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In the last decades, wearable powered orthoses have been introduced in the state of the art for lower-limb rehabilitation. Most of these applications are driven by electric motors. Comparing with electric motor actuators, pneumatic artificial muscle (PAM) actuators are compliant because of the elasticity of PAMs. Consequently, for more safety and comfort of lower-limb rehabilitation, a compliant robotic orthosis powered by PAMs is developed. Based on safe control, a new control method, proxy-based sliding mode control (PSMC), was introduced into rehabilitation robotics a few years ago. It combined safety and accuracy of tracking to make it suitable for the safe control of PAM actuators. As the reason of low frequency response of PAM actuators and variable loads caused by different human subjects, the fixed parameters of PSMC makes the tracking performance vary from subject to subject, and lacks robustness. This paper presents a modification of PSMC by using neural network to tune PSMC gains online, and implements both PSMC and modified PSMC control schemes in the robotic orthosis. Experimental results demonstrate that the improved PSMC method performs better on tracking with little degradation on safety for different loads and human subjects. [ABSTRACT FROM AUTHOR]

A robotic orthosis has been developed for spinal cord injury patients which consists of an exoskeleton robotic suit with actuators at the hip and knee joints. In this article, a presentation is made on how to generate a gait pattern to control the robotic orthosis. First, the features needed to characterize a gait pattern are defined, and then a description is given of how to extract features from the normal gait patterns contained in experimental encoder data on the hip and knee joints of a person walking normally. Next, the physical meaning of these features in a gait pattern is discussed, and methods of varying the walking speed and step size through the adjustment of features are then considered. It is shown that the desired gait pattern can be obtained by connecting these features with spline interpolation. Finally, a crutch gait pattern is produced using these features, and this is applied to the exoskeleton robot ROBIN-P1 in order to verify the proposed method. [ABSTRACT FROM AUTHOR]

This paper focuses on the development and position control of a robotic system specifically designed for passive rehabilitation. The system is a two-degree-of-freedom robot, dedicated to elbow and a wrist joints. The objective of this research is to create a robotic rehabilitation emulation test that allows therapists to implement their therapeutic programs smoothly and efficiently while minimizing any excessive movements or overshooting. The real-time experiments were conducted to compare the performances of four controllers: model predictive controller (MPC), proportional–integral (PI) controller, RST controller, and constrained MPC (CMPC). Sinusoidal and filtered trajectories were studied to determine the optimal controller for energy efficiency. The obtained results demonstrate that the embedded MPC outperforms the other two controllers. The constrained model predictive control design is employed to address the stringent constraints of the robot. Using a rapid quadratic programming solver based on the Hildreth method ensures compliance with the short real-time implementation sampling time. This approach achieves reduced computational costs while balancing control effort against closed-loop performance. [ABSTRACT FROM AUTHOR]

In this research activity, a new methodology for the synthesis of hand exoskeleton mechanisms has been developed and validated through real prototypes. The innovative methodology is based on a new parallel mechanism and has been tested by building a robotic assistive device for hand opening disabilities applied to real cases. The studied robotic orthosis is designed to be a low-cost, adaptable and portable hand exoskeletons to assist people with hand opening disabilities in their activities of daily livings. As regards the methodology for the synthesis of hand exoskeleton mechanism, the authors propose to use a motion capture system to acquire the real hand phalanx trajectories and the geometrical characteristics of the patient's hand, and to use optimization algorithms to properly defines the novel kinematic mechanism that better fits the finger trajectories. The preliminary testing phase of the prototype on a single patient is concluded; currently, through the collaboration with an Italian rehabilitation center, a group of patients are testing the proposed HES methodology. [ABSTRACT FROM AUTHOR]

Patients with motor impairments (e.g., stroke, bone fracture, Parkinson's) are sensitive to the wearing experience of rehabilitation devices, and they often have difficulty accurately positioning them at an accurate position. While solutions involving optical systems or IMUs could potentially help alleviate the issue, they often introduce other challenges such as privacy concerns or discomforting experiences. With the emergence of wearable soft sensors during the last few decades, researchers widely apply soft sensors in rehabilitation to improve the wearing experience. However, these approaches have primarily focused on analyzing the sensor readings to improve accuracy rather than addressing the needs of patients and healthcare providers, and there is a lack of comprehensive design investigation and need assessment based on soft sensor-based rehabilitation systems for motor-impaired patients and their doctors. In this study, we developed an application, RehabFAB, utilizing fabric sensors for rehabilitation purposes. Besides, we evaluated our application and device and investigated the needs of patients and doctors for potential home rehabilitation applications. The investigation was conducted through thematic analysis, correlation analysis and System Usability Scale. The experimental results validated the efficacy, reliability and usability of our approach, with a SUS score of 81.75. In addition, the RehabFAB meets the expectations of motor-impaired patients and medical professionals as a home rehabilitation tool. Our core contributions lie in a thorough evaluation of the needs of motor-impaired patients in order to design a stable and reliable motion-tracking device based on soft sensors for their recovery. [ABSTRACT FROM AUTHOR]

In recent technological advancements, designing of exoskeleton have gained attraction from various scientists and researchers across the world. Exoskeletons are assistive devices which are used by the subjects with limb impairments. Various trajectory tracking control schemes have been developed in the past, however there are several challenges like human machine interaction, external disturbances and model uncertainties present that should be handled in the efficient designing of a controller. This paper deals with the development of 2-link lower limb extremity manipulator for rehabilitation purposes in bio-medical engineering. A novel control strategy is proposed to track the desired trajectory for assistive devices using the robust adaptive sliding mode controller coupled with Radial Basis Function (RBF) neural network and fuzzy techniques for function approximation. The simulations are carried out on a musculoskeletal model Gait2354 which can be scaled according to the physical dimensions of the experimental subject developed in OpenSim software. The controller designed is robust in terms of uncertainties and disturbances present in the system and reduces chattering problems. In addition, Minimum Learning Parameter is also incorporated in the system which reduces the number of parameters to only one, thereby reducing the computational cost and enhancing the real time performance of the system. For validation of the proposed model, Matlab Simulink environment is used. The different performance index parameters used in comparative analysis are Integral Absolute Error (IAE), Integral Square Error (ISE), Integral Time Absolute Error (ITAE) and Integral Time Square Error (ITSE). The simulation results show that SMC-RBF network has better performance and tracking rate as compared to other controllers implemented. The results obtained were also compared with the existing method to prove their effectiveness. The simulation results obtained ensures to provide effective and comfortable rehabilitation services for lower limb extremity in the future. [ABSTRACT FROM AUTHOR]

The use of social assistive robots for interactive stimulation has strong potential in neurorehabilitation therapies. It is of particular interest in the case of pediatric patients to promote children's motivation and adherence, specially when those robots are able of guide gamified activities, as it is the case of NAOTherapist. NAOTherapist is a Social Assistive Robotics (SAR) platform for hands-off rehabilitation based on upper-limb activities, that was originally designed for pediatric patients with Cerebral Palsy (CP) or Obstetric Braxial Plexus Palsy (OBPP). Formerly, it endowed the therapists with tools to perform rehabilitation exercises. This paper proposes the gamification of NAOTherapist in order to incorporate additional characteristics which allow its intensive use in new rehabilitation procedures, such as the Hand-Arm Bimanual Intensive Therapy (HABIT). This intensive therapy setting involves daily activities in several consecutive days, which require a strong engagement of the patients with the therapeutic methods and the acceptation of the NAOTherapist as a rehabilitation system. The gamified system shows very accurate results considering the different aspects defined in the USUS methodology; namely Usability, Social acceptance, User experience and Societal impact. [ABSTRACT FROM AUTHOR]

Using lower limb rehabilitation robots (LLRRs) to help stroke patients recover their walking ability is attracting more and more attention presently. Previous studies have shown that gait rehabilitation training with natural gait pattern can improve the therapeutic outputs. However, how to generate the personalized gait trajectory has not been well researched. In this paper, a personalized gait generation method based anthropometric features is proposed. Firstly, gait trajectories are fitted and simplified into Fourier coefficient vectors, which are used to represent gait trajectories. Secondly, fourteen body features are used to generate the personalized gait trajectories and the feature set is further optimized based on the minimal redundancy maximal relevance criterion for easy application on the LLRR. Then, the relationship between the optimized feature set and gait trajectories is modeled by using the RF algorithm. Finally, the performance of the proposed method is demonstrated by several comparison experiments. [ABSTRACT FROM AUTHOR]

Due to the rapid advancements in the human–robot interface, soft robotics is getting attention in recent times. Conventional robots or robotic devices are not compatible with being used for internal application of the patient's body even outer applications are also inhibited due to lack of flexibility, heavyweight, and less motion creation. As physically challenged persons require different degree of comfortability and flexibility in their everyday work or therapy, textile-based soft robots have diverse advantages including safety, easiness to use, and proper deformability to assist them. Any robotic system for rehabilitation of the impaired patients is composed of three different units of sensing, actuation, and controlling. The accommodation of these three units in conventional robotic systems makes the device heavy, rigid, and mechanically less functional. Textile materials have a higher level of inherent and programmable properties regarding physical and chemical structure, weight, durability, and deformability. Leveraging textile structures, many efforts have already been done for the fabrication of soft robots. This review will discuss the present scenario of textile-based soft actuators that are made to assist physically challenged individuals. Materials, fabrication methods, outcomes of the devices, requirement specific applications of soft robotics for physically challenged individuals have been discussed. Finally, we suggest some future directions for textile-based soft robots addressing some challenges in this field. [ABSTRACT FROM AUTHOR]

An adjustable torsional stiffness control system with a magnetic coupling that offers compliance for robotic devices to cooperate with humans is presented in this paper. Most robotic devices, such as robotic grippers or robotic arms, achieve torque control by manipulating current in actuators. As power consumption derived from the actuator current is not precise enough to represent the output torque, series elastic actuation (SEA) is instead proposed and examined in this research that involves adding an elastic component between the actuator and the load, allowing the force to be measured by calculating the deformation of this elastic component. In this paper, an axial magnetic coupling having 24 mm in diameter and 3 mm in thickness forming is adopted as the elastic component of the magnetic series elastic actuation (MSEA) system. Through the MSEA controller, the load stiffness can be controlled as a variable spring in a range of 100 to 500 mN·m/rad, producing a maximum output torque of 300 mN·m, force resolution of 0.1 mN·m and control error less than 6 mN·m. By adding the SEA component between the actuator and the load, one can control system's output torque and output stiffness simultaneously, making the actuator more flexible and compliant. [ABSTRACT FROM AUTHOR]

Over the past decade, there has been a rapid increase in applications of multibody system dynamics to the predictive simulation of human movement. Using predictive "what-if" human dynamic simulations that do not rely on experimental testing or prototypes, new medical interventions and devices can be developed more quickly, cheaply, and safely. In this paper, we provide a comprehensive review of research into the predictive multibody dynamic simulation of human movements, with applications in clinical practice, medical and assistive device design, sports, and industrial ergonomics. Multibody models of human neuromusculoskeletal systems are reviewed, including models of joints, contacts, and muscle forces or torques, followed by a review of simulation approaches that use optimal control methods and a cost function to predict human movements. Modelling and optimal control software are also reviewed, and directions for future research are suggested. [ABSTRACT FROM AUTHOR]

Recent studies have shown that physical exercise and the chance of increased mobility have a beneficial effect on ALS patients, improving functional ability and pulmonary function [[3]]. General assumptions for the development of wearable systems for ALS patients The exoskeleton presented in this work has been designed to be versatile and adaptable to the phenotypic heterogeneity characterising ALS patients. Dear Sirs, Amyotrophic lateral sclerosis (ALS) is a heterogeneous neurodegenerative disease, characterized by the degeneration of both upper and lower motor neurons. Regarding the clinical presentation, ALS shows wide phenotypic variability, including the following variants: bulbar, classic spinal, flail arm, flail leg, and predominant upper or lower motor neuron ALS phenotypes. [Extracted from the article]

The observation of gait phases provides essential information for controller design and performance evaluation of lower extremity wearable robots. Specifically, gait events are often defined and detected to distinguish the transition of gait phases. To achieve this, rule-based gait event detection algorithms detect gait events by utilizing the repetitive features in human walking with very few sensors and simple logic. Besides, many of these algorithms define gait events as characteristic features that are detectable from the sensor measurements. However, conventional methods have not fully considered the correlation between the sensor measurement and characteristics of the human motion. Moreover, these methods were only accurate for a limited condition of human motion, for example, walking only or running without sensor noise. Therefore, in this paper, we propose a gait event detection algorithm considering the full kinematic characteristics of the lower extremity under various gait conditions. The proposed algorithm demonstrates robust performance for both walking and running. Besides, to minimize the time delay and the false information in the detected gait events, this paper also proposes a robust signal dithering algorithm that reduces the sensor noise with a limited phase delay. Overall, the performances of the proposed methods are verified through gait experiments with human subjects. [ABSTRACT FROM AUTHOR]

Pneumatic artificial muscle (PAM) is a potential actuator in human–robot interaction systems, especially rehabilitation systems. However, PAM is a nonlinear actuator with uncertainty and a considerable delay in characteristics, making control challenging. This study presents a discrete-time sliding mode control approach combined with the adaptive fuzzy algorithm (AFSMC) to deal with the unknown disturbance of the PAM-based actuator. The developed fuzzy logic system has parameter vectors of the component rules that are automatically updated by an adaptive law. Consequently, the developed fuzzy logic system can reasonably approximate the system disturbance. When operating the PAM-based system in multi-scenario studies, experimental results confirm the efficiency of the proposed strategy. [ABSTRACT FROM AUTHOR]

Brain-Computer Interface (BCI) research is considered one of the significant interdisciplinary fields. It assists people with severe motor disabilities to recover and improve their motor actions through rehabilitation sessions using Motor Imagery (MI) based BCI systems. Several smart criteria, such as virtual reality, plays a significant role in training people for motor recovery in a virtual environment. Accordingly, Smart Training Environments (STEs) based on virtual reality for MI-BCI users provide a safe environment. They are cost-effective for real-life conditions and scenarios with severe motor disabilities. Fundamentally, the literature presents a lack of comparison of the STE applications considering the smart and effective criteria of the developed applications. Accordingly, three key issues faced the comparison process: importance, multi-evaluation criteria, and data variation, which falls under complex Multi-Criteria Decision Making (MCDM). Performance issues increased comparison complexity caused by the rapidly changing market demands of the MI-BCI. Therefore, this study developed two methodology phases for evaluating and benchmarking the STE applications for the MI-BCI community; making effective decisions is vital. In the first phase, formulate the STE Decision Matrix (DM) based on two main dimensions: the evaluation of ten smart criteria of STE and the alternatives (27 STE applications) developed in the literature for MI-BCI. In the second phase, integration methods of MCDM have been formulated: Analytic Hierarchy Process (AHP) for weighting the ten smart criteria and Fuzzy Decision by Opinion Score Method (FDOSM) for benchmarking STE applications based on constructed AHP weights. The evaluation results show importunity in the obtained weights among the ten STE criteria to distinguish the greatest and lowest important weights. Through the benchmarking performance, FDOSM processes prioritized all STE applications. The ranking results were objectively validated based on five groups of alternatives, and the results were systematically ranked. Finally, this study argued three important summary points concerning the STE dataset, formulated a DM of STE applications, and smart criteria for STE applications to support the MI-BCI community and market. Developing the appropriate STE application for MI-BCI is a better choice to support a large BCI community by identifying the ten smart criteria and considering the presented methodology to establish a robust, practical, cost-efficient, and reliable BCI system. [ABSTRACT FROM AUTHOR]

The proposed cable-driven articulated robot (CDAR) is composed mainly of an articulated-multibody system which is connected to three cables, and can perform sitting type rehabilitation therapies for lower extremity. Adding torsion springs to the rotary joints of CDAR ensures that all cables are always in tension. The arrangement of kinematics in the sagittal plane is presented for motion control and the corresponding optimization problems with strongly nonlinear characteristics are developed. After, the particle swarm optimization method is used to determine the dimensions of the mechanism and the optimum attachment position of the cable. Further, the system dynamics is derived based on Newton-Euler method to help analyze the force profiles and the stiffness of the robot is described when considering compliant impacts of the structural joints. Finally, experimental results demonstrated that the designed robot can fulfill lower limb rehabilitation with advantages such as being lightweight, low-cost, and having simple transmission. [ABSTRACT FROM AUTHOR]

This paper proposes a gait intention detection (GID) method based on motion information from a single leg. This method detects three gait states: stair ascending (SA), stair descending (SD), and level walking (LW). The GID is a highly important factor for the control of gait assistive devices. If the GID is limited to the motion information from a single leg, analyzing the gait motions becomes more challenging, owing to the absence of information regarding the phase difference between both legs. To address this challenging, previous GID methods have used fusion data from several sensors. In this paper, only three inertial measurement units are used for proposed single leg GID method. To analyze gait motions using these limited sensors, the divergent component of motion (DCM) of the user is computed. The DCM is a physical quantity incorporating the linear position and linear velocity of the center of mass; these can be computed from the kinematic data of a support leg. In each of the three gait states SA, SD, and LW, the DCM shows different patterns. To classify and detect these different patterns, a pattern recognition method based on an artificial neural network algorithm is used. The GID performance of the proposed method was experimentally evaluated, and it showed 99% success rate for the SA, SD, and LW states. [ABSTRACT FROM AUTHOR]

In recent years, the neural control mechanisms of the arms and legs during human bipedal walking have been clarified. Rhythmic leg stepping leads to suppression of monosynaptic reflex excitability in forearm muscles. However, it is unknown whether and how corticospinal excitability of the forearm muscle is modulated during leg stepping. The purpose of the present study was to investigate the excitability of the corticospinal tract in the forearm muscle during passive and voluntary stepping. To compare the neural effects on corticospinal excitability to those on monosynaptic reflex excitability, the present study also assessed the excitability of the H-reflex in the forearm muscle during both types of stepping. A robotic gait orthosis was used to produce leg stepping movements similar to those of normal walking. Motor evoked potentials (MEPs) and H-reflexes were evoked in the flexor carpi radialis (FCR) muscle during passive and voluntary stepping. The results showed that FCR MEP amplitudes were significantly enhanced during the mid-stance and terminal-swing phases of voluntary stepping, while there was no significant difference between the phases during passive stepping. Conversely, the FCR H-reflex was suppressed during both voluntary and passive stepping, compared to the standing condition. The present results demonstrated that voluntary commands to leg muscles, combined with somatosensory inputs, may facilitate corticospinal excitability in the forearm muscle, and that somatosensory inputs during walking play a major role in monosynaptic reflex suppression in forearm muscle. [ABSTRACT FROM AUTHOR]

Neuromodulation via spinal stimulation has been investigated for improving motor function and reducing spasticity after spinal cord injury (SCI) in humans. Despite the reported heterogeneity of outcomes, few investigations have attempted to discern commonalities among individual responses to neuromodulation, especially the impact of stimulation frequencies. Here, we examined how exposure to continuous lumbosacral transcutaneous spinal stimulation (TSS) across a range of frequencies affects robotic torques and EMG patterns during stepping in a robotic gait orthosis on a motorized treadmill. We studied nine chronic motor-incomplete SCI individuals (8/1 AIS-C/D, 8 men) during robot-guided stepping with body-weight support without and with TSS applied at random frequencies between 1 and up to 100 Hz at a constant, individually selected stimulation intensity below the common motor threshold for posterior root reflexes. The hip and knee robotic torques needed to maintain the predefined stepping trajectory and EMG in eight bilateral leg muscles were recorded. We calculated the standardized mean difference between the stimulation conditions grouped into frequency bins and the no stimulation condition to determine changes in the normalized torques and the average EMG envelopes. We found heterogeneous changes in robotic torques across individuals. Agglomerative clustering of robotic torques identified four groups wherein the patterns of changes differed in magnitude and direction depending mainly on the stimulation frequency and stance/swing phase. On one end of the spectrum, the changes in robotic torques were greater with increasing stimulation frequencies (four participants), which coincided with a decrease in EMG, mainly due to the reduction of clonogenic motor output in the lower leg muscles. On the other end, we found an inverted u-shape change in torque over the mid-frequency range along with an increase in EMG, reflecting the augmentation of gait-related physiological (two participants) or pathophysiological (one participant) output. We conclude that TSS during robot-guided stepping reveals different frequency-dependent motor profiles among individuals with chronic motor incomplete SCI. This suggests the need for a better understanding and characterization of motor control profiles in SCI when applying TSS as a therapeutic intervention for improving gait. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine whether the control of lateral balance can be improved by applying repeated lateral perturbation force to the pelvis during swing versus stance phase walking in individuals with spinal cord injury (SCI). Fourteen individuals with incomplete SCI were recruited in this study. Each participant visited the lab once and was tested in two experimental sessions that consisted of (1) treadmill walking with bilateral perturbation force applied to the pelvis in the lateral direction during either swing or stance phase of each leg and (2) overground walking pre- and post-treadmill walking. Applying the swing-phase perturbation during walking induced a greater increase in the muscle activation of hip abductors and ankle plantar flexors and a greater improvement in lateral balance control after the removal of perturbation force, in comparison to the results of the stance-phase perturbation condition (P ≤ 0.03). Participants also exhibited a greater reduction in overground step width and a greater improvement in overground walking speed after a session of treadmill walking practice with the swing-phase perturbation, compared with the result of the stance-phase perturbation (P = 0.01). These findings suggest that applying perturbation force to the pelvis during the swing phase of gait while walking may enhance muscle activities of hip abductors and improve lateral balance control in individuals with SCI. A walking practice with the swing-phase pelvis perturbation can be used as a rehabilitation approach to improve the control of lateral balance during walking in people with SCI. [ABSTRACT FROM AUTHOR]

In recent years, our group and others have reported multiple cases of consistent neurological recovery in people with spinal cord injury (SCI) following a protocol that integrates locomotion training with brain machine interfaces (BMI). The primary objective of this pilot study was to compare the neurological outcomes (motor, tactile, nociception, proprioception, and vibration) in both an intensive assisted locomotion training (LOC) and a neurorehabilitation protocol integrating assisted locomotion with a noninvasive brain–machine interface (L + BMI), virtual reality, and tactile feedback. We also investigated whether individuals with chronic-complete SCI could learn to perform leg motor imagery. We ran a parallel two-arm randomized pilot study; the experiments took place in São Paulo, Brazil. Eight adults sensorimotor-complete (AIS A) (all male) with chronic (> 6 months) traumatic spinal SCI participated in the protocol that was organized in two blocks of 14 weeks of training and an 8-week follow-up. The participants were allocated to either the LOC group (n = 4) or L + BMI group (n = 4) using block randomization (blinded outcome assessment). We show three important results: (i) locomotion training alone can induce some level of neurological recovery in sensorimotor-complete SCI, and (ii) the recovery rate is enhanced when such locomotion training is associated with BMI and tactile feedback (∆Mean Lower Extremity Motor score improvement for LOC = + 2.5, L + B = + 3.5; ∆Pinprick score: LOC = + 3.75, L + B = + 4.75 and ∆Tactile score LOC = + 4.75, L + B = + 9.5). (iii) Furthermore, we report that the BMI classifier accuracy was significantly above the chance level for all participants in L + B group. Our study shows potential for sensory and motor improvement in individuals with chronic complete SCI following a protocol with BMIs and locomotion therapy. We report no dropouts nor adverse events in both subgroups participating in the study, opening the possibility for a more definitive clinical trial with a larger cohort of people with SCI. Trial registration: http://www.ensaiosclinicos.gov.br/ identifier RBR-2pb8gq. [ABSTRACT FROM AUTHOR]

The application of lower limb exoskeletons for gait assistance has attracted extensive attention in recent decades. Reference trajectory and assistive control are the important aspects determining the performance of the exoskeleton. During overground walking, there are certain variations in touchdown position, step length and time as subjects have the requirement of adjusting gait pattern to maintain balance. In order to adapt to the different starting and ending foot location on the swing phase of gait, in this study, a dynamic movement primitives (DMPs)-based gait trajectory generation method is proposed to provide a flexible manner to online adjust the reference trajectory. Meanwhile, a time-independent path-based trajectory controller is developed to exert the assistance and guidance for the swing leg motion, where a lookahead distance is employed to search for the desired angular position in 4D hip-knee joint space. The combination of DMP-based reference generation and path-based trajectory control enables lower limb exoskeletons to reduce gait trajectory deviation while still allowing subjects’ own effort to change the gait pattern. Simulations and experiments involving five healthy subjects walking on level ground with a commercial lower limb exoskeleton, were conducted to validate the feasibility and effectiveness of the proposed approach. The results showed that the exoskeleton assistance improved subjects’ leg movement performance, and did not limit subjects’ active effort to adjust step length and time. [ABSTRACT FROM AUTHOR]

Exoskeletons for human performance augmentation have been widely applied in many environments, ranging from military, industry, to construction. For load-carrying augmentation exoskeletons, one of the key issues is to control the human-robot interaction (HRI) force. This paper firstly proposes a unified framework for scale force control (SFC) of human-bearing augmentation exoskeleton (HBAE) and robot-bearing augmentation exoskeleton (RBAE). Furthermore, a mid-level SFC method was proposed, in the light of both cognitive and physical HRIs (cHRI and pHRI). On this basis, a hybrid low-level controller was designed for load-carrying exoskeletons (LCEs). Finally, the proposed method was simulated on an LCE. The simulation results demonstrate the effectiveness of our SFC approach: the pilot is always provided with an arbitrary scaled-down interaction force, regardless of the load state. [ABSTRACT FROM AUTHOR]

A common issue with many commercial rehabilitative exoskeletons and orthoses are that they can be prohibitively expensive for an average individual to afford without additional financial support. Due to this a user may have limited to the usage of such devices within set rehabilitation sessions as opposed to a continual usage. The purpose of this review is therefore to find which actuator implementations would be most suitable for a simplistic, low-cost powered orthoses capable of assisting those with pathologic gait disorders by collating literature from Web of Science, Scopus, and Grey Literature. In this systematic review paper 127 papers were selected from these databases via the PRISMA guidelines, with the financial costs of 25 actuators discovered with 11 distinct actuator groups identified. The review paper will consider a variety of actuator implementations used in existing lower-limb exoskeletons that are specifically designed for the purpose of rehabilitating or aiding those with conditions inhibiting natural movement abilities, such as electric motors, hydraulics, pneumatics, cable-driven actuators, and compliant actuators. Key attributes such as technical simplicity, financial cost, power efficiency, size limitations, accuracy, and reliability are compared for all actuator groups. Statistical findings show that rotary electric motors (which are the most common actuator type within collated literature) and compliant actuators (such as elastic and springs) would be the most suitable actuators for a low-cost implementation. From these results, a possible actuator design will be proposed making use of both rotary electric motors and compliant actuators. [ABSTRACT FROM AUTHOR]

This paper presents a discrete-time adaptive parameter integral sliding mode controller to improve the trajectory tracking performance of two pneumatic artificial muscles in an antagonistic configuration. The main contribution of the paper is a combination of an extended observer and an integral sliding mode controller. Therefore, the proposed controller is not only capable of adapting to the uncertainty in model parameters but also reducing the chattering phenomenon in the traditional sliding mode controller. To develop the control approach, the dynamic behavior of the system is first described by a discrete-time second-order model with two uncertain parameters. After that, an adaptive extended observer is designed for the estimation of variation of the model parameter and unknown disturbance of the system. Finally, an integral sliding mode controller combined with the extended observer is proposed to achieve a good tracking performance. Multi-scenarios experiments are carried out to verify the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

STUDY DESIGN:: Clinical study on six spinal cord-injured subjects. The performance of two automatic gait-pattern adaptation algorithms for automated treadmill training rehabilitation of locomotion (called DJATA1 and DJATA2) was tested and compared in this study. OBJECTIVES:: To test the performance of the two algorithms and to evaluate the corresponding patient satisfaction. We also wanted to evaluate the motivation of the patients to train with a fixed gait pattern versus training where they can influence and change the gait pattern (gait-pattern adaptation). SETTING:: Spinal Cord Injury Center Paracare, Balgrist, Zürich, Switzerland. METHODS:: The experimental data were collected during six blinded and randomized training trials (comprising three different conditions per algorithm) split into two training sessions per patient. During the experiments, we have recorded the time courses of the six parameters describing the adaptation. Additionally, a special patient questionnaire was developed that allowed us to collect data regarding the quality, perception, speed, and required effort of the adaptation, as well as patients’ opinion that addressed their motivation. The achieved adaptation was evaluated based on the time course of adaptation parameters and based on the patient questionnaire. A statistical analysis was made in order to quantify the data and to compare the two algorithms. RESULTS:: Significant adaptation of the gait pattern took place. The patients were in most cases able to change the gait pattern to a desired one and have always perceived the adaptation. No statistically significant differences were found between the performances of the two algorithms based on the evaluated data. However, DJATA2 achieved better adaptation scores. All patients preferred treadmill training with gait-pattern adaptation. CONCLUSION:: In the future, the patients would like to train with gait-pattern adaptation. Besides the subjective opinion indicating the choice of this training modality, gait-pattern adaptation also might lead to additional improvement of the rehabilitation of locomotion as it increases and promotes active training. SPONSORSHIP:: The work was supported by The Swiss Commission for Technology and Innovation (Project No. 4005.1).Spinal Cord (2003) 41, 657-666. doi:10.1038/sj.sc.3101518 [ABSTRACT FROM AUTHOR]

Exoskeletons aim to provide required torques in a patient’s lower limbs generating anatomical patterns. In this sense, this work presents an AGoRA lower limb exoskeleton aimed at rehabilitating patients who suffer hemiparesis. Two control strategies for physical Human-Robot Interaction are tested in a pilot study with a healthy user. On the one hand, the assistance mode uses a gait phase detection to generate a desired gait cycle in the paretic limb. On the other hand, the transparency mode is presented to simulate a back-drivable device using a mass-damper system. As a result, in the assistance mode, torques in the range of (20 - 30 Nm) for the knee and hip joints were generated to complete the user’s gait cycle. Moreover, the user can execute unrestricted movements using the AGoRA exoskeleton in the transparency mode. [ABSTRACT FROM AUTHOR]

This article analyzes the current state of studies of aspects of the neurophysiological control of parameters of atypical postures in humans associated with the use of external exoskeleton-type devices. The main results of studies in the last five years relate to the formation of scientific concepts of neurophysiological rearrangements of the motor control system on use of robotic devices. The resulting information can be used in sports physiology, motor rehabilitation, development of upper and lower limb exoskeletons, organizing the work of operators, and training to specific movements. [ABSTRACT FROM AUTHOR]

Gait disabilities empowered intensive research on the field of human-robot interaction to promote effective gait rehabilitation. Assist-as-needed strategies are becoming prominent, appealing to the users’ participation in their rehabilitation therapy. This study proposes and assesses the biomechanical effects of an adaptive impedance control strategy that innovatively allows adaptability in interaction-based stiffness and gait trajectory towards a fully assist-as-needed therapy. By modulating the interaction-based stiffness per gait phase, we hypothesize that the strategy appeals to a symbiotic human-orthotic cooperation, augmenting the user’s muscular activity. The interaction stiffness was estimated by modelling the human-orthosis interaction torque vs angle curve with a linear regression model. The strategy also allows for real-time trajectory adaptations at different gait phases to fulfil the users’ needs. The biomechanical assessment of the impedance-controlled ankle orthosis involved eight healthy volunteers walking at 1.0 and 1.6 km/h. The results revealed a stronger muscular activation regarding the non-assisted leg for the gastrocnemius lateralis (increment ratio ≥ 1.0 for both gait speeds) and for the tibialis anterior muscle (increment ratio ≥ 1.0 for 1.6 km/h). The strategy guided users successfully on a healthy gait pattern while allowing deviations (median error < 5.0°) given the users’ intention weighted by interaction stiffness. Findings showed the relevance for adapting gait trajectory as users prefer higher trajectories as the speed increases. No significant temporal variations or neither knee angular compensations were observed (p value ≥0.11). Overall results support that this strategy may be applied for intensity-adapted gait training, allowing different human-robot compliant levels. [ABSTRACT FROM AUTHOR]

The brain–computer interface (BCI) technique represents one of the furthermost active interdisciplinary study domains and includes a wide knowledge spectrum from a different disciplines such as medicine, neuroscience, machine learning and rehabilitation. The motor imagery (MI) technique based on BCI has been broadly applied in rehabilitation especially for upper limb motor movement where people with disabilities need to restore or improve their walking capability. Nowadays, virtual reality is a beneficial scheme for BCI users because it proposes a relatively cost-effective, safe way for BCI users to train and explain themselves in using BCI in a computer-generated environment earlier than in a real-life scenario. Depicting the whole picture for signal processing techniques and methods utilised in MI-based BCI training environments is difficult. In addition, numerous challenges and open issues regarding signal processing and pattern recognition exist in the literature of the current topic; however, to the best of our knowledge, this is the first attempt to highlight these challenges and open issues in signal processing methods, techniques and pattern recognition in smart BCI training environments. This work illustrates the effect of the theoretical perspectives associated with BCI works for research development in smart training environments. Consequently, this research copes with these issues via a systematic review protocol to help the large community of BCI users, especially people with disabilities. Fundamentally, four substantial databases, namely, IEEE, ScienceDirect, Scopus and PubMed contain a considerable amount of technical and scientific articles relevant to smart BCI training systems. A set of 375 articles is collected from 2010 to 2020 to reveal a clear picture and a better understanding of all the academic literature through a final set of 25 articles. In addition, this research provides the state of the art for signal processing, feature extraction, classification techniques and smart training environment characteristics for MI-based BCI applications. This study also reports the challenges and issues identified by the researchers as well as recommended solutions to solve the persistent problems. This study introduces the state-of-the art virtual and augmented reality environments as a smart platform and the neurofeedback schemes used for MI-based smart BCI training systems. Moreover, this study highlights for the first time 10 concepts of smart training in a virtual environment applied in MI and BCI, and investigates the evaluation of these concepts against the literature to gain only 45.55%. Collectively, the implication of this study will offer the opportunity of deploying an efficient smart BCI training system in terms of data acquisition and recording, pattern recognition and smart environment for BCI users and rehabilitation programmes. [ABSTRACT FROM AUTHOR]

This study introduces the design of an asynchronous event-driven adaptive robust control that regulates a mobile limb orthosis position for the hind legs of a Canis lupus familiaris (CLF). The application of a suitable stability analysis based on a controlled Lyapunov function results in the laws to adjust the adaptive gains of a proportional integral derivative controller (PID). The controller succeeded in compensating external bounded perturbations and non-modeled uncertainties in the active orthosis device. This compensation forces the tracking between the current positions and some reference trajectories obtained by a biomechanical gait cycle analysis of the CLF. The controller starts with the event triggered from the power of the electromyography signal from the frontal legs. If the signal power is higher than a predefined threshold, the movement of the orthosis will initiate. Electromyographic signals were acquired offline and injected into a virtualized orthosis model to test the event-driven control design. A set of numerical simulations confirmed a better performance of tracking reference trajectories and the effect of the event-driven controller on the orthosis operation. The experimental validation of the proposed output feedback controller on the designed orthosis seems to justify a potential automatized rehabilitation therapy based on the proposed electromyography-driven strategy. [ABSTRACT FROM AUTHOR]

Exoskeleton robot-assisted physical therapy has recently been studied extensively due to its proven effectiveness in providing different forms of physical therapy at any stage of physical recovery. The efficacy of robot-assisted physical therapy depends on the maneuverability of the robot during the rehabilitation application. Robot dynamics is inherently nonlinear. Often, robot control algorithms are developed based on an approximate robot dynamic model which leads to system instability and tracking errors. Accurately determining a rehabilitation robot's payload (human limb masses and inertial properties) is frequently impractical. An adaptive control scheme can handle modeling errors very efficiently. In this paper, a 7 degrees of freedom (DOF) human lower extremity dynamic model was developed using the Newton Euler's method. To simulate joint friction, a realistic friction model is included. A direct adaptive controller is designed so that the robot can follow the prescribed trajectory with high speed and accuracy. A total of 31 model parameters were considered for adaption. To ensure system stability, the controller's adaptive gains are determined based on the Lyapunov stability approach. Simulation results show excellent tracking performance of the developed controller in the presence of joint friction. [ABSTRACT FROM AUTHOR]