Search Results (493)

Exoskeleton-assisted bodyweight support training (BWST) has demonstrated enhanced neurorehabilitation outcomes in which joint motion prediction serves as the critical foundation for adaptive human–machine interactive control. However, joint angle prediction under dynamic unloading conditions remains unexplored. This study introduces an adaptive wavelet-denoising fusion (AWDF) model to predict lower-limb joint angles during BWST. Utilizing a custom human-tracking bodyweight support system, time series data of surface electromyography (sEMG), and inertial measurement unit (IMU) from ten adults were collected across graded bodyweight support levels (BWSLs) ranging from 0% to 40%. Systematic comparative experiments evaluated joint angle prediction performance among five models: the sEMG-based model, kinematic fusion model, wavelet-enhanced fusion model, late fusion model, and the proposed AWDF model, tested across prediction time horizons of 30–150 ms and BWSL gradients. Experimental results demonstrate that increasing BWSLs prolonged gait cycle duration and modified muscle activation patterns, with a concomitant decrease in the fractal dimension of sEMG signals. Extended prediction time degraded joint angle estimation accuracy, with 90 ms identified as the optimal tradeoff between system latency and prediction advancement. Crucially, this study reveals an enhancement in prediction performance with increased BWSLs. The proposed AWDF model demonstrated robust cross-condition adaptability for hip and knee angle prediction, achieving average root mean square errors (RMSE) of 1.468° and 2.626°, Pearson correlation coefficients (CC) of 0.983 and 0.973, and adjusted R² values of 0.992 and 0.986, respectively. This work establishes the first computational framework for BWSL-adaptive joint prediction, advancing human–machine interaction in exoskeleton-assisted neurorehabilitation. Full article

Background and Objectives: Horse riding simulators (HRS) provide rhythmic, repetitive, and multidirectional movements analogous to horseback riding, which may facilitate postural control, balance, and functional abilities in children with cerebral palsy (CP). This study aimed to investigate the effects of the HRS application on the muscle tone of the lower extremity, gross motor function, trunk postural control, balance, gait functions, and functional independence in children with CP. Materials and Methods: A quasi-experimental study included 30 children with cerebral palsy (17 hemiparetic, 13 diparetic; mean age, 9.3 ± 3.2 years). All participants received Neurodevelopmental Therapy (NDT) for eight weeks, followed by eight weeks of HRS plus NDT, in a sequential design. Outcomes included the Modified Ashworth Scale (MAS), Myoton^®PRO, Gross Motor Function Measures (GMFM)-88, Pedalo^® Sensamove Balance Test (Pedalo^® SBT), Pediatric Balance Scale (PBS), Trunk Impairment Scale (TIS), gait analysis parameters, and Functional Independence Measure (WeeFIM). Assessments were made at baseline, the 8th, and the 16th week. Results: At week 16, after incorporating HRS, all MAS parameters demonstrated greater improvements compared to those achieved during the first eight weeks of NDT alone (ES: 0.728–0.931, p < 0.05). Myoton^®PRO measurements showed a significant reduction in gastrocnemius stiffness (ES = 0.672, p < 0.05) in hemiparetic children and decreases in hip adductor (ES: 0.649, p < 0.05) and gastrocnemius-soleus (ES: 0.766–0.865, p < 0.05) stiffness from week 8 to 16 in diparetic children following HRS intervention. Total scores on the GMFM-88, WeeFIM, TIS, and PBS improved significantly, with large effect sizes observed both from baseline to week 16 and from week 8 to 16 (ES: 0.771–0.886, p < 0.05). Additionally, Pedalo^® SBT scores increased following HRS intervention from baseline to week 16 (ES = 0.599–0.602, p < 0.05). Conclusions: HRS integrated with conventional NDT may improve muscle tone, motor function, balance, gait, and functional independence in children with cerebral palsy, representing a valuable adjunct to standard rehabilitation. These findings provide the first evidence that simulator-assisted interventions may benefit daily activities in children with cerebral palsy. Full article

This paper presents the design and evaluation of a lightweight, minimally actuated lower limb exoskeleton that emphasizes hip–knee coordination for natural and efficient gait assistance. The system adopts a hip-only motorized actuation strategy in combination with an electromagnetically controlled knee locking mechanism, ensuring rigid stability during stance while providing compliant assistance during swing. To support sit-to-stand transitions, a gas spring–ratchet mechanism is integrated, which remains disengaged in the seated position, delivers assistive torque during rising, and provides cushioning during the descent to enhance safety and comfort. The control framework fuses foot pressure and thigh-mounted IMU signals for finite state machine (FSM)-based gait phase detection and employs a fuzzy PID controller to achieve adaptive hip torque regulation with coordinated hip–knee control. Preliminary human-subject experiments demonstrate that the proposed design enhances lower-limb coordination, reduces muscle activation, and improves gait smoothness. By integrating a minimal-actuation architecture, a practical sit-to-stand assist module, and an intelligent control strategy, this exoskeleton strikes an effective balance between mechanical simplicity, functional support, and gait naturalness, offering a promising solution for everyday mobility assistance in elderly or mobility-impaired users. Full article

Systems presenting haptic information have emerged as an important technological advance in assisting individuals with sensory impairments or amputations, where the aim is to enhance sensory perception or provide sensory substitution through tactile feedback. These systems provide information on limb positioning, environmental interactions, and gait events, significantly improving mobility in amputees and their confidence about using such devices. This review summarizes recent progress in haptic feedback systems by providing a comparative analysis of different feedback approaches, evaluating their clinical effectiveness and usability, tactile feedback system design, and user experience, while identifying key gaps in the literature. These insights can contribute to the advancement of more effective, user-centered haptic feedback systems tailored for lower limb prosthetics. The findings are aimed at guiding future research in designing adaptive, intuitive, and clinically viable feedback mechanisms, fostering the widespread implementation of haptic systems in both assistive and rehabilitative applications. Full article

This study proposes a methodology for conducting computational simulations of pathological gait. The literature shows a consensus that biomechanical models for gait analysis should be formulated as control problems. To achieve this, it is common practice to guide the solution using kinematic or kinetic data to prevent temporal instability. The aim of this study is to implement a biomechanical model of the Charcot–Marie–Tooth disease in OpenSim software that enables more comprehensive simulations, which may in future involve the musculoskeletal system of patient and predictive studies. In this way, it will be possible to design specific active assistive devices tailored to each patient. Experimental gait data from six Charcot–Marie–Tooth patients were used. The dataset comprises three-dimensional trajectories of reflective markers placed according to the Davis-Heel protocol. The acquired data allowed a patient-specific adjustment of the biomechanical model. The inverse kinematic was solved, and the results were validated by comparing them with those obtained using the commercial BTS Bioengineering^® software. The results show a strong alignment in ankle kinematics between the OpenSim model and the data generated by BTS Bioengineering^®. Additionally, the kinematic results have been compared with normative curves, allowing the identification of potential areas for intervention using active assistive devices aimed at improving movement patterns of patients. Full article

Exoskeletons are a fast-growing technology that enables multiple use-cases in clinical scenarios. They can be useful tools for the rehabilitation of patients with motor dysfunctions caused by neurological conditions, aging or trauma. Assistive exoskeletons modulate the torque exerted by the electrical motors moving their joints to allow the patients wearing them to achieve an intended movement, such as gait, correctly. Their effectiveness, therefore, requires accurate online control of such torques to complement those generated by the patient. Hereby we explored Deep Learning (DL) models to generate an online prediction of the gait phase, i.e., stance or swing, during assisted walking with a lower-limb exoskeleton based on surface electromyography (sEMG) data. We leveraged the lead of muscular activation with respect to the movement of the limbs to adjust the labeling based on joints kinematics. The cross-subject design allowed to generalize over subjects not considered for training A hyperparameter optimization algorithm was also implemented to further explore the capabilities of DL models of a reduced size. We simulated a use case scenario to assess whether online implementation of the proposed technique is feasible. We also proposed a new metric called trade-of score (TOS) for evaluating the cost-performance compromise of the optimized models which lead to identifying a DL model capable of classifying gait phases with an accuracy of about 95% while significantly reducing the number of parameters compared to the full architecture. Its mean computational time of less than 10 ms offers the opportunity for accurate, online exoskeleton control based on sEMG data. Full article

Stroke remains one of the leading causes of disability worldwide, often resulting in persistent impairments in gait and balance. Traditional rehabilitation methods—though beneficial—are limited by factors such as therapist dependency, low patient adherence, and restricted access. In recent years, sensor-supported technologies, including virtual reality (VR), robotic-assisted gait training (RAGT), and wearable feedback systems, have emerged as promising adjuncts to conventional therapy. This systematic review evaluates the effectiveness of wearable and immersive technologies for gait and balance rehabilitation in adult stroke survivors. Following PRISMA guidelines, a systematic search of the PubMed and ScienceDirect databases retrieved 697 articles. After screening, eight studies published between 2015 and 2025 were included, encompassing 186 participants. The interventions included VR-based gait training, electromechanical devices (e.g., HAL, RAGT), auditory rhythmic cueing, and smart insoles, compared against conventional rehabilitation or baseline function. Most studies reported significant improvements in motor function, dynamic balance, or gait speed, particularly when interventions were intensive, task-specific, and personalized. Patient engagement, adherence, and feasibility were generally high. However, heterogeneity in study design, small sample sizes, and limited long-term data reduced the strength of the evidence. Technologies were typically implemented as complementary tools rather than standalone treatments. In conclusion, wearable and immersive systems represent promising adjuncts to conventional stroke rehabilitation, with potential to enhance motor outcomes and patient engagement. However, the heterogeneity in protocols, small sample sizes, and methodological limitations underscore the need for more robust, large-scale trials to validate their clinical effectiveness and guide implementation. Full article

(1) Background: Functional Electrical Stimulation (FES) is a recognized method for post-stroke gait rehabilitation but remains underutilized due to workflow complexity and the need for manual configuration. NeuroSkin^®, a wearable FES system integrating AI-driven stimulation and sensor-based gait monitoring, was developed to streamline clinical use by automating phase-specific, multi-muscle stimulation. (2) Methods: This retrospective multicenter feasibility study evaluated the integration of NeuroSkin^® into routine inpatient rehabilitation. Fifteen subacute stroke patients across seven centers underwent 10 to 20 FES-assisted gait training sessions. Standardized assessments (10MWT, 6MWT, TUG, NFAC) were performed pre- and post-intervention. Therapists completed the System Usability Scale (SUS) questionnaire. (3) Results: All outcomes showed statistically significant improvement: walking speed and endurance increased by 70% and 171% respectively, TUG time decreased by 39%, and ambulation level improved by three NFAC categories. No adverse events were reported, and usability was rated as excellent (mean SUS score: 84.6). (4) Conclusions: NeuroSkin^® was safely and effectively implemented in diverse clinical settings, demonstrating strong usability and promising functional benefits. These findings support the need for prospective controlled trials to confirm its clinical efficacy and broader applicability in stroke rehabilitation. Full article

A small-scale electro-hydrostatic actuator, termed miniHydrA, was developed based on biomechanical requirements for gait and integrated into an ankle exoskeleton. The key advantage of this actuator concept lies in its compact size and the low mass of its output stage, combined with the ability to deliver high support torques, sufficient for full human assistance. During development, hydraulic cylinder leakage and friction were identified as key challenges. To address control requirements, a dedicated control strategy was proposed and implemented. The prototype exoskeleton was evaluated for joint torque tracking performance across a range of torques (0–120 Nm), both in benchtop tests and during treadmill walking trials. In benchtop experiments, zero-torque tracking was achieved with a mean absolute error ranging from 0.03 to 2.26 Nm across frequencies from 0 to 5 Hz. During treadmill walking, torque tracking errors ranged from 0.70 to 0.95 Nm, with no observable deviations in ankle joint kinematics among the three test subjects. These results show the feasibility of the miniHydrA for remote actuation. Compared to Bowden cables, commonly used in exoskeletons and exosuits, the proposed actuator concept offers two key advantages: it is better suited for high-torque applications, and its friction characteristics can be more accurately predicted and modeled, enabling more effective feedforward control. Full article

The effective use of nursing assistant robots requires an understanding of key acceptance factors. The study examined the differences in attitudes among 58 nursing students while performing ambulation tasks with and without an Adaptive Robotic Nursing Assistant (ARNA) robot. An ARNA is driven by tactile cues from the patient through a force–torque-measuring handlebar, whose signals are fed into a neuro-adaptive controller to achieve a specific admittance behavior regardless of patient strength, weight, or floor incline. Ambulation tasks used two fall-prevention devices: a gait belt and a full-body harness. The attitude toward the robot included perceived satisfaction, usefulness, and assistance, replacing the perceived ease-of-use construct found in the standard technology acceptance model. The effects of external demographic variables on those constructs were also analyzed. The modified technology acceptance model was validated with the simultaneous estimation of the effects of perceived usefulness and assistance on satisfaction. Our analysis employed an integrated hierarchical linear mixed-effects regression model to analyze the complex relationships between model variables. Our results suggest that nursing students rated the ARNA’s performance higher across all model constructs compared to a human assistant. Furthermore, male subjects rated the perceived usefulness of the robot higher than female subjects. Full article

Transverse myelitis is a rare spinal cord condition that can cause severe motor, sensory, and autonomic dysfunction. This case report describes a 16-year-old male with incomplete paraplegia due to idiopathic transverse myelitis who underwent robotic-assisted gait training (RAGT) using the EKSO exoskeleton, integrated into an intensive rehabilitation programme. After one month, he showed significant improvements in gait speed, dynamic balance, effort tolerance, and trunk mobility. RAGT promoted better weight distribution and reduced compensatory patterns during ambulation. The intervention proved safe and clinically beneficial, highlighting the potential of robotic technologies as effective adjuncts in paediatric spinal cord injury rehabilitation. Full article

Background/Objectives: Hip-level amputees face ambulatory challenges due to the lack of a lower limb and prosthetic hip power. Some hip-level amputees restore mobility by using a prosthesis with hip, knee, and ankle joints. Powered prosthetic joints contain an actuator that provides external flexion-extension moments to assist with movement. Powered knee and powered ankle-foot units are on the market, but no viable powered hip unit is commercially available. This research details the development of a novel powered four-bar prosthetic hip joint that can be integrated into a full-leg prosthesis. Methods: The hip joint design consisted of a four-bar linkage with a harmonic drive DC motor placed in the inferior link and an additional linkage to transfer torque from the motor to the hip center of rotation. Link lengths were determined through engineering optimization. Device strength was demonstrated with force and finite element analysis and with ISO 15032:2000 A100 static compression tests. Walking tests with a wearable hip-knee-ankle-foot prosthesis simulator, containing the novel powered hip, were conducted with three able-bodied participants. Each participant walked back and forth on a level 10 m walkway. Custom hardware and software captured joint angles. Spatiotemporal parameters were determined from video clips processed in the Kinovea software (ver. 0.9.5). Results: The powered hip passed all force and finite element checks and ISO 15032:2000 A100 static compression tests. The participants, weighing 96 ± 2 kg, achieved steady gait at 0.45 ± 0.11 m/s with the powered hip. Participant kinematic gait profiles resembled those seen in transfemoral amputee gait. Some gait asymmetries occurred between the sound and prosthetic legs. No signs of mechanical failure were seen. Most design requirements were met. Areas for powered hip improvement include hip flexion range, mechanical advantage at high hip flexion, and device mass. Conclusions: The novel powered four-bar hip provides safe level-ground walking with a full-leg prosthesis simulator and is viable for future testing with hip-level amputees. Full article

Introduction: Chronic primary low back pain (CPLBP) is a prevalent condition in primary care and a leading cause of disability and absenteeism worldwide. Multidimensional approaches may be necessary to achieve physical and mental health benefits in individuals with CPLBP. Objective: The BACKFIT randomized controlled trial aims to evaluate the effectiveness of a multidimensional intervention—combining supervised exercise and mindfulness—on pain, physical fitness, mental health, and functional outcomes in individuals with CPLBP. Hypothesis: Both the supervised exercise program focused on motor control and trunk muscle strength (IG1) and the multidimensional intervention combining supervised exercise with mindfulness training (IG2) are expected to produce significant health improvements in individuals with CPLBP. It is further hypothesized that IG2 will yield greater improvements compared to IG1, both immediately post-intervention and at the two-month follow-up. Design: Randomized controlled trial. Setting: Virgen de las Nieves University Hospital, Granada (Spain). Participants: 105 individuals. Inclusion criteria: Previously diagnosed with CPLBP, aged ≥18 and ≤65 years, able to read and understand the informed consent, and able to walk, move, and communicate without external assistance. Exclusion criteria: serious lumbar structural disorders, acute or terminal illness, physical injury, mental illness, and medical prescriptions that prevent participation in the study. Intervention: Individuals will be randomly assigned to a supervised physical exercise group (2 days per week, 45 min per session), a multidimensional intervention group (same as supervised physical exercise group, and mindfulness 1 day per week, 2.5 h per session) or an active control group (usual care, 2 days per week, 45 min per session). The intervention will last 8 weeks. Main Outcome Measures: Primary outcome: pain threshold, perceived acute pain, and disability due to pain. Secondary measures: body composition, muscular fitness, gait parameters, device-measured physical activity and sedentary behavior, self-reported sedentary behavior, quality of life, pain catastrophizing, mental health, sleep duration and quality, and central sensitization. The groups will undergo pre-intervention, post-intervention, and a 2-month follow-up after a detraining period. Statistical Analysis: Both per-protocol and intention-to-treat approaches (≥70% attendance) will be used. Program effects will be assessed via one-way ANCOVA for between-group changes in primary and secondary outcomes. Conclusions: Given the complex nature of CPLBP, multidimensional approaches are recommended. If effective, this intervention may provide low-cost alternatives for health professionals. Full article

Human gait analysis is essential for clinical evaluation and rehabilitation monitoring, particularly in post-stroke individuals, where joint kinematics provide valuable insights into motor recovery. While optical motion capture (OMC) is the gold standard, its high cost and restricted use in laboratory settings limit its accessibility. This study aimed to evaluate the accuracy of REEV SENSE, a novel magnetometer-free inertial measurement unit (IMU), in capturing knee and ankle joint angles during overground walking in post-stroke individuals using assistive devices. Twenty participants with chronic stroke walked along a 10-m walkway with their usual assistive device (cane or walker), while joint kinematics were simultaneously recorded using OMC and IMUs. Agreement between the systems was assessed using the mean absolute error, root mean square error, 95% confidence intervals, and Pearson’s correlation coefficient. Knee angles measured with the IMUs showed a strong correlation with the OMC (r > 0.9) and low errors (MAE < 5°), consistent with clinical acceptability. Ankle angle accuracy was lower for participants using walkers, while knee measurements remained stable regardless of the assistive device. These findings demonstrate that REEV SENSE IMUs provide clinically relevant kinematic data and support their use as a practical wearable tool for gait analysis in real-world or remote clinical settings. Full article

Smart walking aids represent a growing trend in assistive technologies designed to support individuals with mobility impairments in their daily lives and rehabilitation. Previous research has introduced sensor-integrated systems that provide user feedback to enhance safety and functional mobility. However, a comprehensive overview of their technological and functional characteristics is lacking. To address this gap, this scoping review systematically mapped the current state of research in sensor-based walking aids, focusing on device types, sensor technologies, application contexts, target populations, and reported outcomes. In addition, integrated artificial intelligence (AI)-based approaches for functional support and health monitoring were examined. Following PRISMA-ScR guidelines, 35 peer-reviewed articles were identified from three databases: ACM Digital Library, IEEE Xplore, and Web of Science. Extracted data were thematically analyzed and synthesized across device types (e.g., walking canes, crutches, walkers, rollators) and use cases, including gait training, fall prevention, and daily support. Findings show that, while many prototypes show promising features, few have been evaluated in clinical settings or over extended periods. A lack of standardized methods for sensor location assessment, often the superficial implementation of feedback modalities, and limited integration with other assistive technologies were identified. In addition, system validation and user testing lack consensus, with few long-term studies and often incomplete demographic data. Diversity in data communication approaches and the heterogeneous use of AI algorithms were also notable. The review highlights key challenges and research opportunities to guide the future development of intelligent, user-centered mobility systems. Full article