Search

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The current rehabilitation evaluation methods for patients with hand dysfunction face issues such as inconsistent standards and incomplete quantification processes. To address these challenges, this paper introduces a rehabilitation evaluation system that integrates various rehabilitation training modes and leverages an exoskeleton finger rehabilitation robot. This system is carefully designed and thoroughly analyzed based on the diverse training modes offered by the rehabilitation robot. Twenty stroke patients and six healthy subjects were recruited to perform grasping of static objects and gesture movement experiments, which were evaluated by Brunnstrom's motor evaluation and rehabilitation evaluation tests, respectively, and the results were compared. The experimental results showed that the results of the robotic rehabilitation evaluation of the 20 patients were consistent with the clinical Brunnstrom motor grades, which verified the accuracy of the rehabilitation evaluation system that was designed in this study. [ABSTRACT FROM AUTHOR]

Injuries and diseases such as wrist nerve injuries, stroke, neurological disorders, and other wrist-related conditions have significantly impacted people's quality of life. This study aims to develop a lightweight, affordable, and portable ForeWrist (forearm and wrist) exoskeleton. This device is intended to assist and rehabilitate individuals with wrist disabilities, mainly stroke survivors, to enhance wrist range of motion and strength. The device can offer one active degree of freedom (DOF) responsible for pronation-supination (PS) of the forearm and two passive DOFs for the wrist joint. The design of the ForeWrist PS mainly consists of a cable-driven C-shaped guide rail and stationary bearing-carriage mechanism that can be attached to the user's wrist. The simulation and experimental analysis are conducted for the design validation and performance analysis. The experimental results indicate that the designed device should demonstrate promising potential for practical applications. The root mean squared error for joint position and velocity exhibit low values, and the peak torque for an average weight of the human lower arm was found to be under 10% of the device's total capacity. The developed exoskeleton provides a full range of motion for daily activities and covers 75% of the forearm's total range of motion with a consistency error of less than 1 ∘ . The device can be effective for both at home and outdoor assistance and rehabilitation training with its low weight of 300 g and peak velocity and torque of 70 deg/sec and 6 Nm, respectively. [ABSTRACT FROM AUTHOR]

The increasing utilization of upper limb rehabilitation robots in rehabilitation therapy has brought to light significant safety concerns regarding their mechanical structures and control systems. This study focuses on a six degrees of freedom (DOF) upper limb rehabilitation robot, which has been designed with an emphasis on safety through careful consideration of its mechanical structure and trajectory planning. Various parameter schemes for the shoulder joint angles were proposed, and the robotic arm's structure was developed by analyzing the spatial motion trajectories of the shoulder joint motor. This design successfully achieves the objective of minimizing the installation space while maximizing the range of motion. Additionally, an enhanced artificial field method is introduced to facilitate the planning of self-collision avoidance trajectories for dual-arm movements. This approach effectively mitigates the risk of collisions between the robotic arm and the human body, as well as between the two robotic arms, during movement. The efficacy of this method has been validated through experimental testing. [ABSTRACT FROM AUTHOR]

In this study, a hybrid impedance control-based portable ankle rehabilitation robot that can perform therapeutic exercises for the ankle has been developed. This 1-DOF robot can perform plantar flexion and dorsiflexion movements for the ankle. The capacity of the robotic system to perform therapeutic exercises was tested with subjects. Isotonic and resistive exercises were tested with 10 healthy subjects and stretching exercise was tested with a patient. The results showed that the hybrid impedance controlled robotic system can successfully perform passive stretching, active isotonic and active resistive exercises by providing a safe human-robot interaction. Especially when a sudden resistance increase occurs in the joint, the hybrid impedance controller acts like a physiotherapist and performs the movement without damaging the joint. [ABSTRACT FROM AUTHOR]

Abstract Purpose This pilot study aimed to investigate the effects of REX exoskeleton rehabilitation robot training on the balance and lower limb function in patients with sub-acute stroke. Methods This was a pilot, single-blind, randomized controlled trial. Twenty-four patients with sub-acute stroke (with the course of disease ranging from 3 weeks to 3 months) were randomized into two groups, including a robot group and a control group. Patients in control group received upright bed rehabilitation (n = 12) and those in robot group received exoskeleton rehabilitation robot training (n = 12). The frequency of training in both groups was once a day (60 min each) for 5 days a week for a total of 4 weeks. Besides, the two groups were evaluated before, 2 weeks after and 4 weeks after the intervention, respectively. The primary assessment index was the Berg Balance Scale (BBS), whereas the secondary assessment indexes included the Fugl-Meyer Lower Extremity Motor Function Scale (FMA-LE), the Posture Assessment Scale for Stroke Patients (PASS), the Activities of Daily Living Scale (Modified Barthel Index, MBI), the Tecnobody Balance Tester, and lower extremity muscle surface electromyography (sEMG). Results The robot group showed significant improvements (P

In this paper, a new type of cable-driven lower limb rehabilitation robot (CDLLRR) based on bilateral control is proposed. Compared with the traditional rehabilitation robot, it has multiple cooperative remote rehabilitation modes of patient-therapist interaction with better safety, mechanical compliance and adaptability. Firstly, according to the characteristics of gait movement of human lower limbs and the performance requirements of the robot in therapy and rehabilitation, the overall structure, main components and assembly arrangement of the robot are designed in detail. Secondly, in order to ensure that the rehabilitation robot has excellent force transparency and system stability while still maintaining a certain position tracking, according to the performance measurement index based on the introduced bilateral control strategy, this paper proposes an improved PD controller based on Anderson method (IPDAM). Finally, the feasibility of the proposed CDLLRR and the effectiveness of the IPDAM are verified by simulation experiments. [ABSTRACT FROM AUTHOR]

Severe coronavirus disease 2019 (COVID-19) infection may decrease respiratory and physical functions. To evaluate whether robot-assisted stair climbing training (RASCT) would improve pulmonary and physical functions in a patient post-severe COVID-19 infection. A 48-year-old woman who had experienced severe COVID-19 underwent a 6-week inpatient rehabilitation. She persistently exhibited impaired pulmonary and physical functions, including walking and balance impairment. We provided a 30-min outpatient RASCT biweekly for 6 weeks. After training, maximal inspiratory and maximal expiratory pressures improved from 81 and 74 cmH2O to 104 and 81 cmH2O, respectively. The walking speed improved from 1.15 to 1.21 m/s. In balance ability, physical performance battery score and timed up-and-go test improved from 8 to 11 s and 10.89 to 9.95 s, respectively. Regarding exercise capacity, the 6-min walk test distance improved from 453 to 482 m, and the number of 1-min sit-to-stand test improved from 20 to 23, with improved pulse rate and saturation level. The physical and psychological domain scores of the World Health Organization Quality-of-Life Scale-BREF improved from 44 to 63 and 69 to 81, respectively; Falls Efficacy Scale-International scores improved from 38 to 21. RASCT, as part of a rehabilitation plan, was feasible and effective for this patient after severe COVID-19 infection. [ABSTRACT FROM AUTHOR]

Purpose: This pilot study aimed to investigate the effects of REX exoskeleton rehabilitation robot training on the balance and lower limb function in patients with sub-acute stroke. Methods: This was a pilot, single-blind, randomized controlled trial. Twenty-four patients with sub-acute stroke (with the course of disease ranging from 3 weeks to 3 months) were randomized into two groups, including a robot group and a control group. Patients in control group received upright bed rehabilitation (n = 12) and those in robot group received exoskeleton rehabilitation robot training (n = 12). The frequency of training in both groups was once a day (60 min each) for 5 days a week for a total of 4 weeks. Besides, the two groups were evaluated before, 2 weeks after and 4 weeks after the intervention, respectively. The primary assessment index was the Berg Balance Scale (BBS), whereas the secondary assessment indexes included the Fugl-Meyer Lower Extremity Motor Function Scale (FMA-LE), the Posture Assessment Scale for Stroke Patients (PASS), the Activities of Daily Living Scale (Modified Barthel Index, MBI), the Tecnobody Balance Tester, and lower extremity muscle surface electromyography (sEMG). Results: The robot group showed significant improvements (P < 0.05) in the primary efficacy index BBS, as well as the secondary efficacy indexes PASS, FMA-LE, MBI, Tecnobody Balance Tester, and sEMG of the lower limb muscles. Besides, there were a significant differences in BBS, PASS, static eye-opening area or dynamic stability limit evaluation indexes between the robotic and control groups (P < 0.05). Conclusions: This is the first study to investigate the effectiveness of the REX exoskeleton rehabilitation robot in the rehabilitation of patients with stroke. According to our results, the REX exoskeleton rehabilitation robot demonstrated superior potential efficacy in promoting the early recovery of balance and motor functions in patients with sub-acute stroke. Future large-scale randomized controlled studies and follow-up assessments are needed to validate the current findings. Clinical trials registration: URL: https://www.chictr.org.cn/index.html.Unique identifier: ChiCTR2300068398. [ABSTRACT FROM AUTHOR]

Although robot-assisted physiotherapy has gained increasing attention in recent years, the use of wearable rehabilitation robots for lower limbs has shown reduced efficiency due to additional equipment and motors located at the center of the joint, increasing complexity and load on disabled patients. This paper proposes a novel rehabilitation approach by eliminating motors and equipment from the center of joints and placing them on a fixed platform using cable-based power transmission. A proposed model of a 14 cable-driven bipedal robot with 7 degrees of freedom has been used to model a lower limb rehabilitation robot corresponding to it. The dynamic equations of the robot are derived using the Euler-Lagrange method. The sliding mode control technique is utilized to offer accurate control for tracking desired trajectories, ensuring smoothness despite disturbances, and reducing tracking errors. This approach is employed to help prevent patients from falling and support them in maintaining balance during rehabilitative exercises. To ensure that cables exert positive tension, the sliding mode controller was combined with quadratic programming optimization, minimizing path error while constraining the controller input torque to be non-negative. The performance of the proposed controller was assessed by considering several control gains resulting in K = 10 identified as the most effective one. The feasibility of this approach to rehabilitation is demonstrated by the numerical results in MATLAB simulation, which show that the RMSE amount of the right and left hip and thigh angles are 0.29, 0.37, 0.31, and 0.44, respectively which verified an improved rehabilitation process. Also, the correlation coefficient between the Adams and MATLAB simulation results for motor torque was found to be 0.98, indicating a high degree of correlation between the two simulation results. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid adaptive control strategy for upper limb rehabilitation robots using impedance learning. The hybrid adaptation consists of a differential updating mechanism for the estimation of robotic modeling uncertainties and periodic adaptations for the online learning of time-varying impedance. The proposed hybrid adaptive controller guarantees asymptotical control stability and achieves variable impedance regulation for robots without interaction force measurements. According to Lyapunov's theory, we proved that the proposed impedance learning controller guarantees the convergence of tracking errors and ensures the boundedness of the estimation errors of robotic uncertainties and impedance profiles. Simulations and experiments conducted on a parallel robot validated the effectiveness and the superiority of the proposed impedance learning controller in robot-assisted rehabilitation. The proposed hybrid adaptive control has potential applications in rehabilitation, exoskeletons, and some other repetitive interactive tasks. [ABSTRACT FROM AUTHOR]

As important auxiliary equipment, rehabilitation robots are widely used in rehabilitation treatment and daily life assistance. The rehabilitation robot proposed in this paper is mainly composed of an omnidirectional mobile platform module, a lower limb exoskeleton module, and a support module. According to the characteristics of the robot's omnidirectional mobility and good stiffness, the overall kinematic model of the robot is established using the analytical method. Passive and active training control strategies for an omnidirectional mobile lower limb exoskeleton robot are proposed. The passive training mode facilitates the realization of the goal of walking guidance and assistance to the human lower limb. The active training mode can realize the cooperative movement between the robot and the human through the admittance controller and the tension sensor and enhance the active participation of the patient. In the simulation experiment, a set of optimal admittance parameters was obtained, and the parameters were substituted into the controller for the prototype experiment. The experimental results show that the admittance-controlled rehabilitation robot can perceive the patient's motion intention and realize the two walking training modes. In summary, the passive and active training control strategies based on admittance control proposed in this paper achieve the expected purpose and effectively improve the patient's active rehabilitation willingness and rehabilitation effect. [ABSTRACT FROM AUTHOR]

Aimed at the problem of human–machine interaction between patients and robots in the process of using rehabilitation robots for rehabilitation training, this paper proposes a human–machine interactive control method based on an independently developed upper limb rehabilitation robot. In this method, the camera is used as a sensor, the human skeleton model is used to analyse the moving image, and the key points of the human body are extracted. Then, the three-dimensional coordinates of the key points of the human arm are extracted by depth estimation and spatial geometry, and then the real-time motion data are obtained, and the control instructions of the robot are generated from it to realise the real-time interactive control of the robot. This method can not only improve the adaptability of the system to individual patient differences, but also improve the robustness of the system, which is less affected by environmental changes. The experimental results show that this method can realise real-time control of the rehabilitation robot, and that the robot assists the patient to complete the action with high accuracy. The results show that this control method is effective and can be applied to the fields of robot control and robot-assisted rehabilitation training. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It has been proven that robot-assisted rehabilitation training can effectively promote the recovery of upper-limb motor function in post-stroke patients. Increasing patients’ active participation by providing assist-as-needed (AAN) control strategies is key to the effectiveness of robot-assisted rehabilitation training. In this paper, a greedy assist-as-needed (GAAN) controller based on radial basis function (RBF) network combined with 3 degrees of freedom (3-DOF) potential constraints was proposed to provide AAN interactive forces of an end-effect upper limb rehabilitation robot. The proposed 3-DOF potential fields were adopted to constrain the tangential motions of three kinds of typical target trajectories (one-dimensional (1D) lines, two-dimensional (2D) curves and three-dimensional (3D) spirals) while the GAAN controller was designed to estimate the motor capability of a subject and provide appropriate robot-assisted forces. The co-simulation (Adams-Matlab/Simulink) experiments and behavioral experiments on 10 healthy volunteers were conducted to validate the utility of the GAAN controller. The experimental results demonstrated that the GAAN controller combined with 3-DOF potential field constraints enabled the subjects to actively participate in kinds of tracking tasks while keeping acceptable tracking accuracies. 3D spirals could be better in stimulating subjects’ active participation when compared to 1D and 2D target trajectories. The current GAAN controller has the potential to be applied to existing commercial upper limb rehabilitation robots.

IntroductionIsokinetic exercise can improve joint muscle strength and stability, making it suitable for early rehabilitation of stroke patients. However, traditional isokinetic equipment is bulky and costly, and cannot effectively avoid external environmental interference.MethodsThis paper designed a lightweight upper limb joint isokinetic rehabilitation training equipment, with a control system that includes a speed planning strategy and speed control with disturbance rejection. Based on the established human-machine kinematic closed-loop model between the equipment and the user, a dynamic evaluation method of torque at the joint level was proposed.ResultsTo validate the effectiveness of the equipment, experiments were conducted by manually applying random disturbances to the equipment operated at an isokinetic speed. The results showed that the root mean square error between the observed torque curve of the second-order linear extended state observer used in this paper and the actual disturbance curve was 0.52, and the maximum speed tracking error of the speed control algorithm was 1.27%. In fast and slow sinusoidal speed curve tracking experiments, the root mean square errors of the speed tracking results for this algorithm were 9.65 and 5.27, respectively, while the tracking errors for the PID speed control algorithm under the same environment were 19.94 and 12.11.DiscussionThe research results indicate that compared with traditional PID control method, the proposed control strategy demonstrates superior performance in achieving isokinetic control and suppressing external disturbances, thereby exhibiting significant potential in promoting upper limb rehabilitation among patients.

Based on the 3-UU parallel mechanism, a prototype robot for wrist joint rehabilitation is developed to assist stroke patients in wrist joint rehabilitation training. Based on the constraint relation and geometric characteristics of the 3-UU mechanism, the inverse kinematics of 3-UU mechanism is analyzed by spherical coordinate method and roll-pitch-yaw (RPY) method， and the relation between the platform and the driver is obtained. Compared with the classic 3-RRR wrist joint rehabilitation mechanism, the developed prototype has no advantages such as multi-solution and singular value. The motion performance of the prototype and the electromyographic signals of the two major muscle groups in the forearm are measured. The experimental results show that the maximum roll angle is -90° to 90°, the pitch angle is -90° to 90°， and the virtual yaw angle is -180° to 180°. The maximum electromyogram (EMG) signal can be generated at 950 mV. The results show that the developed model can meet the requirements of the wrist motion and train the forearm muscle group.

Background: Artificial intelligence (AI) technology has made breakthroughs in spinal cord neural injury and restoration in recent years. It has a positive impact on clinical treatment. This study explores AI research's progress and hotspots in spinal cord neural injury and restoration. It also analyzes research shortcomings related to this area and proposes potential solutions. Methods: We used CiteSpace 6.1.R6 and VOSviewer 1.6.19 to research WOS articles on AI research in spinal cord neural injury and restoration. Results: A total of 1,502 articles were screened, in which the United States dominated; Kadone, Hideki (13 articles, University of Tsukuba, JAPAN) was the author with the highest number of publications; ARCH PHYS MED REHAB (IF = 4.3) was the most cited journal, and topics included molecular biology, immunology, neurology, sports, among other related areas. Conclusion: We pinpointed three research hotspots for AI research in spinal cord neural injury and restoration: (1) intelligent robots and limb exoskeletons to assist rehabilitation training; (2) brain-computer interfaces; and (3) neuromodulation and noninvasive electrical stimulation. In addition, many new hotspots were discussed: (1) starting with image segmentation models based on convolutional neural networks; (2) the use of AI to fabricate polymeric biomaterials to provide the microenvironment required for neural stem cell-derived neural network tissues; (3) AI survival prediction tools, and transcription factor regulatory networks in the field of genetics were discussed. Although AI research in spinal cord neural injury and restoration has many benefits, the technology has several limitations (data and ethical issues). The data-gathering problem should be addressed in future research, which requires a significant sample of quality clinical data to build valid AI models. At the same time, research on genomics and other mechanisms in this field is fragile. In the future, machine learning techniques, such as AI survival prediction tools and transcription factor regulatory networks, can be utilized for studies related to the up-regulation of regeneration-related genes and the production of structural proteins for axonal growth. [ABSTRACT FROM AUTHOR]

Lower-limb rehabilitation exoskeletons offer a transformative approach to enhancing recovery in patients with movement disorders affecting the lower extremities. This comprehensive systematic review delves into the literature on sensor technologies and the control strategies integrated into these exoskeletons, evaluating their capacity to address user needs and scrutinizing their structural designs regarding sensor distribution as well as control algorithms. The review examines various sensing modalities, including electromyography (EMG), force, displacement, and other innovative sensor types, employed in these devices to facilitate accurate and responsive motion control. Furthermore, the review explores the strengths and limitations of a diverse array of lower-limb rehabilitation-exoskeleton designs, highlighting areas of improvement and potential avenues for further development. In addition, the review investigates the latest control algorithms and analysis methods that have been utilized in conjunction with these sensor systems to optimize exoskeleton performance and ensure safe and effective user interactions. By building a deeper understanding of the diverse sensor technologies and monitoring systems, this review aims to contribute to the ongoing advancement of lower-limb rehabilitation exoskeletons, ultimately improving the quality of life for patients with mobility impairments. [ABSTRACT FROM AUTHOR]

The finger workspace is crucial for performing various grasping tasks. Thus, various soft rehabilitation gloves have been developed to assist individuals with paralyzed hands in activities of daily living (ADLs) or rehabilitation training. However, most soft robotic glove designs are insufficient to assist with various hand postures because most of them use an underactuated mechanism for design simplicity. Therefore, this paper presents a methodology for optimizing the design of a high-degree-of-freedom soft robotic glove while not increasing the design complexity. We defined the required functional workspace of the index finger based on ten frequently used grasping postures in ADLs. The design optimization was achieved by simulating the proposed finger–robot model to obtain a comparable workspace to the functional workspace. In particular, the moment arm length for extension was optimized to facilitate the grasping of large objects (precision disk and power sphere), whereas a torque-amplifying routing design was implemented to aid the grasping of small objects (lateral pinch and thumb–two-finger pinch). The effectiveness of the optimized design was validated through testing with a stroke survivor and comparing the assistive workspace. The observed workspace demonstrated that the optimized glove design could assist with nine out of the ten targeted grasping posture functional workspaces. Furthermore, the assessment of the grasping speed and force highlighted the glove's usability for various rehabilitation activities. We also present and discuss a generalized methodology to optimize the design parameters of a soft robotic glove that uses an underactuated mechanism to assist the targeted workspace. Overall, the proposed design optimization methodology serves as a tool for developing advanced hand rehabilitation robots, as it offers insight regarding the importance of routing optimization in terms of the workspace. [ABSTRACT FROM AUTHOR]

BACKGROUND: Robot-assisted gait training (RAGT) has been reported to treat motor dysfunction in patients with Parkinson's disease (PD) in the last few years. However, the benefits of RAGT for treating motor dysfunction in PD are still unclear. OBJECTIVES: To investigate the efficacy of RAGT for motor dysfunction in PD patients. METHODS: We searched PubMed, Web of Science, Cochrane Library, Embase, CNKI, Wanfang, Chinese Biomedical Literature Database (CBM), and Chinese VIP Database for randomized controlled trials investigating RAGT to improve motor dysfunction in PD from the databases' inception dates until September 1, 2022. The following outcome indexes were employed to evaluate motor dysfunction: the Berg Balance Scale (BBS), Activities-specific Balance Confidence Scale (ABC), 10-Meter Walk Test gait speed (10-MWT), gait speed, stride length, cadence Unified Parkinson Disease Rating Scale Part III (UPDRS III), 6-Minute Walk Test (6MWT), and the Timed Up and Go test (TUG). The meta-analysis was performed using the proper randomeffect model or fixed-effect model to evaluate the difference in efficacy between the RAGT and the control groups. The Cochrane Risk of Bias Tool was used for the included studies and Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) was used to interpret the certainty of the results. RESULTS: The results consisted of 17 studies comprising a total of 670 participants. Six hundred and seven PD patients with motor dysfunction were included: 335 in the RAGT group and 335 in the control group. This meta-analysis results established that when compared with the control group, robot-assisted gait training improved the BBS results of PD patients (MD: 2.80, 95%CI: 2.11–3.49, P < 0.00001), ABC score (MD: 7.30, 95%CI: 5.08–9.52, P < 0.00001), 10-MWT (MD: 0.06, 95%CI: 0.03–0.10, P = 0.0009), gait speed (MD: 3.67, 95%CI: 2.58–4.76, P < 0.00001), stride length (MD: 5.53, 95%CI: 3.64–7.42, P < 0.00001), cadence (MD: 4.52, 95%CI: 0.94–8.10, P = 0.01), UPDRS III (MD: - 2.16, 95%CI: - 2.48– - 1.83, P < 0.00001), 6MWT (MD: 13.87, 95%CI: 11.92–15.82, P < 0.00001). However, RAGT did not significantly improve the TUG test result of patients with PD (MD = - 0.56, 95% CI: - 1.12–0.00, P = 0.05). No safety concerns or adverse reactions among robot-assisted gait training patients were observed. CONCLUSION: Even though RAGT can improve balance function, walking function, and gait performance and has demonstrated positive results in several studies, there is currently insufficient compelling evidence to suggest that it can improve all aspects of lower motor function. [ABSTRACT FROM AUTHOR]

Objective：To observe the therapeutic effect of low- frequency repetitive transcranial magnetic stimulation combined with rehabilitation robot on hand dysfunction in stroke patients. Method：Totally 36 patients with stroke hand dysfunction treated in the Rehabilitation Department of Jinshan Hospital were randomly divided into the experimental group （n=18） and the control group （n=18）. Both groups of patients received routine treatment. Both groups were treated with 1Hz repetitive transcranial magnetic stimulation on the unaffected side，and the experimental group was added by a hand function rehabilitation robot. Both groups were treated 5 times a week for 4 weeks. The assessment were evaluated before treatment， 2 weeks and 4 weeks after treatment, including the latency of cortical motor evoked potential （MEP），central motor conduction time（CMCT），Fugl-Meyer assessment（FMA）score and motor intensity index（MI）score. Result：After 4 weeks of treatment，MEP，CMCT，FMA and MI of the two groups were significantly improved compared with those before treatment（P<0.001）. After 4 weeks of treatment，MEP，CMCT，FMA and MI in the experimental group were significantly improved compared with those in the control group（P<0.05）. Conclusion：The contralateral low-frequency repetitive transcranial magnetic stimulation combined with rehabilitation robot can significantly improve hand dysfunction in stroke patients. [ABSTRACT FROM AUTHOR]

Different patients have different rehabilitation requirements. It is essential to ensure the safety and comfort of patients at different recovery stages during rehabilitation training. This study proposes a multi-mode adaptive control method to achieve a safe and compliant rehabilitation training strategy. First, patients’ motion intention and motor ability are evaluated based on the average human–robot interaction force per task cycle. Second, three kinds of rehabilitation training modes—robot-dominant, patient-dominant, and safety-stop—are established, and the adaptive controller can dexterously switch between the three training modes. In the robot-dominant mode, based on the motion errors, the patient’s motor ability, and motion intention, the controller can adaptively adjust its assistance level and impedance parameters to help patients complete rehabilitation tasks and encourage them to actively participate. In the patient-dominant mode, the controller only adjusts the training speed. When the trajectory error is too large, the controller switches to the safety-stop mode to ensure patient safety. The stabilities of the adaptive controller under three training modes are then proven using Lyapunov theory. Finally, the effectiveness of the multi-mode adaptive controller is verified by simulation results.

This paper introduces a lightweight bilateral underactuated upper limb exoskeleton (UULE) designed to assist chronic stroke patients with distal joint (Elbow-Wrist) impairments during bimanual activities of daily living (ADL). The UULE aims to assist patients in shoulder flexion/extension, elbow flexion/extension, forearm pronation/supination, and wrist flexion/extension. Notable features include (i) a cable-driven mechanism maintaining a lightweight structure (1.783 kg); (ii) passive joints conforming to less-impaired proximal joints, reducing restrictions on their movements; (iii) a compact design with passive ball joints enabling bilateral configuration for scapula protraction/retraction; and (iv) implementation of the master-slave joint assistance training strategy in an underactuated exoskeleton, achieving symmetric robot joint motion in bimanual ADL. Experiments with ten healthy subjects demonstrated the UULE’s effectiveness by revealing significant reductions in muscle activity in a symmetric bimanual ADL task. These advancements address critical limitations of current exoskeletons, showcasing the UULE as a promising contribution to lightweight and effective robotic rehabilitation strategies for chronic stroke patients.

Abstract Background The original version of the Tenodesis-Induced-Grip Exoskeleton Robot (TIGER) significantly improved the motor and functional performance of the affected upper extremity of chronic stroke patients. The assist-as-needed (AAN) technique in robot-involved therapy is widely favored for promoting patient active involvement, thereby fostering motor recovery. However, the TIGER lacked an AAN control strategy, which limited its use in different clinical applications. The present study aimed to develop and analyze the training effects of an AAN control mode to be integrated into the TIGER, to analyze the impact of baseline patient characteristics and training paradigms on outcomes for individuals with chronic stroke and to compare training effects on the upper limb function between using the AAN-equipped TIGER and using the original prototype. Methods This was a single-arm prospective interventional study which was conducted at a university hospital. In addition to 20 min of regular task-specific motor training, each participant completed a 20-min robotic training program consisting of 10 min in the AAN control mode and 10 min in the functional mode. The training sessions took place twice a week for 9 weeks. The primary outcome was the change score of the Fugl–Meyer Assessment of the Upper Extremity (FMA-UE), and the secondary outcomes were the change score of the Box and Blocks Test (BBT), the amount of use (AOU) and quality of movement (QOM) scales of the Motor Activity Log (MAL), the Semmes–Weinstein Monofilament (SWM) test, and the Modified Ashworth Scale (MAS) for fingers and wrist joints. The Generalized Estimating Equations (GEE) and stepwise regression model were used as the statistical analysis methods. Results Sixteen chronic stroke patients completed all steps of the study. The time from stroke onset to entry into the trial was 21.7 ± 18.9 months. After completing the training with the AAN-equipped TIGER, they exhibited significant improvements in movement reflected in their total score (pre/post values were 34.6 ± 11.5/38.5 ± 13.4) and all their sub-scores (pre/post values were 21.5 ± 6.0/23.3 ± 6.5, 9.5 ± 6.2/11.3 ± 7.2, and 3.6 ± 1.0/3.9 ± 1.0 for the shoulder, elbow, and forearm sub-category, the wrist and hand sub-category, and the coordination sub-category, respectively) on the FMA-UE (GEE, p

With the rapid development of modern robotics and medical industry, the use of rehabilitation robots for the treatment of patients with lower limb sports injuries has become one of the research hotspots at home and abroad. In order to achieve better rehabilitation treatment effect, trajectory planning of a three-degree-of-freedom lower limb rehabilitation robot is studied, and the motion law of healthy ankle joint is simulated to optimize the trajectory design of the lower limb rehabilitation robot. Based on the rehabilitation, injury mechanism, and motion form of the ankle joint, a comparative analysis of several low degree of freedom planar symmetric parallel mechanisms is conducted, and a design prototype using a 2UU-UPU mechanism as the rehabilitation actuator is determined; research on trajectory planning for lower limb rehabilitation training of lower limb rehabilitation robots, trajectory planning motion formulas for cycloidal motion laws and superimposed cycloidal motion laws are derived, and the rationality of trajectory under different rehabilitation training conditions is verified. In order to further explore a more reasonable rehabilitation trajectory, the optical motion capture system is used to collect the ankle joint movement trajectory in the sitting position, establish the rehabilitation trajectory constraints, improve the similarity between the planned trajectory and the human movement, and achieve better rehabilitation effect.

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.

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.

The increasing utilization of upper limb rehabilitation robots in rehabilitation therapy has brought to light significant safety concerns regarding their mechanical structures and control systems. This study focuses on a six degrees of freedom (DOF) upper limb rehabilitation robot, which has been designed with an emphasis on safety through careful consideration of its mechanical structure and trajectory planning. Various parameter schemes for the shoulder joint angles were proposed, and the robotic arm’s structure was developed by analyzing the spatial motion trajectories of the shoulder joint motor. This design successfully achieves the objective of minimizing the installation space while maximizing the range of motion. Additionally, an enhanced artificial field method is introduced to facilitate the planning of self-collision avoidance trajectories for dual-arm movements. This approach effectively mitigates the risk of collisions between the robotic arm and the human body, as well as between the two robotic arms, during movement. The efficacy of this method has been validated through experimental testing.

OBJECTIVE: To systematically evaluate the efficacy of rehabilitation exoskeleton robots on the lower limb motor function of stroke patients using Meta-analysis and to compare the efficacy of different lower limb exoskeleton robots, so as to provide a theoretical basis for the scientific selection of suitable exoskeleton robots for patients with post-stroke lower limb motor dysfunction. METHODS: Computer searches of the Cochrane Library, PubMed, Web of Science, Embase, CNKI, VIP, and WanFang Data were conducted to collect randomized controlled clinical studies on exploring lower extremity rehabilitation exoskeleton robots to improve lower limb motor function in stroke patients published from database inception to November 2022. Two researchers conducted the literature search and screening. The quality of the included literature was evaluated using the Cochrane 5.1.0 risk of bias assessment tool and the Jadad scale. Meta-analysis was performed using RevMan 5.4 and Stata 17.0 software. RESULTS: (1) Finally 22 publications were included, involving 865 patients (n=436 in the test group and n=429 in the control group), and the Jadad score showed that all the included articles were of high quality. (2) Meta-analysis results showed that the exoskeleton robot significantly improved the Fugl-Meyer Assessment of Lower Extremity score (mean difference [MD]=2.63, 95% confidence interval [CI]: 1.87-3.38, P < 0.05), Berg Balance Scale score (MD=3.62, 95% CI: 1.21-6.03, P < 0.05), Timed Up and Go score (MD=-2.77, 95% CI: -4.48 to -1.05, P < 0.05) and step frequency score (MD=3.15, 95% CI: 1.57-4.72, P < 0.05) in stroke patients compared with the control group. However, there was no significant improvement in the Functional Ambulation Category Scale score (MD=0.30, 95% CI: -0.01 to 0.61, P > 0.05) and 6-minute walk test score (MD=3.77, 95% CI: -6.60 to 14.14, P > 0.05). (3) Network Meta-analysis results showed that compared with the conventional rehabilitation therapy, both the level-walking exoskeleton (MD=10.23, 95% CI: 3.81-27.49, P < 0.05) and the bodyweight support exoskeleton (MD=33.66, 95% CI: 11.49-98.54, P < 0.05) improved the Fugl-Meyer Assessment of Lower Extremity score. Compared with the conventional rehabilitation therapy, body-weight support exoskeleton significantly improved the Berg Balance Scale scores (MD=79.86, 95% CI: 2.34-2 725.99, P < 0.05). In terms of Fugl-Meyer Assessment of Lower Extremity and Berg Balance Scale scores, the ranking results were body-weight support exoskeleton > level-walking exoskeleton > conventional rehabilitation therapy. Compared with the conventional rehabilitation therapy, level-walking exoskeleton significantly improved the Functional Ambulation Category Scale score (MD=1.38, 95% CI: 1.00-1.90, P < 0.05) and body-weight support exoskeleton significantly improved the Timed Up and Go score (MD=0.07, 95% CI: 0.01-0.51, P < 0.05). In terms of Functional Ambulation Category Scale and Timed Up and Go scores, the ranking results were level-walking exoskeleton > body-weight support exoskeleton > conventional rehabilitation therapy. CONCLUSION: Rehabilitation exoskeleton robots can improve balance, walking and activities of daily living in stroke patients, with body-weight support exoskeleton being more effective in improving lower limb motor function and balance and level walking exoskeleton being more effective in improving functional walking and transfer. [ABSTRACT FROM AUTHOR]

Background: The original version of the Tenodesis-Induced-Grip Exoskeleton Robot (TIGER) significantly improved the motor and functional performance of the affected upper extremity of chronic stroke patients. The assist-as-needed (AAN) technique in robot-involved therapy is widely favored for promoting patient active involvement, thereby fostering motor recovery. However, the TIGER lacked an AAN control strategy, which limited its use in different clinical applications. The present study aimed to develop and analyze the training effects of an AAN control mode to be integrated into the TIGER, to analyze the impact of baseline patient characteristics and training paradigms on outcomes for individuals with chronic stroke and to compare training effects on the upper limb function between using the AAN-equipped TIGER and using the original prototype. Methods: This was a single-arm prospective interventional study which was conducted at a university hospital. In addition to 20 min of regular task-specific motor training, each participant completed a 20-min robotic training program consisting of 10 min in the AAN control mode and 10 min in the functional mode. The training sessions took place twice a week for 9 weeks. The primary outcome was the change score of the Fugl–Meyer Assessment of the Upper Extremity (FMA-UE), and the secondary outcomes were the change score of the Box and Blocks Test (BBT), the amount of use (AOU) and quality of movement (QOM) scales of the Motor Activity Log (MAL), the Semmes–Weinstein Monofilament (SWM) test, and the Modified Ashworth Scale (MAS) for fingers and wrist joints. The Generalized Estimating Equations (GEE) and stepwise regression model were used as the statistical analysis methods. Results: Sixteen chronic stroke patients completed all steps of the study. The time from stroke onset to entry into the trial was 21.7 ± 18.9 months. After completing the training with the AAN-equipped TIGER, they exhibited significant improvements in movement reflected in their total score (pre/post values were 34.6 ± 11.5/38.5 ± 13.4) and all their sub-scores (pre/post values were 21.5 ± 6.0/23.3 ± 6.5, 9.5 ± 6.2/11.3 ± 7.2, and 3.6 ± 1.0/3.9 ± 1.0 for the shoulder, elbow, and forearm sub-category, the wrist and hand sub-category, and the coordination sub-category, respectively) on the FMA-UE (GEE, p < 0.05), as well as their scores on the BBT (pre/post values were 5.9 ± 6.5/9.5 ± 10.1; GEE, p = 0.004) and the AOU (pre/post values were 0.35 ± 0.50/0.48 ± 0.65; GEE, p = 0.02). However, the original TIGER exhibited greater improvements in their performance on the FMA-UE than the participants training with the AAN-equipped TIGER (GEE, p = 0.008). The baseline score for the wrist and hand sub-category of the FMA-UE was clearly the best predictor of TIGER-mediated improvements in hand function during the post-treatment assessment (adjusted R2 = 0.282, p = 0.001). Conclusions: This study developed an AAN-equipped TIGER system and demonstrated its potential effects on improving both the function and activity level of the affected upper extremity of patients with stroke. Nevertheless, its training effects were not found to be advantageous to the original prototype. The baseline score for the FMA-UE sub-category of wrist and hand was the best predictor of improvements in hand function after TIGER rehabilitation. Clinical trial registration ClinicalTrials.gov, identifier NCT03713476; date of registration: October19, 2018. https://clinicaltrials.gov/ct2/show/NCT03713476 [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this work is to present a multivariate analysis of the kinematics of an upper limb rehabilitation robot. Comparing multiple concepts of kinematic chains makes it possible to identify advantages and disadvantages and, as a consequence, choosing the optimal solution to create a physical device. Such actions shall contribute towards automation of the rehabilitation process, bringing benefits to both therapists and patients in comparison with conventional rehabilitation. Methods: Multivariate analysis of kinematics was performed on the basis of three concepts of the kinematic chain of an exoskeleton, enabling the rehabilitation of both right and left upper limb within the area of the shoulder joint, elbow joint and wrist. The kinematic chain allows the performance of simple and complex movements. Results: The results of the conducted multivariate kinematic analysis define specific movements and angular ranges, which may be performed while applying one of the proposed concepts of the robot design. The results made it possible to determine the optimum solution to the kinematic diagram and construction design, which best satisfy the expectations for effective rehabilitation. Conclusions: The analysis of the kinematic diagram concept of the exoskeleton should be done in relation to its design (construction form). Considering the obtained parameters, it is necessary to find an optimum concept and wisely manoeuvre the values, in order to avoid a situation in which one significant parameter influences another, equally important one. It is noteworthy that the introduction of changes into particular segments of the kinematic chain often has a significant impact on other segments. [ABSTRACT FROM AUTHOR]

Lower limb exoskeleton rehabilitation robots have become an important direction for development in today's society. These robots can provide support and power to assist patients in walking and movement. In order to achieve better interaction between humans and machines and achieve the goal of flexible driving, this paper addresses the shortcomings of traditional elastic actuators and designs a series elastic–damping actuator (SEDA). The SEDA combines elastic and damping components in parallel, and the feasibility of the design and material selection is demonstrated through finite element static analysis. By modeling the dynamics of the SEDA, using the Bode plot and Nyquist plot, open-loop and closed-loop frequency domain comparisons and analyses were carried out, respectively, to verify the effect of damping coefficients on the stability of the system, and the stiffness coefficient ks = 25.48 N/mm was selected as the elastic element and the damping coefficient cs = 1 Ns/mm was selected as the damping element. A particle swarm optimization (PSO)-based algorithm was proposed to introduce the fuzzy controller into the PID control system, and five parameters, namely the the fuzzy controller's fuzzy factor (ke, kec) and de-fuzzy factor (kp1, ki1, kd1), are taken as the object of the algorithm optimization to obtain the optimal fuzzy controller parameters of ke = 0.8, kec = 0.2, kp1 = 0.5, ki1 = 8, kd1 = −0.1. The joint torque output with and without external interference is simulated, and the simulation model is established in the MATLAB/Simulink environment The results show that when fuzzy PID control is used, the amount of overshooting in the system is 14.6%, and the regulation time is 0.66 s. This has the following advantages: small overshooting amount, short rise time, fast response speed, short regulation time, good stability performance, and strong anti-interference ability. The SEDA design structure and control method breaks through limitations of the traditional series elastic actuator (SEA) such as its lack of flexibility and stability, which is very helpful to improve the output effect of flexible joints. [ABSTRACT FROM AUTHOR]

Objective: To investigate the test- retest reliability and criterion validity of the Robotic Brace for Dynamic Trunk Support System (RoboBDsys) in evaluating seated trunk control function in patients with spinal cord injury(SCI). Method:From October to December, 2022, 20 patients with spinal cord injury who were unable to walk were recruited. All participants completed RoboBDsys trunk control test including static control test and dynamic control test on the seated platform. The evaluation indexes were the supporting torque regression slope exerted by the robot platform on the direction of x, y, α, and θ, recorded as Kx, Ky, Kθ, Kα; the average supporting torque recorded as Fx, Fy, Mθ, Mα in the static control test; and the the Range of Motion (ROM) of trunk in four directions in the dynamic control test. All the participants completed Trunk Control Test (TCT) as criterion assessment. The test interval was one week, intra class correlation coefficient(ICC) values were calculated between two RoboBDsys trunk control test. Result: There were 13 participants completed two tests. In the static control test, the ICCs of four indexes were 0.418-0.742 between 2 tests; the ICCs of all indexes in dynamic control test were 0.633-0.848, which all showed moderate to high test-retest reliability. In regards of criterion validity, there was low to moderate correlation between Ky, Mθ, Mα in static control test and TCT (|r|=0.467-0.561, P<0.05); all indexes except right lateral flection ROM in dynamic control test showed moderate to high correlation with TCT (r= 0.559-0.758, P<0.05). Conclusion: RoboBDsys is a reliable and valid tool for evaluating seated trunk control function in patients with SCI. [ABSTRACT FROM AUTHOR]

The structural characteristics of a Body Weight Support System (BWSS) significantly influence the rehabilitation effect and control system of a lower-limb robot. This study considers vibration characteristics to design and optimize a BWSS. First, the design principles of the BWSS are formulated, and the force of the system during the rehabilitation training process is analyzed. Second, the BWSS optimization model is established by considering the vibration characteristics, including the design object, selected materials, design variables, optimization objective functions and constraints in static and dynamic analyses. Third, a hybrid multi-objective optimization method combining the finite element method, Kriging metamodel and Multi-Objective Genetic Algorithm (MOGA) is proposed. After performing optimization, the Kriging metamodel is discussed, and the final design parameters of a single-arm support structure are obtained by the MOGA. Finally, different MOGA parameters and the screening algorithm are compared and analyzed. Simultaneously, the experimental vibration acceleration data and the shock data of the actual original rehabilitation robot are compared with the numerical results of the optimized design. The numerical results indicate that the proposed hybrid multi-objective optimization method is reliable in terms of designing the BWSS, and the vibration characteristics of the designed BWSS are improved over those of the actual original BWSS. [ABSTRACT FROM AUTHOR]

For patients with lower limb motor hemiplegia, a 1R2T-1R type rope-driven lower limb rehabilitation robot is proposed and its complete model is built based on human lower limb movement patterns. The kinematic forward and inverse solution analysis is carried out for this robot and its correctness is verified. Based on the Monte Carlo method, the controllable workspace and the actual workspace of this robot under the premise of force closure are solved in the Matlab environment and compared. The results show that the rehabilitation robot has a reasonable configuration and its working range can meet the needs of patients' rehabilitation training, which provides some reference for subsequent parameter optimization and trajectory planning.

With the complex changes in social and demographic structure, the problem of aging is becoming increasingly serious, and the demand for equipment in the field of medical rehabilitation is also increasing. Given the above situation, various countries have proposed using rehabilitation robots to assist patients in treatment, and have achieved certain results. The rehabilitation robot helps the injured area gradually recover autonomous movement through repeated auxiliary actions, thereby achieving the goal of physical rehabilitation training. However, most robot configurations face insufficient energy and power, as well as bulky and inflexible equipment. Based on the above issues, this article adopts an improved fuzzy algorithm control strategy to optimize the energy storage device and applies the energy system to the wrist rehabilitation training robot device. Firstly, a fuzzy adaptive algorithm is added to the traditional energy controller to combine system control with operational status, achieving precise control and dynamic adjustment of energy allocation. The research has added filter optimization design, which improves the accuracy and effectiveness of fuzzy algorithms. Finally, this article establishes an upper limb motion model of the rehabilitation robot and configures relevant hardware systems. At the same time, this article also uses manufacturing materials that meet functional requirements to construct auxiliary equipment. The results indicate that the improved fuzzy algorithm can improve the auxiliary effect in energy control of wrist rehabilitation robots. It has obvious advantages in the process of wrist rehabilitation training.

This paper presents a hybrid adaptive control strategy for upper limb rehabilitation robots using impedance learning. The hybrid adaptation consists of a differential updating mechanism for the estimation of robotic modeling uncertainties and periodic adaptations for the online learning of time-varying impedance. The proposed hybrid adaptive controller guarantees asymptotical control stability and achieves variable impedance regulation for robots without interaction force measurements. According to Lyapunov’s theory, we proved that the proposed impedance learning controller guarantees the convergence of tracking errors and ensures the boundedness of the estimation errors of robotic uncertainties and impedance profiles. Simulations and experiments conducted on a parallel robot validated the effectiveness and the superiority of the proposed impedance learning controller in robot-assisted rehabilitation. The proposed hybrid adaptive control has potential applications in rehabilitation, exoskeletons, and some other repetitive interactive tasks.

As important auxiliary equipment, rehabilitation robots are widely used in rehabilitation treatment and daily life assistance. The rehabilitation robot proposed in this paper is mainly composed of an omnidirectional mobile platform module, a lower limb exoskeleton module, and a support module. According to the characteristics of the robot’s omnidirectional mobility and good stiffness, the overall kinematic model of the robot is established using the analytical method. Passive and active training control strategies for an omnidirectional mobile lower limb exoskeleton robot are proposed. The passive training mode facilitates the realization of the goal of walking guidance and assistance to the human lower limb. The active training mode can realize the cooperative movement between the robot and the human through the admittance controller and the tension sensor and enhance the active participation of the patient. In the simulation experiment, a set of optimal admittance parameters was obtained, and the parameters were substituted into the controller for the prototype experiment. The experimental results show that the admittance-controlled rehabilitation robot can perceive the patient’s motion intention and realize the two walking training modes. In summary, the passive and active training control strategies based on admittance control proposed in this paper achieve the expected purpose and effectively improve the patient’s active rehabilitation willingness and rehabilitation effect.

Aimed at the problem of human–machine interaction between patients and robots in the process of using rehabilitation robots for rehabilitation training, this paper proposes a human–machine interactive control method based on an independently developed upper limb rehabilitation robot. In this method, the camera is used as a sensor, the human skeleton model is used to analyse the moving image, and the key points of the human body are extracted. Then, the three-dimensional coordinates of the key points of the human arm are extracted by depth estimation and spatial geometry, and then the real-time motion data are obtained, and the control instructions of the robot are generated from it to realise the real-time interactive control of the robot. This method can not only improve the adaptability of the system to individual patient differences, but also improve the robustness of the system, which is less affected by environmental changes. The experimental results show that this method can realise real-time control of the rehabilitation robot, and that the robot assists the patient to complete the action with high accuracy. The results show that this control method is effective and can be applied to the fields of robot control and robot-assisted rehabilitation training.

Abstract Background Upper-limb rehabilitation robots provide repetitive reaching movement training to post-stroke patients. Beyond a pre-determined set of movements, a robot-aided training protocol requires optimization to account for the individuals’ unique motor characteristics. Therefore, an objective evaluation method should consider the pre-stroke motor performance of the affected arm to compare one’s performance relative to normalcy. However, no study has attempted to evaluate performance based on an individual’s normal performance. Herein, we present a novel method for evaluating upper limb motor performance after a stroke based on a normal reaching movement model. Methods To represent the normal reaching performance of individuals, we opted for three candidate models: (1) Fitts’ law for the speed-accuracy relationship, (2) the Almanji model for the mouse-pointing task of cerebral palsy, and (3) our proposed model. We first obtained the kinematic data of healthy (n = 12) and post-stroke (n = 7) subjects with a robot to validate the model and evaluation method and conducted a pilot study with a group of post-stroke patients (n = 12) in a clinical setting. Using the models obtained from the reaching performance of the less-affected arm, we predicted the patients’ normal reaching performance to set the standard for evaluating the affected arm. Results We verified that the proposed normal reaching model identifies the reaching of all healthy (n = 12) and less-affected arm (n = 19; 16 of them showed an R 2 > 0.7) but did not identify erroneous reaching of the affected arm. Furthermore, our evaluation method intuitively and visually demonstrated the unique motor characteristics of the affected arms. Conclusions The proposed method can be used to evaluate an individual’s reaching characteristics based on an individuals normal reaching model. It has the potential to provide individualized training by prioritizing a set of reaching movements.