Search Results (1,181)

Introduction: Currently, there is an ongoing debate regarding the benefits of kinematic alignment (KA) versus mechanical alignment (MA) in total knee arthroplasty (TKA). Robotic-assisted TKA has been shown to improve implant positioning and precision of the KA technique, enabling successful kinematic alignment. However, its impact on early postoperative and functional outcomes remains unclear. This study aims to examine how imageless, table-mounted, robotic-assisted KA-TKA compares with conventional MA-TKA. Methods: Registry data of all primary TKAs using ATTUNE™ cruciate-retaining implants (January 2021–December 2024) performed by a single, experienced surgeon in a high-volume arthroplasty center were retrospectively reviewed. A total of 64 patients who underwent robotic-assisted KA-TKA were compared to 39 patients who underwent conventional MA-TKA. The mean age was 70.3 ± 7.71 and 69.3 ± 9.47 in the KA-TKA group and the MA-TKA group, respectively, while the male proportion was 32.8% and 30.7%, respectively. Early postoperative outcomes (static/dynamic pain score, ambulation distance, length of stay) and 6-month functional outcomes (range of motion, Knee Society Score, Oxford Knee Score, SF-36, patient expectation/satisfaction scores) were analyzed. Delta changes in outcome scores and proportion of patients attaining a minimum clinically important difference (MCID) were studied. Results: Robotic-assisted KA-TKA displayed benefits in the majority of the early postoperative outcomes, with significant improvements in ambulation distance (23.3 vs. 14.7 m, p = 0.002) compared to conventional MA-TKA. Both groups showed significant improvements in the majority of the functional outcomes at 6 months. Robotic-assisted KA-TKA also shows significant improvements in selected mental health aspects of SF-36, namely vitality (p = 0.001), mental health (p = 0.048), mental component summary (MCS) (p = 0.004), and a larger proportion attaining SF-36 vitality MCID (p = 0.045). Following false discovery rate correction for multiple comparisons, postoperative ambulation distance, SF-36 vitality, and MCS remained statistically significant between groups. No significant differences in KSS, OKS, and satisfaction/expectation fulfillment were noted. Conclusions: Robotic-assisted KA-TKA demonstrated early rehabilitation and select mental health-related quality of life improvements compared to conventional MA-TKA. Further studies are needed to examine its long-term clinical outcomes. Full article

Integrating neural and muscular signals into wearable robotics enables adaptive assistance during real-world tasks. This study proposes a multimodal neural interface for passive exoskeletons that combines electroencephalography (EEG) and electromyography (EMG) signals to classify motor gestures and estimate real-time cognitive and muscular effort, supported by finite-element-based biomechanical modeling. The system was implemented on the Ottobock Shoulder X passive exoskeleton© and validated using synchronous EEG–EMG acquisition via the LiveAmp platform©, a commercially available platform that was not developed specifically for this study. A hybrid CNN–LSTM architecture with deep fusion was employed to enhance robustness and responsiveness under realistic operating conditions. This study proposes a multimodal neural interface for the software-level adaptive assistance of passive upper-limb exoskeletons. While the physical device maintains a static mechanical profile, the proposed digital framework achieves adaptation by interpreting the user’s physiological and motor states. Ten healthy participants performed three functional tasks (screwing, moving the box, and lifting the box) under five assistive conditions. Finite element modeling (FEM) was used to characterize the torque–angle relationship of the passive exoskeleton and to support the interpretation of experimentally observed assistive torque profiles. The FEM model, used as an offline biomechanical analysis tool to aid in the interpretation of experimental results, has not been integrated into the real-time control loop. Results showed an average classification accuracy of 90%, an F1-score of 0.85, and inference latency below 180 ms, confirming real-time applicability. Cognitive indices such as the Cognitive Load Index (CLI) and Frontal Asymmetry Index (FAI) enabled adaptive modulation of assistance strategies without requiring active actuation, thereby preserving the device’s intrinsic passive nature. Comparative torque analysis highlighted the ergonomic benefits of passive systems in mid-range postures, while Finite Element Method (FEM) supported analysis clarified their limitations under highly dynamic loads compared to active solutions. These findings advance multimodal brain–machine interfaces for wearable robotics by integrating physiological sensing, deep learning, and biomechanical modeling, offering a safe, energy-efficient, and adaptive approach with potential rehabilitation, occupational ergonomics, and human–robot applications. Full article

Inertial Measurement Units (IMUs) enable portable, multibody motion capture in diverse environments beyond the laboratory, making them a desirable choice for diagnosing mobility disorders and supporting rehabilitation in clinical or home settings. However, challenges associated with IMU measurements, including magnetic distortions and errors due to integration drift, complicate their broader use for motion capture. In this work, we propose a tightly coupled motion-capture approach that directly integrates IMU measurements with multibody dynamic models via an iterated extended Kalman filter to simultaneously estimate the system’s kinematics and kinetics. By enforcing the complete multibody system dynamics and utilizing only accelerometer and gyroscope data, our method accurately estimates joint kinematics and kinetics. Our algorithm is designed to fuse different sensor data, such as optical motion-capture measurements and joint torque readings, to further enhance estimation accuracy. We validated our approach using highly accurate ground-truth data from a 3-degree-of-freedom pendulum and a 6-degree-of-freedom collaborative robot. We demonstrate a maximum root-mean-square difference of 3.75° in the pendulum’s computed joint angles with respect to the marker motion-capture inverse kinematics. For the robot, we observed a maximum joint angle root-mean-square difference of 3.24° with respect to the joint encoders, while the maximum joint angle root-mean-square difference of the optical motion-capture inverse kinematics with respect to the encoders was 1.16°. With regard to kinetic estimates, we report a maximum joint torque root-mean-square difference of 3.02 Nm in the pendulum with respect to the marker motion-capture inverse dynamics and 4.27 Nm in the robot relative to its joint torque sensors. Full article

Multimodal sensor fusion is increasingly used to improve observability for fault diagnosis and prognostics, enabling Remaining Useful Life estimation in complex mechatronic and robotic systems. Yet, real-world deployments remain vulnerable to sensor faults and data integrity issues—including bias and drift, miscalibration, dropouts, saturation, cross-talk, time desynchronization, and domain shift—which can propagate through fusion pipelines and lead to optimistic validation and poor generalization. These challenges are particularly consequential in safety- and health-adjacent applications such as collaborative robots, wearable/rehabilitation devices, and human-centric mechatronic systems where decisions based on faulty sensing may affect both reliability and user safety. This review synthesizes the state of the art on (i) sensor fault taxonomies and fault models relevant to multimodal fusion, (ii) fault-aware fusion strategies spanning data-, feature-, and decision-level integration, and (iii) how sensor faults and uncertainty impact diagnosis and remaining-life estimators. We will conduct a systematic scoping review of peer-reviewed literature, extracting sensor modalities, fault characterization or injection protocols, fusion architectures, validation settings (simulation, hardware-in-the-loop, bench, and in-field/on-body studies), and reporting completeness. Beyond summarizing methods, we provide practical reporting standards for sensor-fusion-based diagnosis and prognostics, including a minimum disclosure set covering synchronization, fault ground truth, missingness handling, leakage controls, uncertainty calibration, and task-relevant metrics. Reusable checklists and evidence tables are included to support more comparable, reproducible, and deployment-ready research. Full article

This work presents a pilot study to analyze the effect of aging on motor performance of young adults (YAs) and older adults (OAs) through wrist movement assessment, using an upper limb rehabilitation robot (ULRR) in passive mode coupled to a maze-solving task serious video game. The proposed approach considers the use of kinematic metrics, such as ROM, path accuracy, and movement smoothness, as quantitative biomarkers that evidence differences between YAs and OAs. An experimental protocol was conducted with 20 participants: 10 OAs and 10 YAs. Standardized wrist movements corresponding to flexion (F), extension (E), radial deviation (R), and ulnar deviation (U) were assessed at each level of the maze. The kinematic analysis was based on metrics for range of motion (ROM), path accuracy, smoothness, and root-mean-square error (RMSE) in trajectory tracking. The results revealed clear differences between the groups: the YAs achieved a greater ROM and made fewer errors on mean (2.167 errors for YAs compared to 6.000 errors for OAs), and showed a lower RMSE, while the OAs showed greater smoothness in their movements, because the YAs exhibit greater variability and disturbances in movement when correcting and controlling their movements to achieve good performance, reflecting more precise motor control and a greater capacity for error correction during movements with trajectory constraints. Full article

People living with neurological disorders frequently experience gait impairments that substantially reduce mobility, independence, and quality of life. This pilot study aimed to evaluate the feasibility, safety, and preliminary functional outcomes of integrating the EksoNR robotic exoskeleton (Ekso Bionics, San Rafael, CA, USA) into outpatient neurorehabilitation practice in individuals with chronic neurological impairments. Over an eight-month period, five participants with heterogeneous neurological conditions (two spinal cord injuries, one cerebellar ataxia, one ischemic stroke, and one spastic paraparesis) completed a four-week robotic gait training program consisting of 15 intervention sessions. Functional outcomes were assessed before and after the intervention using standardized clinical tests. Cardiovascular endurance was evaluated using the 6-Minute Walk Test (6MWT), while physical and psychological well-being were assessed with the Functional Independence Measure (FIM) and the Barthel Index, in addition to the WHO Quality of Life (WHOQOL) and EQ-5D-5L questionnaires. Mobility and balance were evaluated using the Timed Up and Go (TUG), Berg Balance Scale (BBS), Tinetti Performance-Oriented Mobility Assessment (POMA), and Walking Index for Spinal Cord Injury II (WISCI II), where applicable. In addition, device-recorded gait parameters, including step count, step length, walking distance, and walking duration, were analyzed. Significant improvements were observed in several device-derived gait parameters, including the number of steps performed with the exoskeleton (p < 0.001), step length (p = 0.003), walking distance (p = 0.002), and walking duration (p < 0.05). Significant improvements were also identified in balance performance (BBS: p = 0.006; Tinetti POMA: p = 0.001), cardiovascular endurance (6MWT: p = 0.017), and EQ-5D-5L scores (p = 0.038). Functional independence measures (FIM and BI), TUG performance, and WHOQOL domains did not demonstrate statistically significant changes. No serious adverse events or device-related injuries occurred during the intervention period. Due to the small and clinically heterogeneous sample, these findings should be interpreted as preliminary exploratory results. Nevertheless, the study supports the feasibility and potential clinical utility of EksoNR-assisted gait rehabilitation and provides a basis for larger controlled investigations. Full article

Robotic assistive devices, such as exoskeletons, are increasingly employed in walking rehabilitation. Therefore, the measurement of both movement kinematics and cognitive workload is important to understand this human–robot interaction in real-world contexts. To address this need this study presents the validation of a framework integrating inertial motion capture (Xsens) and eye-tracking sensor (Pupil Neon) within a Mixed Reality (Meta Quest 3) architecture. We developed an overground dual-task paradigm in which holographic numbers appear in the user’s peripheral vision. This setup actively stimulates visuospatial attention while quantifying kinematic and cognitive output. To validate the framework, the protocol has been tested on 30 healthy subjects across repeated exoskeleton training sessions. Statistical analyses revealed that the Coefficient of Multiple Correlation (CMC) and Spectral Arc Length (SPARC), calculated on the shank angular velocity, together with the Step Length Variability, exhibited significant time effects (p < 0.01), mapping the transition toward automated gait. Concurrently, pupillometric data demonstrated a measurable reduction in neurocognitive demand; specifically, the Task-Evoked Pupillary Response (TEPR) decreased significantly across progressive training sessions (p < 0.05). With this work, we validated a measurement protocol that aims to provide a novel methodology for objectively evaluating motor and cognitive adaptation in wearable assistive devices. Full article

Disabilities of the lower extremities significantly affect a person’s ability to perform activities of daily living. Many people have been affected by this type of disability due to birth disease or injury from accidents, strokes or even old age. The technical aids used in this type of disability are very basic, and rehabilitation is mainly performed by therapists. Rehabilitation consists of repetitions of exercises with normal movements that must be performed for prolonged periods of time. On the other hand, therapists, having to support the weight of the patient, tend to get injured. This paper introduces the design and modeling a robotic device intended to assist the therapist in the rehabilitation of sit-to-stand (STS) and walking movements, focusing primarily on the technical aspects of the system. The robot is designed to safely support the user’s weight and guide the user with appropriate movements according to the usual biomechanics of STS. This paper presents the solution of the inverse kinematic modeling of both the position and velocity of the robot mechanism, as well as the dynamic analysis. A series of simulations is conducted to evaluate the performance of the proposed mechanical architecture during the STS task, providing quantitative information on the system dynamics and the interaction forces between the user and the robot. The mathematical model was employed in the design of a prototype intended for children aged 8–12 years, capable of supporting up to 50 kg and providing a vertical motion range of 20–90 cm. The main structural elements of the robot, its control architecture, and its operation during the execution of the STS task are described. Finally, the conclusions of this work are discussed, and future work derived from this research is outlined. Full article

Objectives: Bilateral robotic arm training (BRT) may enhance poststroke motor recovery by reducing interhemispheric inhibition and promoting bilateral motor network engagement. However, previous reviews have often pooled bilateral and unilateral robotic approaches, potentially masking differential effects. This systematic review and meta-analysis compared the effects of BRT with those of unilateral robotic training (URT) and conventional rehabilitation on upper-limb motor function after stroke. Methods: Randomized controlled trials were identified through systematic searches of major electronic databases and trial registries in accordance with PRISMA guidelines. The risk of bias was assessed via the Cochrane Risk of Bias 2 tool. Random effects meta-analyses were performed using standardized mean differences (SMDs). Predefined subgroup and sensitivity analyses were used to examine the influence of participant characteristics, training dose, intervention duration, and robotic device type. Results: Fourteen randomized controlled trials involving 440 participants were included. Overall, compared with control interventions, BRT did not significantly improve upper-limb motor function, as measured using the Fugl–Meyer Assessment for Upper Extremity (SMD = 0.18, 95% CI −0.01–0.36). Significant effects were observed in participants younger than 60 years, with training doses > 15 h, intervention durations > 4 weeks, and use of Bi-Manu-Track systems. Conclusions: BRT did not demonstrate a significant overall advantage over URT or conventional rehabilitation. However, subgroup analyses suggest that treatment effects may vary according to patient characteristics, training dose, duration of the intervention, and device type. Full article

Stroke-related gait impairments are frequently associated with deficits in trunk control, movement coordination, and dynamic stability. Although robotic-assisted gait rehabilitation has shown promising clinical benefits, phase-specific biomechanical adaptations following rehabilitation remain incompletely understood. This study investigated phase-specific biomechanical adaptations following robotic-assisted gait rehabilitation in individuals with stroke using sensor-derived waveform analysis. Rehabilitation was performed three times per week over approximately 5–6 weeks using treadmill-based robotic gait training under dynamic body-weight support conditions. Pre- and post-intervention kinematic data were collected using a sensor-based motion analysis system. Joint kinematics, trunk motion, and center of gravity (COG) displacement were analyzed across the normalized gait cycle using waveform-based effect size analysis, statistical parametric mapping, principal component analysis, and k-means clustering to explore inter-individual adaptation patterns. Thirteen post-stroke hemiplegia patients (10 males; age = 63.9 ± 13.8 years), including six subacute and seven chronic stroke survivors, completed 16 rehabilitation sessions. The most prominent improvements were observed in trunk lateral flexion, particularly during loading response (d = 0.47, p < 0.01), indicating enhanced frontal plane trunk stability. Trunk flexion–extension showed reduced compensatory motion, whereas hip and knee adaptations were smaller and phase-dependent. COG displacement decreased across the gait cycle, reflecting improved dynamic stability. Step length increased significantly on both hemiplegic (Δ = +5.73 cm, p = 0.024) and intact sides (Δ = +8.83 cm, p = 0.007), while cadence and load symmetry remained unchanged. Clustering analysis revealed heterogeneous adaptation profiles rather than distinct responder groups. Chronic participants demonstrated greater variability within the Principal Component Analysis space compared to subacute participants, suggesting more variable and individualized biomechanical reorganization patterns rather than clearly separable recovery categories. Overall, robotic rehabilitation induced inter-individual biomechanical adaptations, predominantly involving proximal trunk control and stabilization strategies. Full article

Stroke is the second leading cause of death globally and a major contributor to lower-limb disability, affecting gait, balance, and functional independence in elderly populations. While robot-assisted rehabilitation has demonstrated effectiveness in motor recovery, access remains limited due to high costs and geographic barriers, particularly in Latin America. This study presents ExoKnee, a low-cost knee exoskeleton designed through biomimetic principles and 3D-printed fabrication as a proof-of-concept device targeting gait rehabilitation in elderly adults. The system integrates a single-degree-of-freedom pneumatic actuator controlled by electromyography (EMG) signals from the quadriceps muscle, enabling knee flexion and extension (90° to 180°). The design was evaluated through finite element analysis and dynamic simulations in MATLAB/Simulink R2024a under constant, stepwise, and sinusoidal reference inputs in a digital-twin environment. Expert validation using the Content Validity Coefficient yielded a mean score of 0.8747, reflecting preliminary expert agreement on the conceptual design’s coherence and relevance. The prototype demonstrated controlled movements through a 6-bar pneumatic system with EMG-triggered relay activation, validated at the proof-of-concept level through simulation and single-subject threshold calibration. ExoKnee addresses critical gaps by offering an anthropometrically informed, biosignal-driven, and locally manufacturable rehabilitation platform for low- and middle-income countries, pending clinical validation. Future work will focus on clinical trials and adaptive EMG control strategies. Full article

This paper reviews recent advances in assistive devices based on multimodal information fusion control, designed for individuals with motor dysfunction. The prevalence of motor dysfunction is increasingly concerning amidst global population aging. Information fusion technology, widely adopted in rehabilitation, enhances the efficacy and specificity of rehabilitation treatments. This paper introduces the concept of multimodal information fusion control into rehabilitation equipment design. It highlights the advantages and disadvantages of data-level, feature-level, and decision-level fusion, along with commonly employed fusion algorithms. By summarizing and analyzing the current state of research, this paper aims to provide a valuable reference for the further development and optimization of assistive devices for motor dysfunction. Full article

Composite materials, particularly polymers reinforced with carbon, glass, and aramid fibers, enable the development of lightweight yet mechanically robust structures that enhance user comfort and functional performance. Their high strength-to-weight ratio and fatigue resistance make them ideal for applications requiring repetitive movements in rehabilitation and assistive robotics. However, challenges remain related to cost-effective production, durability under complex loading conditions, and ergonomic fit to human anatomy. Recent advances in materials science and smart materials are expanding the possibilities of multifunctional composites with embedded sensors. Furthermore, machine learning methods are increasingly being used to optimize material selection and structural design. Future advances are expected to improve scalability, personalization, and system integration, positioning composite fibers as a key assistive technology in next-generation robotic systems. Full article

Stroke is a leading cause of chronic disability, with heterogeneous sensorimotor impairments that are not well captured by standard clinical assessments. While advanced motion capture and robotic systems provide precise measurements, they are not scalable for widespread clinical use. We developed C-MORE (Computer Vision for Movement Observation and Recovery Enhancement), a smartphone-based framework that uses computer vision and machine learning to automatically score the Box and Blocks Test (BBT) and extract quantitative kinematic metrics. The system combines hand tracking with a custom machine learning (ML) architecture to identify valid block transfers and segment task phases. We evaluated C-MORE in 7 individuals with chronic stroke and a cohort of 10 healthy adults. The system achieved 99.0% agreement with ground-truth scoring, with errors below clinically meaningful thresholds. Kinematic measures derived from the system were sensitive to stroke-related impairments, including reduced movement velocity and increased task duration in affected limbs. Exploratory analyses indicated that grasp-related metrics, particularly the ratio of grasp-to-transfer duration, were correlated with independent measures of proprioception. These findings demonstrate that smartphone-based computer vision can provide accurate, scalable assessment of upper-extremity function. C-MORE offers a practical approach for enhancing clinical evaluation and enabling more precise, individualized rehabilitation strategies. Full article

Objective: To systematically evaluate the rehabilitation effect of brain-computer interface (BCI)-controlled hand robot training on post-stroke motor functions, especially upper limb functions. Methods: PubMed, Embase, Web of Science, Cochrane Library, CNKI, SinoMed, WanFang Data, and VIP Database were searched from inception to 13 March 2026. Randomized controlled trials (RCTs) with dose-matched designs were included, where the test group underwent BCI-controlled hand robot training and the control group received either pure hand robot training or routine rehabilitation. Meta-analysis was performed on RevMan 5.4. Results: Totally 11 RCTs involving 380 patients were included. Compared with hand robot training alone, BCI-controlled hand robot training significantly improved Fugl-Meyer Assessment for Upper Extremity (FMA-UE) scores (MD = 4.87, 95% CI: 1.04 to 8.69) and FMA-UE proximal scores (MD = 4.44, 95% CI: 0.15 to 8.74), and significantly reduced finger flexor spasticity (MD = −0.44, 95% CI: −0.68 to −0.21), but showed no significant difference in distal upper limb motor function or Action Research Arm Test (ARAT) scores. Compared with routine rehabilitation, BCI-controlled hand robot training significantly improved FMA-UE scores (MD = 6.55, 95% CI: 3.49 to 9.61). Conclusions: BCI-controlled hand robot training can effectively improve overall upper limb and proximal motor function after stroke and alleviate finger flexor spasticity, but the evidence for distal hand function and long-term efficacy remains limited. Full article