Robotic Rehabilitation System Research Articles

Optimal Design of Lower Limb Rehabilitation System Based on Parallel and Serial Mechanisms

This paper presents the structure and model of a hybrid modular structure of a robotic system for lower limb rehabilitation. It is made of two modules identical in structure, including an active 3-PRRR manipulator for moving the patient’s foot and a passive orthosis based on the RRR mechanism for supporting the lower limb. A mathematical model has been developed to describe the positions for the links of the active and passive mechanisms of two modules, as a function of the angles in the joints of the passive orthosis, considering constraints for attaching the active manipulators to the moving platform and their configurations. A method has been formulated for a parametric synthesis of the hybrid robotic system proposed with modular structure, taking into account the generated levels of parametric constraints depending on the ergonomic and manufacturability features. The proposed design is based on a criterion in the form of a convolution, including two components, one of which is based on minimizing unattainable points of the trajectory, considering the characteristics of anthropometric data, and the other is based on the compactness of the design. The results of the mathematical modeling are discussed as well as the analysis results towards a prototype validation.

Resist-as-Needed ADL Training With SPINDLE for Patients With Tremor.

Individuals with neurological disorders often exhibit altered manual dexterity and muscle weakness in their upper limbs. These motor impairments with tremor lead to severe difficulties in performing Activities of Daily Living (ADL). There is a critical need for ADL-focused robotic training that improves individual's strength when engaging with dexterous ADL tasks. This research introduces a new approach to training ADLs by employing a novel robotic rehabilitation system, Spherical Parallel INstrument for Daily Living Emulation (SPINDLE), which incorporates Virtual Reality (VR) to simulate ADL tasks. The study results present the feasibility of training individuals with movements similar to ADLs while interacting with the SPINDLE. A new game-based robotic training paradigm is suggested to perform ADL tasks at various intensity levels of resistance as needed. The proposed system can facilitate the training of various ADLs requiring 3-dimensional rotational movements by providing optimal resistance and visual feedback. We envision this system can be utilized as a table-top home device by restoring the impaired motor function of individuals with tremor and muscle weakness, guiding to improved ADL performance and quality of life.

Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base.

In the process of rehabilitation treatment for stroke patients, rehabilitation evaluation is a significant part in rehabilitation medicine. Researchers intellectualized the evaluation of rehabilitation evaluation methods and proposed quantitative evaluation methods based on evaluation scales, without the clinical background of physiatrist. However, in clinical practice, the experience of physiatrist plays an important role in the rehabilitation evaluation of patients. Therefore, this paper designs a 5 degrees of freedom (DoFs) upper limb (UL) rehabilitation robot and proposes a rehabilitation evaluation model based on Belief Rule Base (BRB) which can add the expert knowledge of physiatrist to the rehabilitation evaluation. The motion data of stroke patients during active training are collected by the rehabilitation robot and signal collection system, and then the upper limb motor function of the patients is evaluated by the rehabilitation evaluation model. To verify the accuracy of the proposed method, Back Propagation Neural Network (BPNN) and Support Vector Machines (SVM) are used to evaluate. Comparative analysis shows that the BRB model has high accuracy and effectiveness among the three evaluation models. The results show that the rehabilitation evaluation model of stroke patients based on BRB could help physiatrists to evaluate the UL motor function of patients and master the rehabilitation status of stroke patients.

Retraction Notice Upper Limb Rehabilitation Robot System Based on Internet of Things Remote Control

Retraction Notice Upper Limb Rehabilitation Robot System Based on Internet of Things Remote Control

Design and analysis of a compatible exoskeleton rehabilitation robot system based on upper limb movement mechanism.

Rehabilitation robots are used to promote structural and functional recovery of the nervous system with repetitive, task-oriented training and have been gradually applied to clinical rehabilitation training. This paper proposes an upper limb exoskeleton rehabilitation robot system that could realize shoulder-elbow-wrist joint rehabilitation training. Firstly, a motion equivalent model was established based on the upper limb movement mechanism, the robot mechanism configuration was designed, and the optimization algorithm and spatial mechanism theory were used to optimize and analyze the structural parameters and human-machine compatibility of the robot, which will guide the design of the robot's model. Then, the robot kinematics were solved, and its maximum motion range, dexterity distribution, and daily motion trajectory were simulated. Finally, a system prototype was built to test the maximum range of robot-assisted human upper limb training by laser tracker, while the pressure of human-machine interaction during training was captured and analyzed by flexible sensors. The results show that the proposed rehabilitation robot could nearly completely cover the range of motion of upper limb joints and meet the needs of trajectory training, and the linear velocity dexterity and angular velocity dexterity in the motion space are maximum 0.55 and 0.89, and the human-machine interaction pressures during the training process are all less than 10kPa. Besides, this paper also conducted a system evaluation based on the fuzzy comprehensive evaluation model, and the evaluation result was 0.39, with an excellent evaluation grade, it indirectly indicates that the robot's overall performance was good.

Evaluation of finger strength and spasticity in hemiplegic patients using hand-finger robotic device: A validity and reliability study.

We aimed to investigate the validity, reliability, and clinical relevance of Amadeo hand-finger robotic rehabilitation system measurements for evaluating spasticity and strength in hemiplegic patients. In total, 161 participants (107 hemiplegic patients and 54 sex- and age-matched healthy controls) were included in this study. Spasticity was evaluated using the Modified Ashworth Scale, hand motor functions were evaluated using the Fugl-Meyer Assessment Hand Subscale, and hand grip and pinch strength were evaluated using the Jamar hand grip and pinch dynamometer. The Amadeo (Tyromotion) hand-finger robotic rehabilitation system was used to evaluate finger spasticity and strength of the participants. A statistically significant difference was found between the median values of the Modified Ashworth Scale (both clinical and robotic evaluation results) and the mean values of hand flexor and extensor strength measured with the robotic device in patients compared to healthy subjects (P < .01). Statistically, excellent agreement was obtained between the clinical and robotic test-retest results of the scale (P < .01) (intra-class correlation coefficient, ICC = .98-.99; ICC = .98-.99, respectively). There was a statistically significant positive correlation between the clinical and robotic device results of the Modified Ashworth Scale (r = .72; P < .01). There was a statistically significant positive correlation between the hand strength values measured with the robotic device, Jamar grip, pinch, and Fugl-Meyer Assessment Hand Subscale scores (P < .01) in the patient group. Hand finger spasticity and strength measurements of the Amadeo hand-finger robotic rehabilitation system were valid, reliable, and clinically correlated in stroke patients.

Adaptive neural tracking control for upper limb rehabilitation robot with output constraints

AbstractThe authors investigate the trajectory tracking control problem of an upper limb rehabilitation robot system with unknown dynamics. To address the system's uncertainties and improve the tracking accuracy of the rehabilitation robot, an adaptive neural full‐state feedback control is proposed. The neural network is utilised to approximate the dynamics that are not fully modelled and adapt to the interaction between the upper limb rehabilitation robot and the patient. By incorporating a high‐gain observer, unmeasurable state information is integrated into the output feedback control. Taking into consideration the issue of joint position constraints during the actual rehabilitation training process, an adaptive neural full‐state and output feedback control scheme with output constraint is further designed. From the perspective of safety in human–robot interaction during rehabilitation training, log‐type barrier Lyapunov function is introduced in the output constraint controller to ensure that the output remains within the predefined constraint region. The stability of the closed‐loop system is proved by Lyapunov stability theory. The effectiveness of the proposed control scheme is validated by applying it to an upper limb rehabilitation robot through simulations.

Trends of Robotic Systems for Hand Rehabilitation

Trends of Robotic Systems for Hand Rehabilitation

A novel robotic system enabling multiple bilateral upper limb rehabilitation training via an admittance controller and force field

Patients with hemiplegia are usually restricted to performing general bilateral activities of daily life (gbADLs). Bilateral training has been verified to contribute to the rehabilitation of physical functions. Although robotic systems are gradually being employed in the field of rehabilitation, few studies have performed simulations with regards to gbADLs for training. Therefore, a novel end-effector bilateral rehabilitation robotic system (EBReRS) for the upper limb is developed in this article for the task rendering of gbADLs, in which the gbADL-corresponding workspace is obtained via modularly designed bilateral parallelogram mechanisms. In addition, the interaction rendering of multiple bimanual modes (uncoupled, trans-soft-coupled, trans-semi-coupled, and rotation-coupled) is achieved by implementing the admittance model, the inner force field between robotic end-effectors, and the outer force field distributed around. Experiments of the proposed four rehabilitation training modes were carried out on the healthy subject, with the results showing a feasible method of the EBReRS in the simulation of multiple bimanual coordinated rehabilitation training tasks. In the future, the constructed EBReRS is expected to be exploited for home rehabilitation as a coordinated training device.

A Portable Robotic System for Ankle Joint Rehabilitation

The design of a new exoskeleton is presented based on four electric linear actuators for the flexion motion range of the ankle of a user. The exoskeleton system is designed to be easy to operate and fairly inexpensive so that it can be used as a tool for exercising and the rehabilitation of the ankle. A prototype of the ankle exoskeleton is presented with its electrical circuit and components, such as servomotors, microcontrollers, sensors, and power supplies. The prototype is distinguished by an innovative design, which uses linear electric actuators for angular-assisted motion, providing a controlled adaptive movement of the ankle joint in its basic movements, separately and combined. The key elements of the ankle exoskeleton prototype consist of a lower leg housing, front and rear servomotor mounts, and ball joints with the aim of mimicking and supporting the natural movements of the ankle for users with walking and mobility problems. The innovation of the proposed work can be recognized in the portable mechanical design with a proper mechatronic design with four actuators that control ankle motion in all its possibilities.

Design of a Flexible mechatronic system for mechanical impedance measurement of the wrist as one step towards robotic rehabilitation

Abstract Home rehabilitation is a great complement to inpatient rehabilitation, because it allows for more regular training, especially in rural areas, where the access to physiotherapists is limited. However, unsupervised training carries the risk of deviation from the prescribed protocol. An obvious solution is to introduce a technical means for rehabilitation and continuous monitoring of performance. This paper presents a robotic system for rehabilitation of three degrees of freedom of the wrist independently, implemented with only one actuator, optimizing the design and size of the system. The device provides measurements of joint torque-position dependence, allowing objective assessment of mechanical impedance and range of motion to evaluate the progress of rehabilitation. Derived from research on haptic devices, a measurement method was implemented to measure hand impedance in the high frequency range between 100 and 200 Hz using a combination of exciters and accelerometers in the handles. This allows continuous monitoring of gripping forces without the need for complex contact-based force sensors. In collaboration between the authors’ technical and medical universities, a set of two-level passive and active safety measures was implemented to ensure the safety of the system’s users. Therefore, the presented system shows the potential to evaluate the mechanical impedance of the wrist at both low and high frequencies and to monitor the progress during rehabilitation. In the next step, a baseline of wrist impedance will be measured in a target group of healthy volunteers in order to prepare for studies in motion-restricted subjects.

Special Issue on Exoskeleton Robotic Systems

Over the past 20 years, the therapies available in rehabilitation clinics have improved substantially, aided by robotic rehabilitation systems, which have seen rapid progress and increased performance [...]

Implementation of cable-driven waist rehabilitation robotic system using fractional-order controller

This paper presents the design and implementation of a fractional-order controller for a cable-driven waist rehabilitation robotic system (CDWRRS) based on an improved Oustaloup numerical approximation method. First, the mechanical structure and hardware design of the CDWRRS are established based on the demands of waist rehabilitation training. Next, a fractional-order controller is designed and implemented. The function of the proposed controller is realised using integer-order systems for approximation. Additionally, an improved numerical approximation method is proposed based on the Oustaloup numerical approximation method. In the Oustaloup method, the zeros and poles of the integer-order transfer functions are different, and the approximation errors near the endpoints of the frequency range are not considered. To address these problems, the proposed numerical approximation method improves the approximation accuracy and reduces the calculation cost. Finally, numerical examples and experimental results are presented to verify the feasibility and effectiveness of the improved Oustaloup numerical approximation method and the designed fractional-order controller for the developed CDWRRS.

Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home

Robotic devices are gaining popularity for the physical rehabilitation of stroke survivors. Transition of these robotic systems from research labs to the clinical setting has been successful, however, providing robot-assisted rehabilitation in home settings remains to be achieved. In addition to ensure safety to the users, other important issues that need to be addressed are the real time monitoring of the installed instruments, remote supervision by a therapist, optimal data transmission and processing. The goal of this paper is to advance the current state of robot-assisted in-home rehabilitation. A state-of-the-art approach to implement a novel paradigm for home-based training of stroke survivors in the context of an upper limb rehabilitation robot system is presented in this paper. First, a cost effective and easy-to-wear upper limb robotic orthosis for home settings is introduced. Then, a framework of the internet of robotics things (IoRT) is discussed together with its implementation. Experimental results are included from a proof-of-concept study demonstrating that the means of absolute errors in predicting wrist, elbow and shoulder angles are 0.89180,2.67530 and 8.02580, respectively. These experimental results demonstrate the feasibility of a safe home-based training paradigm for stroke survivors. The proposed framework will help overcome the technological barriers, being relevant for IT experts in health-related domains and pave the way to setting up a telerehabilitation system increasing implementation of home-based robotic rehabilitation. The proposed novel framework includes:•A low-cost and easy to wear upper limb robotic orthosis which is suitable for use at home.•A paradigm of IoRT which is used in conjunction with the robotic orthosis for home-based rehabilitation.•A machine learning-based protocol which combines and analyse the data from robot sensors for efficient and quick decision making.

BIO‐inspired fuzzy inference system—For physiological signal analysis

AbstractWhen a person's neuromuscular system is affected by an injury or disease, Activities‐for‐Daily‐Living (ADL), such as gripping, turning, and walking, are impaired. Electroencephalography (EEG) and Electromyography (EMG) are physiological signals generated by a body during neuromuscular activities embedding the intentions of the subject, and they are used in Brain–Computer Interface (BCI) or robotic rehabilitation systems. However, existing BCI or robotic rehabilitation systems use signal classification technique limitations such as (1) missing temporal correlation of the EEG and EMG signals in the entire window and (2) overlooking the interrelationship between different sensors in the system. Furthermore, typical existing systems are designed to operate based on the presence of dominant physiological signals associated with certain actions; (3) their effectiveness will be greatly reduced if subjects are disabled in generating the dominant signals. A novel classification model, named BIOFIS is proposed, which fuses signals from different sensors to generate inter‐channel and intra‐channel relationships. It explores the temporal correlation of the signals within a timeframe via a Long Short‐Term Memory (LSTM) block. The proposed architecture is able to classify the various subsets of a full‐range arm movement that performs actions such as forward, grip and raise, lower and release, and reverse. The system can achieve 98.6% accuracy for a 4‐way action using EEG data and 97.18% accuracy using EMG data. Moreover, even without the dominant signal, the accuracy scores were 90.1% for the EEG data and 85.2% for the EMG data. The proposed mechanism shows promise in the design of EEG/EMG‐based use in the medical device and rehabilitation industries.

Patent Review of Lower Limb Rehabilitation Robotic Systems by Sensors and Actuation Systems Used.

Robotic systems for lower limb rehabilitation are essential for improving patients' physical conditions in lower limb rehabilitation and assisting patients with various locomotor dysfunctions. These robotic systems mainly integrate sensors, actuation, and control systems and combine features from bionics, robotics, control, medicine, and other interdisciplinary fields. Several lower limb robotic systems have been proposed in the patent literature; some are commercially available. This review is an in-depth study of the patents related to robotic rehabilitation systems for lower limbs from the point of view of the sensors and actuation systems used. The patents awarded and published between 2013 and 2023 were investigated, and the temporal distribution of these patents is presented. Our results were obtained by examining the analyzed information from the three public patent databases. The patents were selected so that there were no duplicates after several filters were used in this review. For each patent database, the patents were analyzed according to the category of sensors and the number of sensors used. Additionally, for the main categories of sensors, an analysis was conducted depending on the type of sensors used. Afterwards, the actuation solutions for robotic rehabilitation systems for upper limbs described in the patents were analyzed, highlighting the main trends in their use. The results are presented with a schematic approach so that any user can easily find patents that use a specific type of sensor or a particular type of actuation system, and the sensors or actuation systems recommended to be used in some instances are highlighted.

Neurorehabilitation robotics: how much control should therapists have?

Robotic technologies for rehabilitating motor impairments from neurological injuries have been the focus of intensive research and capital investment for more than 30 years. However, these devices have failed to convincingly demonstrate greater restoration of patient function compared to conventional therapy. Nevertheless, robots have value in reducing the manual effort required for physical therapists to provide high-intensity, high-dose interventions. In most robotic systems, therapists remain outside the control loop to act as high-level supervisors, selecting and initiating robot control algorithms to achieve a therapeutic goal. The low-level physical interactions between the robot and the patient are handled by adaptive algorithms that can provide progressive therapy. In this perspective, we examine the physical therapist's role in the control of rehabilitation robotics and whether embedding therapists in lower-level robot control loops could enhance rehabilitation outcomes. We discuss how the features of many automated robotic systems, which can provide repeatable patterns of physical interaction, may work against the goal of driving neuroplastic changes that promote retention and generalization of sensorimotor learning in patients. We highlight the benefits and limitations of letting therapists physically interact with patients through online control of robotic rehabilitation systems, and explore the concept of trust in human-robot interaction as it applies to patient-robot-therapist relationships. We conclude by highlighting several open questions to guide the future of therapist-in-the-loop rehabilitation robotics, including how much control to give therapists and possible approaches for having the robotic system learn from therapist-patient interactions.

A Motor Recovery Training and Evaluation Method for the Upper Limb Rehabilitation Robotic System

Stroke can cause acute damage to blood vessels in the brain, which often lead to hemiplegia and imbalances in mobility. It is a challenge to develop a motor recovery training and evaluation method with less help of therapists. In this paper, a motor recovery training and evaluation method for upper limb rehabilitation robotic system is proposed. This system has two rehabilitation unit, one is active, the other is passive. The developed rehabilitation robotic system includes exoskeleton rehabilitation robot and PHANTOM1.5. The patients do rehabilitation training with the help of robot in the passive unit, and virtual reality game is designed in active unit. Patients with mild motor impairments observe the virtual reality game interface while manipulating the PHANTOM to do rehabilitation training. Three experiments are proposed in this paper. The fuzzy neural network, spring-damper model, and the method to evaluate the training trajectory are designed and validated. The sEMG signals and grip force during rehabilitation training are collected to set up a fuzzy neural network and achieve evaluation. The accuracy of the network is 0.96 which is calculated in validation set. In chapter IV, the rehabilitation evaluation method is compared with the state of art on rehabilitation evaluation method. The method proposed in this paper can reach high accuracy. It is easy to use and understand for patients even without the help of therapists. The problem of lacking therapists can be solved to some extent by the proposed upper limb rehabilitation system.

Machine learning full 3-D lower-body kinematics and kinetics on patients with osteoarthritis from electromyography

Osteoarthritis (OA) is a degenerative disease that causes severe pain and reduces the range of motion of the joint, decreasing the quality of life for millions of individuals in the United States. Electromyography (EMG) sensors have been widely studied in biomechanics, showing applications in prosthetics, robotics, and control. While complex musculoskeletal models have been well established, the attempt of directly correlating EMG with kinematics and kinetics is still quite limited. Particularly, little work has been conducted on OA patients. In this work, we propose a method for estimating lower body joint angles (JAs) and ground reaction forces (GRFs) from surface-EMG sensors during a step-down task for individuals diagnosed with OA. The JAs and GRFs were measured by a Vicon motion capture system and force plates, respectively. EMG, JAs, and GRFs were used to train an echo state network (ESN) which afforded JAs with relative errors of 3.78% and 3.71% and the GRFs with relative errors of 3.619% and 4.596% for training and testing datasets, respectively. This study suggests the high fidelity of the ESN in automatically predicting full lower body kinematics and kinetics from the EMG signals. The results of this work promote the development of an EMG-controlled lower limb rehabilitation robot system for patients with OA.

Multiresolution Dual-Polynomial Decomposition Approach for Optimized Characterization of Motor Intent in Myoelectric Control Systems.

Surface electromyogram (sEMG) is arguably the most sought-after physiological signal with a broad spectrum of biomedical applications, especially in miniaturized rehabilitation robots such as multifunctional prostheses. The widespread use of sEMG to drive pattern recognition (PR)-based control schemes is primarily due to its rich motor information content and non-invasiveness. Moreover, sEMG recordings exhibit non-linear and non-uniformity properties with inevitable interferences that distort intrinsic characteristics of the signal, precluding existing signal processing methods from yielding requisite motor control information. Therefore, we propose a multiresolution decomposition driven by dual-polynomial interpolation (MRDPI) technique for adequate denoising and reconstruction of multi-class EMG signals to guarantee the dual-advantage of enhanced signal quality and motor information preservation. Parameters for optimal MRDPI configuration were constructed across combinations of thresholding estimation schemes and signal resolution levels using EMG datasets of amputees who performed up to 22 predefined upper-limb motions acquired in-house and from the public NinaPro database. Experimental results showed that the proposed method yielded signals that led to consistent and significantly better decoding performance for all metrics compared to existing methods across features, classifiers, and datasets, offering a potential solution for practical deployment of intuitive EMG-PR-based control schemes for multifunctional prostheses and other miniaturized rehabilitation robotic systems that utilize myoelectric signals as control inputs.