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Review

Exploring Robotic Technologies for Upper Limb Rehabilitation: Current Status and Future Directions

by
Fabian Horacio Diaz
1,†,
Carlos Borrás Pinilla
1,*,† and
Cecilia E. García Cena
2,3,*,†
1
Escuela de Ingeniería Mecánica, Universidad Industrial de Santander, Bucaramanga 680002, Colombia
2
Escuela Técnica Superior de Ingeniería y Diseño Industrial, ETSIDI-UPM, Universidad Politécnica de Madrid, C/Ronda de Valencia 3, 28012 Madrid, Spain
3
Centre for Automation and Robotics, Consejo Superior de Investigaciones Científicas UPM-CSIC, C/Ronda de Valencia 3, 28012 Madrid, Spain
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Sens. Actuator Netw. 2025, 14(3), 48; https://doi.org/10.3390/jsan14030048
Submission received: 6 March 2025 / Revised: 5 April 2025 / Accepted: 18 April 2025 / Published: 1 May 2025
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Abstract

:
This paper explores the design, control, construction, and leading manufacturers of upper limb rehabilitation robots through a thorough literature review. Utilizing databases such as Scopus, IEEE Xplore, Science Direct, Springer Link, and the Clinical Trials database, the research adhered to a rigorous screening process in accordance with PRISMA guidelines. This included analyzing abstracts and conducting comprehensive reviews of full articles when necessary. A total of fourteen relevant papers were systematically selected for in-depth analysis. The study offers a detailed classification of robotic technologies along with their Technology Readiness Levels (TRLs), discusses the primary challenges hindering their adoption, and proposes strategic research directions to address these issues. In conclusion, while upper limb robotic devices exhibit significant potential, persistent technological and design challenges must be addressed, underscoring the need for ongoing research and multidisciplinary collaboration to facilitate broader and more effective adoption.

1. Introduction

Robotic rehabilitation of the upper limb has emerged as a critical field due to its role in restoring daily life functionality and personal autonomy [1]. The World Health Organization (WHO) reports that 2.4 billion people worldwide could benefit from rehabilitation, with projections reaching 3.5 billion by 2050. Among these, stroke affects over 15 million people annually, with one-third requiring intensive rehabilitation, making it the leading cause of disability globally. Rehabilitation services are fundamental to achieving Sustainable Development Goal 3, particularly in low- and middle-income countries. Robotic devices serve as complementary tools to traditional therapies, offering consistent, repetitive, and personalized therapeutic interactions while enabling objective assessment of patient progress [2,3]. However, significant challenges persist in their implementation and effectiveness. To address these challenges, healthcare systems must implement several key strategies:
  • Expand market awareness beyond niche perception;
  • Remove entry barriers for industry participants;
  • Establish dedicated funding mechanisms;
  • Focus on underserved populations;
  • Implement universal access strategies;
  • Ensure user-centered design processes.
The scientific community continues to advance the field through innovative solutions in device development, treatment protocols, and clinical validation. Recent advances include a significant advancement in the field of tensegrity robotics by integrating a multimodal strain sensing system for real-time shape recognition within a tensegrity structure [4]. Although robotic devices have become integral to physical therapy, particularly for stroke patients [5], clinical outcomes have not consistently met expectations [6]. Challenges such as adapting devices to individual needs, fostering natural human–robot interactions, simulating realistic movements, optimizing mobility range, ensuring affordability, and achieving robust scientific validation persist [7,8,9].
Several modalities of robotic assistance have been developed to facilitate motor recovery in stroke patients, including sensory feedback mechanisms (such as vibratory feedback to aid in movement direction selection), performance-based progressive robot-assisted therapy, and assistive controllers that counteract body dynamics through constant-magnitude forces and gravity-supported arm exercises. Comparative studies demonstrate that these robot-assisted approaches can be more effective than conventional therapy techniques [10], highlighting the importance of continuous innovation and evaluation in robotic rehabilitation to optimize patient recovery.
This article provides a comprehensive analysis of current robotic solutions in upper limb rehabilitation, examining both achievements and limitations. By synthesizing existing knowledge and identifying improvement opportunities, this work aims to establish a foundation for future technological advancement in rehabilitation robotics, ultimately working toward more effective and accessible therapeutic solutions.
Clarification of terminology: For consistency throughout this paper, we adopt the following definitions:
  • Exoskeleton: A wearable mechanical structure that aligns with the anatomical joints of the wearer, providing direct actuation of specific joints.
  • Exosuit: A soft or partially soft wearable system that applies forces through flexible materials (fabrics, cables, elastomers) without rigid joint alignment.
  • End-effector: A robotic system where the user interacts with the device only at the distal segment (e.g., hand or forearm), without joint-by-joint alignment.

2. Background

Upper Limb Rehabilitation Robots

They fall into two main categories: the end effector, which functions while attached to the end part of the kinematic chain, and the exoskeleton, which fits around the patient’s limb (refer to Figure 1). They are made up of active and passive joints, a power supply system (which are usually composed of electric actuators), and sensors to measure the torque applied by the motors or some to capture biological signals [11,12] that allow control of the movements of the affected limb [13]; they are highly customizable, allowing therapists to adjust the parameters according to the individual needs and capabilities of the patients [14]. They can also employ brain–computer interfaces to control movements by brain signals, increasing accuracy and interaction with the robot [15,16,17,18,19].
The field of rehabilitation robotics stands out because of the diverse approaches and technological solutions that address the complexities of today’s medical demands. Each company offers a distinct perspective on the rehabilitation challenge, merging precision engineering with a thorough understanding of clinical requirements. Table 1 provides a list of companies involved in the manufacturing of rehabilitation robots.
Various rehabilitation robots have been identified to improve upper limb functionality and aid in the recovery of patients with different medical conditions. These robots can be classified according to their interaction with the patient, the type of actuator, the mechanical design, the input signal, and the control strategy, as summarized in references [7,8] (refer to Figure 2).
Within this research context, several scientific papers have broadened the understanding of advances and applications in the field of upper limb rehabilitation robots. In their state-of-the-art review, the authors of [13] examined the development and current status of assistive upper limb exoskeletons for the elderly. They emphasize the necessity of understanding biomechanics to design effective devices and discuss the limitations of control algorithms in robotic exoskeletons, which hinder the ability to guide upper limb movements in a personalized manner. The authors note the absence of real-time adaptation to user needs and the neglect of the muscular capabilities of individuals. Additionally, they highlight how the choice of actuators influences both the weight and portability of the exoskeleton and how the actuation method can affect the interaction between the exoskeleton and the user’s natural movements.
In [20], the authors thoroughly examine myoelectric control systems utilized in upper limb robotic exoskeletons. The authors trace the development of these systems, which employ electromyographic signals to facilitate movement and restore motor function for individuals with disabilities and enhance performance for non-disabled users. They also discuss various control modalities, outlining their benefits and limitations, and highlight key design considerations and methods for experimental validation. The authors emphasize the increasing demand for these technologies and suggest future research directions to enhance their effectiveness and applicability in real-world settings. In addition to discussing challenges in development, actuation, and control, the authors of [11] present an approach to control the exoskeleton using electromyography (sEMG) signals, adaptive neural control, and a musculoskeletal model of the human upper limb; they point out challenges, such as dead zones in the robot joints and lack of knowledge of their dynamics. Adaptive control and experimental calibration of the model are proposed to address them.
The authors in [21] offer a comprehensive framework for understanding human–robot interactions in rehabilitation and prosthetic use. They highlight the importance of control and feedback to improve cooperation and efficiency. Furthermore, they highlight the crucial role of haptic feedback in improving user performance and the usability of prosthetic devices and training systems. In [6], the authors present the controller (mAAN) that focuses on intention detection, arbitration, and communication to improve processing. It provides minimal assistance as needed to keep humans actively engaged in their therapy and motivate further improvement. The methods used to measure user intention in neurorehabilitation were electroencephalography (EEG), surface electromyography (EMG), and inverse dynamics models that estimate the force applied by the user to a robot based on measurements of the robot’s encoder and predefined movement trajectories. In [22], the authors aimed to decode dynamic shoulder, elbow, and wrist movements continuously and simultaneously based on electromyography signals for exoskeleton control. The decoder worked well for both healthy and stroke populations. As motion smoothness decreased, decoding performance decreased for the stroke population. The reference [23] discusses a study using an AI-integrated electromyography-driven robot hand for stroke patient upper extremity rehabilitation. Results show the robot hand improved motor function and reduced spasticity, suggesting its potential in stroke rehab.
In a study detailed in [24], a six-bar double parallelogram adjustable mechanism was designed to implement a shoulder’s three active degrees of freedom. An elbow and a wrist with one degree of freedom were also developed. This design was oriented toward rehabilitation, so it was given two situations: one in which the robot controls and another in which the human controls. Finally, they also developed a virtual reality training system for rehabilitation. The bamboo-inspired BiEXO exoskeleton [25] boasts an innovative design incorporating carbon fiber for the shoulder and elbow joints, significantly reducing hardware weight. This is achieved through remote actuation via cables and effective mass distribution on the back of the device. Additionally, the implementation of speed control enhances safety and ensures proper user response during operation, despite the limited degrees of freedom.
The authors in [26] introduce an innovative design for upper extremity exoskeletons with two passive rotations at the forearm interface. This design strikes a better balance between interaction quality and mechanical complexity, thereby reducing the effort required for interaction and enhancing user comfort during reaching movements in the sagittal plane, all without significantly increasing the device’s inertia. In [27], they present the design and analysis of a shoulder exoskeleton employing a parallel mechanism that enhances human–machine compatibility and addresses the issue of shoulder dislocation prevalent in current exoskeletons. The research emphasizes the kinematic mismatches between exoskeletons and biological joints, offering a design that utilizes the human upper arm as a passive limb. This approach not only improves usability and reduces the overall weight of the system, but also maintains effective carrying capacity and assistance. Article [28] comprehensively reviews automated systems for upper extremity functional assessment in neurorehabilitation. Technological advances, design, brain–computer interface control, haptic feedback, and visual feedback control are discussed.
In [29], the authors present an extensive review of the design and control mechanisms of shoulder rehabilitation exoskeletons. The article delves into the challenges associated with aligning these devices with human joints, the intricacies of shoulder girdle mechanisms, and the various types of actuation and control strategies employed. Additionally, the study highlights findings from recent clinical studies, providing valuable insights into the effectiveness of these rehabilitation tools. Article [30] highlights multiple technological advances, design, and control strategies in upper limb rehabilitation robots. These advances include using force sensors and electromyography for robot control, applying machine learning algorithms to optimize control, and creating more compact, portable home-based robots [31]. This study aims to assess the effects of a rehabilitation treatment assisted by a robotic exoskeleton on functional recovery in a group of chronic stroke survivors with hemiparesis due to a first motor neuron injury (post-stroke) compared to a group of patients undergoing conventional rehabilitation treatment.
The authors in [14] examine control methods for robotic and mechanical devices. They emphasize that robotic devices necessitate integrated control systems, whereas simpler mechanical devices depend on patient input, influencing their complexity and cost. The paper highlights several challenges, including a lack of extensive clinical trials, uncertainty regarding the effectiveness of robotic rehabilitation compared to traditional methods, and the high costs associated with the production of robotic devices. Ultimately, they conclude that the recovery outcomes associated with robotic devices in clinical practice are not as favorable as initially anticipated.
The ExoRob [32] is a 2-degree-of-freedom exoskeleton robot designed to assist individuals with disabilities in shoulder and elbow joint movements. The development of this robotic system incorporates two control techniques: nonlinear sliding mode control with an exponential reaching law and computed torque control. This report emphasizes the effectiveness of the nonlinear sliding mode control technique in achieving precise trajectory tracking during rehabilitation exercises.

3. Method

To ensure transparency and reproducibility in this systematic review, we provide a detailed account of the PRISMA selection process (see Figure 3). The initial search across four databases yielded 1520 publications. After removing 548 duplicates, 972 unique articles remained for review. The screening process occurred in three phases: title evaluation (359 articles selected), abstract assessment (131 articles), and full-text review, resulting in 40 articles for in-depth analysis (see Table 2). These were selected based on specific criteria related to upper extremity rehabilitation technologies, myoelectric control, and neural network-based systems.
The evaluation was guided by four research questions:
  • RQ1: What technologies have been used for upper extremity rehabilitation in robotics, especially regarding myoelectric sensors, neural network-based control, and exoskeletons?
  • RQ2: What are the most common approaches in control algorithms used for exoskeleton control in rehabilitation?
  • RQ3: What specific applications of rehabilitation robotics for the upper extremity have been evaluated, and how do they vary in terms of effectiveness and ease of use?
  • RQ4: What research gaps exist in the field of upper extremity rehabilitation robotics that have not yet been addressed?
To address these questions, we searched for systematic reviews in the Scopus database using the equation: (TITLE-ABS-KEY (“rehabilitation robotics”) OR TITLE-ABS-KEY (exoskeleton) AND TITLE-ABS-KEY (“upper limb”)) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “re”)) AND (LIMIT-TO (SUBJAREA, “ENGI”)). No publication year limitation was applied, though the search was restricted to the engineering field. Subsequently, we created a bibliographic linkage network using VOSviewer, with a minimum cluster size of 20 and a resolution of 0.7. Figure 4 provides an overview of key terms, illustrating the evolution of research topics and their connections to the predefined search criteria. Three additional databases were consulted using the same equation. Results were integrated into Mendeley software to eliminate duplicates. After screening according to inclusion/exclusion criteria (Table 3 and Table 4), full texts were reviewed for final inclusion. We also searched the US Clinical Trials database (https://clinicaltrials.gov/, accessed on 8 February 2025) using the keyword combination: Condition/disease-Therapy/Rehabilitation Intervention/treatment-Upper limb rehabilitation robot.
Data analysis. The information analyzed was structured in two subsections: one dedicated to present trends in terms of technologies, design, or control strategies and the other, where a summary table with information on the main existing upper limb robots is presented; this information supports movements, degrees of freedom, main control input, type of actuator implemented, and its information about the clinical trial or pilot study.

4. Discussion

Clinical studies have reported encouraging outcomes associated with robotic rehabilitation devices. For example, the MAHI Exo II has significantly improved the Jebsen–Taylor Hand Function Test (JTHFT) and the Action Research Arm Test (ARAT) from pre-training to post-training, highlighting its clinical benefits in enhancing motor function [33]. Similarly, the CRUX exosuit has shown potential efficacy in rehabilitation, illustrated by a stroke survivor’s ability to don the device and complete exercises, although specific quantitative outcomes were not provided [34]. These findings underscore the effectiveness of robotic systems in supporting recovery for stroke patients. Various performance metrics are recommended to evaluate the impact of robotic systems on rehabilitation outcomes. These include velocity profile-based metrics, speed peaks, mean arrest period ratio (MAPR), and smoothness correlation coefficients, all serving as valuable indicators of upper limb movement quality [31].
The user experience during rehabilitation is crucial for the effectiveness of robotic devices. Innovative designs and control systems play a significant role in enhancing user engagement. The incorporation of physiotherapy controllers and the “assist-as-needed” paradigm fosters patient involvement, which is essential for effective rehabilitation. Additionally, devices that utilize electromyography (EMG) and electroencephalography (EEG) to detect user intent provide tailored assistance, improving interaction and support during therapy [35]. The lightweight and compliant designs of these devices also enhance comfort, promoting ease of use and potentially increasing therapy session frequency [34]. The paper [11] provides a detailed review of wearable device designs for shoulder rehabilitation and assistance; presents the different types of mechanisms, power transmission systems, and sensors; and describes several specific control strategies, such as adaptive performance-based control, challenge-based control, and constraint-based control.
Feedback mechanisms play a crucial role in the rehabilitation process by motivating users and enhancing the effectiveness of exercises. Research indicates that utilizing visual feedback, haptic feedback, and functional electrical stimulation (FES) can significantly boost user motivation and engagement during rehabilitation sessions [14]. Moreover, advanced control algorithms that align robotic exoskeletons with human joints facilitate customized and natural arm movements, effectively addressing the complex biomechanics involved in rehabilitation [29].
The integration of multimodal feedback has also emerged as a vital component in improving rehabilitation outcomes. Visual–haptic integration, which combines visual and force feedback, has been shown to enhance motor learning retention compared to unimodal feedback in certain tasks [36,37].
The improvements observed are rooted in multisensory integration, which enhances motor learning by activating complementary neural pathways. For example, combining visual error feedback with proprioceptive input improves neuroplasticity in the sensorimotor cortex during rehabilitation.
A comparison of technologies shows notable differences in clinical efficacy and feasibility. Impedance-based devices like MAHI Exo II and SUEFUL-7 (TRL 7–8) have improved functionality in the Jebsen–Taylor Hand Function Test and Action Research Arm Test by 15–20%. Meanwhile, EEG-based exoskeletons, though innovative, face challenges with complex setup and calibration. EMG-based systems like the Myoelectric Hand exoskeleton offer a good balance of efficacy and ease of use compared to EEG systems. Economically, cable-driven devices provide a lower-cost alternative (cheaper than DC motors) but lack precision and force control. This comparison aids clinical decision-making based on resources and rehabilitation requirements.
The review highlights the need for personalized and adaptable systems tailored to individual patient needs. It stresses the importance of user-friendly designs for varying disabilities, cost-effectiveness, portability, and compatibility with different platforms. Accurate feedback is crucial for ensuring patients perform correct movements to avoid injuries and maintain therapeutic efficacy.
Each study offers unique insights, with most algorithms fitting into three main categories. Figure 5 shows the relationship between control methodologies and actuator technologies, emphasizing the prevalence of DC motors, alongside specialized actuators for certain approaches. This study seeks to create a concise framework for designing upper limb exoskeleton systems.
Several sources are linked to practical design considerations and rigorous clinical studies to evaluate the efficacy of these systems in clinical practice. Table 5 presents a summary of the main upper limb robots, detailing the motions they can perform, the degrees of freedom, the main control input, and the type of actuators they employ; this compilation of related publications was obtained from article [8] and supplemented by other relevant papers [1,5,7,18].
In this study, we classified the TRLs for upper limb rehabilitation robots using standardized criteria to ensure consistency in assessment. The TRL scale from 1–9 was applied as follows:
  • TRL 1–3 (research phase): Conceptual studies, laboratory testing without human subjects;
  • TRL 4–6 (Development phase): Testing with healthy subjects (TRL 4: n < 10, TRL 5: n ≥ 10), preliminary patient studies (TRL 6: n ≥ 5);
  • TRL 7–8 (Clinical validation): Registered clinical trials (TRL 7: single-center trials, TRL 8: multi-center trials with n ≥ 20);
  • TRL 9 (Commercial deployment): Regulatory approval obtained, commercial availability with post-market surveillance.
Objective metrics considered for each device included:
  • Clinical validation: Registration in clinical trial databases (e.g., NCT number), patient sample size, published clinical outcomes;
  • Technical validation: Peer-reviewed publications on performance metrics, mechanical specifications, control system validation;
  • Development status: Current stage (prototype, pre-commercial, commercial product).
Figure 6 presents the distribution of Technology Readiness Levels (TRL) among upper limb rehabilitation robots analyzed in this study. The data indicate that the majority of these devices (57.9%) fall within the medium TRL range (4–6), while a substantial proportion (40.4%) have attained a high TRL (7–9). In contrast, only 1.7% of the devices remain in the low TRL category (1–3), suggesting that most rehabilitation robotic technologies have advanced beyond the initial conceptual stages. This distribution highlights the notable maturity of the field, with many devices undergoing significant clinical validation and technological development. The absence of devices in the low TRL range further reinforces the notion that the analyzed systems have surpassed early development phases, underscoring their potential for practical implementation in upper limb robotic rehabilitation.
As illustrated in Figure 7, upper extremity rehabilitation robotics face several interconnected challenges that require careful consideration. The primary challenge stems from the need for individualized design approaches that accommodate patient-specific characteristics. Patients present with diverse anatomical variations (in limb length and joint angles), different levels of motor impairment and muscle strength, and unique natural movement patterns, all of which complicate the development of universally effective exoskeleton designs.
Effective customization must address multiple dimensions simultaneously: the specific rehabilitation goals based on the patient’s condition and recovery stage; comfort and wearability requirements for extended therapy sessions; and consideration of cognitive and emotional factors that influence therapy engagement. From a technical perspective, designers must navigate workspace constraints to ensure proper alignment with the human biomechanical range of motion, address mechanical singularities where control becomes problematic, and achieve the delicate balance between structural integrity and lightweight construction. These multifaceted challenges highlight why a holistic, interdisciplinary approach to rehabilitation robotics development is essential for meaningful advancement in the field.
Human–robot interaction is a vital component, especially in comprehending human intentions for controlling active exoskeletons. This understanding is essential for promoting neuroplasticity and motor learning, as well as for differentiating among various types of learning and adaptation in patients. Currently, there is a pressing need for enhanced assessment tools that deliver quantitative and individualized measures of sensorimotor function, thus addressing the shortcomings of existing evaluations.
Control strategies in robotic exoskeletons for rehabilitation encompass various approaches that can be combined according to the patient’s needs and therapeutic goals. In Figure 8, we present a comparative analysis of control strategies. These include:
  • Impedance/Admittance Control: Enables the exoskeleton to behave as a “virtual” mechanical system with desired stiffness or compliance characteristics, facilitating more natural interaction between the robot and the user. While offering excellent stability and safety for patient interaction, these methods face challenges in personalizing assistance levels without explicit models of patient capability. Advanced implementations now incorporate adaptive parameters that evolve based on performance metrics (completion time, movement smoothness).
  • Position Control: Primarily used in the early stages of rehabilitation, where the patient requires full assistance in movement.
  • Proportional–Derivative (PD) Control: Provides precise movement control, suitable for specific tasks requiring high precision.
  • Assist-as-Needed Strategy: Adjusts the level of assistance based on the patient’s performance, encouraging active participation.
  • Neural Network Control: Models the complex dynamics of human–robot interactions and adapts in real-time to the user’s movements.
  • Myoelectric Control: Uses electromyography (EMG) signals to infer intended movements, promoting a high level of user engagement. These systems provide intuitive control but suffer from signal variability due to electrode placement, muscle fatigue, and cross-talk between muscle groups. Recent advances employ deep learning techniques to improve signal classification accuracy.
  • Biofeedback Control: Provides feedback to the user through sensory channels, such as visual, haptic, or auditory, enhancing motivation and learning. Although offering the advantage of direct brain–computer interfaces, these systems face significant challenges in signal reliability and classification accuracy.
  • Force Control: Applies force directly to the limb, enabling strength training and precise control of interaction.
  • Hybrid Systems: Combine various control strategies, such as position control and myoelectric control, to maximize therapeutic effectiveness.
  • Patient-Cooperative Control: Modulates the robot’s behavior in response to patient-initiated movements, promoting active participation and motor learning.
Future directions in this field are both diverse and promising. The objective is to develop smarter and more adaptive systems that can assess and respond to individual patient progress in real time. The integration of virtual reality and gamification is anticipated to enhance patient engagement, while the creation of lighter and more comfortable exoskeletons will enable their use beyond clinical settings. Additionally, advancements in neural interfaces are expected to facilitate more intuitive control schemes, and telerehabilitation will enhance access to robotic therapy. Moreover, data-driven personalization, leveraging big data analytics and machine learning, holds significant potential to improve rehabilitation programs. However, key concerns regarding accessibility and affordability remain [92,93,94].
Before commercialization and clinical implementation, these devices must comply with rigorous regulatory standards. In the United States, the Food and Drug Administration (FDA) classifies these devices as Class II under regulation 21 CFR 890.3500, typically requiring clearance through the 510(k) process. This regulatory pathway mandates that manufacturers demonstrate substantial equivalence to a previously approved device concerning safety and efficacy. In cases where no predicate device is available, the device must undergo the more stringent Premarket Approval (PMA) process, which involves comprehensive clinical studies. Furthermore, manufacturers are required to adhere to Good Manufacturing Practices (GMP-21 CFR 820) and prove conformity with international standards, including ISO 13485 [95], as well as electrical safety and electromagnetic compatibility requirements outlined by IEC 60601-1 [96] and IEC 60601-1-2 [97].
In the European Union, device certification is governed by the Medical Device Regulation (MDR 2017/745) and necessitates CE marking. Manufacturers must submit a Technical File that includes safety evidence, biocompatibility data, clinical documentation, and risk assessment. Rehabilitation devices are generally classified as Class IIa or IIb, which requires evaluation by an authorized Notified Body. Additionally, ISO 13485 certification is essential for manufacturers intending to market their devices in the European market, and adherence to safety standards such as IEC 60601-1 and IEC 60601-1-2 is also required.
It is crucial to enhance our understanding of robot–human collaboration, improve methods for measuring outcomes, and conduct rigorous clinical trials to validate the effectiveness of these devices. The importance of ethical considerations and accessibility in the deployment of advanced technologies cannot be overstated, especially regarding rehabilitation solutions for underserved populations. To fully leverage the advantages of these innovations, it is essential to engage in interdisciplinary collaboration that integrates cutting-edge technologies, user-centered design principles, and clinical expertise. This comprehensive approach is vital for developing innovative and personalized rehabilitation strategies that significantly improve therapeutic outcomes. By prioritizing these facets, we can ensure that technological advancements reach those who need them most, thereby promoting equity in healthcare.

5. Conclusions

It is essential to develop and validate unified protocols for evaluating robotic rehabilitation devices. These protocols should incorporate standardized, clinically accepted metrics—such as the Fugl-Meyer Assessment, the Wolf Motor Function Test, and the Action Research Arm Test (ARAT)—and be supported by robust statistical frameworks to ensure reproducibility and clinical relevance across diverse populations. Standardization will enable direct comparisons between technologies and facilitate high-quality meta-analyses.
Future research should prioritize the development of hybrid control algorithms that integrate multiple modalities—such as myoelectric signals, electroencephalography (EEG), and force feedback—capable of dynamically adapting to the patient’s progress throughout therapy. While current control strategies target specific stages of rehabilitation, considerable technological challenges remain. These include achieving full-range movement assistance, adapting to highly individualized patient needs, and enhancing user experience through intuitive, responsive interfaces. Stronger connections between these adaptive control systems and clinical outcomes could further validate their effectiveness.
As the demand for personalized, flexible, and user-friendly rehabilitation solutions grows, systems must be designed to meet the diverse needs of patients in both clinical and home-based settings. Compatibility with a variety of devices and platforms, coupled with intuitive human–robot interactiosn and realistic motion simulation, represents a significant design challenge that must be addressed to ensure widespread adoption.
In this context, the continued development of patient-centered, versatile rehabilitation systems is paramount. These systems must balance technological sophistication with accessibility and cost-effectiveness, ensuring their utility across different healthcare environments while adhering to safety regulations and data privacy standards.
This review also presents an updated summary table of the main upper limb exoskeletons, categorized by development status and clinical validation. A comprehensive analysis of recent literature confirms the effectiveness of robotic-assisted rehabilitation in clinical studies. However, major challenges—particularly in achieving real-time adaptability and delivering high-quality sensory feedback—remain unresolved. The accuracy and responsiveness of such feedback, whether visual, auditory, or haptic, are crucial for ensuring correct movement execution, reducing injury risk, and maximizing therapeutic outcomes.

Author Contributions

Conceptualisation, F.H.D., C.B.P. and C.E.G.C.; Methodology: F.H.D.; Investigation, F.H.D.; Original draft preparation, F.H.D.; Review and editing, C.B.P. and C.E.G.C.; Supervision, F.H.D., C.B.P. and C.E.G.C.; Project administration, C.E.G.C. All authors have read and agreed to the published version of the manuscript. .

Funding

Prof. Cecilia E. Garcia Cena is partially supported by the R&D Project with ID Ref. PID2023-147965NB-I00 with financial support from the Spanish Government, Ministry of Science, Innovation and Universities MICIU/AEI/10.13039/501100011033 and by iRoboCity2030-CM, Robótica inteligente para ciudades sostenibles (TEC-2024/TEC-62), funded by Programas de Actividades I+D en tecnologías de la Comunidad de Madrid.

Institutional Review Board Statement

This study did not require ethical approval.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data supporting the findings of this study are available from publicly accessible sources. All relevant references have been cited in the manuscript.

Acknowledgments

This work is supported by the Vicerrectoría de Investigación y Extension UIS ((VIE-4162 y VIE 2897) Semillero de investigación de Robótica, DICBoT) of the Universidad Industrial Santander UIS and DICBoT research lab, where the research projects support PhD, Master’s, and Bachelor’s students in Mechanical Engineering in the Dynamic, Control, and Robotic research areas.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Robotics devices: left—final effector and right—exoskeleton power.
Figure 1. Robotics devices: left—final effector and right—exoskeleton power.
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Figure 2. Robotics devices classification.
Figure 2. Robotics devices classification.
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Figure 3. Robotics devices classification.
Figure 3. Robotics devices classification.
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Figure 4. Temporal evolution and relationships among the reviewed topics.
Figure 4. Temporal evolution and relationships among the reviewed topics.
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Figure 5. Correlation between control type and actuator technology.
Figure 5. Correlation between control type and actuator technology.
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Figure 6. Distribution of Technology Readiness Levels (TRL).
Figure 6. Distribution of Technology Readiness Levels (TRL).
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Figure 7. Challenges and considerations in rehabilitation robotics.
Figure 7. Challenges and considerations in rehabilitation robotics.
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Figure 8. Comparative analysis of control strategies.
Figure 8. Comparative analysis of control strategies.
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Table 1. Classification of rehabilitation robots by company type and application.
Table 1. Classification of rehabilitation robots by company type and application.
Company CategoryManufacturers and Country of OriginClinical Application
Global Leading CompaniesHocoma (Switzerland)Lokomat (gait training systems)
Ekso Bionics (USA)EksoNR (rehabilitation exoskeletons)
Tyromotion (Austria)Physical and cognitive rehabilitation systems
InMotion Robots/Bionik Laboratories (USA)Robots for neurorehabilitation
Cyberdyne (Japan)HAL (exoskeleton based on bio-signals)
ReWalk Robotics (Israel/USA/Germany)Exoskeletons for assisted mobility
Specialized ManufacturersMotorika Medical (Israel)Devices for motor rehabilitation
Myomo (USA)Lightweight arm exoskeletons
Rex Bionics (New Zealand/Australia)Full-body exoskeletons
Honda (Japan)Walking Assist Device
Toyota Motor Corporation (Japan)Robots for rehabilitation
AlterG (USA)Anti-gravity treadmills
European CompaniesTechnaid (Spain)Motion analysis systems
Gogoa Mobility Robots (Spain)Exoskeletons for mobility rehabilitation
ABLE Human Motion (Spain)Affordable exoskeletons
KUKA Robotics (Germany)Collaborative robots for rehabilitation
Ottobock (Germany)Advanced prosthetics and mobility solutions
Japanese CompaniesFourier Intelligence (China)Adaptive robots for rehabilitation
Yaskawa Electric (Japan)Industrial and medical exoskeletons
Panasonic (Japan)Robotic solutions for medical care
ATOUN (Japan)Robots for assistance and mobility
Startups and Emerging CompaniesWandercraft (France/Spain)Atalante (autonomous exoskeleton)
Marsi Bionics (Spain)Pediatric exoskeletons
SuitX/US Bionics (USA)Accessible exoskeletons for various applications
Axosuits (Romania)Affordable exoskeletons
B-Temia (Canada)Mobility assistance devices
ExoAtlet (Luxembourg/Russia)Clinical exoskeletons
Companies with Medical Robotics DivisionsÖssur (Iceland)Robotic prosthetics
Kinova Robotics (Canada)Robots for assistance and personal care
Barrett Technology (USA)Medical robots for neurorehabilitation
Meditouch (Israel)Technologies for physical therapy
Rehab-Robotics (Hong Kong)Robots for extremity rehabilitation
Focal Meditech (Netherlands)Customized robotic solutions
Research and Development CompaniesCorindus (USA)Robots for cardiovascular procedures
Medexo Robotics (Hong Kong)Robots for clinical rehabilitation
Curexo (South Korea)Robots for orthopedic rehabilitation
Instead Technologies (Spain)Innovative solutions in assistive technology
Table 2. Search query and total number of results from the selected databases.
Table 2. Search query and total number of results from the selected databases.
QueryKeywordsIEEE XploreScience DirectScopusSpringer LinkTotal
1“rehabilitation robotics” AND “exoskeleton” AND “upper limb”1576053693891520
Total1520
Total duplicates removed972
Total titles screened359
Total abstracts screened131
Total full text screened18
Total full text selection40
Table 3. Inclusion criteria for article selection.
Table 3. Inclusion criteria for article selection.
No.Inclusion Criteria
1The article focuses on upper limb rehabilitation robotics.
2The article focuses on myoelectric control research.
3The article focuses on neural network-based or predictive control for exoskeletons.
Table 4. Exclusion criteria for article selection.
Table 4. Exclusion criteria for article selection.
No.Exclusion Criteria
1The article is not published in English or Spanish.
2The article describes only prosthetics without control systems.
3The article is not focused on upper limb rehabilitation.
Table 5. Control methods and characteristics of rehabilitation robots.
Table 5. Control methods and characteristics of rehabilitation robots.
Main Control MethodRobotic DeviceTRLTargeted JointDoFsType of ActuatorsSample Testing
Admittance controlMAHI [33]8Forearm, wrist.5Frameless DC motorYes, Clinical trial NCT01948739
Hand exoskeleton [38]4Fingers.6Cable-drivenNot given
Impedance controlUpper limb exoskeleton [39]3Shoulder, forearm.3EC motorNot given
WOTAS [40]5Forearm, wrist.3Brushless DC motorYes
SUEFUL-7 [41]7Shoulder, elbow, forearm, wrist.7DC motorYes
Harmony [42,43,44]7Shoulder, elbow.7Series Elastic ActuatorYes
Force controlBONES [45]6Shoulder, elbow.4Pneumatic actuatorsYes
ABLE [46]6Shoulder, elbow.4Screw-cableYes
MARSE-7 [47,48]7Shoulder, elbow, forearm, wrist.7Brushless DC motorYes
Stuttgart Exo Jacket [49]7Shoulder, elbow.12EC Motor with Spring mechanismYes
Exo for the shoulder joint [50]5Shoulder.6DC motorNot given
ASR Glove [51]6Fingers.3 for each fingerShape memory alloyYes
Yes
FEX [52]6Fingers.4DC motorYes
ExoK’ab 2016 [53]6Fingers.6DC motorYes
Upper limb exoskeleton [54]6Shoulder, elbow.4DC motorYes
Upper limb exoskeleton [34]6Shoulder, forearm.6DC motorYes
Active elbow orthosis [55]6Elbow.1DC motorYes
Electroencephalography (EEG) based controlBCI powered exoskeleton [56]8Fingers.1DC motorYes, Clinical trial NCT02552368
EMG and sEMG
based control		RehaArm [57]8Shoulder.3Artificial muscleYes, Clinical trial NCT02321254
NESM [58]7Elbow, wrist.4Brushless DC motorYes
Wearable robotic [59]7Elbow.1Twisted stringYes
NMES-Robot [60]7Fingers.2 for each fingerLinear actuatorYes
Myoelectric Hand Exoskeleton [61]7Fingers.3 for each fingerServo motorYes
Robot-assisted wrist [62]6Wrist.1DC motorYes
The eWrist [63]6Wrist.1DC motorYes
Exoneuromusculoskeleton [64]8Elbow, wrist.2Soft pneumatic musclesYes, Clinical trial NCT03752775
Joint angle controlElbow and wrist Exoskeleton [65]6Elbow, wrist.2DC motorYes
ORTE [66]8Shoulder, elbow, forearm.6Servo motorYes
Upper limb Robotic Exo [67]6Shoulder, elbow, forearm.6Servo motorYes
Parallel actuated shoulder exoskeleton [68]5Shoulder.5DC motorNot given
NEURO-Exos [69]5Elbow.1DC motorYes
Upper arm exoskeleton [70]5Shoulder, elbow.3Cable-drivenNot given
Gravity balanced exoskeleton [71]5Shoulder, elbow.5Brushless Servo motorNot given
Wearable elbow exoskeleton [72]5Elbow.2SMANot given
Hand exoskeleton [73]5Fingers.5DC motorNot given
Wearable upper limb exoskeleton [74]6Shoulder, elbow.5Cable-driven mechanismYes
Upper limb exoskeleton [75]5Shoulder, forearm.3DC motorNot given
Cable-driven soft exoskeleton [35]6Elbow.1Cable-drivenYes
CAREX [76]8Shoulder, elbow, wrist.7Cable-driven by motorYes, Clinical trial NCT02726204
CRUX [77]6Shoulder, forearm.3DC motorYes
Upper limb exoskeleton [78]6Shoulder, elbow.2DC motorYes
NTUH-II [79]8Shoulder, elbow, wrist8DC motorYes, Clinical trial NCT06113380
CADEN-7 [80]6Shoulder, elbow, forearm, wrist.7Brushed motors with cable-driven reduction pulleysYes
6-REXOS [81]5Elbow, forearm, wrist.6DC motorNot given
ExoRob [32]5Forearm, wrist.2Maxon EC-45Not given
Master-slave controlHand motion assist robot for therapy [82]5Wrist, fingers.18DC motorNot given
Visual, haptic, and auditory-based controlHEnRiE [83]5Shoulder, elbow, wrist.5DC motorNot given
Haptic robotic glove [36]5Fingers.3 for each fingerPneumatic driveNot given
RobHand [37]8Fingers.5Linear actuatorYes, Clinical trial NCT05598892.
EAsoftM exoskeleton [84]6Elbow, wrist.4Pneumatic actuatorYes
Arm Assist (AA) [85]8Shoulder, elbow.2DC motor Cable-drivenYes, Clinical trial NCT02729649
REAPlan [86]8Shoulder, elbow.2DC motorYes, Clinical trial NCT02543424
HAL-SJ [87]8Elbow.2DC motorYes, Clinical trial UMIN000014336
Gloreha-hand [88]9Wrist, fingers.1 for each fingersCable-drivenYes, Clinical trial NCT02711787
InMotion 2 [89,90]9Shoulder, elbow.2Brushless DC motorYes, Clinical trial NCT00453843
RH-UL-LEXOS-10 [31]9Shoulder, elbow, wrist.7Cable-drivenYes, Clinical trial NCT03319992
Armeo power [91]9Shoulder, elbow, forearm, wrist.5DC motorYes, Clinical trial NCT01485354
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MDPI and ACS Style

Diaz, F.H.; Borrás Pinilla, C.; García Cena, C.E. Exploring Robotic Technologies for Upper Limb Rehabilitation: Current Status and Future Directions. J. Sens. Actuator Netw. 2025, 14, 48. https://doi.org/10.3390/jsan14030048

AMA Style

Diaz FH, Borrás Pinilla C, García Cena CE. Exploring Robotic Technologies for Upper Limb Rehabilitation: Current Status and Future Directions. Journal of Sensor and Actuator Networks. 2025; 14(3):48. https://doi.org/10.3390/jsan14030048

Chicago/Turabian Style

Diaz, Fabian Horacio, Carlos Borrás Pinilla, and Cecilia E. García Cena. 2025. "Exploring Robotic Technologies for Upper Limb Rehabilitation: Current Status and Future Directions" Journal of Sensor and Actuator Networks 14, no. 3: 48. https://doi.org/10.3390/jsan14030048

APA Style

Diaz, F. H., Borrás Pinilla, C., & García Cena, C. E. (2025). Exploring Robotic Technologies for Upper Limb Rehabilitation: Current Status and Future Directions. Journal of Sensor and Actuator Networks, 14(3), 48. https://doi.org/10.3390/jsan14030048

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