Gait pattern generation for robotic gait rehabilitation system on treadmill

Robotic gait rehabilitation devices enable efficient and convenient gait rehabilitation by mimicking the functions of physical theraphists. In manual gait rehabilitation training, physical theraphists have patients practice and memorize normal gait patterns by applying assistive torque to the patient's joint once the patient's gait deviates from the normal gait. Thus, one of the most important factors in robotic gait rehabilitation devices is to determine the assistive torque to the patient's joint during rehabilitation training. In order to calculate the assistive torque, the desired gait trajectory for affected leg should be determined. This reference trajectory is obtained from his healthy leg. The obtained signal has many noisy factors. Therefore, after conditioning the signal, the suitable pattern is applied to the developed system. This procedure is called "gait pattern generation" in this paper. It is described and discussed in detail.

The Robotic Gait Rehabilitation (RGR) Trainer has been designed to address secondary gait deviations in stroke survivors undergoing rehabilitation. In this paper we describe the operating principle of the RGR Trainer and the systems ability to record the pelvic obliquity patterns (during normal gait and during hip hiking simulated by healthy subjects). Furthermore, we present results of experiment designed to teach new gait pattern in healthy subjects.

Control strategies for effective robot assisted gait rehabilitation: The state of art and future prospects

Robot assisted gait training is a rapidly evolving rehabilitation practice. Various robotic orthoses have been developed during the past two decades for the gait training of patients suffering from neurologic injuries. These robotic orthoses can provide systematic gait training and reduce the work load of physical therapists. Biomechanical gait parameters can also be recorded and analysed more precisely as compared to manual physical therapy. A review of robotic orthoses developed for providing gait training of neurologically impaired patients is provided in this paper. Recent developments in the mechanism design and actuation methods of these robotic gait training orthoses are presented. Control strategies developed for these robotic gait training orthoses in the recent years are also discussed in detail. These control strategies have the capability to provide customised gait training according to the disability level and stage of rehabilitation of neurologically impaired subjects. A detailed discussion regarding the mechanism design, actuation and control strategies with potential developments and improvements is provided at the end of the paper. A number of robotic orthoses and novel control strategies have been developed to provide gait training according to the disability level of patients and have shown encouraging results. There is a need to develop improved robotic mechanisms, actuation methods and control strategies that can provide naturalistic gait patterns, safe human-robot interaction and customized gait training, respectively. Extensive clinical trials need to be carried out to ascertain the efficacy of these robotic rehabilitation orthoses.

Enhanced Kapandji test evaluation of a soft robotic thumb rehabilitation device by developing a fiber-reinforced elastomer-actuator based 5-digit assist system

A major criterion for the application of rehabilitation robots in gait therapy is the question to what extent the machine is able to facilitate physiologically correct muscle activation patters in the patients leg muscles in order to achieve an optimal gait training effect. The EMG data presented in this paper is based on intermediate results of a study with 8 healthy subjects (5 male, 3 female) to evaluate the end-effector based gait rehabilitation robot HapticWalker in position controlled mode. The study investigated two different walking trajectories (floor, upstairs) at three different cadences (45, 60, 90 steps/min) in three different modes (free walking, HapticWalker with vertical CoM motion, HapticWalker without vertical CoM motion). Results show that muscle EMGs measured from all relevant leg muscles have the same phasic and rhythmic muscle activation patterns on the HapticWalker as with free walking. Even though there are differences in patterns of dedicated muscles, we observed reduced amplitudes and slightly delayed activation on the HapticWalker compared to free walking. No differences in EMGs were observed between the two different HapticWalker modes (with vertical CoM motion, cancelled CoM motion), which might eliminate the need for an active trunk suspension system in the latter case. A passive patient lifter would significantly reduce the complexity of the machine construction, all advanced training modes (e.g. dynamic body weight reduction) could then be accomplished via compliant behavior of the freely programmable footplates. Numerous EMG measurements with healthy subjects and non-ambulatory stroke patients were performed on the predecessing electromechanical Gait Trainer GT I and showed that physiologically relevant findings from healthy subjects (e.g. correct phasic muscle activation) can be transferred to a certain extent to stroke patients, but nevertheless studies with stroke patients on the robotic gait trainer HapticWalker are needed to confirm the results presented in this paper.

Following stroke, upper limb impairment is common and frequently limits ability to perform everyday activities. Due to limited resources, current therapy levels are insufficient to optimise functional improvement. Robotic devices have potential to augment upper limb stroke rehabilitation, but knowledge regarding the optimal device features and intervention parameters is limited. This systematic review and meta-analysis aimed to determine the efficacy of upper limb robotic rehabilitation compared with conventional rehabilitation, and to critically explore the device features and programme parameters that influence rehabilitation outcomes. Six electronic databases were searched for RCTs that compared dose-matched robotic versus conventional rehabilitation following stroke, and measured activity level changes in upper limb outcomes. The efficacy of robotic compared with conventional rehabilitation was evaluated using random-effects (I2 ≥ 50%) or fixed-effect (I2 < 50%) models. A systematic categorization of robotic device features and intervention parameters was conducted to facilitate subgroup analyses and meta-regression, enabling exploration of how these factors influence rehabilitation outcomes. The review included 54 studies, involving 2744 participants. Meta-analysis demonstrated that robotic rehabilitation had a small, statistically significant positive effect on upper limb capacity compared with conventional rehabilitation (SMD 0.14, 95% CI [0.02, 0.26]), however these gains were not maintained at follow-up (SMD 0.05, 95% CI [-0.13, 0.24]). No significant differences were found between robotic and conventional rehabilitation for ADL outcomes either post-treatment (SMD 0.04, 95% CI [- 0.05, 0.13]) or at follow-up (SMD 0.05, 95% CI [-0.13, 0.24]). Subgroup analyses provided crucial insights into the factors influencing robotic rehabilitation efficacy, revealing significant effects of device assistance (p = 0.0046), joints mobilized (p = 0.0133), degrees of freedom (p = 0.012), device laterality (p = 0.0048), and the number of devices used (p = 0.0001). The results suggest that robotic rehabilitation does not result in clinically meaningful improvement in either upper limb capacity or ADL performance. However, this study's novel subgroup analyses highlight specific device features and intervention parameters that significantly influence efficacy. These findings provide critical guidance for the design, implementation, and future research of robotic rehabilitation.

Robotic rehabilitation devices are more frequently used for the physical therapy of people with upper limb weakness, which is the most common type of stroke-induced disability. Rehabilitation robots can provide customized, prolonged, intensive, and repetitive training sessions for patients with neurological impairments. In most cases, the robotic exoskeletons have to be aligned with the human joints and provide natural arm movements. This is a challenging task to achieve for one of the most biomechanically complex joints of human body, i.e., the shoulder. Therefore, specific considerations have been made in the development of various existing robotic shoulder rehabilitation orthoses. Different types of actuation, degrees of freedom (DOFs), and control strategies have been utilized for the development of these shoulder rehabilitation orthoses. This paper presents a comprehensive review of these shoulder rehabilitation orthoses. Recent advancements in the mechanism design, their advantages and disadvantages, overview of hardware, actuation system, and power transmission are discussed in detail with the emphasis on the assisted DOFs for shoulder motion. A brief overview of control techniques and clinical studies conducted with the developed robotic shoulder orthoses is also presented. Finally, current challenges and directions of future development for robotic shoulder rehabilitation orthoses are provided at the end of this paper.

Many people have been exposed to lower extremity function losses due to neurological, pathological or traffic accidents. In the physical therapy and rehabilitation of these patients, treatment programs based on robotic systems have started to be preferred instead of conventional methods. In robotic gait rehabilitation, mobilized lower extremity exoskeletons such as Rewalk or un-mobilized lower extremity exoskeletons such as RoboGait are used. It is important to evaluate the rehabilitation process in patients with lower extremity problems. Measurement of surface electromyogram (EMG) signals during the treatment process give information about the functional activities of the muscles. Obtained information plays an important role in determining the intention of patient motion in musculoskeletal design and musculoskeletal activities of the musculoskeletal. Changes in muscle activation timing and amplitude during the use of lower extremity exoskeleton can be determined by analysis of EMG. In this study, muscles involved in walking movement during robotic rehabilitation were examined. The examined iliopsoas, gluteus maximus, gluteus medius muscles provide flexion, extension and abduction movements of the hip, while the medial gastrocnemius and tibialis anterior muscles perform flexion and dorsiflexion movements of the foot. During the gait, the knee joint patency is controlled by the Vastus Medialis and Biceps Femoris muscles. In this study, while 6 patients with lower limb dysfunction were walking on the RoboGait device, the muscle activation potentials obtained from 7 different muscle groups were transferred to the computer simultaneously and wirelessly and displayed in the Matlab environment. The EMG signals measured with the MicroCor Lab device are shaped according to the activation of the muscles during walking. The electrode placement plan is critical for the analysis of EMG signals, and an appropriate electrode placement plan was obtained as a result of the study. Examined measured signals by following with the electrode placement plan, the maximum gluteus and iliopsoas muscles responsible for the extension and flexion movements of the hips are more effective during walking. Gletous maximum muscle was found to be the most effective muscle in walking while the iliopsoas muscle group was involved in the first movement of the leg. As a result of this study, these findings will help to follow the development of the treatment process and to develop EMG controlled mobilized lower extremity exoskeletons.

The present study highlights the advantages of robotic systems for post-stroke rehabilitation of the upper limb. The latest demographic studies illustrate a continuous increase of the average life span, which leads to a continuous increase of stroke incidents and patients requiring rehabilitation. Some studies estimate that by 2030 the number of physical therapists will be insufficient for the patients requiring physical rehabilitation, imposing a shift in the current methodologies. A viable option is the implementation of robotic systems that assist the patient in performing rehabilitation exercises, the physical therapist role being to establish the therapeutic program for each patient and monitor their individual progress. Using a set of clinical measurements for the upper limb motions, the analysis of rehabilitation robotic systems provides a comparative study between the motions required by clinicians and the ones that robotic systems perform for different therapeutic exercises. A critical analysis of existing robots is performed using several classifications: mechanical design, assistance type, actuation and power transmission, control systems and human robot interaction (HRI) strategies. This classification will determine a set of pre-requirements for the definition of new concepts and efficient solutions for robotic assisted rehabilitation therapy.

IntroductionElectromechanically-assisted gait training has been introduced in stroke rehabilitation as a means to enable gait training with a large number of reproducible and symmetrical task repetitions, i.e. steps. However, few studies have evaluated its impact on gait pattern functions. This study includes persons with no independent ambulation function at the start of a 4-week neurorehabilitation period in the sub-acute phase after stroke. The primary aim of the study was to evaluate whether the addition of electromechanically-assisted gait training to conventional training resulted in better gait pattern function than conventional training alone. The secondary aim was to identify correlations between overall gait quality and standardized clinical assessments.Participants and methodsSeventeen patients with no independent ambulation function who participated in a Prospective Randomized Open Blinded End-point study in the sub-acute phase after stroke were randomized into two groups; one group (n = 7) to undergo conventional training only (CONV group) and the other group (n = 10) to undergo conventional training with additional electromechanically-assisted gait training (HAL group). All patients were assessed with 3D gait analysis and clinical assessments after the 4-week intervention period. Overall gait quality as per the Gait Profile Score (GPS), as well as kinematic, and kinetic and other spatiotemporal metrics were collected and compared between intervention groups. Correlations between biomechanical and clinical outcomes were evaluated.ResultsBoth the CONV and HAL groups exhibited similar gait patterns with no significant differences between groups in any kinematic, kinetic parameters or other spatiotemporal metrics. The GPS for the paretic limb had a median (IQR) of 12.9° (7.8°) and 13.4° (4.3°) for the CONV and HAL groups, respectively (p = 0.887). Overall gait quality was correlated with independence in walking, walking speed, movement function and balance. We found no added benefit in gait pattern function from the electromechanically-assisted gait training compared to the conventional training alone.DiscussionThis finding raises new questions about how to best design effective and optimal post-stroke rehabilitation programs in patients with moderate to severe gait impairments to achieve both independent walking and optimal gait pattern function, and about which patients should be in focus in further studies on the efficacy of electromechanically-assisted gait training.Clinical trial registrationThe study was retrospectively registered at ClinicalTrials.gov, identifier (NCT02410915) on April 2015.

Use of end-effector type robots might be more effective to gait rehabilitation than use of exoskeleton type robots, but there has been little work-in-progress to investigate gait pattern when using end-effector type robots. In this study, wearable inertial measurement units (IMU) signals of body segment during walking with the end-effector type robot were compared with during the walking on the treadmill. Different characteristics during the walking with the two configurations were observed; small range of angular velocity and no impact acceleration on the heel-strike during walking with the robot were shown. Since the foot of the subject was fixed on the footplate (i.e., end-effector) of the robot and footplate trajectory of the robot indirectly generates joint motions of lower limbs, the trajectory should be carefully designed. Therefore, the results are applicable to identify the desirable and effective trajectory of an end-effector type gait rehabilitation robot.

Robotic systems for gait rehabilitation have been actively developed in recent years; many of the rehabilitation robots have been commercialized and utilized for treatment of real patients in hospitals. The first generation of gait rehabilitation robots was a tethered exoskeleton system on a treadmill. While these robots have become a new trend in rehabilitation medicine, there are several arguments about the effectiveness of such robots due to the passiveness of the motions that the robots generate, i.e., the continuous passive motions may limit the active involvement of patients’ voluntary motion control. In order to let a patient be more actively involved by requiring the self-control of whole-body balance, untethered powered exoskeletons, wearable robots that patients can wear and walk on the ground, are receiving great attention. While several powered exoskeletons have been commercialized already, the question about their effectiveness has not been cleared in the viewpoint of rehabilitation medicine because most of the powered exoskeletons provide still continuous passive motions, even though they are on the ground without tethering. This is due to their control strategy; the joints of a powered exoskeleton are position-controlled to repeatedly follow a predefined angle trajectory. This may be effective when a wearer is completely paraplegic such that the powered exoskeleton must generate full actuation power for walking. For people with muscular weakness due to various reasons, the powered exoskeleton must assist only the lack of muscular force without constraining human motion. For assistance and rehabilitation of people with partial impairment in walking ability, Angel Legs is introduced in this paper. The proposed powered exoskeleton system is equipped with a transparent actuation system such that the assistive force is accurately generated. The overall design and control of Angel Legs are introduced in this paper, and a clinical verification with a human subject is also provided.

This paper introduces an approach to generate ground-collision-free gait motion by learning a statistical model of walking motion and applies assist-as-needed (AAN) training scheme in learned statistical model which is efficient for robotic gait rehabilitation. The method utilizes a nonlinear dimensionality reduction technique, which is based on Gaussian process, to construct the model using gait motion data obtained from several dozens of healthy subjects. The model is a common, averaged in statistical sense, low-dimensional representation of walking motion. Using the model, it is possible to generate a ground-collision-free gait trajectory at an arbitrary walking speed for a subject on the gait rehabilitation robot, and apply AAN training paradigm around the generated motion. We simulate the framework of learning and generation of motion with gait data from 50 healthy subjects, who walked on a motorized treadmill at 3 different speeds.

Adaptive Trajectory Control to Achieve Smooth Interaction Force in Robotic Rehabilitation Device