Stroke is a leading cause of long-term disability worldwide, with survivors often facing significant challenges in regaining upper-limb functionality. In response, robotic rehabilitation systems have emerged as promising tools to enhance post-stroke recovery by delivering precise, adaptable, and patient-specific therapy. This paper presents a review of robotic interfaces developed specifically for upper-limb rehabilitation. It analyses existing exoskeleton- and end-effector-based systems, with respect to three core design pillars: assistance types, control philosophies, and actuation methods. The review highlights that most solutions favor electrically actuated exoskeletons, which use impedance- or electromyography-driven control, with active assistance being the predominant rehabilitation mode. Resistance-providing systems remain underutilized. Furthermore, no hybrid approaches featuring the combination of robotic manipulators with actuated interfaces were found. This paper also identifies a recent trend towards lightweight, modular, and portable solutions and discusses the challenges in bridging research prototypes with clinical adoption. By focusing exclusively on upper-limb applications, this work provides a targeted reference for researchers and engineers developing next-generation rehabilitation technologies.

Stroke affects over 100 million people worldwide, and over two-thirds of survivors experience lasting upper-limb impairments, which significantly impact their quality of life. The global shortage of rehabilitation providers, which cannot attend to all patients who need it, creates an urgent, not yet answered, need for reliable and accessible rehabilitation innovations. Robotic rehabilitation has been emerging as an effective alternative to traditional physical therapy. This paper presents the development and evaluation of 2 degree-of-freedom exoskeleton, coupled to a collaborative robotic manipulator, which performs upperlimb rehabilitation. The system targets elbow flexion/extension and forearm pronation/supination, using two direct current brushless actuators. To accommodate a wide range of users, the mechanical design is modular and adjustable, allowing the rehabilitation of a broad range of arm lengths, while mechanical barriers prevent unsafe joint motions. Furthermore, limit switches ensure the movements are performed within safe values and an emergency button is also available for emergency stop. Safety assessment confirmed the actuators’ performance and the integrity of the physical barriers. Three different rehabilitation modes were implemented: passive assist, active assist and active resist. Passive assistance tests achieved consistent trajectory tracking with a root mean square error of 4.85º for pronation/supination and 0.87º for elbow flexion/extension, while maintaining smooth motion profiles with spectral arc length values of -1.603 and -1.56, respectively. Active resistance generated stable bidirectional torque across the full range of motion, reaching up to 1 Nm for forearm pronation/supination and 7 Nm for elbow flexion/extension. The adaptive active assistance strategy modified the assistance torque in real time according to the detected user performance. These findings establish a foundation for future clinical evaluation and real-world applications, with the system’s modular design and multiple therapy modes showing potential to support diverse rehabilitation needs.

Reliable Human Pose Estimation (HPE) is essential for vision-based monitoring of upper-limb movements in exoskeleton-assisted rehabilitation. However, the robotic structure, camera placement, and arm orientation may challenge pose-estimation performance, and widely used markerless models remain insufficiently benchmarked under these conditions. This paper benchmarks 10 representative HPE models on video sequences of a healthy participant wearing an upper-limb exoskeleton (idle, with the collaborative manipulator locked), recorded with an OAK-D camera from frontal, top-down, and lateral placements, covering elbow flexion and extension under shoulder flexion and shoulder abduction. Performance is assessed across accuracy, robustness, temporal stability, and speed. RTMO-M achieved the highest accuracy (Percentage of Correct Keypoints (PCK) = 0.921 ± 0.081), while YOLO26m offered the best accuracy-throughput trade-off (PCK = 0.896 ± 0.125, 27.0 ± 3.5 frames per second). Camera placement influenced accuracy more than model selection: frontal placement achieved a mean PCK of 0.947 versus 0.719 for lateral, a gap exceeding the largest inter-model difference. An ablation across five YOLO size variants and three recording resolutions further characterizes accuracy–speed trade-offs and resolution sensitivity. Results highlight the importance of model and camera configuration for vision-based HPE with upper-limb exoskeletons in laboratory scenarios representing rehabilitation conditions.

Upper-limb impairments affect a substantial portion of the global population, often limiting the ability to perform daily activities. Robotic rehabilitation systems offer a promising solution by enabling high-dose, task-oriented therapy with consistent and objective feedback. However, user acceptance and perceived comfort are critical for their successful adoption. This work presents a feasibility, performance, and comfort evaluation of a 2-degree-of-freedom upper-limb rehabilitation exoskeleton capable of performing elbow flexion/extension and forearm pronation/supination. A total of 47 healthy participants were enrolled and tested across three rehabilitation modalities: passive assist, active assist, and active resist. Passive assist enabled full range-of-motion execution, active assist supported movement, and active resist provided variable resistance via a sliding bar (0–100%). Objective performance metrics, including position, current, and temperature, were recorded and analyzed, revealing trajectory-tracking errors during passive assistance of 4.82° ± 0.02° for forearm movement and 1.20° ± 0.04° for elbow movement, with actuator temperatures remaining below their rated limits throughout the study. The active assist mode did not achieve a true assist-as-needed performance, indicating a need for further refinement. Subjective evaluation included the System Usability Scale, yielding a score of 87.1 ± 9.6, indicating excellent usability, and a safety and comfort assessment averaging 4.4 ± 0.4 out of 5. Perceived effort was assessed using the Borg CR-10 scale and generally scaled appropriately across modalities, although some variability suggests the need for further investigation. Qualitative feedback identified areas for improvement, particularly in ergonomics and control behavior. Overall, the results support the feasibility, usability, and safe operation of the proposed exoskeleton and provide insights for future device refinement and evaluation with target user populations.