Rehabilitation Exoskeleton Research Articles

An overview about hand exoskeleton actuation system and thumb motions analysis

Abstract. Stroke patients experience impaired hand function which significantly impacts their daily activities. Robot-assisted training is essential for restoring motor function, with hand exoskeletons serving as assistive devices to support, amplify, replace, or counteract body movements. Designing an exoskeleton that accurately mimics the natural movement of the thumb and provides adequate support is a key challenge in rehabilitation. This paper designs a retrieval model for hand rehabilitation exoskeletons from 2009 to 2023, analyzing the hand anatomy and thumb motion range. It discusses various driving mechanisms for hand exoskeletons, including cable, linkage, pneumatic, gear motor, and hybrid systems. While progress has been made in experimental stages, more research is needed for clinical applications. A universal hand exoskeleton actuation system that meets all patient preferences does not exist. Ongoing research on hybrid drive systems aims to enhance hand exoskeletons for better rehabilitation and daily use for patients with hand dysfunction.

Progress and development trends of lower limb exoskeleton rehabilitation robots

Abstract. Currently, as the aging population intensifies, many elderly people are trapped by lower limb dysfunctions caused by diseases such as stroke sequelae, spinal cord injuries, and bone injuries. Research on lower limb exoskeleton robots has made progress both domestically and internationally. Foreign lower limb exoskeleton robot technology is very mature and widely applied; although starting later, domestic development in this field has been rapid. This paper studies the key technologies of lower limb exoskeleton robots from several aspects: the degrees of freedom in human lower limb movement, the cyclic movement of human gait, structural design, driving methods, and interactive control. With the continuous advancement of technology, lower limb exoskeleton robots will become more intelligent and focus more on personalized services and wearing comfort in future development, aiming to bring more convenience and help to disabled individuals, rehabilitation patients, and groups with special needs.

Assessment of gamified mixed reality environments for upper limb robotic rehabilitation: pilot study on healthy adults

Exoskeletons for rehabilitation have the potential to aid movement and promote intensive, task-oriented, and personalized motor training. However, robotic-assisted training and user experience could be further improve by including Mixed Reality Environments (MREs) and using a gamification approach. This paper presents the design and evaluation of a MR solution based on Microsoft Hololens 2 for robot-assisted bilateral shoulder training. It is based on gamified MREs designed to guide and encourage bilateral synchronous and asynchronous movements of the upper extremities while a 6 Degrees of Freedom (DOF) exoskeleton (FLOAT) provides motor assistance only to the impared limb. The robotic-assisted bimanual tasks involve the use of rehabilitative tools (such as a dowel rod), as a means to interact with the virtual world and to enable the intact limb guide and control the motions of the disable limb. The gamified MR training solution generates meaningful performance metrics from the kinematic analysis of hands movement, captured by Hololens. The subjective evaluation of the gamified MR solution focused on usability, cognitive load, and user experience. Meanwhile, the objective evaluation encompassed the analysis of the robot-assisted movements with and without gamified MREs, a comparative analysis between metrics obtained from Hololens and Vicon data, and the collection of reference data and trajectories. There were twenty-one healthy adults involved in the evaluation of the system. The results with the gamified MREs highlight excellent system usability, low cognitive load, and high user experience. Additionally, integrating gamified MREs into robot-assisted movements enhances shoulder movements. Data collection with Hololens demonstrated to be reliable and consistent. Furthermore, the normal reference values, paths, and velocity profiles obtained from healthy individuals offer a foundation for assessing the performance of individuals with disabilities. In summary, the introduction of gamified MREs for robot-assisted bilateral shoulder movements marks a significant and promising advancement in rehabilitation technology.

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.

Control Method of Upper Limb Rehabilitation Exoskeleton for Better Assistance: A Comprehensive Review

Control Method of Upper Limb Rehabilitation Exoskeleton for Better Assistance: A Comprehensive Review

Wireless PID-Based Control for a Single-Legged Rehabilitation Exoskeleton

The demand for remote rehabilitation is increasing, opening up convenient and effective home-based therapy for the sick and elderly. In this study, we use AnyBody simulations to analyze muscle activity and determine key parameters for designing a rehabilitation exoskeleton, as well as selecting the appropriate motor torque to assist patients during rehabilitation sessions. The exoskeleton was designed with a PID control mechanism for the precise management of motor positions and joint torques, and it operates in both automated and teleoperation modes. Hip and knee movements are monitored via smartphone-based IMU sensors, enabling real-time feedback. Bluetooth communication ensures seamless control during various training scenarios. Our study demonstrates that remotely controlled rehabilitation systems can be implemented effectively, offering vital support not only during global health crises such as pandemics but also in improving the accessibility of rehabilitation services in remote or underserved areas. This approach has the potential to transform the way physical therapy can be delivered, making it more accessible and adaptable to the needs of a larger patient population.

Lower Limb Rehabilitation Exoskeleton Robots, A review

Using a lower limb exoskeleton for rehabilitation (LLE) Lower limb exoskeleton rehabilitation robots (LER) are designed to assist patients with daily duties and help them regain their ability to walk. Even though a substantial portion of them is capable of doing both, they have not yet succeeded in conducting agile and intelligent joint movement between humans and machines, which is their ultimate goal. The typical LLE products, rapid prototyping, and cutting-edge techniques are covered in this review. Restoring a patient's athletic prowess to its pr-accident level is the aim of rehabilitation treatment. The core of research on lower limb exoskeleton rehabilitation robots is the understanding of human gait. The performance of common prototypes might be used to match wearable robot shapes to human limbs. To imitate a normal stride, robot-assisted treatment needs to be able to control the movement of the robot at each joint and move the patient's limb.

Lower limb exoskeleton for gait rehabilitation with adaptive nonsingular sliding mode control

Abstract This paper introduces a lower limb exoskeleton for gait rehabilitation, which has been designed to be adjustable to a wide range of patients by incorporating an extension mechanism and series elastic actuators (SEAs). This configuration adapts better to the user’s anatomy and the natural movements of the user’s joints. However, the inclusion of SEAs increases actuator mass and size, while also introducing nonlinearities and changes in the dynamic response of the exoskeletons. To address the challenges related to the human–exoskeleton dynamic interaction, a nonsingular terminal sliding mode control that integrates an adaptive parameter adjustment strategy is proposed, offering a practical solution for trajectory tracking with uncertain exoskeleton dynamics. Simulation results demonstrate the algorithm’s ability to estimate unknown parameters. Experimental tests analyze the performance of the controller against uncertainties and external disturbances.

A Recurrent Deep Network for Gait Phase Identification from EMG Signals During Exoskeleton-Assisted Walking

Lower limb exoskeletons represent a relevant tool for rehabilitating gait in patients with lower limb movement disorders. Partial assistance exoskeletons adaptively provide the joint torque needed, on top of that produced by the patient, for a correct and stable gait, helping the patient to recover an autonomous gait. Thus, the device needs to identify the different phases of the gait cycle to produce precisely timed commands that drive its joint motors appropriately. In this study, EMG signals have been used for gait phase detection considering that EMG activations lead limb kinematics by at least 120 ms. We propose a deep learning model based on bidirectional LSTM to identify stance and swing gait phases from EMG data. We built a dataset of EMG signals recorded at 1500 Hz from four muscles from the dominant leg in a population of 26 healthy subjects walking overground (WO) and walking on a treadmill (WT) using a lower limb exoskeleton. The data were labeled with the corresponding stance or swing gait phase based on limb kinematics provided by inertial motion sensors. The model was studied in three different scenarios, and we explored its generalization abilities and evaluated its applicability to the online processing of EMG data. The training was always conducted on 500-sample sequences from WO recordings of 23 subjects. Testing always involved WO and WT sequences from the remaining three subjects. First, the model was trained and tested on 500 Hz EMG data, obtaining an overall accuracy on the WO and WT test datasets of 92.43% and 91.16%, respectively. The simulation of online operation required 127 ms to preprocess and classify one sequence. Second, the trained model was evaluated against a test set built on 1500 Hz EMG data. The accuracies were lower, yet the processing times were 11 ms faster. Third, we partially retrained the model on a subset of the 1500 Hz training dataset, achieving 87.17% and 89.64% accuracy on the 1500 Hz WO and WT test sets, respectively. Overall, the proposed deep learning model appears to be a valuable candidate for entering the control pipeline of a lower limb rehabilitation exoskeleton in terms of both the achieved accuracy and processing times.

Variable Admittance Control of High Compatibility Exoskeleton Based on Human–Robotic Interaction Force

The wearable exoskeleton system is a typical strongly coupled human–robotic system. Human–robotic is the environment for each other. The two support each other and compete with each other. Achieving high human–robotic compatibility is the most critical technology for wearable systems. Full structural compatibility can improve the intrinsic safety of the exoskeleton, and precise intention understanding and motion control can improve the comfort of the exoskeleton. This paper first designs a physiologically functional bionic lower limb exoskeleton based on the study of bone and joint functional anatomy and analyzes the drive mapping model of the dual closed-loop four-link knee joint. Secondly, an exoskeleton dual closed-loop controller composed of a position inner loop and a force outer loop is designed. The inner loop of the controller adopts the PID control algorithm, and the outer loop adopts the adaptive admittance control algorithm based on human–robot interaction force (HRI). The controller can adaptively adjust the admittance parameters according to the HRI to respond to dynamic changes in the mechanical and physical parameters of the human–robot system, thereby improving control compliance and the wearing comfort of the exoskeleton system. Finally, we built a joint simulation experiment platform based on SolidWorks/Simulink to conduct virtual prototype simulation experiments and recruited volunteers to wear rehabilitation exoskeletons to conduct related control experiments. Experimental results show that the designed physiologically functional bionic exoskeleton and adaptive admittance controller can significantly improve the accuracy of human–robotic joint motion tracking, effectively reducing human–machine interaction forces and improving the comfort and safety of the wearer. This paper proposes a dual-closed loop four-link knee joint exoskeleton and a variable admittance control method based on HRI, which provides a new method for the design and control of exoskeletons with high compatibility.

Deep Learning-Driven Analysis of a Six-Bar Mechanism for Personalized Gait Rehabilitation

Abstract Recent advances in robotics and artificial intelligence have highlighted the potential for the integration of computational intelligence in enhancing the functionality and adaptability of robotic systems, particularly in rehabilitation. Designing robotic exoskeletons for the lower limb rehabilitation of post-stroke patients requires frequent adjustments to accommodate individual differences in leg anatomy. This complex engineering challenge necessitates a deep understanding of human physiology, robotics, and optimization to develop adaptive robotic systems and also to swiftly quantify the required adjustments and implement them for each patient. The conventional approaches, which mostly rely on heuristics and manual tuning, often struggle to achieve optimal results. This paper presents a novel method that integrates a genetic algorithm (GA) with a deep learning approach to generate a gait trajectory of the ankle joint from a six-bar linkage mechanism of fixed dimensions. Later, using the same approach, the inverse kinematics solution for this mechanism is also devised whereby, the set of the link dimensions of the six-bar linkage mechanism is obtained for the given gait trajectory of an individual to achieve customization. We simulated the kinematic behavior of the six-bar linkage mechanism within defined mechanical constraints and utilized the generated data for training a feedforward neural network and long short-term memory models. The proposed model, when trained, can produce accurate lengths for the desired gait trajectories in the sagittal plane and vice-versa which further validates our proposed approach for inverse kinematics solution.

Design and Optimization of a Custom-Made Six-Bar Exoskeleton for Pulp Pinch Grasp Rehabilitation in Stroke Patients.

Stroke often causes neuromotor disabilities, impacting index finger function in daily activities. Due to the role of repetitive, even passive, finger movements in neuromuscular re-education and spasticity control, this study aims to design a rehabilitation exoskeleton based on the pulp pinch movement. The exoskeleton uses an underactuated RML topology with a single degree of mobility, customized from 3D scans of the patient's hand. It consists of eight links, incorporating two consecutive four-bar mechanisms and the third inversion of a crank-slider. A two-stage genetic optimization was applied, first to the location of the intermediate joint between the two four-bar mechanisms and later to the remaining dimensions. A targeted genetic optimization process monitored two quality metrics: average mechanical advantage from extension to flexion, and its variability. By analyzing the relationship between these metrics and key parameters at different synthesis stages, the population evaluated is reduced by up to 96.2%, compared to previous studies for the same problem. This custom-fit exoskeleton uses a small linear actuator to deliver a stable 12.45 N force to the fingertip with near-constant mechanical advantage during flexion. It enables repetitive pulp pinch movements in a flaccid finger, improving rehabilitation consistency and facilitating home-based therapy.

Gait self-learning control based on reference trajectory generation online for an asymmetric limb rehabilitation exoskeleton

Lower limb exoskeleton (LEX) are widely used to assist stoke survivors with walking dysfunction, which is lack of a more flexible trajectory and fails to address the control challenge posed by gait variability and asymmetry in rehabilitation training. This paper introduces an asymmetric self-learning lower exoskeleton (AS-LEX) based on reference trajectory generation for the affected side. Motor intent of the unaffected limb based on thresholds was identified to classify the gait phase of stance and swing. A parameterized gait trajectory was generated online, namely a combination of circular trajectory in the stance phase and an elliptical trajectory in the swing phase. Gait self-learning control is presented to make the affected limb adaptively learn the gait parameters generated by the unaffected limb. Feasibility of the AS-LEX is demonstrated experimentally using three healthy subjects. Resuls demonstrate that overground walking in a more natural speed (with a stride length 600 mm and 700 mm) make subjects more actively learn gait of the affected side from the unaffected side. Additionally, experiments of the fatigue level of the affected limb and human-robot interaction torques were carried out, and the results indicate a more natural gait and reduced interaction forces with the AS-LEX.

Data-Driven Model-Free Adaptive Containment Control for Uncertain Rehabilitation Exoskeleton Robots with Input Constraints

This paper presents a data-driven model-free adaptive containment control (MFACC) scheme for uncertain rehabilitation exoskeleton robots, where the robotic exoskeleton dynamics are uncertain with saturation constraints. To handle uncertainties of the robotic dynamics, a model-free adaptive control (MFAC) strategy is established by linearizing the robotic exoskeleton dynamics into an equivalent data model. Considering the integral additive effect of the traditional MFAC method, an improved MFAC controller is designed in this paper. Since actuators with saturation constraints constantly affect the safety of patients during rehabilitation training, we construct a new criterion function with active constraints for the critical function of the MFAC algorithm and adopt the Hildreth quadratic programming algorithm to find the constrained optimal solution to overcome this limitation. The proposed MFACC scheme is rigorously proven by the compression mapping method to demonstrate model-free stability. Finally, the proposed control scheme is verified to be effective by simulation studies of the robotic SimMechanics model.

Design, Simulation and Functional Testing of a Novel Ankle Exoskeleton with 3DOFs

This paper presents a study on developing a new exoskeleton for ankle joint rehabilitation with three degrees of freedom (3 DOFs). The primary attention is paid to the process of designing and modelling the device aimed at restoring the lost functions of joint mobility. The authors conducted a complex analysis of the functional requirements of the exoskeleton based on research into the potential user’s needs, which allowed for the development of a conceptual model of the proposed device. In this study, a prototype of the exoskeleton is designed using modern additive technologies. The prototype underwent virtual testing in conditions maximally close to reality, which confirmed its effectiveness and comfort of use. The main results of this study indicate the promising potential of the proposed solution for application in rehabilitation practices, especially for patients with ankle joint injuries and diseases.

EMG model calibration and trajectory tracking of rehabilitation exoskeleton by asynchronous tele-training

ABSTRACT This study presents a novel kinematic tracking model, designed for a networked exoskeleton system that is asynchronously taught by a remote therapist. The therapist’s rehabilitation exercises are quantitatively assessed using a monocular vision system. The resultant metrics are then transmitted asynchronously over the network to patients equipped with exoskeletons. The exoskeleton utilises these metrics as reference paths for exercises, complemented by electromyography (EMG) feedback. This work introduces a calibration approach aimed at estimating angular positions by utilising EMG observations. The calibration model establishes real-time correlations between polynomial reference positions. We further explore redundant kinematics, incorporating an EMG observer for linear, time-variant rehabilitation tracking control. Our methodology is validated using vision-based metric data and experimental EMG measurements including shoulder flexion, elbow flexion, and rowing-like movements. Computer simulations demonstrate the system’s ability to reliably, robustly, and effectively follow desired trajectories. This research offers a promising approach for remote personalized rehabilitation.

Research on the Motion Control Strategy of a Lower-Limb Exoskeleton Rehabilitation Robot Using the Twin Delayed Deep Deterministic Policy Gradient Algorithm.

The motion control system of a lower-limb exoskeleton rehabilitation robot (LLERR) is designed to assist patients in lower-limb rehabilitation exercises. This research designed a motion controller for an LLERR-based on the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to control the lower-limb exoskeleton for gait training in a staircase environment. Commencing with the establishment of a mathematical model of the LLERR, the dynamics during its movement are systematically described. The TD3 algorithm is employed to plan the motion trajectory of the LLERR's right-foot sole, and the target motion curve of the hip (knee) joint is deduced inversely to ensure adherence to human physiological principles during motion execution. The control strategy of the TD3 algorithm ensures that the movement of each joint of the LLERR is consistent with the target motion trajectory. The experimental results indicate that the trajectory tracking errors of the hip (knee) joints are all within 5°, confirming that the LLERR successfully assists patient in completing lower-limb rehabilitation training in a staircase environment. The primary contribution of this study is to propose a non-linear control strategy tailored for the staircase environment, enabling the planning and control of the lower-limb joint motions facilitated by the LLERR.

Exploring the Application of Upper Limb Exoskeletons in Pediatric Rehabilitation: A Literature Review

Exploring the Application of Upper Limb Exoskeletons in Pediatric Rehabilitation: A Literature Review

Research status of lower limb exoskeleton rehabilitation robot

Lower limb exoskeleton rehabilitation robots are used to improve or restore the walking and movement ability of people with lower limb movement disorders. However, the required functions for patients differ based on various diseases. For example, patients with weak muscle strength require power assistance, patients with spinal cord injuries require motion compensation, patients with gait abnormalities require gait correction, and patients with strokes require neural rehabilitation. To design a more targeted lower limb exoskeleton rehabilitation robot for different diseases, this article summarised and compared existing lower limb exoskeleton rehabilitation robots according to their main functions and the characteristics and rehabilitation needs of various lower limb movement disorders. The correlations between the functions of existing devices and diseases were summarised to provide certain references for the development of new lower limb exoskeleton rehabilitation robots.