Rehabilitation Robot Research Articles

Combining Transcranial Direct Current Stimulation With Hand Robotic Rehabilitation in Chronic Stroke Patients: A Double-Blind Randomized Clinical Trial.

This study aimed to assess the impact of combining transcranial direct current stimulation with end-effector robot-assisted treatment on upper limb function, spasticity, and hand dexterity in chronic stroke patients. This was a prospective, double-blind randomized trial with 20 equally allocated stroke patients. The experimental group received dual transcranial direct current stimulation (anode over affected M1, cathode over contralateral M1) alongside robot-assisted treatment, while the control group received sham transcranial direct current stimulation with the same electrode placement + robot-assisted treatment. Each patient underwent 20 combined transcranial direct current stimulation and robot-assisted treatment sessions. The primary outcome measure was the Fugl-Meyer Upper Limb motor score, with secondary outcomes including AMADEO kinematic measures, Action Research Arm Test, and Functional Independence Measure. Assessments were conducted at baseline, after rehabilitation, and 3 mos later. Combining bilateral transcranial direct current stimulation with robot-assisted treatment did not yield additional improvements in Fugl-Meyer Upper Limb motor score, Functional Independence Measure, or Action Research Arm Test scores among stroke patients. However, the real transcranial direct current stimulation group showed enhanced finger flexion in the affected hand based on AMADEO kinematic measures. The addition of transcranial direct current stimulation to robot-assisted treatment did not result in significant overall functional improvements in chronic stroke patients. However, a benefit was observed in finger flexion of the affected hand.

Dynamic Motion Primitives-Based Trajectory Learning for Physical Human–Robot Interaction Force Control

One promising function of interactive robots is to provide a specific interaction force to human users. For example, rehabilitation robots are expected to promote patients' recovery by interacting with them with a prescribed force. However, motion uncertainties of different individuals, which are hard to predict due to the varying motion speed and noises during motion, degrade the performance of existing control methods. This paper proposes a method to learn a desired reference trajectory for a robot based on dynamic motion primitives (DMPs) and iterative learning (IL). By controlling the robot to follow the generated desired reference trajectory, the interaction force can achieve a desired value. In our proposed approach, DMPs are first employed to parameterize the demonstration trajectories of the human user. Then a recursive least square (RLS)-based estimator is developed and combined with the Adam optimization method to update the trajectory parameters so that the desired reference trajectory of the robot is iteratively obtained by resolving the DMPs. Since the proposed method parameterizes the trajectories depending on the phrase variable, it removes the essential assumption of traditional IL methods where the iteration period should be invariant, and thus has improved robustness compared with the existing methods. Experiments are performed using an interactive robot to validate the effectiveness of our proposed scheme.

Design of Human–Machine Compatible Ankle Rehabilitation Robot Based on Equivalent Human Ankle Model

Design of Human–Machine Compatible Ankle Rehabilitation Robot Based on Equivalent Human Ankle Model

Development of a low-cost upper-body rehabilitation robot for home use.

The inability to use one's hands or arms greatly restricts the ability to perform daily activities. After a developmental or acquired injury, the intensity and frequency of rehabilitation exercises are essential. To alleviate the burden on the healthcare system, robotic systems have been developed to support clinicians' interventions. However, these systems are often bulky and expensive, limiting their use to specific clinical settings and making them impractical for home use. This paper presents the development of an affordable and easy to install 2-DOF five-bar linkage robot designed to be used at home. This work aims to reduce the cost of the robot through actuation optimization, mechanical optimization and 3D printing. The architecture and links length are chosen to optimize the robot's performance in the required workspace. Using sensor feedback, impedance control algorithms and multiple types of exercise such as virtual walls guidance are implemented. Finally, a user interface was programmed to facilitate the robot's use.

Multimode Control Strategy for Robotic Rehabilitation on Special Orthogonal Group SO(3)

Robot-assisted rehabilitation for three-degree-of-freedom joints, such as hip and ankle, is significant for patients with motor function injuries. The control of such robots involves attitude control. To adapt to different disease stages, multi-mode hybrid control is considered to be one of the best choices. Passive mode is based on trajectory tracking control, while active mode is based on field-based assist-as-needed (AAN) control. The key to AAN control is the solution of the closest attitude point. However, the attitude point belongs to a special orthogonal group SO(3), and its topology is completely different from Euclidean space, which causes difficulties to the solution. Both passive and active control methods are affected by the inaccuracy of model parameters and external disturbances. Therefore, this paper proposes a multi-mode hybrid control method on SO(3). First, the expressions of trajectory tracking and contour tracking errors are proposed. To solve the contour tracking error, a feedback linearization algorithm based on sliding surface was used. A radial basis function (RBF) neural network was used for adaptive compensation. Subsequently, a controller for different modes was designed and its stability was analysed. Experiments were conducted using a hip exoskeleton, and the results verified the effectiveness of the proposed control method.

Effect of Robotic Rehabilitation on Hand Functions and Quality of Life in Children With Cerebral Palsy: A Prospective Randomized Controlled Study.

This study aimed to examine the impact of robotic hand rehabilitation on hand function and quality of life in children with cerebral palsy. Children with cerebral palsy aged 7-16 yrs were divided into robotic rehabilitation ( n = 9) or conventional rehabilitation ( n = 10) groups for hand rehabilitation of 30 sessions. The primary outcomes were the Fugl-Meyer Assessment for Upper Extremity and Box and Block Test. The secondary outcomes were the Manual Ability Classification System, modified Ashworth scale, hand grasp and finger strengths, ABILHAND-Kids, Functional Independence Measure for Children, and PedsQL Quality of Life Inventory-CP Module. In the robotic rehabilitation group, a significant improvement was found in all parameters after treatment ( P < 0.05), except for the Functional Independence Measure ( P = 0.081). In the conventional rehabilitation group, there was significant improvement after treatment in the modified Ashworth scale, Fugl-Meyer Assessment for Upper Extremity, hand grasp strength, Box and Block Test, ABILHAND-Kids, and PedsQL Quality of Life Inventory-CP Module ( P < 0.05). Before and after treatment, all outcome parameters in the groups were similar ( P > 0.05). Robotic hand rehabilitation is effective in improving motor function, manual dexterity, spasticity, and quality of life in children with cerebral palsy. However, it was not demonstrated to be superior to conventional rehabilitation.

Adaptive control for shape memory alloy actuated systems with applications to human-robot interaction.

Shape memory alloy (SMA) actuators are attractive options for robotic applications due to their salient features. So far, achieving precise control of SMA actuators and applying them to human-robot interaction scenarios remains a challenge. This paper proposes a novel approach to deal with the control problem of a SMA actuator. Departing from conventional mechanism models, we attempt to describe this nonlinear plant using a gray-box model, in which only the input current and the output displacement are measured. The control scheme consists of the model parameters updating and the control law calculation. The adaptation algorithm is founded on the multi-innovation concept and incorporates a dead-zone weighted factor, aiming to concurrently reduce computational complexities and enhance robustness properties. The control law is based on a PI controller, the gains of which are designed by the pole assignment technique. Theoretical analysis proves that the closed-loop performance can be ensured under mild conditions. The experiments are first conducted through the Beckhoff controller. The comparative results suggest that the proposed adaptive PI control strategy exhibits broad applicability, particularly under load variations. Subsequently, the SMA actuator is designed and incorporated into the hand rehabilitation robot. System position tracking experiments and passive rehabilitation training experiments for various gestures are then conducted. The experimental outcomes demonstrate that the hand rehabilitation robot, utilizing the SMA actuator, achieves higher position tracking accuracy and a more stable system under the adaptive control strategy proposed in this paper. Simultaneously, it successfully accommodates hand rehabilitation movements for multiple gestures. The adaptive controller proposed in this paper takes into account both the computational complexity of the model and the accuracy of the control results, Experimental results not only demonstrate the practicality and reliability of the controller but also attest to its potential application in human-machine interaction within the field of neural rehabilitation.

Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures.

The quantification of comfort in binding parts, essential human-machine interfaces (HMI) for the functioning of rehabilitation robots, is necessary to reduce physical strain on the user despite great achievements in their structure and control. This study aims to investigate the physiological impacts of binding parts by measuring electrodermal activity (EDA) and tissue oxygen saturation (StO2). In Experiment 1, EDA was measured from 13 healthy subjects under three different pressure conditions (10, 20, and 30 kPa) for 1 min using a pneumatic cuff on the right thigh. In Experiment 2, EDA and StO2 were measured from 10 healthy subjects for 5 min. To analyze the correlation between EDA parameters and the decrease in StO2, a survey using the visual analog scale (VAS) was conducted to assess the level of discomfort at each pressure. The EDA signal was decomposed into phasic and tonic components, and the EDA parameters were extracted from these two components. RM ANOVA and a post hoc paired t-test were used to determine significant differences in parameters as the pressure increased. The results showed that EDA parameters and the decrease in StO2 significantly increased with the pressure increase. Among the extracted parameters, the decrease in StO2 and the mean SCL proved to be effective indicators. Such analysis outcomes would be highly beneficial for studies focusing on the comfort assessment of the binding parts of rehabilitation robots.

Preliminary Study on Current Research Status and Clinical Application of Lower Limb Rehabilitation Robot Mechanisms

As an assistive device to restore lower limb athletic ability, human lower limb rehabilitation robots are becoming increasingly important in the field of rehabilitation and clinical applications. With the advancement of science and technology, both domestic and foreign research in this field has been developed. This study provides a detailed overview of the development of lower limb rehabilitation robots and reviews the current status of clinical applications, with a focus on mechanism research, from the perspectives of degrees of freedom, workspace, singularity, gait simulation, kinematic simulation and human-robot interaction, etc. The results show that domestic research on lower limb rehabilitation robots focus on the design and optimization of the mechanism configuration, while foreign research focus on the improvement and innovation of the control system and training mode based on human-computer interaction. Finally, the current state of research is combined with an outlook on future trends.

Optimizing lower limb rehabilitation: the intersection of machine learning and rehabilitative robotics.

Lower limb rehabilitation is essential for recovery post-injury, stroke, or surgery, improving functional mobility and quality of life. Traditional therapy, dependent on therapists' expertise, faces challenges that are addressed by rehabilitation robotics. In the domain of lower limb rehabilitation, machine learning is progressively manifesting its capabilities in high personalization and data-driven approaches, gradually transforming methods of optimizing treatment protocols and predicting rehabilitation outcomes. However, this evolution faces obstacles, including model interpretability, economic hurdles, and regulatory constraints. This review explores the synergy between machine learning and robotic-assisted lower limb rehabilitation, summarizing scientific literature and highlighting various models, data, and domains. Challenges are critically addressed, and future directions proposed for more effective clinical integration. Emphasis is placed on upcoming applications such as Virtual Reality and the potential of deep learning in refining rehabilitation training. This examination aims to provide insights into the evolving landscape, spotlighting the potential of machine learning in rehabilitation robotics and encouraging balanced exploration of current challenges and future opportunities.

Retracted: Sensor-Based Exercise Rehabilitation Robot Training Method

Retracted: Sensor-Based Exercise Rehabilitation Robot Training Method

Post-stroke rehabilitation in the peri-pandemic COVID-19 era.

The coronavirus disease 2019 (COVID-19), which arosein late 2019, caused extensive destruction, impacting a substantial proportion of the worldwide population and leading to millions of deaths. Although COVID-19 is mainly linked to respiratory and pulmonary complications, it has the potential to affect neurologic structures as well. Neurological involvement may manifest as minimal and reversible; however, a notable proportion of cases have exhibited pronounced neurological consequences, such as strokes. Endothelial inflammation, hypercoagulation, renin-angiotensin-aldosterone system alterations, and cardiogenic embolism are the pathophysiological mechanismsof stroke under COVID-19 circumstances. Physical activity and exercise have improved several aspects of post-stroke recovery, including cardiovascular health, walking capacity, and upper limb strength. They are commonly used to assist stroke survivors in overcomingtheir motor restrictions. Furthermore, stroke rehabilitation can incorporate a range of specific techniques, including body-weight-supported treadmill applications, constraint-induced movement therapy, robotic rehabilitation interventions, transcranial direct current stimulation, transcranial magnetic stimulation, and prism adaptation training. Under pandemic conditions, there were several barriers to neurological rehabilitation. The most significant of these were individual's fear of infection, which caused them to postpone their rehabilitation applicationsand rehabilitation areas being converted into COVID-19 units. The primary emphasis had turned to COVID-19 treatment. Several valuable data and views were gained in reorganizing rehabilitation during the pandemic, contributing to establishing future views in this regard.

Powered single hip joint exoskeletons for gait rehabilitation: a systematic review and Meta-analysis.

Gait disorders and as a consequence, robotic rehabilitation techniques are becoming increasingly prevalent as the population ages. In the area of rehabilitation robotics, using lightweight single hip joint exoskeletons are of significance. Considering no prior systematic review article on clinical outcomes, we aim to systematically review powered hip exoskeletons in terms of gait parameters and metabolic expenditure effects. Three databases of PubMed, Scopus, and Web of science were searched for clinical articles comparing outcomes of gait rehabilitation using hip motorized exoskeleton with conventional methods, on patients with gait disorder or healthy individuals. Of total number of 37 reviewed articles, 14 trials were quantitatively analyzed. Analyses performed in terms of gait spatiotemporal parameters like speed (self-speed and maximum speed), step length, stride length, cadence, and oxygen consumption. Improved clinical outcomes of gait spatiotemporal parameters with hip joint exoskeletons are what our review's findings show. In terms of gait values, meta-analysis indicates that rehabilitation with single hip joint exoskeleton enhanced parameters of maximum speed by 0.13 m/s (0.10-0.17) and step length by 0.06 m (0.05-0.07). For the remaining investigated gait parameters, no statistically significant difference was observed. Regarding metabolic parameters, oxygen consumption was lower in individuals treated with hip exoskeleton (- 1.23 ml/min/kg; range - 2.13 to - 0.32). Although the analysis demonstrated improvement with just specific gait measures utilizing powered hip exoskeletons, the lack of improvement in all parameters is likely caused by the high patient condition heterogeneity among the evaluated articles. We also noted in patients who rehabilitated with the hip exoskeleton, the oxygen cost was lower. More randomized controlled trials are needed to verify both the short- and long-term clinical outcomes, including patient-reported measures. Level I (systematic review and meta-analysis).

Clinical evaluation of a patient participation assessment system for upper extremity rehabilitation exercises.

In conventional and robotic rehabilitation, the patient's active participation in exercises is essential for the maximum functional output to be received from therapy. In rehabilitation exercises performed with robotic devices, the difficulty levels of therapy tasks and the device assistance are adjusted based on the patient's therapy performance to improve active participation. However, the existing therapy performance evaluation methods are based on either some specific device designs or certain therapy tasks, which limits their widespread use. In this paper, the effectiveness of a participation assessment system, which can evaluate patients' therapy performance, tiredness, and slacking independent of any device design and therapy exercise, was clinically tested on ten patients diagnosed with frozen shoulder syndrome. The patients performed exercises using the system once a week throughout their 4-week treatment period. Multiple clinical measurements and scales were employed during the clinical study to assess patients' progress and status, such as tiredness throughout the therapy process. The clinical data, along with the patient findings obtained from the participation assessment system, were statistically analyzed and compared. The findings revealed that the patients' improvements and progress during the therapy process clinically coincide with the variations in the performance evaluation results of the system, and the implemented method successfully assesses the patients' participation during the rehabilitation exercises.

A Review of Design and Modeling of Pneumatic Artificial Muscle

Soft robots, which are often considered safer than rigid robots when interacting with humans due to the reduced risk of injury, have found utility in various medical and industrial fields. Pneumatic artificial muscles (PAMs), one of the most widely used soft actuators, have proven their efficiency in numerous applications, including prosthetic and rehabilitation robots. PAMs are lightweight, responsive, precise, and capable of delivering a high force-to-weight ratio. Their structure comprises a flexible, inflatable membrane reinforced with fibrous twine and fitted with gas-sealing fittings. For the optimal design and integration of these into control systems, it is crucial to develop mathematical models that accurately represent their functioning mechanisms. This paper introduces a general concept of PAM's construction, its various types, and operational mechanisms, along with its key benefits and drawbacks, and also reviews the most common modeling techniques for PAM representation. Most models are grounded in PAM architecture, aiming to calculate the actuator's force across its full axis by correlating pressure, length, and other parameters that influence actuator strength.

How should robots exercise with people? Robot-mediated exergames win with music, social analogues, and gameplay clarity.

Introduction: The modern worldwide trend toward sedentary behavior comes with significant health risks. An accompanying wave of health technologies has tried to encourage physical activity, but these approaches often yield limited use and retention. Due to their unique ability to serve as both a health-promoting technology and a social peer, we propose robots as a game-changing solution for encouraging physical activity. Methods: This article analyzes the eight exergames we previously created for the Rethink Baxter Research Robot in terms of four key components that are grounded in the video-game literature: repetition, pattern matching, music, and social design. We use these four game facets to assess gameplay data from 40 adult users who each experienced the games in balanced random order. Results: In agreement with prior research, our results show that relevant musical cultural references, recognizable social analogues, and gameplay clarity are good strategies for taking an otherwise highly repetitive physical activity and making it engaging and popular among users. Discussion: Others who study socially assistive robots and rehabilitation robotics can benefit from this work by considering the presented design attributes to generate future hypotheses and by using our eight open-source games to pursue follow-up work on social-physical exercise with robots.

Adaptive-Robust Controller for Smart Exoskeleton Robot.

Rehabilitation robotics has seen growing popularity in recent years due to its immense potential for improving the lives of people with disabilities. However, the complex, uncertain dynamics of these systems present significant control challenges, requiring advanced techniques. This paper introduces a novel adaptive control framework integrating modified function approximation (MFAT) and double-integral non-singular terminal sliding mode control (DINTSMC). The goal is to achieve precise tracking performance, high robustness, a fast response, a finite convergence time, reduced chattering, and effective handling of unknown system dynamics. A key feature is the incorporation of a higher-order sliding mode observer, eliminating the need for velocity feedback. This provides a new solution for overcoming the inherent variations and uncertainties in robot manipulators, enabling improved accuracy within fixed convergence times. The efficacy of the proposed approach was validated through simulations and experiments on an exoskeleton robot. The results successfully demonstrated the controller's effectiveness. Stability analysis using Lyapunov theory proved the closed-loop system's uniform ultimate boundedness. This contribution is expected to enable enhanced control for rehabilitation robots and improved patient outcomes.

The facilitators and barriers to home-based robotic rehabilitation in India: a pilot feasibility study

IntroductionRobot-assisted rehabilitation has emerged as a promising approach for enhancing motor function in stroke survivors. However, the feasibility and effectiveness of home-based robotic training in this population are underexplored, especially in low/middle-income countries.MethodsThis feasibility study aimed to address this gap by examining the feasibility and effectiveness of independent home-based training using PLUTO, a robotic device for hand training. A total of 7 chronic stroke survivors were recruited, with 5 completing the study.ResultsThe results revealed high engagement and adherence to the home-based training program, with participants averaging 1659.8 min of training over 24.8 days. The PLUTO system demonstrated excellent usability and elicited positive user perceptions. Significant improvements were observed in functional outcomes, as evidenced by a noteworthy increase in Fugl-Meyer Assessment scores (mean increase of 6.2 points, exceeding the minimal clinically important difference (MCID) of 5.35 points). Furthermore, participants showed improvements in the ABILHAND measure (mean improvement of 1.24 logits, surpassing the MCID of 0.2 logits) and the Barthel Index (mean increase of 8.8 points).ConclusionThese findings demonstrate the feasibility and effectiveness of home-based robotic rehabilitation for chronic stroke survivors. This has implications for expanding access to rehabilitation services in low- and middle-income countries, enhancing patient engagement and adherence, and improving functional outcomes. Larger controlled studies are warranted to evaluate the effectiveness of home-based robotic rehabilitation programs.

Research on the method of identifying upper and lower limb coordinated movement intentions based on surface EMG signals.

Rehabilitation robots have gained considerable focus in recent years, aiming to assist immobilized patients in regaining motor capabilities in their limbs. However, most current rehabilitation robots are designed specifically for either upper or lower limbs. This limits their ability to facilitate coordinated movement between upper and lower limbs and poses challenges in accurately identifying patients' intentions for multi-limbs coordinated movement. This research presents a multi-postures upper and lower limb cooperative rehabilitation robot (U-LLCRR) to address this gap. Additionally, the study proposes a method that can be adjusted to accommodate multi-channel surface electromyographic (sEMG) signals. This method aims to accurately identify upper and lower limb coordinated movement intentions during rehabilitation training. By using genetic algorithms and dissimilarity evaluation, various features are optimized. The Sine-BWOA-LSSVM (SBL) classification model is developed using the improved Black Widow Optimization Algorithm (BWOA) to enhance the performance of the Least Squares Support Vector Machine (LSSVM) classifier. Discrete movement recognition studies are conducted to validate the exceptional precision of the SBL classification model in limb movement recognition, achieving an average accuracy of 92.87%. Ultimately, the U-LLCRR undergoes online testing to evaluate continuous motion, specifically the movements of "Marching in place with arm swinging". The results show that the SBL classification model maintains high accuracy in recognizing continuous motion intentions, with an average identification rate of 89.25%. This indicates its potential usefulness in future rehabilitation robot-active training methods, which will be a promising tool for a wide range of applications in the fields of healthcare, sports, and beyond.

Force/position-based velocity control strategy for the lower limb rehabilitation robot during active training: design and validation.

Aiming at the shortcomings of most existing control strategies for lower limb rehabilitation robots that are difficult to guarantee trajectory tracking effect and active participation of the patient, this paper proposes a force/position-based velocity control (FPVC) strategy for the hybrid end-effector lower limb rehabilitation robot (HE-LRR) during active training. The configuration of HE-LRR is described and the inverse Jacobian analysis is carried out. Then, the FPVC strategy design is introduced in detail, including normal velocity planning and tangential velocity planning. The experimental platform for the HE-LRR system is presented. A series of experiments are conducted to validate the FPVC strategy's performance, including trajectory measurement experiments, force and velocity measurement experiments, and active participation experiments. Experimental studies show that the end effector possesses good following performance with the reference trajectory and the desired velocity, and the active participation of subjects can be adjusted by the control strategy parameters. The experiments have verified the rationality of the FPVC strategy, which can meet the requirements of trajectory tracking effect and active participation, indicating its good application prospects in the patient's robot-assisted active training.