Search Results (315)

Background: There are only a few available options for early rehabilitation of severely impaired individuals who must remain bedbound, as most exercise paradigms focus on out-of-bed exercises. To enable these individuals to exercise, we developed a novel hybrid rehabilitation system combining a brain–computer interface (BCI), functional electrical stimulation (FES), and a robotic device. Methods: The BCI assessed the presence of a movement-related cortical potential (MRCP) and triggered the administration of FES to produce movement of the lower limb. The exercise trajectory was supported by the robotic device. To demonstrate the system, an experiment was conducted in an out-of-lab setting by ten able-bodied participants. During exercise, the performance of the BCI was assessed, and the participants evaluated the system using the NASA Task Load Index, Intrinsic Motivation Inventory, and by answering a few subjective questions. Results: The BCI reached a true positive rate of 62.6 ± 9.2% and, on average, predicted the movement initiation 595 ± 129 ms prior to the MRCP peak negativity. All questionnaires showed favorable outcomes for the use of the system. Conclusions: The developed system was usable by all participants, but its clinical feasibility is uncertain due to the total time required for setting up the system. Full article

Transparency is a key requirement for some classes of robots, exoskeletons, and assistive devices (READs), where safe and efficient human–robot interaction is crucial. Typical fields that require transparency are rehabilitation and industrial contexts. However, the definitions of transparency adopted in the literature are heterogeneous. It follows that there is a need to clarify, summarize, and assess how transparency is commonly defined and measured. Thus, the goal of this review is to systematically examine how transparency is conceptualized and evaluated across studies. To this end, we performed a structured search across three major scientific databases. After a thorough screening process, 20 out of 400 identified articles were further examined and included in this review. Despite being recognized as a desirable and essential characteristic of READs in many domains of application, our findings reveal that transparency is still inconsistently defined and evaluated, which limits comparability across studies and hinders the development of standardized evaluation frameworks. Indeed, our screening found significant heterogeneity in both terminology and evaluation methods. The majority of the studies used either a mechanical or a kinematic definition, mostly focusing on the intrinsic behavior of the device and frequently giving little attention to the device impact of the user and on the user’s perception. Furthermore, user-centered or physiological assessments could be examined further, since evaluation metrics are usually based on kinematic and robot mechanical metrics. Only a few studies have examined the underlying motor control strategies, using more in-depth methods such as muscle synergy analysis. These findings highlight the need for a shared taxonomy and a standardized framework for transparency evaluation. Such efforts would enable more reliable comparisons between studies and support the development of more effective and user-centered READs. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

The development of wearable robotic exoskeletons has seen rapid progress in recent years, driven by the growing need for technologies that support motor rehabilitation, assist individuals with physical impairments, and enhance human capabilities in both clinical and everyday contexts. Within this field, elbow exoskeletons have emerged as a key focus due to the joint’s essential role in upper limb functionality and its frequent impairment following neurological injuries such as stroke. With increasing research activity, there is a strong interest in evaluating these systems not only from a technical perspective but also in terms of user comfort, adaptability, and clinical relevance. This review investigates recent advancements in elbow exoskeleton technology, evaluating their effectiveness and identifying key design challenges and limitations. Devices are categorized based on three main criteria: mechanical structure (rigid, soft, or hybrid), actuation method, and sensing technologies. Additionally, the review classifies systems by their supported range of motion, flexion–extension, supination–pronation, or both. Through a systematic analysis of these features, the paper highlights current design trends, common trade-offs, and research gaps, aiming to guide the development of more practical, effective, and accessible elbow exoskeletons. Full article

Background/Objectives: Robotic-assisted gait training (RAGT) is a promising adjunct to conventional rehabilitation for stroke survivors. However, its additive benefit over standard therapy remains to be fully clarified. This systematic review and meta-analysis evaluated the effectiveness of combining RAGT with conventional rehabilitation in improving gait-related outcomes among individuals with stroke. Methods: We searched PubMed, Embase, CINAHL, and Cochrane CENTRAL through September 2024 for randomized controlled trials (RCTs) comparing combined RAGT and conventional rehabilitation versus conventional rehabilitation alone in adults post-stroke. Data were synthesized using a random-effects model, and subgroup analyses examined effects by intervention duration, stroke chronicity, and robotic system type. Results: Twenty-three RCTs (n = 907) were included. The combined intervention significantly improved gait function (SMD = 0.51, p = 0.001), gait speed (SMD = 0.47, p = 0.010), balance (MD = 4.58, p < 0.001), and ADL performance (SMD = 0.35, p = 0.001). Subgroup analyses revealed that end-effector robotic systems yielded superior outcomes compared to exoskeletons, particularly in subacute stroke patients. The most pronounced benefits were seen in gait velocity and dynamic balance, especially with ≤15 training sessions. Conclusions: Integrating RAGT with conventional rehabilitation enhances motor recovery and functional performance in stroke survivors. End-effector devices appear most effective in subacute phases, supporting individualized RAGT application based on patient and device characteristics. Full article

Background/Objectives: Robot-led rehabilitative exercise offers a promising avenue to enhance the care provided by exercise specialists (ESs). ESs, such as physical and occupational therapists, prescribe exercise regimens to clinical populations to improve patients’ adherence to prescribed exercises outside the clinic, such as at home. Collaborative efforts among roboticists, clinical ESs, and patients are essential for developing interactive, personalized exercise systems that meet each stakeholder’s needs. This work builds upon research involving individuals with Parkinson’s disease (PD) that evaluated a robotic rehabilitative exercise system designed to address strength and flexibility deficits. Methods: To complement the findings of our previous work in people with PD (PWP), we conducted a pilot user study in which 11 ESs evaluated a novel robot-led exercise system for PWP, focusing on perceptions of the system’s efficacy and acceptance. Utilizing a mixed-methods approach, including technology acceptance questionnaires, task load questionnaires, and inductively coded semi-structured interviews, we gathered comprehensive insights into ES perspectives and experiences after interacting with the system. Results: Findings reveal a broadly positive reception, which highlights the system’s capacity to augment traditional rehabilitative exercise for PD, enhance patient engagement, and ensure consistent exercise support. We also identified two key areas for improvement: incorporating more human-like feedback systems and increasing the robot’s ease of use. Conclusion: This research emphasizes the value of incorporating robotic assistants into rehabilitative exercise for PD, offering insights that can guide the development of more effective and user-friendly rehabilitation technologies. Full article

This article presents a series of innovative systems developed through student laboratory projects, comprising two autonomous vehicles, a quadrupedal walking robot, an active ankle-foot orthosis, a ball-on-beam balancing mechanism, a ball-on-plate system, and a manipulator arm, all actuated by pneumatic artificial muscles (PAMs). Due to their flexibility, low weight, and compliance, fluidic muscles demonstrate substantial potential for integration into various mechatronic systems, robotic platforms, and manipulators. Their capacity to generate smooth and adaptive motion is particularly advantageous in applications requiring natural and human-like movements, such as rehabilitation technologies and assistive devices. Despite the inherent challenges associated with nonlinear behavior in PAM-actuated control systems, their biologically inspired design remains promising for a wide range of future applications. Potential domains include industrial automation, the automotive and aerospace sectors, as well as sports equipment, medical assistive devices, entertainment systems, and animatronics. The integration of self-constructed laboratory systems powered by PAMs into control systems education provides a comprehensive pedagogical framework that merges theoretical instruction with practical implementation. This methodology enhances the skillset of future engineers by deepening their understanding of core technical principles and equipping them to address emerging challenges in engineering practice. Full article

This paper presents the design and evaluation of a novel soft pneumatic actuator featuring two independent bending chambers, enabling independent joint actuation and localization for rehabilitation purposes. The actuator’s dual-chamber configuration provides flexibility for applications requiring customized bending profiles. To measure the bending angle of the finger joints in real time, a camera-based system is employed, utilizing a deep learning detection model to localize the joints and estimate their bending angles. This approach provides a non-intrusive, sensor-free alternative to hardware-based measurement methods, reducing complexity and wiring typically associated with wearable devices. Experimental results demonstrate the effectiveness of the proposed actuator in achieving bending angles of 105 degrees for the metacarpophalangeal (MCP) joint and 95 degrees for the proximal interphalangeal (PIP) joint, as well as a gripping force of 9.3 N. The vision system also captures bending angles with a precision of 98%, indicating potential applications in fields such as rehabilitation and human–robot interaction. Full article

Background/Objective: Stroke is one of the leading causes of death and disability worldwide, with older survivors (aged > 65 years) bearing significant health and economic impacts, particularly in industrialized countries. While gait rehabilitation is a cornerstone in post-stroke recovery and robotic technologies offer promising tools to enhance its effectiveness, the existing literature has largely overlooked elderly populations. Most studies on robot-assisted gait training (RAGT)—which uses exoskeleton or end-effector devices to support and guide movement—either exclude older adults or do not analyze their outcomes separately. This review aims to critically evaluate the current evidence on RAGT in elderly post-stroke patients, addressing a significant gap in the literature and providing novel insights into the effectiveness and adaptability of RAGT for this specific population. Methods: The search included databases such as PubMed, Scopus, Embase, Web of Science, and ClinicalTrials. The inclusion criteria covered studies published up to March 2025, focusing on post-stroke individuals aged >65 years, who underwent RAGT. Results: 25 studies were included in the review, 21 involving exoskeleton and 4 end-effector devices. The primary focus was on motor outcomes, such as gait independence, gait parameters, and balance control. Only a few studies examined non-motor aspects, including cognitive and emotional functions, fatigue, pain, and neuroplasticity. Moreover, data on the long-term effects on the elderly population remain scarce. Conclusions: RAGT is an effective strategy for promoting motor recovery and improving functional outcomes, from independence in daily activities to quality of life, in the post-stroke elderly population. Early and high-intensity interventions are particularly useful with positive effects on neuronal plasticity, cognitive function, and well-being. Full article

(1) Background: Peripheral artery disease (PAD) and related conditions significantly impair walking ability. Previous studies demonstrated that passive lightweight exosuits can improve walking biomechanics. However, most of these devices focus on assisting hip flexion. The aim of this study was to investigate the effects of flexion and extension assistance on joint kinetics and muscle activation. We hypothesized that there would be an optimal combination of flexion and extension assistance for measured parameters. (2) Methods: Four patients with PAD and six healthy individuals walked on a treadmill while wearing a passive exosuit with adjustable hip flexion and extension assistance. Lower limbs’ power, moment, and muscle activation were recorded. (3) Results: We found that passive hip assistance effectively reduced hip kinetics in both healthy and PAD participants. We also found different effects between the groups, with the PAD group utilizing the exosuit to reduce plantarflexion kinetics and gastrocnemius activity. (4) Conclusions: These findings suggest that patients with PAD can leverage the exosuit to ameliorate impairment-specific deficits. Future research should explore more real-world applicability of passive exosuits. Full article

To address the precision challenges in upper-limb joint motion prediction, this study proposes a novel artificial neural network (COANN) enhanced by the Cheetah Optimization Algorithm (COA). The model integrates surface electromyography (sEMG) signals with joint angle data through multi-source information fusion, effectively resolving the local optima issue in neural network training and improving the accuracy limitations of single sEMG predictions. Experimental results demonstrate that the COANN achieves significant performance improvements: compared with RBF neural networks, it reduces the root mean square error (RMSE) by 24.32% (ΔR² + 18.75%) with a 22.6% shorter system runtime; relative to conventional ANNs, it decreases the RMSE by 31.59% (ΔR² + 12.15%) while reducing computational time by 35.1%; compared with CNN neural networks, it reduces the root mean square error (RMSE) by 14.9% (ΔR² + 3.84%); and relative to conventional LSTM, it decreases the RMSE by 15.31% (ΔR² + 4.86%). Multi-source integration enhanced elbow joint prediction accuracy by 5.7% and shoulder joint accuracy by 6.9% compared with single sEMG approaches. This methodology provides theoretical foundations for human–robot interaction systems in upper-limb rehabilitation robotics and motion-assistive devices. Full article

Neurological disorders like stroke are one of the main causes of motor dysfunction and gait function disabilities in humans. These disorders impact the mobility of patients often leading to weakened and impaired ankle joints which further compromise their balance and walking abilities. Over the span of the last twenty years, there has been a rising interest in designing, developing, and using rehabilitative robots for patients suffering from various ankle joint disabilities. These robotic devices are developed by employing diverse mechanical designs, materials, and control strategies. The aim of this study is to provide a detailed overview of the recent developments in mechanical design, actuation, and control strategies of ankle rehabilitation robots. Experimental evaluation of the discussed ankle robots has also been carried out discussing their results and limitations. This article concludes by highlighting future challenges and opportunities for the advancement of ankle rehabilitation robots, stressing the need for robust and effective devices to better serve patients. Full article

The ability to walk is often lost after neural injury, leading to multiple secondary complications that reduce quality of life and increase healthcare costs. The current rehabilitation interventions primarily focus on restoring leg movements through intensive training on a treadmill or using robotic devices, but ignore engaging the arms. Several groups have recently shown that simultaneous arm and leg (A&L) cycling improves walking function and interlimb connectivity. These findings highlight the importance of neuronal pathways between the arm (cervical) and leg (lumbar) control regions in the spinal cord during locomotion, and emphasize the need for activating these pathways to improve walking after neural injury or disease. While the findings to date provide important evidence about actively including the arms in walking rehabilitation, these strategies have yet to be optimized. Moreover, improvements beyond A&L cycling alone may be possible with conjunctive targeted strategies to enhance spinal interlimb connectivity. The aim of this review is to highlight the current evidence for improvements in walking function and neural interlimb connectivity after neural injury or disease with cycling-based rehabilitation paradigms. Furthermore, strategies to enhance the outcomes of A&L cycling as a rehabilitation strategy are explored. These include the use of functional electrical stimulation-assisted cycling in acute care settings, utilizing non-invasive transcutaneous spinal cord stimulation to activate previously inaccessible circuitry in the spinal cord, and the use of paired arm and leg rehabilitation robotics. This review aims to consolidate the effects of exercise interventions that incorporate the arms on improved outcomes for walking, functional mobility, and neurological integrity, underscoring the importance of integrating the arms into the rehabilitation of walking after neurological conditions affecting sensorimotor function. Full article

In the context of aging populations, it has become necessary to develop new methods and devices for the daily home-based self-rehabilitation of elderly people. To this end, this paper proposes and evaluates the use of an easy-to-use single battery-powered device including a 3D accelerometer and a 3D gyroscope, where light algorithms, such as the complementary filter and the Kalman filter, are implemented to estimate the elbow joint angle. During experiments, a robotic arm and a human arm were used to obtain an error interval for each tested algorithm; the robotic arm allows for reproducible movements and reproducible results, which allows us to independently verify the impact of parameters such as the sensor’s movement speed on the algorithm precision. The experimental results show that the algorithm that uses only accelerometer data is one of the most relevant since it allows us to obtain a Root Mean Square Error between 1.83° and 5.52° at a sensor data rate of 100 Hz, which is similar to the results obtained using the data fusion algorithms tested. Nevertheless, it has a lower power consumption since it requires only 58 cycles when using an ARM Cortex-M4 processor (which is lower than that of the other data fusion algorithms tested by a factor of at least two), and it does not necessitate the additional sensor required by the other data fusion algorithms tested (such as a gyroscope or a magnetometer). The algorithm using only accelerometer data also seems to be the algorithm with the lowest power consumption and should be preferred. Moreover, its power consumption can be reduced by more than the increase in the error when reducing the rate of the data output by the sensor. In this work, a reduction in the data rate from 100 Hz to 10 Hz increased the RMSE by a factor of 1.8 but could reduce the power consumption associated with the sensor and the algorithm’s computation by a factor of 10. Finally, the experimental results show that the higher the speed of the sensor’s motion, the higher the error obtained using only accelerometer data. Nevertheless, the algorithm that uses only accelerometer data remains well suited to rehabilitation exercises or mobility evaluations since the speed of the sensor’s movement is also moderate. Full article

Artificial intelligence (AI) is revolutionizing the field of orthopedic bioengineering by increasing diagnostic accuracy and surgical precision and improving patient outcomes. This review highlights using AI for orthopedics in preoperative planning, intraoperative robotics, smart implants, and bone regeneration. AI-powered imaging, automated 3D anatomical modeling, and robotic-assisted surgery have dramatically changed orthopedic practices. AI has improved surgical planning by enhancing complex image interpretation and providing augmented reality guidance to create highly accurate surgical strategies. Intraoperatively, robotic-assisted surgeries enhance accuracy and reduce human error while minimizing invasiveness. AI-powered smart implant sensors allow for in vivo monitoring, early complication detection, and individualized rehabilitation. It has also advanced bone regeneration devices and neuroprosthetics, highlighting its innovation capabilities. While AI advancements in orthopedics are exciting, challenges remain, like the need for standardized surgical system validation protocols, assessing ethical consequences of AI-derived decision-making, and using AI with bioprinting for tissue engineering. Future research should focus on proving the reliability and predictability of the performance of AI-pivoted systems and their adoption within clinical practice. This review synthesizes recent developments and highlights the increasing impact of AI in orthopedic bioengineering and its potential future effectiveness in bone care and beyond. Full article