Search Results (710)

Background/Objectives: Advanced heart failure is associated with reduced functional capacity and impaired quality of life. Left ventricular assist devices (LVADs) are increasingly used as a long-term treatment option in patients with end-stage heart failure. Despite improvements in hemodynamic function after LVAD implantation, many patients continue to experience limitations in daily functioning. The aim of this study was to evaluate the relationship between physical activity and functional status in patients with LVAD support. Methods: This study included 262 adult participants divided into four groups according to LVAD support and declared physical activity. Functional status and quality of life were assessed using the Short Form-36 Health Survey (SF-36) and the Minnesota Living with Heart Failure Questionnaire (MLHFQ). Results: Significant differences were observed between the analyzed groups in both SF-36 and MLHFQ scores. Physically active patients with LVAD achieved the most favorable results, indicating a better functional status and lower symptom burden, whereas inactive individuals demonstrated poorer outcomes. Significant correlations were found between physical activity and selected aspects of daily functioning, including walking, climbing stairs, performing household activities, and carrying groceries. Higher levels of physical activity were associated with better quality of life and fewer functional limitations. Conclusions: Physical activity may positively influence functional status and quality of life in patients with LVAD support. The findings suggest that regular physical activity should be considered an important component of rehabilitation and long-term management in patients with advanced heart failure treated with LVAD therapy. 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

Introduction: Congenital myasthenic syndrome (CMS) is a rare hereditary disorder of the neuromuscular junction caused by pathogenic variants that affect acetylcholine transmission. We report three pediatric cases with CMS, including a rare homozygous CHRNE mutation previously described only once, a novel CHRNE compound heterozygous variant, and two novel DPAGT1 variants associated with limb-girdle CMS (LG-CMS), thereby expanding the known genetic and phenotypic spectrum of the disorder. Case presentation: The first patient, a 4-year-old girl born to consanguineous parents, presented with bilateral ptosis and fatigable weakness since infancy. Whole-genome sequencing revealed a homozygous CHRNE variant, c.991C>T. The second patient, a 4-year-old boy born to non-consanguineous parents, presented with congenital bilateral ptosis and ophthalmoplegia without generalized weakness. Genetic analysis identified compound heterozygous CHRNE variants, c.905C>G and c.1040T>C. Both patients demonstrated marked improvement with pyridostigmine therapy. The third patient, a 3-year-old girl born to non-consanguineous parents, presented with severe limb weakness requiring assistance in walking and performing daily activities with minimal ocular involvement, suggesting a diagnosis of LG-CMS. Genetic testing identified two novel variants in the DPAGT1 gene in the compound heterozygous form, c.710G>T and c.858C>A. The initial response to pyridostigmine diminished over time. Conclusions: These cases underscore the phenotypic heterogeneity of CMS, even within the same genetic subtype, and expand the existing mutational spectrum of CHRNE and DPAGT1 genes. This study also highlights the essential role of molecular diagnosis in distinguishing CMS from other neuromuscular disorders. Early genetic confirmation facilitates genotype-targeted therapy, prevents inappropriate immunosuppression, and enables informed reproductive counseling. Full article

Integrating depth sensors into mobile robots enables automated gait monitoring with potential applications in neurological disorders. This pilot study aims to evaluate the preliminary feasibility of robot-assisted gait assessment using Azure Kinect against Vicon, including individuals with multiple sclerosis, while simultaneously examining between-system, within-system, and environmental effects. A total of 20 participants were recruited to complete the eight-meter straight-line and 32 m corridor walking tests in the laboratory on the same day. Following independent data acquisition by both systems, temporal alignment was achieved through foot-event anchoring and interval trimming. On a unified timeline, 8 joint kinematic signals and 26 descriptors were extracted. Generalized estimating equations were applied, with a Bonferroni correction implemented for the 26 parallel tests to control the family error rate. The results showed: The spatiotemporal gait metrics exhibited general stability between systems and environments. Vicon better revealed variations in hip and pelvic amplitudes and restricted extension phenotypes, while the robotic system demonstrated greater sensitivity to knee posture and relative swing amplitude. The corridor environment induced an increase in stride length and a reduced step time compared to the laboratory, accompanied by a greater peak of hip and knee flexion and a greater forward lean of the trunk, with a largely preserved temporal organization. Within the Vicon-referenced framework, Azure Kinect-based robotic assessment demonstrated preliminary feasibility for capturing gait-related characteristics in individuals with multiple sclerosis. However, due to the limited number of analyzed MS participants, these findings should be interpreted as exploratory rather than as definitive clinical validation. The two systems exhibit complementary kinematic advantages. We recommend adopting an evaluation protocol that combines laboratory baseline with corridor validation, supplemented by descriptor-level mapping for cross-system data integration when necessary. This approach may support future tiered assessment, disease progression monitoring, and efficacy evaluation, but larger clinical cohorts are required to confirm its applicability in individuals with multiple sclerosis. Full article

Background: Equine-assisted programs have been shown to promote psychosocial outcomes, but quantitative evidence of motor benefits in correctional settings is scarce. Aim: The present study examined the effects of a one-week equine-assisted riding program on riding performance, movement quality, and well-being among young inmates in an open German prison. Methods: Ten male participants (24.5 ± 0.71 years) completed a five-day program combining practical riding exercises, cooperative activities, and guided reflection. Riding performance was assessed using standardized expert video ratings based on the German performance testing guidelines on a 10-point scale, movement quality using a semantic differential with bipolar adjective pairs assessing telic and autotelic dimensions on a six-point scale, and well-being using the WHO-5 Well-Being Index. A single-group pre–post repeated-measures design without a control group was applied. Results: Significant improvements were found in riding performance for both walk and trot, with large effect sizes (n = 10). Participants also reported a significant enhancement in the autotelic, experience-oriented dimension of movement quality, whereas no significant change occurred in the telic, goal-oriented dimension. Well-being increased significantly from pre- to post-test. Conclusions: Even a short, experience-focused equine-assisted program can produce meaningful improvements in motor performance, positive movement experience, and well-being in a correctional context. Equine-assisted programs may therefore represent a promising complementary approach to rehabilitation by integrating physical, emotional, and social learning processes. Full article

Background: Human locomotion requires coordinated torque production across multiple joints, yet conventional gait analysis typically evaluates joint behavior independently, limiting insight into inter-joint coordination. This study aimed to quantify task-dependent reorganization of ankle–knee mechanical coordination using a moment–moment phase space framework. Methods: A normative dataset of healthy adults (N = 50) performing natural-speed walking, toe walking, heel walking, stair ascent, and stair descent was analyzed. Sagittal-plane external ankle and knee moments were extracted from time-normalized stride cycles and z-score normalized within each stride to emphasize coordination topology. Ankle–knee trajectories were represented in moment–moment space and characterized using three geometric metrics: loop magnitude (|Area|), principal axis orientation, and anisotropy. Metrics were aggregated within subject and analyzed using linear mixed-effects models with planned contrasts against walking. Results: Loop magnitude differed significantly across tasks (p < 0.001), with the largest increases observed during toe walking (+3.45 relative to walking) and stair descent (+2.41). Principal axis orientation also showed a significant task effect (p = 0.026), with stair descent producing the largest rotation of the coordination axis (−29.8°). Anisotropy varied significantly across tasks (p < 0.001), indicating systematic changes in the dimensionality and strength of inter-joint torque coupling. Conclusions: Locomotor tasks induce structured, task-dependent reorganization of ankle–knee coordination topology. Moment–moment phase space analysis provides a compact and interpretable framework for quantifying inter-joint torque coupling, with potential applications in biomechanics research and the development of activity-aware assistive technologies. Full article

Falls in older adults and individuals with balance impairments remain a major concern because they are closely associated with injury, reduced mobility, and loss of independence. This study presents a preclinical proof-of-concept for a cognitive smart walker architecture that combines user-compatible walking assistance with active safety intervention. The system integrates a 2D LiDAR sensor for contactless lower-limb monitoring, a six-degree-of-freedom (6-DOF) force/torque sensor to measure user–walker interaction, and an inertial measurement unit (IMU) for dynamic monitoring, with all data processed in real time on a Raspberry Pi/ROS-based platform. Normal walking assistance is provided through a command-level variable admittance-based controller that converts interaction forces into a smoothed signed duty-cycle command rather than a rigid speed-control signal. Safety decisions are managed by a Hierarchical State Machine (HSM). Early-risk conditions are handled through motor-based dynamic braking, whereas severe physical crises additionally deploy lateral support legs to enlarge the base of support. Within this framework, the system can detect and manage foot entanglement, grip loss, forward fall, vertical collapse, lateral fall, successive crises, and recovery-abort events. In experiments across multiple scenarios, the system correctly detected all 50 crisis cases and did not issue unnecessary interventions in 30 non-crisis cases. These findings show that the proposed architecture can preserve transparent walking assistance during normal gait while providing graded, context-sensitive active safety when risk emerges. Full article

The number of patients with lower-limb dysfunction is increasing each year due to aging, illness, accidents, and other factors. Without timely rehabilitation and rapid recovery of walking function, further physical and mental deterioration may be accelerated, potentially leading to long-term bedriddenness. This study discusses gait training in rehabilitation therapy from the perspectives of kinesiology, cognitive science, walking function, and safety, and an omnidirectional gait training robot was developed. This study proposed a control system construction method for an omnidirectional gait training robot based on both prescription-based training and autonomous training. In the prescription-based training system, the target values are derived from the training prescription, and the control objective is to guide the patient to walk along the robot’s prescribed path and speed. In the autonomous training system, the target values are automatically generated based on the patient’s walking intentions, and the control objective is for the robot to safely follow the patient’s movement. A necessary condition for robot-assisted autonomous gait training is effective fall prevention. A fall prediction strategy based on foot position information and handrail pressure data was developed. Using this strategy, the robot can predict falls immediately before they occur, similar to a physical therapist, thereby reducing the risk of falls during gait training. Experimental results demonstrate the feasibility of implementing this strategy. Full article

To address the high physical demands faced by personnel engaged in power maintenance operations, this study develops a hip assistive exoskeleton capable of state recognition between level-ground walking and transmission tower climbing. The mechanical structure of the exoskeleton is designed based on motion data analysis of human level-ground walking and tower climbing activities. A dynamic model of the human lower limb is conducted to support state-based torque control of the actuators. To accommodate different locomotion scenarios, a control strategy based on a hierarchical finite state machine (HFSM) is proposed to achieve adaptive state recognition and enable the exoskeleton to provide state-specific torque output. State recognition and transition experiments, alongside laboratory and field transmission tower climbing experiments, are conducted. The results show that the exoskeleton can reliably recognize transitions between walking and climbing, providing effective assistance during transmission tower climbing operations. Furthermore, laboratory and field transmission tower climbing experiments show that exoskeleton assistance reduces integrated EMG (IEMG), root mean square (RMS) and maximum absolute value (MAXABS) values of the biceps femoris (BF), rectus femoris (RF), and vastus medialis (VM), demonstrating the effectiveness of the exoskeleton. Full article

Background: Wearable powered gait orthoses offer a clinically flexible alternative to treadmill-based robotic systems, yet evidence on different device configurations matched to the site of neuromuscular impairment remains limited. Methods: In this multicenter prospective pilot study, 75 participants with neurological gait impairment were allocated to a hip orthosis (HO; n = 39) or a knee–ankle–foot orthosis (KAFO; n = 36) group based on clinical assessment of predominant gait pattern. Both groups completed six overground gait-training sessions over three weeks. Primary outcomes were the Six-Minute Walk Test (6MWT) and Ten-Meter Walk Test (10MWT), assessed without (WO) and with (WITH) the device. Secondary outcomes were the Berg Balance Scale (BBS), Timed Up and Go Test (TUG), and Dynamic Gait Index (DGI), all assessed without the device. Wilcoxon signed-rank tests were used for pre-to-post comparisons. Results: Both groups demonstrated significant improvements in primary walking outcomes, with consistent gains in unassisted (WO) 6MWT and 10MWT performance across groups and in device-assisted (WITH) 10MWT speed; the one exception was a small statistically significant but clinically negligible decrease in device-assisted 6MWT in the KAFO group (−4.1 m, below established MCID). In the KAFO group, BBS improved by a median of 5.5 points (43.5 to 49.0, p = 0.0005), TUG decreased by 5.1 s (p < 0.001), and DGI improved by 6.0 points (p = 0.002); all three changes exceeded published minimum detectable change thresholds. In the HO group, pre-to-post differences in BBS (+1.0), TUG (+0.8 s; an unfavorable direction), and DGI (−2.0; an unfavorable direction) were statistically detectable but small in absolute magnitude, fell at or below published thresholds for minimum detectable change, and should not be interpreted as clinically meaningful improvement. The WO-WITH performance gap in the KAFO group narrowed substantially after training, with 10MWT time no longer differing significantly between conditions at post-training (p = 0.116). Conclusions: Six sessions of gait pattern-matched powered gait orthosis training produced clinically meaningful within-group improvements in walking outcomes in both groups. In the KAFO group, balance and functional mobility outcomes also showed clinically meaningful improvements; in the HO group, balance and functional mobility outcomes showed only statistically detectable but clinically non-meaningful fluctuations around near-ceiling baseline scores. Walking benefits generalized to unassisted ambulation in both groups. These findings support the feasibility of an individualized orthosis prescription framework and provide a basis for future randomized controlled trials. Full article

Navigating through everyday environments, like walking down a sidewalk, which many people often take for granted, is a difficult task for millions of people with vision impairments since it involves sophisticated object detection, depth perception, and situational awareness, all working seamlessly to guide a person through complex surroundings. Many current assistive devices for vision-impaired people are either expensive, information-overabundant, or missing critical information. This paper details our Vision Alarming System (VAS), which can improve the safety for blind and vision-impaired people by providing awareness of both positions and nature of nearby obstacles; thus, assisting users to make decisions to avoid collisions, reduce accidents and casualties, while enhance their experience, independence, and confidence when participating in traffic. VAS is an Artificial Intelligence/Internet-of-Things (AI/IoT)—powered system developed utilizing the cutting-edge Raspberry Pi 5, a Light Detection and Ranging (LiDAR) sensor, and an AI depth camera, operating as different containers in a Docker architecture, and leveraging a Robotic Operating System 2 (ROS 2) backbone. VAS communicates the obstacle detections to users via Bluetooth interface, using the neural Text-To-Speech (TTS) system, namely, Piper, and the Sound eXchange (SoX) technologies. Our proof-of-concept system proves that VAS can be a standalone, open-source, extremely low cost, low power consumption assistive device which can synergistically utilize the cutting-edge AI/IoT technologies to provide blind and vision-impaired users with an appropriate amount of critical information about their surrounding environments. Full article

Data-driven exoskeletons offer the potential for adaptive augmentation of human mobility. Yet their widespread adoption is hindered by labor-intensive biomechanical data collection and manual tuning. Herein, this study presents a highly efficient synthetic data approach to facilitate data-driven pipelines. We leveraged an Adversarial Motion Priors (AMP) agent to learn stylized walking within a massively parallel, physics-based simulation. The resulting high-fidelity data were collected and validated against OpenSim inverse dynamics pipelines. Further, we trained an end-to-end torque prediction algorithm using the collected data. A novel CNN-Transformer architecture was developed to map contralateral swing-phase data to variable-length push-off torque profiles. This enabled real-time, adaptive torque assistance of exoskeletons for variable-speed walking. A custom ankle exoskeleton was used to demonstrate robust sim-to-real transferability. Our system achieved an average root mean square error of approximately 0.081 ± 0.015 newton-meters per kilogram and an average R² of 0.836 ± 0.050 across speeds ranging from 0.6 to 1.75 m·s⁻¹. The controller significantly reduced user-positive ankle mechanical work by up to 14 ± 6.30%. Finally, our multi-sensor configuration exhibited inherent fault tolerance, ensuring safe operation even under partial sensor failure. By taking a scalable, data-driven approach, this work offers a practical pathway toward deploying autonomous exoskeletons in versatile, real-world environments. Full article

Wearable lower-limb exoskeletons can enhance mobility, reduce metabolic cost, and aid rehabilitation. Effective human-exo cooperation requires personalized assistance profiles that match biomechanical principles. Existing methods often rely on fixed curves, involve complex tuning, and lack biomechanical interpretability. To address this, we propose a “Physics-guided perception and physiology-driven optimization” approach. First, a Physics-guided Dynamic Fusion Model (PDFM) is proposed, which integrates Newton–Euler dynamics, LSTM, and NTM to estimate multi-plane hip joint moments without ground reaction forces, employing biomechanical models as complementary fusion factors rather than the embedded hard constraints used in conventional physics-informed neural networks (PINNs). The model achieved correlation coefficients of 0.938, 0.924, and 0.929, and relative root mean square error (rRMSE) values of 5.29%, 9.79%, and 5.61%, in the sagittal, coronal, and transverse planes, respectively. These results outperformed all single-network baselines across all three anatomical planes. Second, an assistance profile derived from estimated moments is individually optimized using Bayesian optimization based on multi-muscle sEMG. Compared to no-exo walking, the optimized system reduced target muscle loading by 49.31% and metabolic cost by 14.75%; relative to the pre-optimized profile, the reductions were 23.64% and 5.74%, respectively. This work provides a laboratory-validated framework for personalized hip exoskeleton assistance in healthy adults, establishing a foundation for future clinical translation. Full article