training, such as the robust adaptive neural controller (29), fuzzy adaptive backstepping controller (30), neural proportional–integral–derivative (PID) controller (31), fuzzy sliding mode controller (32), and neuron PI controller (33).

The major disadvantage of patient-passive training is that the active participation of patients is neglected during therapeutic treatment (34). Several studies suggest that, for the patients who have regained parts of motor functions, the rehabilitation treatment integrated with the voluntary efforts of patients facilitates the recovery of lost motor ability (35). The patient-active control, normally referred as patient-cooperative control and assist-as-needed control, is capable of regulating the human–robot interaction depending on the motion intention and the disability level of patients. Keller et al. proposed an exoskeleton for pediatric arm rehabilitation. A multimodal patient-cooperative control strategy was developed to assist upper limb movements with an audiovisual game-like interface (36). Duschauwicke et al. proposed an impedance-based control approach for patient-cooperative robot-aided gait rehabilitation. The affected limb was constrained with a virtual tunnel around the desired spatial path (37). Ye et al. proposed an adaptive electromyography (EMG) signals-based control strategy for an exoskeleton to provide efficient motion guidance and training assistance (38). Oldewurtel et al. developed a hybrid admittance–impedance controller to maximize the contribution of patients during rehabilitation training (39). Banala et al. developed a force-field assist-as-need controller for intensive gait rehabilitation training (40). However, there are two limitations in the existing patient-cooperative control strategies. Firstly, the rehabilitation training process is not completely patient-active, as the patient needs to perform training tasks along a certain predefined trajectory. Secondly, existing control strategies are executed in self-designed virtual scenarios that are generally too simple, rough, and uninteresting. Besides, applying a certain control strategy to different virtual reality scenarios is difficult.

Taking the above issues into consideration, the main contribution of this paper is to develop a control strategy for an upper limb exoskeleton to assist disabled patients in performing active rehabilitation training in a virtual scenario based on their own active motion intentions. Firstly, the overall structure design and the real-time control system of the exoskeleton system are briefly introduced. A dynamic model of the human–robot interaction system is then established using the Lagrangian approach. After that, an admittance-based patient-active controller combined with an audiovisual therapy interface is proposed to induce the active participation of patients during training. Existing commercial virtual games without a specific predetermined training trajectory can be integrated into the controller via a virtual keyboard unit. Finally, three types of experiments, namely the position tracking experiment without interaction force, the free arm movement experiment, and the virtual airplane-game operation experiment, are conducted with healthy and disabled subjects. The experimental results demonstrate the feasibility of the proposed exoskeleton and control strategy.

Exoskeleton Robot Design

The architecture of the proposed exoskeleton is shown in Figure 1. This wearable force-feedback exoskeleton robot has seven actuated degrees of freedom (DOFs) and two passive DOFs covering the natural range of movement (ROM) of humans in ADL. The robot has been designed with an open-chain structure to mimic the anatomy of the human right arm and provide controllable assistance torque to each robot joint. There are three actuated DOFs at the shoulder for internal/external rotation, abduction/adduction, and flexion/extension; two DOFs at the elbow for flexion/extension and pronation/supination; and two DOFs at the wrist for flexion/extension and ulnal/radial deviation. Besides, since the center of rotation of the glenohumeral joint varies with the shoulder girdle movement, the robot is mounted on a self-aligning platform with two passive translational DOFs to compensate the human–robot misalignment and to guarantee interaction comfort. […]

Figure 1. Architecture of upper limb rehabilitation exoskeleton (1-Self-aligning platform; 2-AC servo motor; 3-Bowden cable components; 4-Support frame; 5-Wheelchair; 6-Elbow flexion/extension; 7-Proximal force/torque sensor; 8-Wrist flexion/extension; 9-Wrist ulnal/radial deviation; 10-Distal force/torque sensor; 11-Forearm pronation/supination; 12-Auxiliary links; 13-Shoulder flexion/extension; 14-Shoulder abduction/adduction; 15-Shoulder internal/external; 16-Free-length spring).

 

Continue —>  Frontiers | Patient-Active Control of a Powered Exoskeleton Targeting Upper Limb Rehabilitation Training | Neurology

[Abstract] Development of an RBFN-based neural-fuzzy adaptive control strategy for an upper limb rehabilitation exoskeleton

Abstract

The patients of paralysis with motion impairment problems require extensive rehabilitation programs to regain motor functions. The great labor intensity and limited therapeutic effect of traditional human-based manual treatment have recently boosted the development of robot-assisted rehabilitation therapy. In the present work, a neural-fuzzy adaptive controller (NFAC) based on radial basis function network (RBFN) is developed for a rehabilitation exoskeleton to provide human arm movement assistance. A comprehensive overview is presented to describe the mechanical structure and electrical real-time control system of the therapeutic robot, which provides seven actuated degrees of freedom (DOFs) and achieves natural ranges of upper extremity movement. For the purpose of supporting the disable patients to perform repetitive passive rehabilitation training, the RBFN-based NFAC algorithm is proposed to guarantee trajectory tracking accuracy with parametric uncertainties and environmental disturbances. The stability of the proposed control scheme is demonstrated through Lyapunov stability theory. Further experimental investigation, involving the position tracking experiment and the frequency response experiment, are conducted to compare the control performance of the proposed method to those of cascaded proportional-integral-derivative controller (CPID) and fuzzy sliding mode controller (FSMC). The comparison results indicate that the proposed RBFN-based NFAC algorithm is capable of obtaining lower position tracking error and better frequency response characteristic.

 

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[Abstract] Ethical Considerations in Providing an Upper Limb Exoskeleton Device for Stroke Patients

Abstract

The health care system needs to face new and advanced medical technologies that can improve the patients’ quality of life by replacing lost or decreased functions. In stroke patients, the disabilities that follow cerebral lesions may impair the mandatory daily activities of an independent life. These activities are dependent mostly on the patient’s upper limb function so that they can carry out most of the common activities associated with a normal life. Therefore, an upper limb exoskeleton device for stroke patients can contribute a real improvement of quality of their life. The ethical problems that need to be considered are linked to the correct adjustment of the upper limb skills in order to satisfy the patient’s expectations, but within physiological limits. The debate regarding the medical devices dedicated to neurorehabilitation is focused on their ability to be beneficial to the patient’s life, keeping away damages, injustice, and risks.

Source: Ethical Considerations in Providing an Upper Limb Exoskeleton Device for Stroke Patients – Medical Hypotheses