Considerations for designing robotic upper limb rehabilitation devices

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

More than 130,000 people have strokes each year in the United States [1]. Of these victims, 76% are left with disabilities that cost the nation over $54 billion in lost work and medical fees. One prominent disability is upper extremity hemiplegia, which occurs among approximately 50% of stroke sufferers [2]. Robotic technology has the potential to provide an automated platform for controlled rehabilitation and assisted, task-oriented therapy. Several systems have been designed to assist in patient articulation of an impaired hand using rigid robotic components. While these products have been successful in articulating the pure bending motions of the four fingers, the limited capabilities of rigid technologies fail to reproduce the complicated motion path [5] of the thumb during opposition grasp (see Figure 1). This is the most important articulation for normal hand function and specifically for picking up everyday objects. To date, robotic systems for thumb rehabilitation have not been widely investigated [3] apart from some recent work [4] that used a multi-joint rigid robot.

Following stroke, upper limb impairment is common and frequently limits ability to perform everyday activities. Due to limited resources, current therapy levels are insufficient to optimise functional improvement. Robotic devices have potential to augment upper limb stroke rehabilitation, but knowledge regarding the optimal device features and intervention parameters is limited. This systematic review and meta-analysis aimed to determine the efficacy of upper limb robotic rehabilitation compared with conventional rehabilitation, and to critically explore the device features and programme parameters that influence rehabilitation outcomes. Six electronic databases were searched for RCTs that compared dose-matched robotic versus conventional rehabilitation following stroke, and measured activity level changes in upper limb outcomes. The efficacy of robotic compared with conventional rehabilitation was evaluated using random-effects (I2 ≥ 50%) or fixed-effect (I2 < 50%) models. A systematic categorization of robotic device features and intervention parameters was conducted to facilitate subgroup analyses and meta-regression, enabling exploration of how these factors influence rehabilitation outcomes. The review included 54 studies, involving 2744 participants. Meta-analysis demonstrated that robotic rehabilitation had a small, statistically significant positive effect on upper limb capacity compared with conventional rehabilitation (SMD 0.14, 95% CI [0.02, 0.26]), however these gains were not maintained at follow-up (SMD 0.05, 95% CI [-0.13, 0.24]). No significant differences were found between robotic and conventional rehabilitation for ADL outcomes either post-treatment (SMD 0.04, 95% CI [- 0.05, 0.13]) or at follow-up (SMD 0.05, 95% CI [-0.13, 0.24]). Subgroup analyses provided crucial insights into the factors influencing robotic rehabilitation efficacy, revealing significant effects of device assistance (p = 0.0046), joints mobilized (p = 0.0133), degrees of freedom (p = 0.012), device laterality (p = 0.0048), and the number of devices used (p = 0.0001). The results suggest that robotic rehabilitation does not result in clinically meaningful improvement in either upper limb capacity or ADL performance. However, this study's novel subgroup analyses highlight specific device features and intervention parameters that significantly influence efficacy. These findings provide critical guidance for the design, implementation, and future research of robotic rehabilitation.

The upper-limb rehabilitation robots can be developed as an efficient tool for motor function assessments. Circle-drawing has been used as a specific task for robot-based motor function measurement. The upper-limb movement-related kinematic and kinetic parameters measured by motion and force sensors embedded in the rehabilitation robots have been widely studied. However, the muscle synergies characterized by multiple surface electromyographic (sEMG) signals in upper limbs during human-robot interaction (HRI) with circle-drawing movements are rarely investigated. In this research, the robot-assisted and constrained circle-drawing movements for upper limb were used to increase the consistency of muscle synergy features. Both clockwise and counterclockwise circle-drawing tasks were implemented by all healthy subjects using right hands. The sEMG signals were recorded from six muscles in upper limb, and nonnegative matrix factorization (NMF) analysis was utilized to obtain muscle synergy information. Both synergy pattern and activation coefficient were calculated to represent the spatial and temporal features of muscle synergies, respectively. The results obtained from the experimental study confirmed that high structural similarity of muscle synergies was found among the subjects during HRI with circle-drawing movement by healthy subjects, which indicates healthy people may share a common underlying muscle control mechanism during constrained upper-limb circle-drawing movement. This study indicates the muscle synergy analysis during the HRI with constrained circle-drawing movement could be considered as a task for upper-limb motor function assessment.

Dynamic evaluation mechanisms of the human upper limb are of great value for research and applications in upper limb rehabilitation, especially for the development of robotic upper limb rehabilitation systems. This paper proposes a muscle force prediction method based on the Hill muscle model. The proposed approach, which combines sEMG signals and kinematic data, provides a deep understanding of the dynamic motion mechanisms and parameters that characterize the upper limbs of the human body. The study provides a theoretical benchmark for the evaluation of rehabilitation training practices and for improved designs of upper limb rehabilitation robots that are used for upper limb neuro-rehabilitation. Specifically, the system collected motion data and sEMG signals from the upper limbs of the human body through a high-speed infrared motion capture system and skin sEMG sensors. By applying human kinematics and dynamics theories, real-time joint angle and torque information was obtained and imported into OpenSim. This platform can simulate the real-time muscle force values produced by the upper limbs during movements. The myoelectric signals were first filtered to remove noise, and an exponential model was then used to obtain the muscle activation. These data were then entered into the Hill-type prediction model to determine an individual’s muscle forces. In this paper, grasping movements commonly used in everyday situations were taken as a testing case. The results of the experiments showed that an individual’s muscle forces can be predicted using a Hill-type model. The results are consistent with those from simulated muscle force models and can reflect the real forces experienced during upper limb exercises.KeywordsMuscle forceUpper limb movementHuman–robot interactionBiomechanics analysissEMG signals

Present physical rehabilitation practice implies one-to-one therapist - patient interactions. This leads to shortage of therapists and high costs for patient or healthcare insurance systems. Along with Prokinetic Rehabilitation Clinic, we proposed a new intelligent, adaptive robotic system (RAPMES), which can provide the rehabilitation protocols, defined by a therapist, for the wrist and elbow of upper limb, considering the patient reactions and based on real-time feedback. RAPMES is a passive rehabilitation robotic system (RRS) with 3 degrees of freedom, and assists the rehabilitation process for elbow, forearm and wrist movements. Computation of the kinematic model for the RAPMES robotic device is required in order to determine the parameters associated with the mechanical joints, so that the experimental model executes certain trajectories in space. In this paper, we will present both forward and inverse kinematics determined for the experimental model. The kinematic model was implemented in Matlab environment, and we present a series of simulations, conducted in order to validate the proposed kinematic model. Then, we impose the functional movements (determined using the real-time video motion analysis system, as polynomial movement functions) as input to the kinematic model, and we present a series of simulations and results. The RAPMES control algorithm includes the kinematic model, and uses the polynomial movement functions as control input.

Human Robot Interaction (HRI) is a field of study dedicated to understanding, designing, and evaluating robotic systems for use by, or with, humans. In HRI there is a consensus about the design and implementation of robotic systems that should be able to adapt their behaviour on the basis of user actions and behaviour. The robot should adapt to emotions, personalities, and it should also have memory of past interactions with the user in order to become believable. This is of particular importance in the field of social robotics and social HRI. The aim of this Workshop is to bring together researchers and practitioners who are working on various aspects of social robotics and adaptive interaction.

Robotic gait rehabilitation devices enable efficient and convenient gait rehabilitation by mimicking the functions of physical theraphists. In manual gait rehabilitation training, physical theraphists have patients practice and memorize normal gait patterns by applying assistive torque to the patient’s joint once the patient’s gait deviates from the normal gait. Thus, one of the most important factors in robotic gait rehabilitation devices is to determine the assistive torque to the patient’s joint during rehabilitation training. In order to calculate the assistive torque, the desired gait trajectory for affected leg should be determined. This reference trajectory is obtained from his healthy leg. The obtained signal has many noisy factors. Therefore, after conditioning the signal, the suitable pattern is applied to the developed system. This procedure is called ”gait pattern generation” in this paper. It is described and discussed in detail.

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

IntroductionEmerging evidence suggests that robotic devices for upper limb rehabilitation after a stroke may improve upper limb function. For robotic upper limb rehabilitation in stroke to be successful, patients’ experiences...

Robotic gait rehabilitation devices enable efficient and convenient gait rehabilitation by mimicking the functions of physical theraphists. In manual gait rehabilitation training, physical theraphists have patients practice and memorize normal gait patterns by applying assistive torque to the patient's joint once the patient's gait deviates from the normal gait. Thus, one of the most important factors in robotic gait rehabilitation devices is to determine the assistive torque to the patient's joint during rehabilitation training. In order to calculate the assistive torque, the desired gait trajectory for affected leg should be determined. This reference trajectory is obtained from his healthy leg. The obtained signal has many noisy factors. Therefore, after conditioning the signal, the suitable pattern is applied to the developed system. This procedure is called "gait pattern generation" in this paper. It is described and discussed in detail.

Enhanced Kapandji test evaluation of a soft robotic thumb rehabilitation device by developing a fiber-reinforced elastomer-actuator based 5-digit assist system

Rehabilitation robots play an important role in the rehabilitation field, and effective human-robot interaction contributes to promoting the development of the rehabilitation robots. Though many studies about the human-robot interaction have been carried out, there are still several limitations in the flexibility and stability of the control system. Therefore, we proposed an advanced adaptive control method for lower limb rehabilitation robot. The method was devised with a dual closed loop control strategy based on the surface electromyography (sEMG) and plantar pressure to improve the robustness of the adaptive control for the rehabilitation robots. First, in the outer loop control, an advanced variable impedance controller based on the sEMG and plantar pressure was designed to correct robot's reference trajectory. Then, in the inner loop control, a sliding mode iterative learning controller (SMILC) based on the variable boundary saturation function was designed to achieve the tracking of the reference trajectory. The experiment results showed that, in the designed dual closed loop control strategy, a variable impedance controller can effectively reduce trajectory tracking errors and adaptively modify the reference trajectory synchronizing with the motion intention of patients; the designed sliding mode iterative learning controller can effectively reduce chattering in sliding mode control and excellently achieve the tracking of rehabilitation robot's reference trajectory. This study can improve the performance of the human-robot interaction of the rehabilitation robot system, and expand the application to the rehabilitation field.

Stroke is a leading cause of long-term upper limb disability, severely impacting patients' independence and quality of life. Robot-assisted therapy (RAT) has emerged as a promising, high-intensity rehabilitation alternative. However, conclusions from existing systematic reviews on its efficacy are inconsistent and often lack a holistic framework, limiting their use for guiding personalized clinical decisions. This study aims to systematically synthesize recent evidence on RAT for upper limb rehabilitation after stroke. Guided by the International Classification of Functioning, Disability and Health framework, it moves beyond singular outcomes to provide a multidimensional evaluation across body function, activity, and participation levels. The review aims to provide stratified guidance for clinical decision-making based on patient- and intervention-specific characteristics, thereby supporting evidence-based practice and informing future research. This study included systematic reviews and meta-analyses published from January 1, 2019, to December 26, 2025, comparing RAT with conventional therapy for upper limb rehabilitation after stroke. Overall, 6 databases, including PubMed, Web of Science, and Embase, were searched. Two reviewers (XZ and LZ) independently performed study selection, data extraction, and quality assessment using the AMSTAR 2 tool. The synthesis integrated outcome measures and subgroup analyses derived from the included studies. This umbrella review included 21 meta-analyses encompassing 535 randomized controlled trials and 27,598 patients across acute, subacute, and chronic stroke stages. According to AMSTAR 2, 17 reviews were high quality, 3 moderate, and 1 critically low. The synthesis demonstrated that RAT was superior in improving upper limb motor function, but no statistically significant advantages were observed in activities of daily living compared to conventional therapy. Subgroup analyses revealed that treatment effects were influenced by stroke stage, upper limb motor impairment level, and robot type. RAT is an effective intervention for improving upper limb motor function after stroke. However, its benefits are primarily observed at the level of body function, with limited evidence for long-term maintenance. The current evidence is constrained by significant outcome heterogeneity and methodological limitations inherent to umbrella reviews. Future research should validate these findings in broader clinical practice, focus on translating functional gains into sustained improvements in daily activities and participation, and include cost-effectiveness evaluations. PROSPERO CRD42024497183; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024497183.

BackgroundImpairments in upper limb motor function and cognitive ability are major health problems experienced by stroke patients, necessitating the development of novel and effective treatment options in stroke care. The aim of this study is to examine the effects of robot-assisted therapy on improving upper limb and cognitive functions in stroke patients.MethodsThis will be a single-blinded, 2-arm, parallel design, randomized controlled trial which will include a sample size of 86 acute and subacute stroke patients to be recruited from a single clinical hospital in Shanghai, China. Upon qualifying the study eligibility, participants will be randomly assigned to receive either robot-assisted therapy or conventional therapy with both interventions being conducted over a 6-week period in a clinical rehabilitation setting. In addition to comprehensive rehabilitation, the robot-assisted therapy group will receive a 30-min Armguider robot-assisted therapy intervention 5 days a week. Primary efficacy outcomes will include Fugl-Meyer Assessment for Upper Extremity (FMA-UE) and Mini-Mental Status Examination (MMSE). Other secondary outcomes will include Trail Making Test (TMT), Auditory Verbal Learning Test (AVLT), Digit Symbol Substitution Test (DSST), and Rey–Osterrieth Complex Figure Test (ROCFT). All trial outcomes will be assessed at baseline and at 6-week follow-up. Intention-to-treat analyses will be performed to examine changes from baseline in the outcomes. Adverse events will be monitored throughout the trial period.DiscussionThis will be the first randomized controlled trial aimed at examining the effects of robot-assisted therapy on upper limb and cognitive functions in acute and subacute stroke patients. Findings from the study will contribute to our understanding of using a novel robotic rehabilitation approach to stroke care and rehabilitation.Trial registrationChinese Clinical Trial Registry ChiCTR2100050856. Registered on 5 September 2021.