Robotic Rehabilitation Devices Research Articles

Enhancing stroke rehabilitation with whole-hand haptic rendering: development and clinical usability evaluation of a novel upper-limb rehabilitation device

IntroductionThere is currently a lack of easy-to-use and effective robotic devices for upper-limb rehabilitation after stroke. Importantly, most current systems lack the provision of somatosensory information that is congruent with the virtual training task. This paper introduces a novel haptic robotic system designed for upper-limb rehabilitation, focusing on enhancing sensorimotor rehabilitation through comprehensive haptic rendering.MethodsWe developed a novel haptic rehabilitation device with a unique combination of degrees of freedom that allows the virtual training of functional reach and grasp tasks, where we use a physics engine-based haptic rendering method to render whole-hand interactions between the patients’ hands and virtual tangible objects. To evaluate the feasibility of our system, we performed a clinical mixed-method usability study with seven patients and seven therapists working in neurorehabilitation. We employed standardized questionnaires to gather quantitative data and performed semi-structured interviews with all participants to gain qualitative insights into the perceived usability and usefulness of our technological solution.ResultsThe device demonstrated ease of use and adaptability to various hand sizes without extensive setup. Therapists and patients reported high satisfaction levels, with the system facilitating engaging and meaningful rehabilitation exercises. Participants provided notably positive feedback, particularly emphasizing the system’s available degrees of freedom and its haptic rendering capabilities. Therapists expressed confidence in the transferability of sensorimotor skills learned with our system to activities of daily living, although further investigation is needed to confirm this.ConclusionThe novel haptic robotic system effectively supports upper-limb rehabilitation post-stroke, offering high-fidelity haptic feedback and engaging training tasks. Its clinical usability, combined with positive feedback from both therapists and patients, underscores its potential to enhance robotic neurorehabilitation.

Design and Validation of an Ambulatory User Support Gait Rehabilitation Robot: NIMBLE

Relearning to walk requires progressive training in real scenarios—overground—along with assistance in basic tasks, such as balancing. In addition, user ability must be maximized through compliant robotic assistance as needed. Despite decades of research, gait rehabilitation robotic devices yield controversial results. This article presents the conceptual design of a novel walking assistance and rehabilitation robot, the NIMBLE robot, aimed at providing ambulatory, bodyweight-supported gait training, assisting the user’s center of mass trajectory to aid weight transfer and dynamic balance during walking. NIMBLE consists of a robotic mobile frame, a partial bodyweight support (PBWS) system, an ambulatory lower-limb exoskeleton (Exo-H3) and a cable-driven pelvis-assisting robot. Designed as a modular structure, it differentiates hierarchical communication levels through a Robot Operating System (ROS) 2 network. We present the mechatronic design and experimental results assessing the impact of the mechatronic coupling between the robotic modules on the walking kinematics and the frame movement control performance. The robotic frame hardly affects the walking kinematics up to 2 degrees in both the sagittal and frontal planes, making it feasible for lateral balance and weight translation training. Moreover, it successfully tracks and follows user trajectories. The NIMBLE robotic frame assessment shows promising results for ambulatory gait rehabilitation.

Analysis of Gait Kinematics in Smart Walker-Assisted Locomotion in Immersive Virtual Reality Scenario.

The decline in neuromusculoskeletal capabilities of older adults can affect motor control, independence, and locomotion. Because the elderly population is increasing worldwide, assisting independent mobility and improving rehabilitation therapies has become a priority. The combination of rehabilitation robotic devices and virtual reality (VR) tools can be used in gait training to improve clinical outcomes, motivation, and treatment adherence. Nevertheless, VR tools may be associated with cybersickness and changes in gait kinematics. This paper analyzes the gait parameters of fourteen elderly participants across three experimental tasks: free walking (FW), smart walker-assisted gait (AW), and smart walker-assisted gait combined with VR assistance (VRAW). The kinematic parameters of both lower limbs were captured by a 3D wearable motion capture system. This research aims at assessing the kinematic adaptations when using a smart walker and how the integration between this robotic device and the VR tool can influence such adaptations. Additionally, cybersickness symptoms were investigated using a questionnaire for virtual rehabilitation systems after the VRAW task. The experimental data indicate significant differences between FW and both AW and VRAW. Specifically, there was an overall reduction in sagittal motion of 16%, 25%, and 38% in the hip, knee, and ankle, respectively, for both AW and VRAW compared to FW. However, no significant differences between the AW and VRAW kinematic parameters and no adverse symptoms related to VR were identified. These results indicate that VR technology can be used in walker-assisted gait rehabilitation without compromising kinematic performance and presenting potential benefits related to motivation and treatment adherence.

Effects of training with a rehabilitation device (Rebless®) on upper limb function in patients with chronic stroke: A randomized controlled trial.

Upper limb dysfunction is one of the most common sequelae of stroke and robotic therapy is considered one of the promising methods for upper limb rehabilitation. This study aimed to explore the clinical effectiveness of upper limb training using a rehabilitation robotic device (Rebless®) for patients with stroke. In this prospective, unblinded, randomized controlled trial, patients were randomly assigned to receive robotic training (experimental group, n = 15) or conventional therapy (control group, n = 15). Both groups received upper limb training lasting for 30 minutes per session with a total of 10 training sessions within 4 weeks. Motor function, functional evaluation, and spasticity were clinically assessed before and after the training. Cortical activation was measured using functional near-infrared spectroscopy at the 1st and 10th training sessions. The experimental group demonstrated a significant improvement in the Fugl-Meyer assessment-upper extremity score and the modified Ashworth scale grade in elbow flexors. The cortical activity of the unaffected hemisphere significantly decreased after 10 training sessions in the experimental group compared with the control group. The experimental group showed significant improvement in the Fugl-Meyer assessment-upper extremity score and spasticity of elbow flexors and had significantly decreased cortical activity of the unaffected hemisphere. Training with Rebless® may help patients with chronic stroke in restoring upper limb function and recovering the contralateral predominance of activation in motor function.

Wearable Assistive Rehabilitation Robotic Devices—A Comprehensive Review

This article details the existing wearable assistive devices that could mimic a human’s active range of motion and aid individuals in recovering from stroke. The survey has identified several risk factors associated with musculoskeletal pain, including physical factors such as engaging in high-intensity exercises, experiencing trauma, aging, dizziness, accidents, and damage from the regular wear and tear of daily activities. These physical risk factors impact vital body parts such as the cervical spine, spinal cord, ankle, elbow, and others, leading to dysfunction, a decrease in the range of motion, and diminished coordination ability, and also influencing the ability to perform the activities of daily living (ADL), such as speaking, breathing and other neurological responses. An individual with these musculoskeletal disorders requires therapies to regain and restore the natural movement. These therapies require an experienced physician to treat the patient, which makes the process expensive and unreliable because the physician might not repeat the same procedure accurately due to fatigue. These reasons motivated researchers to develop and control robotics-based wearable assistive devices for various musculoskeletal disorders, with economical and accessible solutions to aid, mimic, and reinstate the natural active range of motion. Recently, advancements in wearable sensor technologies have been explored in healthcare by integrating machine-learning (ML) and artificial intelligence (AI) techniques to analyze the data and predict the required setting for the user. This review provides a comprehensive discussion on the importance of personalized wearable devices in pre- and post-clinical settings and aids in the recovery process.

Novel Methods for Personalized Gait Assistance: Three-Dimensional Trajectory Prediction Based on Regression and LSTM Models.

Enhancing human-robot interaction has been a primary focus in robotic gait assistance, with a thorough understanding of human motion being crucial for personalizing gait assistance. Traditional gait trajectory references from Clinical Gait Analysis (CGA) face limitations due to their inability to account for individual variability. Recent advancements in gait pattern generators, integrating regression models and Artificial Neural Network (ANN) techniques, have aimed at providing more personalized and dynamically adaptable solutions. This article introduces a novel approach that expands regression and ANN applications beyond mere angular estimations to include three-dimensional spatial predictions. Unlike previous methods, our approach provides comprehensive spatial trajectories for hip, knee and ankle tailored to individual kinematics, significantly enhancing end-effector rehabilitation robotic devices. Our models achieve state-of-the-art accuracy: overall RMSE of 13.40 mm and a correlation coefficient of 0.92 for the regression model, and RMSE of 12.57 mm and a correlation of 0.99 for the Long Short-Term Memory (LSTM) model. These advancements underscore the potential of these models to offer more personalized gait trajectory assistance, improving human-robot interactions.

An Experiment Using Functional Near-Infrared Spectroscopy and Robot-Assisted Multi-Joint Pointing Movements of the Lower Limb.

Stroke affects approximately 17 million individuals worldwide each year and is a leading cause of long-term disability. Robotic therapy has shown promise in helping stroke patients regain lost motor functions. One potential avenue for increasing the understanding of how motor recovery occurs is to study brain activation during the movements that are targeted by therapy in healthy individuals. Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising neuroimaging technique for examining neural underpinnings of motor function. This study aimed to investigate fNIRS neural correlates of complex lower limb movements in healthy subjects. Participants were asked to perform cycles of rest and movement for 6 min using a robotic device for motor rehabilitation. The task required coordinated knee and ankle joint movements to point to targets displayed on a computer screen. Two experimental conditions with different levels of movement assistance provided by the robot were explored. The results showed that the fNIRS protocol effectively detected brain regions associated with motor control during the task. Notably, all subjects exhibited greater activation in the contralateral premotor area during the no-assistance condition compared to the assisted condition. In conclusion, fNIRS appears to be a valuable approach for detecting changes in oxyhemoglobin concentration associated with multi-joint pointing movements of the lower limb. This research might contribute to the understanding of stroke motor recovery mechanisms and might pave the way for improved rehabilitation treatments for stroke patients. However, further research is needed to fully elucidate the potential of fNIRS in studying motor function and its applications in clinical settings.

Traditional versus progressive robot-assisted gait training in people with multiple sclerosis and severe gait disability: study protocol for the PROGR-EX randomised controlled trial

Gait disorders are the most frequent symptoms associated to multiple sclerosis (MS). Robot-assisted gait training (RAGT) in people with MS (PwMS) has been proposed as a possible effective treatment option...

User Needs for a Robotic Hand Rehabilitation Device in Home-based Therapy Following a Stroke: A User-centered Approach

User Needs for a Robotic Hand Rehabilitation Device in Home-based Therapy Following a Stroke: A User-centered Approach

Devices for Gait and Balance Rehabilitation: General Classification and a Narrative Review of End Effector-Based Manipulators

Gait and balance have a direct impact on patients’ independence and quality of life. Due to a higher life expectancy, the number of patients suffering neurological disorders has increased exponentially, with gait and balance impairments being the main side effects. In this context, the use of rehabilitation robotic devices arises as an effective and complementary tool to recover gait and balance functions. Among rehabilitation devices, end effectors present some advantages and have shown encouraging outcomes. The objective of this study is twofold: to propose a general classification of devices for gait and balance rehabilitation and to provide a review of the existing end effectors for such purposes. We classified the devices into five groups: treadmills, exoskeletons, patient-guided systems, perturbation platforms, and end effectors. Overall, 55 end effectors were identified in the literature, of which 16 were commercialized. We found a disproportionate number of end effectors capable of providing both types of rehabilitation (2/55) and those focused on either balance (21/55) or gait (32/55). The analysis of their features from a mechanical standpoint (degrees of freedom, topology, and training mode) allowed us to identify the potential of parallel manipulators as driving mechanisms of end effector devices and to suggest several future research directions.

A Review of Wrist Rehabilitation Robots and Highlights Needed for New Devices

Various conditions, including traffic accidents, sports injuries, and neurological disorders, can impair human wrist movements, underscoring the importance of effective rehabilitation methods. Robotic devices play a crucial role in this regard, particularly in wrist rehabilitation, given the complexity of the human wrist joint, which encompasses three degrees of freedom: flexion/extension, pronation/supination, and radial/ulnar deviation. This paper provides a comprehensive review of wrist rehabilitation devices, employing a methodological approach based on primary articles sourced from PubMed, ScienceDirect, Scopus, and IEEE, using the keywords “wrist rehabilitation robot” from 2007 onwards. The findings highlight a diverse array of wrist rehabilitation devices, systematically organized in a tabular format for enhanced comprehension. Serving as a valuable resource for researchers, this paper enables comparative analyses of robotic wrist rehabilitation devices across various attributes, offering insights into future advancements. Particularly noteworthy is the integration of serious games with simplified wrist rehabilitation devices, signaling a promising avenue for enhancing rehabilitation outcomes. These insights lay the groundwork for the development of new robotic wrist rehabilitation devices or to make improvements to existing prototypes incorporating a forward-looking approach to improve rehabilitation outcomes.

Redesign of Leg Assembly and Implementation of Reinforcement Learning for a Multi-Purpose Rehabilitation Robotic Device (RoboREHAB)

Patients who are suffering from neuromuscular disorders or injuries that impair motor control need to undergo rehabilitation to regain mobility. Gait training is commonly prescribed to patients to regain muscle memory. Automated-walking training devices were created to aid this process; while these devices establish accurate ankle-path trajectories, the knee and hip movements are inaccurate. In this work, a redesign of the leg assembly in a multi-purpose rehabilitation robotic device (RoboREHAB) was explored to improve hip- and knee-movement accuracy by adding an extra link and rollers to the assembly. Motion analysis was employed to test feasibility, reinforcement learning was utilized to train the new leg assembly to walk, and the joint motions achieved with the redesign were compared to those achieved by motion-capture (mocap) data. As a key result, the motion analysis showed an improvement in the knee- and hip-path trajectories due to the added roller/joint segment. The redesigned leg assembly, under the reinforcement-learning policy, showed a 5% deviation from the motion-capture joint trajectories with a maximum deviation of 51.177 mm but maintained a similar profile to the mocap trajectory data. This is an improvement over the original two-segment design, which achieved a maximum deviation of 72.084 mm. These results in the knee- and hip-joint movements more closely reflect the mocap and motion-analysis results, validating the redesign and opening it up to further experimentation and technical improvement.

The Use of Robotic Devices in Knee Rehabilitation: A Literature Review

Introduction: Total Knee Replacement (TKR) surgery rehabilitation presents significant challenges, necessitating effective interventions to restore function and mobility. Robotic devices have emerged as promising tools for enhancing knee rehabilitation outcomes. Robotic-Assisted Rehabilitation (RAR) systems offer unique capabilities that may address some of the challenges associated with traditional methods, robotic devices can facilitate early mobilisation and functional training, potentially leading to faster recovery, improved joint function, and enhanced patient satisfaction. Aim: To provide a comprehensive overview of the current state of research on the use of robotic devices in knee rehabilitation. Materials and Methods: A thorough search of databases, including PubMed, Google Scholar, Science Direct, and Medical Literature Analysis and Retrieval System Online (MEDLINE) was conducted by Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines using the terms “rehabilitation”, “robot”, and “total knee replacement”. This approach yielded 37 articles of which six full-text available articles, from 2014 and 2024 were included in the present review. Studies were selected based on predefined inclusion criteria. Pertinent reference lists were examined. Results: The review identified various types of robotic devices utilised in knee rehabilitation, including exoskeletons, robotic-assisted therapy systems, and rehabilitation platforms. Comparative effectiveness studies indicated potential benefits of robotic-assisted rehabilitation in improving range of motion, muscle strength, functional performance and patient satisfaction. Conclusion: It can be concluded that RAR may be an effective treatment in TKR patients. Future research should focus on personalised rehabilitation protocols, longitudinal outcomes assessment and innovative technologies to optimise patient care and rehabilitation efficacy in knee injuries and surgeries.

Design and Development of Portable Wrist Rehabilitation Device for Post-Stroke Subjects: A Pilot Study

Wrist mobility is vital for post-stroke patients to carry out their daily activities. The use of robotic devices in hand rehabilitation has increased significantly in recent years, allowing patients to get training at home and effectively compensating for the therapist-to-patient ratio. Nevertheless, rehabilitation devices are expensive, cumbersome, immobile, and difficult to wear and operate. This work proposes a new method for designing a wrist rehabilitation device that uses airbag technology to minimize device weight and size. A triangular prism-shaped Neoprene (synthetic rubber) airbag is utilized in the experiment; which is inflated and deflated using a DC motorized air pump. An accelerometer sensor serves as the control input for the wrist rehabilitation exercise device. The proposed device trains the impaired hand of ten hemiplegic post-stroke patients. A goniometer and Fugl-Meyer Assessment (FMA) are used to measure wrist motion before and after the training regimen to evaluate the results. The mean angle (in degree) of wrist flexion increases from 8.6 ± 2.59 to 18.4 ± 5.19 (p = 0.005). Likewise, an increase in the wrist extension angle from 12.3 ± 3.62 to 17.7 ± 6.2 (p = 0.008) is noted. The wrist rehabilitation device is lightweight, portable, and simple to use, producing good results for hemiplegic individuals with hand disability. The suggested invention enables the user to extend their wrist’s range of motion.

An original hybrid-architecture finger mechanism for wearable hand exoskeletons

Robotic devices for rehabilitation and assistance are becoming crucial tools for improving the life quality of people with disabilities. The hand is one of the most affected upper-limb parts, but also fundamental for the subject’s interaction with the external environment. Wearability and portability, safety and comfort, lightness and small sizes, independent finger movement, efficacy in daily activities, sense-of-touch preservation, and affordability are the main features such devices should have. This paper will present an in-depth study of a thumb module for wearable hand exoskeletons, designed as a base concept to be adapted to the other fingers. Specifically, it exploits a new hybrid architecture (meaning that rigid and soft elements are included in the finger mechanism) to reduce the overall dimensions while remaining effective in force transmission. An embodiment of the mechanism is also presented and tested. As well as turning out to be small, light, and cheap, the new concept design demonstrates a minimum coverage of the finger range of motion of about 84% and an exerted force up to 16.78 N.

Робототехнические средства реабилитации двигательной активности пациентов в постинсультном периоде

Stroke prevalence is one of the most acute problems in the medical and social aspects of society: strokes are the second most common in the mortality statistics of the population. In the Russian Federation, stroke occurs annually in almost 500,000 people and is the first among the causes of death from neurological diseases and the second most common cause of death after heart disease. The most common consequences of stroke are motor disorders of varying severity, manifested as changes in muscle tone, paresis and paralysis, and impaired walking function. This paper is an overview of the current state of robotic rehabilitation devices used for post-stroke limb paresis and of expected trends of their development. The existing variants of their construction, conditions of kinesiotherapy sessions for obtaining the greatest effect are considered. The authors are of the opinion that the nearest prospect for the development of high-tech devices of this type is not only complex stationary universal complexes for clinics, but also simple mobile specialized simulators with remote medical control for outpatient use.

A novel approach of multi-loop control based-ADRC for improving lower knee position exoskeleton system

AbstractThis study revealed the system of a lower limb exoskeleton created for knee rehabilitation. The exoskeleton has been extensively used in rehabilitation robotic device research, but its practical applicability is limited due to its high nonlinearity and uncertain behavior. As a result, the control technique is critical in increasing the efficacy of rehabilitation devices. For the rehabilitation and help of a patient with a lower-limb condition, a sliding mode control (SMC) with proportional derivative (PD) control approach are used as parallel loops. Active disturbances rejection control (ADRC) is used by these controllers to cancel any external influences. To overcome the degradation of disturbance rejection and robustness caused by a failure to fully adjust for the entire disturbance, a (SMC) loop was introduced to the control regulation. By assessing performance indices related to the estimated inaccuracy, the results demonstrate the effectiveness of the suggested controller. Simulink is used for simulation and analysis.

Assist-as-needed control with a soft robotic glove based on human-object contact estimation

Assist-as-needed control with a soft robotic hand glove for active rehabilitation is studied in this work. There are two resources of the grasping force, the robotic glove and the subject. Compared with traditional passive rehabilitation where the grasping force is merely provided by a robotic hand rehabilitation device (such as hand exoskeleton, robotic glove), assist-as-needed control accounts for the user contribute to performing grasping tasks collaboratively. In this control method, the human muscle strength for grasping is estimated through the myoelectrical signals of the human forearm collected by the MYO armband. A neural network is used for the recognition of human-object contact estimation. The assist-as-needed control is finally implemented to assist humans in grasping tasks. Experiment results on a soft robotic glove show the effectiveness of the proposed assistive control method.

Algorithm for Controlling the Movement of the Actuator of the Device for Active-Passive Mechanotherapy of the Ankle Join

Purpose of research. The article is devoted to the development of an algorithm for controlling the movement of a controlled mobile platform of a passive mechanotherapy ankle joint apparatus. One of the most common injuries sustained by humans is damage to the ankle joint in sports, domestic, industrial exercises, and as a result of car accidents. Falls from heights with a landing on the feet, including parachute jumps, also often result in ankle injuries. In order to increase the effectiveness of rehabilitation, mechanotherapy is usually used. The relevance of the topic is related to the high efficiency of robotic rehabilitation devices, which, thanks to back-linking and electrically controlled actuators, can perform lower limb movements in an optimal way.Methods. The structural diagram of the device control system has been developed. The proposed algorithm uses the strategy of paralleling control actions with the use of corrective and reference controls to calculate control voltages. Presence of force-moment feeling system enables to estimate change of reaction value in time and timely fix the moment of spastic effects appearance, muscle contracture and automatically make changes in foot movement laws in order to exclude patient's traumatisation and pain syndrome elimination.Results. A methodology for constructing the desired dependence of foot rotation angles on time in the sagittal and frontal planes is proposed. We have developed an operating algorithm for the laws of motion and decision-making block, an algorithm for generating control voltages from the reference model, an operating algorithm for the voltage correction block, and an operating algorithm for the reference model correction block. As a result of virtual tests on the mathematical model of the device, we obtained the basic patterns of motion of the actuator in the form of dependences of the rotation angle of the platform from the time.Conclusion. The results of testing the device show that controlling the movement of the actuator for active-passive mechanotherapy of the ankle joint using the algorithms described in the article is reasonable.

Biopsychosocial effects and experience of use of robotic and virtual reality devices in neuromotor rehabilitation: A study protocol.

Robot-assisted therapy (RAT) and virtual reality (VR)-based neuromotor rehabilitation have shown promising evidence in terms of patient's neuromotor recovery, so far. However, still little is known on the perceived experience of use of robotic and VR devices and the related psychosocial impact. The present study outlines a study protocol aiming to investigate the biopsychosocial effects and the experience of use of robotic and non-immersive VR devices in patients undergoing neuromotor rehabilitation. Adopting a prospective, two-arm, non-randomized study design, patients with different neuromotor diseases (i.e., acquired brain injury, Parkinson's Disease, and total knee/hip arthroplasty) undergoing rehabilitation will be included. In a real-world clinical setting, short- (4 weeks) and long-term (6 months) changes in multiple patient's health domains will be investigated, including the functional status (i.e., motor functioning, ADLs, risk of falls), cognitive functioning (i.e., attention and executive functions), physical and mental health-related quality of life (HRQoL), and the psychological status (i.e., anxiety and depression, quality of life satisfaction). At post-intervention, the overall rehabilitation experience, the psychosocial impact of the robotic and VR devices will be assessed, and technology perceived usability and experience of use will be evaluated through a mixed-methods approach, including both patients' and physiotherapists' perspectives. Repeated measures within-between interaction effects will be estimated, and association analyses will be performed to explore the inter-relationships among the variables investigated. Data collection is currently ongoing. The biopsychosocial framework adopted will contribute to expanding the perspective on patient's recovery within the technology-based rehabilitation field beyond motor improvement. Moreover, the investigation of devices experience of use and usability will provide further insight into technology deployment in neuromotor rehabilitation programs, thereby maximising therapy engagement and effectiveness. ClinicalTrials.gov ID: NCT05399043.