Robotic Rehabilitation System Research Articles

The review of innovations and prospects for the development of physical therapy in modern medicine

Aim. To study and summarize the main modern approaches to physical therapy, namely kinesiotherapy, robotic rehabilitation system and virtual platforms; to analyze their effectiveness in different clinical settings and the problem of individualization of rehabilitation programs and the need to develop personalized approaches to patients with different types of pathologies. Materials and methods. The research materials were materials from scientific publications and thematic Internet sites. The methods of data systematization and generalization were used in the study. Results. Physical therapy is an important component of medical care, especially for patients who need to recover from injuries, surgeries, or suffer from chronic diseases of the musculoskeletal system and nervous system. Recent decades have been marked by a significant development in physical rehabilitation methods, including the use of the latest technologies and interdisciplinary approaches. Kinesiotherapy is a method of physical therapy based on the use of motor activity to restore body functions. It is one of the most widespread and scientifically proven approaches to the treatment of various pathologies. Robotic rehabilitation systems are one of the most promising areas of modern medicine. Robotic exoskeletons and rehabilitation platforms provide the ability to perform movements with a high level of control and correction, which helps to avoid incorrect exercise performance and speeds up the recovery process. Virtual reality (VR) and telemedicine allow patients to participate in rehabilitation programs in a safe and controlled environment that can be adapted to their needs. A literature review has shown that the use of a personalized rehabilitation program increases the effectiveness of a patient’s physical therapy. Conclusions. Based on the analysis, it has been found that traditional methods of kinesiotherapy remain the basis of rehabilitation programs, but their effectiveness can be significantly improved through the use of innovative technologies, such as robotic systems, virtual reality and telemedicine platforms. In addition, the combination of different physical rehabilitation methods allow achieving better results in restoring the patients’ physical functions, which is confirmed by the results of many studies. The implementation of such comprehensive approaches can reduce rehabilitation time and improve the quality of life of patient.

The sliding mode controller with composite reaching law for upper limb rehabilitation robot

Purpose Assisted training using upper limb rehabilitation robots is beneficial for flaccid paralysis patients in recovering their functional abilities. In the assisted training mode, the patient’s motor ability is limited by factors such as limb muscle tension, and it is prone for the rehabilitation robot to deviate from the prescribed training trajectory. A sliding mode control method based on a fixed time observer is proposed to address the problem of delayed trajectory tracking response of upper limb rehabilitation robots caused by external disturbances such as patient limbs. Design/methodology/approach First, aiming at the problem of estimating and compensating for external disturbances in the upper limb rehabilitation robot system, a fixed time observer was designed based on the robot’s dynamic model. Second, the composite sliding mode reaching law combining the smooth function and the power-exponential function is proposed to shorten the convergence time of system states in the startup phase, thereby reducing chattering in the control process and realizing the real-time tracking of the training trajectory by the control system. Findings The proposed method provides a solution for the trajectory tracking speed of upper limb rehabilitation robot controllers. In the circular trajectory tracking control, compared to the sliding-mode control method combined with the variable-exponential composite reaching law based on the fixed-time observer, the method in this paper reduces the time for the system state to reach the sliding surface by 0.89 s and improves the response speed by 0.66%. Originality/value The composite sliding mode approach law based on smooth function and power exponent function can reduce the time it takes for the system state to reach and remain on the sliding surface and improve the trajectory tracking speed of upper limb rehabilitation robots. This controller improves the accuracy of trajectory control and ensures the robustness of auxiliary rehabilitation training.

A deep learning-based comprehensive robotic system for lower limb rehabilitation

In the modern era, a significant percentage of people around the world suffer from knee pain-related problems. ‘Knee pain’ can be alleviated by performing knee rehabilitation exercises in the correct posture on a regular basis. In our research, an attention mechanism-based CNN-TLSTM (Convolution Neural Network-tanh Long Sort-Term Memory) network has been proposed for assessing the knee pain level of a person. Here, electroencephalogram (EEG) signals of the frontal, parietal, and temporal lobes, electromyography (EMG) signals of the hamstring and quadriceps muscles, and knee bending angle have been used for knee pain detection. First, the CNN network has been utilized for automated feature extraction from the EEG, knee bending angle, and EMG data, and subsequently, the TLSTM network has been used as a classifier. The trained CNN-TLSTM model can classify the knee pain level of a person into five categories, namely no pain, low pain, medium pain, moderate pain, and high pain, with an overall accuracy of 95.88 %. In the hardware part, a prototype of an automated robotic knee rehabilitation system has been designed to help a person perform three rehabilitation exercises, i.e., sitting knee bending, straight leg rise, and active knee bending, according to his/her pain level, without the presence of any physiotherapist. The novelty of our research lies in (i) designing a novel deep learning-based classifier model for broadly classifying knee pain into five categories, (ii) introducing attention mechanism into the TLSTM network to boost its classification performance, and (iii) developing a user-friendly rehabilitation device for knee rehabilitation.

Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy

AbstractVirtual reality (VR) technology revitalises rehabilitation training by creating rich, interactive virtual rehabilitation scenes and tasks that deeply engage patients. Robotics with immersive VR environments have the potential to significantly enhance the sense of immersion for patients during training. This paper proposes a rehabilitation robot system. The system integrates a VR environment, the exoskeleton entity, and research on rehabilitation assessment metrics derived from surface electromyographic signal (sEMG). Employing more realistic and engaging virtual stimuli, this method guides patients to actively participate, thereby enhancing the effectiveness of neural connection reconstruction—an essential aspect of rehabilitation. Furthermore, this study introduces a muscle activation model that merges linear and non‐linear states of muscle, avoiding the impact of non‐linear shape factors on model accuracy present in traditional models. A muscle strength assessment model based on optimised generalised regression (WOA‐GRNN) is also proposed, with a root mean square error of 0.017,347 and a mean absolute percentage error of 1.2461%, serving as critical assessment indicators for the effectiveness of rehabilitation. Finally, the system is preliminarily applied in human movement experiments, validating the practicality and potential effectiveness of VR‐centred rehabilitation strategies in medical recovery.

Upper limb exoskeleton rehabilitation robot inverse kinematics modeling and solution method based on multi-objective optimization

The inverse kinematics problem of exoskeleton rehabilitation robots is challenging due to the lack of a standard analytical model, resulting in a complex and varied solution process. This complexity is especially pronounced in redundant upper limb exoskeleton robots, where inefficient solutions hinder the robot’s ability to adapt to the kinematic shape of the upper limb. This paper proposes a modeling and solution method based on multi-objective optimization to address the inverse kinematics of upper limb exoskeleton robots. We analyzed and validated this method using a redundant upper limb exoskeleton rehabilitation robot system developed by ourselves. First, we established a multi-objective inverse kinematics solution model by defining the end-position function, joint motion comfort function, system energy consumption function, motion safety, and human-like constraints. Then, the solution was designed based on the Improved Equilibrium Optimization (IEO) algorithm and validated its computational performance in terms of optimization ability, accuracy, and robustness. Finally, we experimentally tested the inverse kinematics solution model with the IEO algorithm on discrete objectives and continuous training trajectories using a redundant upper limb exoskeleton rehabilitation robot system. The results show that by incorporating the joint comfort function, system energy consumption function, and human-like constraints into the inverse kinematics model, we can not only quickly solve the inverse kinematics of redundant upper limb exoskeleton robots but also significantly improve the robot’s motion shape. Furthermore, it has better solution accuracy and stronger robustness than other algorithms when solving this inverse kinematics model based on the IEO algorithm.

An Actively Vision-Assisted Low-Load Wearable Hand Function Mirror Rehabilitation System

The restoration of fine motor function in the hand is crucial for stroke survivors with hemiplegia to reintegrate into daily life and presents a significant challenge in post-stroke rehabilitation. Current mirror rehabilitation systems based on wearable devices require medical professionals or caregivers to assist patients in donning sensor gloves on the healthy side, thus hindering autonomous training, increasing labor costs, and imposing psychological burdens on patients. This study developed a low-load wearable hand function mirror rehabilitation robotic system based on visual gesture recognition. The system incorporates an active visual apparatus capable of adjusting its position and viewpoint autonomously, enabling the subtle monitoring of the healthy side’s gesture throughout the rehabilitation process. Consequently, patients only need to wear the device on their impaired hand to complete the mirror training, facilitating independent rehabilitation exercises. An algorithm based on hand key point gesture recognition was developed, which is capable of automatically identifying eight distinct gestures. Additionally, the system supports remote audio–video interaction during training sessions, addressing the lack of professional guidance in independent rehabilitation. A prototype of the system was constructed, a dataset for hand gesture recognition was collected, and the system’s performance as well as functionality were rigorously tested. The results indicate that the gesture recognition accuracy exceeds 90% under ten-fold cross-validation. The system enables operators to independently complete hand rehabilitation training, while the active visual system accommodates a patient’s rehabilitation needs across different postures. This study explores methods for autonomous hand function rehabilitation training, thereby offering valuable insights for future research on hand function recovery.

Time-Synchronized Convergence Control for n-DOF Robotic Manipulators with System Uncertainties.

A time-synchronized (TS) convergence control method for robotic manipulators is proposed. Adversely to finite-time control, a notion of time-synchronization convergence is introduced based on the ratio persistence property, which can ensure that all system components converge simultaneously in a finite time. Firstly, a robust disturbance observer is constructed to be compatible with the time-synchronized control framework and precisely estimate system uncertainties. Furthermore, we design a (finite) time-synchronized controller to ensure that all states of the robotic manipulator simultaneously converge to an equilibrium point, irrespective of initial conditions. Stability analysis shows the feasibility of the proposed TS control method. At last, simulations are performed with a two-link rehabilitation robotic system, and the comparison results indicate its superiority.

Robotic exoskeleton-assisted gait training in patients with motor incomplete myelopathy

Objectives: To assess the effect of gait training with robotic exoskeleton-assisted rehabilitation systems on gait parameters, balance, and overall disability in motor incomplete myelopathy. Design: Prospective pre-post study. Setting: Neurological rehabilitation unit in a tertiary university teaching hospital. Participants: Thirty-four motor incomplete myelopathy patients [22 males, mean age 36.7 y (12.7)] were included in this prospective, pre-post study. Twenty-two had nontraumatic etiology, and 17 had tetraplegia. American Spinal Injury Association impairment scale suggested 23 as C and 11 as American Spinal Injury Association impairment scale-D. The mean duration of the lesion was 333 days. Intervention: Twenty-four sessions of robotic gait training (1-h session, 5–6 sessions/wk) for 4–6 weeks. Outcome measures: Gait speed was assessed using a 10-meter walk test (10MWT), endurance with a 2-minute walk test (2MWT) and 6-minute walk test (6MWT), walking ability by Walking Index in Spinal Cord Injury-walking index for spinal cord injury II, functionality using Spinal cord Independence Measure-SCIM III, and balance by Timed-up-and-go (TUG) test. Results: At baseline, 20 patients were nonwalkers-NWB, and 14 were walkers-WB. The walking index for spinal cord injury II and SCIM III suggested significant improvement in both groups by the end of the study (P<0.05). Among the WB group, 10MWT, 2MWT, and TUG showed significant improvement (P=0.004, 0.02, and 0.01, respectively) with training. Patients in NWB group were able to perform 10MWT, 2MWT, 6MWT, and TUG by the end of the study. Conclusions: Gait training with robotic exoskeleton-assisted rehabilitation systems can lead to a significant improvement in gait endurance, speed, balance, and functional outcomes in individuals with incomplete myelopathy. The effect is more pronounced among already ambulatory patients.

A compact motorized end-effector for ankle rehabilitation training.

This paper introduces a compact end-effector ankle rehabilitation robot (CEARR) system for addressing ankle range of motion (ROM) rehabilitation. The CEARR features a bilaterally symmetrical rehabilitation structure, with each side possessing three degrees of freedom (DOF) driven by three independently designed actuators. The working intervals of each actuator are separated by a series connection, ensuring they operate without interference to accommodate the dorsiflexion/plantarflexion (DO/PL), inversion/eversion (IN/EV), and adduction/abduction (AD/AB) DOF requirements for comprehensive ankle rehabilitation. In addition, we integrated an actuator and foldable brackets to accommodate patients in varied postures. We decoded the motor intention based on the surface electromyography (sEMG) and torque signals generated by the subjects' ankle joints in voluntary rehabilitation. Besides, we designed a real-time voluntary-triggered control (VTC) strategy to enhance the rehabilitation effect, in which the root mean square (RMS) of sEMG was utilized to trigger and adjust the CEARR rehabilitation velocity support. We verified the consistency of voluntary movement with CEARR rehabilitation support output for four healthy subjects on a nonlinear sEMG signal with an metric of approximately 0.67. We tested the consistency of triggering velocity trends with a linear torque signal for one healthy individual with an metric of approximately 0.99.

Research on gravity compensation control of BPNN upper limb rehabilitation robot based on particle swarm optimization

AbstractA four‐degree‐of‐freedom upper limb exoskeleton rehabilitation robot system with a gravity compensation device is constructed. The objective is to address the rehabilitation training needs of patients with upper limb motor dysfunction. A BP neural network adaptive control method based on particle swarm optimization is proposed. First, the degrees of freedom of the human body are analyzed, and a Lagrange method is employed to construct a dynamic model. Second, a particle swarm optimization back propagation neural network adaptive control algorithm based on particle swarm optimization is presented. Subsequently, the range of motion of the upper limbs is analyzed with reference to muscle anatomy and a three‐dimensional motion capture system. And the robot structure design is analyzed in detail. Finally, simulation experiments were conducted, and the results demonstrated that the proposed method exhibited high effectiveness and accuracy.

Modeling and analysis of a parallel robotic system for lower limb rehabilitation with predefined operational workspace

The paper presents the mathematical modeling and analysis of LegUp, a novel parallel robotic system for lower limb rehabilitation of bedridden patients. The operational workspace of the robotic system is defined based on a set of parameters that describe the motion of the lower limb joints, which is natural in the rehabilitation task. To comply with this representation of the operational workspace, the parallel robot kinematic models describe the dependency between the robotic system actuators and the lower limb joints. Furthermore, the singularity analysis is achieved in the joint space, which shows whether the operational workspace is singularity-free. To achieve a feasible mechanical design for the prescribed operational workspace and to ensure safe operation, the design parameters of the parallel robotic system are determined based on a multi-objective optimization problem. Numerical simulations show that the operational workspace is singularity-free for the selected design parameters, which are then used to construct the experimental model of LegUp.

Soft Robotic Bilateral Rehabilitation System for Hand and Wrist Joints

Upper limb functionality is essential to perform activities of daily living. It is critical to investigate neurorehabilitation therapies in order to improve upper limb functionality in post-stroke patients. This paper presents a soft-robotic bilateral system to provide rehabilitation therapy for hand and wrist joints. A sensorized glove that tracks finger and wrist joint movements is worn on the healthy limb, which guides the movement of the paretic limb. The input of sensors from the healthy limb is provided to the soft robotic exoskeleton attached to the paretic limb to mimic the motion. A proportional derivative flow-based control algorithm is used to perform bilateral therapy. To test the feasibility of the developed system, two different applications are performed experimentally: (1) Wrist exercise with a dumbbell, and (2) Object pick-and-place task. The initial tests of the developed system verified its capability to perform bilateral therapy.

Mechanical design of an upper limb robotic rehabilitation system

Physical diseases have become an alarming social problem worldwide. Several conditions related to limb mobility are more frequent due to a high level of demand in the repetitive execution of activities. Assistive technologies have been proposed to improve the experience of physical rehabilitation. This paper presents a framework with the mechanical design criteria for an upper limb assistive robotic system. We define the design specifications and construction characteristics of an assistive system. As a result, a 7-degree-of-freedom system for upper limb physical rehabilitation is designed with a novel characteristic of arm-switching configuration for the treatment of both arms.

Development of a portable and compact robotic ankle rehabilitation system

As part of this study, an ankle rehabilitation device was developed for patients suffering from motor disorders caused by incomplete spinal cord injury (ISCI), stroke, or cerebral palsy. First, the research models and dynamic equations of the device were considered. Secondly, the prototype ankle rehabilitation device is made of PLA plastic and uses linear actuators to simulate outward and inward flexion exercises (dorsiflexion and plantar flexion) for the patient. The advantages of the device are its simple design, low cost, and the possibility of installation on recovery equipment. The device can work quickly in experiments, in addition, the rehabilitation robot can work stably and is convenient to use for patients.

A 2DoF robotic system for home-based upper limb rehabilitation

A 2DoF robotic system for home-based upper limb rehabilitation

Development of a Novel Robotic Rehabilitation System Using Electrical Stimulation for Upper Limb Motor Recovery

To improve the quality of life for people with neurological disorders or accidents, it is important to create therapy tools for upper limb movement recovery that work and are easy for everyone to use. This study suggests a new artificial therapy device that uses electrical stimulation to help people recover their upper limb movement skills. The technology uses both artificial help and electrical treatment at the same time to help the brain change and learn new skills. The robotic therapy system is made up of a suit that moves with the upper limbs and supports them mechanically. It has sensors that can figure out what the user wants and change the amount of help based on that. In addition, the system has electrical activation probes that are put to target specific muscles or groups of muscles that help move the upper limbs. A closed-loop control system makes the system work. The user's moves are constantly tracked, and the robotic help and electrical stimulation are changed in real time. This flexible method lets recovery plans be made just for each person, based on their wants and results. A pilot study with people who have trouble moving their upper limbs was used to test how well the planned method would work. When the method was used for six weeks, the data showed big gains in motor function, muscle strength, and range of motion. Participants were also very happy with how easy the method was to use and how well it helped them improve their movement skills.

Adaptive Gait Training of a Lower Limb Rehabilitation Robot Based on Human-Robot Interaction Force Measurement.

The existing fixed gait lower limb rehabilitation robots perform a predetermined walking trajectory for patients, ignoring their residual muscle strength. To enhance patient participation and safety in training, this paper aims to develop a lower limb rehabilitation robot with adaptive gait training capability relying on human-robot interaction force measurement. Firstly, a novel lower limb rehabilitation robot system with several active and passive driven joints is developed, and 2 face-to-face mounted cantilever beam force sensors are employed to measure the human-robot interaction forces. Secondly, a dynamic model of the rehabilitation training robot is constructed to estimate the driven forces of the human lower leg in a completely passive state. Thereafter, based on the theoretical moment from the dynamics and the actual joint interaction force collected by the sensors, an adaptive gait adjustment method is proposed to achieve the goal of adapting to the wearer's movement intention. Finally, interactive experiments are carried out to validate the effectiveness of the developed rehabilitation training robot system. The proposed rehabilitation training robot system with adaptive gaits offers great potential for future high-quality rehabilitation training, e.g., improving participation and safety.

An LMI-based robust state-feedback controller design for the position control of a knee rehabilitation exoskeleton robot: Comparative analysis

Rehabilitation exoskeleton robots play a crucial role in restoring functional lower limb movements for individuals with locomotor disorders. Numerous research studies have concentrated on adapting the control of these rehabilitation robotic systems. In this study, we investigate an affine state-feedback control law for robust position control of a knee exoskeleton robot, taking into account its nonlinear dynamic model that includes solid and viscous frictions. To ensure robust stabilization, we employ the Lyapunov approach and propose three methods to establish stability conditions using the Schur complement, the Young inequality, the matrix inversion lemma, and the S-procedure lemma. These conditions are formulated as Linear Matrix Inequalities (LMIs). Furthermore, we conduct a comprehensive comparison among these methods to determine the most efficient approach. At the end of this work, we present simulation results to validate the developed LMI conditions and demonstrate the effectiveness of the adopted control law in achieving robust position control of the knee exoskeleton robot.

Robotic systems for upper-limb rehabilitation in multiple sclerosis: a SWOT analysis and the synergies with virtual and augmented environments.

The robotics discipline is exploring precise and versatile solutions for upper-limb rehabilitation in Multiple Sclerosis (MS). People with MS can greatly benefit from robotic systems to help combat the complexities of this disease, which can impair the ability to perform activities of daily living (ADLs). In order to present the potential and the limitations of smart mechatronic devices in the mentioned clinical domain, this review is structured to propose a concise SWOT (Strengths, Weaknesses, Opportunities, and Threats) Analysis of robotic rehabilitation in MS. Through the SWOT Analysis, a method mostly adopted in business management, this paper addresses both internal and external factors that can promote or hinder the adoption of upper-limb rehabilitation robots in MS. Subsequently, it discusses how the synergy with another category of interaction technologies - the systems underlying virtual and augmented environments - may empower Strengths, overcome Weaknesses, expand Opportunities, and handle Threats in rehabilitation robotics for MS. The impactful adaptability of these digital settings (extensively used in rehabilitation for MS, even to approach ADL-like tasks in safe simulated contexts) is the main reason for presenting this approach to face the critical issues of the aforementioned SWOT Analysis. This methodological proposal aims at paving the way for devising further synergistic strategies based on the integration of medical robotic devices with other promising technologies to help upper-limb functional recovery in MS.

Design and motion control of exoskeleton robot for paralyzed lower limb rehabilitation.

Patients suffering from limb movement disorders require more complete rehabilitation treatment, and there is a huge demand for rehabilitation exoskeleton robots. Flexible and reliable motion control of exoskeleton robots is very important for patient rehabilitation. This paper proposes a novel exoskeleton robotic system for lower limb rehabilitation. The designed lower limb rehabilitation exoskeleton robot mechanism is mainly composed of the hip joint mechanism, the knee joint mechanism and the ankle joint mechanism. The forces and motion of the exoskeleton robot were analyzed in detail to determine its design parameters. The robot control system was developed to implement closed-loop position control and trajectory planning control of each joint mechanism. Multiple experiments and tests were carried out to verify robot's performance and practicality. In the robot angular response experiments, the joint mechanism could quickly adjust to different desired angles, including 15°, 30°, 45°, and 60°. In the trajectory tracking experiments, the exoskeleton robot could complete tracking movements of typical actions such as walking, standing up, sitting down, go upstairs and go downstairs, with a maximum tracking error of ±5°. Robotic wearing tests on normal people were performed to verify the assistive effects of the lower limb rehabilitation exoskeleton at different stages. The experimental results indicated that the exoskeleton robot has excellent reliability and practicality. The application of this exoskeleton robotic system will help paralyzed patients perform some daily movements and sports.