Search Results (1,140)

Robot-based rehabilitation emerges as a promise to enhance mobility and improve the rehabilitation outcomes in children with cerebral palsy. The study aimed to evaluate the preliminary effects of a robot-based therapy program with Atlas-2030 on spatiotemporal gait parameters, pelvis kinematics, gross-motor function, quality of life, and joint range-of-motion in children with cerebral palsy receiving care at a specialized rehabilitation center. This is a single-arm, institution-based, quantitative, longitudinal, pilot study with repeated measures. Sixteen sessions of a robot-based therapy program with the Atlas-2030 wearable exoskeleton were applied to all the children from APAC-IAP in Mexico City with cerebral palsy. Pre-intervention, after eight and sixteen sessions, the GMFM-66, the CP QoL-Child, and gait analysis were performed. The results suggest that an Atlas-2030 robot-based therapy program combined with therapeutic stimulation exhibited better scores on the modified Ashworth scale: hip flexors and extensors: 2.0(1.0), knee flexors and extensors: 2.0(2.9), p > 0.0167, and experience enhanced range of motion in hip flexion: 122.5(5) deg, and extension: 11(5) deg and knee extension: 0(5) deg, p < 0.0167, pelvis rotation approached zero on both sides (left: −1.99(14.04, right: 2.22(13.43), p > 0.0167) reducing the difference in laterality, inducing physiological muscle activation patterns, and higher scores in quality of life regarding well-being and acceptance: 17(1.0) and emotional well-being and self-esteem: 14.5 (1.0), p > 0.0167. The limitations of this study include the following: recruitment from a single specialty care center, the absence of a control group, and the adjusted significance level of p < 0.0167, which may lead to false negatives. Full article

Background: Neurological disorders are the leading cause of disability, affecting over three billion people worldwide. Amyotrophic lateral sclerosis (ALS) is among the most feared and uniformly fatal neurodegenerative diseases, with no therapy capable of restoring lost function. Methods: We report the first application of therapeutic fever to ALS using Computerized Brain-Guided Intelligent Thermofebrile Therapy (CBIT²). This fully noninvasive treatment, delivered through an FDA-approved computerized platform, digitally reengineers the 1927 Nobel Prize-recognized malarial fever therapy into a modern treatment guided by the Brain–Eyelid Thermoregulatory Tunnel. CBIT² induces therapeutic fever through synchronized hypothalamic feedback, activating heat shock proteins, which are known to restore proteostasis and neuronal function. Case presentation: A 56-year-old woman was diagnosed with progressive ALS at the Mayo Clinic, with electromyography (EMG) demonstrating fibrillation and fasciculation indicative of denervation corroborated by neurological and MRI findings; the patient was informed that she had an expected survival of three to five years. A neurologist from Northwestern University confirmed the diagnosis and thus maintained the patient on FDA-approved ALS drugs (riluzole and edaravone). Her condition rapidly worsened despite pharmacological treatment, and she underwent CBIT², resulting in (i) electrophysiological reversal with complete disappearance of denervation; (ii) biomarker correction, including reductions in neurofilament and homocysteine, IL-10 normalization (previously linked to mortality), and robust HSP70 induction; (iii) restoration of gait, swallowing, respiration, speech, and cognition; (iv) reconstitution of tongue structure; and (v) return to complex motor tasks, including golf, pickleball, and swimming. Discussion: This case provides the first documented evidence that ALS can be reversed through digitally reengineered fever therapy aligned with thermoregulation, which induces heat shock response and upregulates heat shock proteins, resulting in the patient no longer meeting diagnostic criteria for ALS and discontinuation of ALS-specific medications. Beyond ALS, shared protein-misfolding pathology suggests that CBIT² may extend to Alzheimer’s, Parkinson’s, and related disorders. By modernizing this Nobel Prize-recognized therapeutic principle with computerized precision, CBIT² establishes a framework for large-scale clinical trials. A century after fever therapy restored lost brain function and so decisively reversed dementia paralytica such that it earned the 1927 Nobel Prize in Medicine, CBIT² now safely harnesses the therapeutic power of fever through noninvasive, intelligent, brain-guided thermal modulation. Amid a global brain health crisis, fever-based therapies may offer a path to preserve thought, memory, movement, and independence for the more than one-third of humanity currently affected by neurological disorders. Full article

Background/Objectives: Functional flat foot (FF) is associated with altered lower limb biomechanics, leading to inefficient load transfer and potential overuse injuries. Customized orthoses, such as 3D-printed insoles and toe spreaders, may mitigate these deficits, but their combined biomechanical and neuromuscular effects remain unclear. The current study investigated the immediate effects of 3D-printed arch support insoles (SI) and toe spreaders (Toe) and their combination (SI+Toe) on gait pattern, center of force (COF), ankle alignment, and lower limb muscle activation in young adults with FF. Methods: Ten FF individuals and ten matched controls performed level walking under four randomized conditions: shoe-only, SI, Toe, and SI+Toe. Gait was analyzed using OptoGait, COF trajectory via F-Scan, ankle angles using Kinovea, and muscle activity (semitendinosus, biceps femoris, tibialis anterior, peroneus longus, gastrocnemius, and soleus) via surface EMG. Results: Compared to controls, FF individuals exhibited medial COF deviation, increased ankle eversion, and altered muscle activity. In the FF group, SI+Toe reduced medial COF deviation, decreased eversion, and prolonged foot flat while shortening the propulsive phase. Semitendinosus and tibialis anterior activity increased under SI+Toe, while gastrocnemius and soleus remained reduced during propulsion. Conclusions: The combined utilization of 3D-printed insoles and toe spreaders produced immediate measurable improvements in foot alignment and muscle activity patterns in FF individuals. These findings support that integrating customized orthotic designs with toe spreader elements may provide a practical, non-invasive approach for improving lower limb biomechanics. Such strategies may help improve foot mechanics and reduce compensatory muscle activation in a clinical setting. Full article

Background/Objectives: Apathy is an independent risk factor for dementia and motoric–cognitive risk syndrome (MCR), a predementia syndrome characterized by slow gait and subjective cognitive complaints (SCCs). Our objective is to assess the cross-sectional association of apathy with gait velocity, SCC, and MCR in a community-based cohort of older adults. Methods: A cross-sectional survey of N = 746 community-dwelling older adults (≥60 years of age) enrolled in the Kerala Einstein Study. Apathy was measured using the Apathy Evaluation Scale (AES). Participants were stratified by AES tertile to evaluate bivariate associations, and multivariate linear and logistic regression models were used to assess the relationship of apathy with gait velocity, SCC, and MCR. Results: Compared with participants in the lowest apathy tertile, those in the highest tertile were significantly older, less physically active, and had slower gait. High-apathy participants also had lower Addenbrooke’s Cognitive Examination scores (79.4 vs. 84.5, p < 0.001) and higher depression scores (9.3 vs. 5.4, p < 0.001). Apathy was associated with slower gait velocity (β = −3.465, p ≤ 0.002), but this relationship was no longer significant after adjusting for ACE score. Apathy and SCC were significantly associated in adjusted models (p < 0.001). Although participants with MCR had higher levels of apathy compared to those without MCR (34.6 vs. 31.4, p < 0.01), prevalent MCR and apathy were not significantly associated in regression models. Conclusions: Among community-dwelling older adults in Kerala, apathy is associated with slower gait and more severe subjective cognitive complaints but not cross-sectional MCR prevalence. These findings suggest that apathy may serve as an early risk factor in dementia pathogenesis across diverse patient populations, warranting further longitudinal investigation. Full article

This work presents a time-domain approach for characterizing the Ground Reaction Forces (GRFs) exerted by a pedestrian during running. It is focused on the vertical component, but the methodology is adaptable to other components or activities. The approach is developed from a statistical perspective. It relies on experimentally measured force-time series obtained from a healthy male pedestrian at eight step frequencies ranging from 130 to 200 steps/min. These data are subsequently used to build a stochastic data-driven model. The model is composed of multivariate normal distributions which represent the step patterns of each foot independently, capturing potential disparities between them. Additional univariate normal distributions represent the step scaling and the aerial phase, the latter with both feet off the ground. A dimensionality reduction procedure is also implemented to retain the essential geometric features of the steps using a sufficient set of random variables. This approach accounts for the intrinsic variability of running gait by assuming normality in the variables, validated through state-of-the-art statistical tests (Henze-Zirkler and Shapiro-Wilk) and the Box-Cox transformation. It enables the generation of virtual GRFs using pseudo-random numbers from the normal distributions. Results demonstrate strong agreement between virtual and experimental data. The virtual time signals reproduce the stochastic behavior, and their frequency content is also captured with deviations below 4.5%, most of them below 2%. This confirms that the method effectively models the inherent stochastic nature of running human gait. Full article

Physical activity trackers are promising for monitoring physical activity in patients after surgery. However, the remobilization of patients following surgery is characterized by low-impact movements. It is often unclear to health care professionals whether a specific physical activity tracker is able to correctly detect steps in this patient population. A protocol is proposed, which allows health care professionals to familiarize themselves with the detection limits of a physical activity tracker. The professional should walk 20 steps under varying conditions mimicking the situation of patients after knee surgery. Conditions vary in step size, walking direction, use of walking aids, and footwear. The protocol was tested in a group of 14 health care professionals. Participants wore four trackers simultaneously, representing different modalities and different locations. For two trackers, the participants could experience a variation in the detection limits across the different conditions. On one hand, the within-participant reproducibility was substantial on average, though the between-participant reproducibility was only fair. On the other hand, experiencing incorrect step counts varied highly across and within participants. In conclusion, the self-familiarization of health care professionals with the detection limits of a physical activity tracker using specific protocols seems to be a feasible approach. Such protocols can provide valuable tools for facilitating the use of physical activity trackers in clinical applications. Additional research may allow for further refinement of the protocol to generate input that is more comparable across participants and closer to the gait of patients. Full article

Background and Objectives: Chronic low back pain (CLBP) is associated with altered neuromuscular control. Dynamic Neuromuscular Stabilization (DNS) targets core–limb coordination; however, its specific impact on lower-limb electromyographic (EMG) activity during gait remains unclear. Materials and Methods: Fifty-five young adults with non-specific CLBP (pain ≥ 3 months with no identifiable specific pathology) completed the trial (overall mean age 23.7 ± 1.3 years). Participants were randomized to an 8-week DNS program or a control. Pre-/Post-intervention surface EMG during gait and clinical outcomes (VAS, ODI) were assessed. Results: Compared with control, DNS showed lower adjusted Post-test VAS (3.08 ± 0.25 vs. 6.13 ± 0.24; ηp² = 0.596) and ODI (15.73 ± 1.55% vs. 34.36 ± 1.52%; ηp² = 0.579). Directionally, DNS was associated with phase-specific EMG modulation: tibialis anterior during mid-stance was lower (ηp² = 0.137), rectus femoris during push-off was lower (ηp² = 0.119), biceps femoris during push-off was lower (ηp² = 0.168), and vastus medialis at heel-strike was higher (ηp² = 0.077) relative to control. Other muscle–phase pairs showed no adjusted between-group differences. Conclusions: An 8-week DNS program was associated with clinically meaningful reductions in pain and disability and with phase-specific changes in lower-limb EMG during gait. These findings support DNS as a promising rehabilitation option for young adults with CLBP; confirmation in larger trials with active comparators is warranted. Full article

Lameness remains a significant welfare and productivity challenge in dairy farming, often underdiagnosed due to the limitations of conventional detection methods. Unlike most previous approaches to lameness detection that rely on a single-sensor or gait-based measurement, this study integrates four complementary data domains—behavioural, physiological, biochemical, and milk composition parameters—collected from 272 dairy cows during early lactation to enhance diagnostic accuracy and biological interpretability. The main objective of this study was to evaluate and compare the diagnostic classification performance of multiple machine learning (ML) algorithms trained on multimodal data collected at the time of clinical lameness diagnosis during early lactation, and to identify the most influential physiological and biochemical traits contributing to classification accuracy. Specifically, six algorithms—random forest (RF), neural network (NN), Ensemble, support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR)—were assessed. The input dataset integrated physiological parameters (e.g., water intake, body temperature), behavioural indicators (rumination time, activity), blood biochemical biomarkers (non-esterified fatty acids (NEFA), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT)), and milk quality traits (fat, protein, lactose, temperature). Among all models, RF achieved the highest validation accuracy (97.04%), perfect validation specificity (100%), and the highest normalized Matthews correlation coefficient (nMCC = 0.94), as determined through Monte Carlo cross-validation on independent validation sets. Lame cows showed significantly elevated NEFA and body temperatures, reflecting enhanced lipid mobilization and inflammatory stress, alongside reduced water intake, milk protein, and lactose content, indicative of systemic energy imbalance and impaired mammary function. These physiological and biochemical deviations emphasize the multifactorial nature of lameness. Linear models like LR underperformed, likely due to their inability to capture the non-linear and interactive relationships among physiological, biochemical, and milk composition features, which were better represented by tree-based and neural models. Overall, the study demonstrates that combining sensor data with blood biomarkers and milk traits using advanced ML models provides a powerful, objective tool for the clinical classification of lameness, offering practical applications for precision livestock management by supporting early, data-driven decision-making to improve welfare and productivity on dairy farms. Full article

Background: The paralyzed ankles of stroke patients show reduced range of motion, muscle tightness, and joint stiffness, further impeding their ability to maintain balance and walk properly. This study aimed to investigate the effects of a combined joint mobilization and active stretching intervention on ankle stiffness, balance, and gait in patients with stroke. Methods: In this study, 24 stroke patients were assigned to two groups of 12 each: the control group (general physical therapy) and the experimental group (joint mobilization technique and active stretching exercise). All interventions were conducted for 30 min a day, 3 times a week, for a total of 4 weeks. Tension and stiffness of the gastrocnemius and tibialis anterior muscles were measured using Myoton^®PRO. Balance was evaluated using BioRescue, the Berg Balance Scale (BBS), and the Timed-Up and Go tests (TUG). All measurements were evaluated before the start of the intervention and after four weeks. Results: The muscle tone and stiffness of the medial and lateral gastrocnemius and tibialis anterior muscles of the experimental group were significantly improved compared with those of the control group. The experimental group had significantly increased moving areas in all directions and BBS scores compared with the control group. The experimental group showed a significant decrease in the time spent on the TUG test compared with the control group. Conclusions: We found that joint mobilization combined with active stretching intervention was more effective than general physical therapy in improving ankle joint movement, balance, and gait ability. Full article

The standard motion analysis systems are limited to laboratory settings; therefore, an individual’s gait may not be realistic, as it is removed from the day-to-day environment in which people ambulate. The modern world and advanced technologies have driven portable, affordable, and wearable sensors for real-world gait assessment that can be used outside the laboratory and during day-to-day activities. Wearable sensors offer a promising solution; however, despite that, the reliability of many wearable systems, especially under unsupervised and real-world-like conditions, remains insufficiently validated. This study aimed to establish intra- and inter-rater reliability of the inertial sensors as a tool used in gait analysis in a quasi-real-world environment. Ninety-eight healthy participants (52% females) aged 19–33 years took part in this reliability study. The research procedures included two separate measurements of gait analysis at participants’ preferred walking speed, conducted by two raters assessing selected spatiotemporal parameters. The reliability was calculated using intraclass correlation coefficients (ICC), and the bias was assessed using the Bland–Altman method. The analysis of intraclass correlation coefficients (ICC) revealed excellent, or near-excellent, reliability for walking speed, cadence, and stride length between raters (ICC = 0.932–0.941, 0.950–0.957, and 0.916–0.938, respectively) and between measurements (ICC = 0.916–0.928, 0.934–0.938, and 0.888–0.906, respectively). Bland–Altman plots confirmed minimal systematic bias for both inter- and intra-rater assessments, with differences in walking speed below 0.038 km/h, cadence below 0.283 steps/min, and stride length below 0.827 cm. The examined sensors are reliable tools for walking speed, cadence, and stride length in a quasi-real-world environment gait assessment. Future studies should include gait analysis involving random path and direction changes, turns, uneven or slippery surfaces, and natural environments. Additionally, research should consider individuals ambulating with various walking aids, or those with unilateral disorders, such as stroke. Full article

Background and Objectives: Parkinson’s disease is the second most common neurodegenerative disorder after Alzheimer’s disease, and its incidence increases with age, being particularly high in people over 70 years of age. For patients with this condition, medical rehabilitation can have a profound impact, helping to improve mobility, preserve functional autonomy, and enhance quality of life. Focal vibration stimulation is a promising, well-tolerated, and easy-to-apply method with potential to facilitate motor activity and support the motor learning process, making it also useful in gait reeducation for patients with various neurological conditions. This systematic review aims to analyze the existing scientific evidence on the effectiveness of focal muscle vibration therapy in managing symptoms of Parkinson’s disease. Materials and Methods: This systematic review of the literature was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the protocol was registered in PROSPERO under the protocol registration number CRD420251120737. Searches were conducted in five databases (PubMed, PEDro, ScienceDirect, Cochrane Library, Web of Science). The selection criteria targeted original clinical studies, published in English between 2010 and the present, that investigated focal muscle vibration therapy in patients with Parkinson’s disease and were fully available, excluding review papers, meta-analyses, books, and articles inaccessible in full text. Version 2 of the Cochrane risk-of-bias tool for randomized trials (RoB 2) was used to assess the quality of the included studies. Results: The results of the studies were interpreted individually for each study, and the main information was synthesized in a comparative table to facilitate analysis. The final analysis included five studies that investigated the effects of focal muscle vibration in patients with Parkinson’s disease. The results suggest that this form of stimulation may offer benefits for patients with gait disorders, improving balance and stability. Among the study’s limitations are the small number of included articles (n = 5) and the restriction to English-language publications, which may limit the applicability of the results. Conclusions: Given the promising results, focal muscle vibration therapy could represent a useful option in the management of Parkinson’s disease. Integrating this method into rehabilitation plans could bring significant functional benefits, but further studies are needed to confirm its long-term effectiveness and to establish standardized application protocols. No external funding was received for the conduct of this review. Full article

Children with intoeing gait are at increased risk of tripping and consequent injury, reduced mobility, and psychological issues. Quantification of tripping is needed outside the gait lab during daily life for improved clinical assessment and treatment evaluation and to enrich the database for artificial intelligence (AI) learning. This paper presents the development of a low-cost, wearable tripping monitor to log a child’s Tripping Hazard Events (THEs) and steps taken during two weeks of everyday activity. A combination of sensors results in a high probability of THE detection, even during rapid gait, while guarding against false positives and minimizing power and therefore monitor size. A THE is logged when the feet come closer than a predefined threshold during the intoeing foot swing phase. Foot proximity is determined by a Radio Frequency Identification (RFID) reader in “sniffer” mode on the intoeing foot and a target of passive Near-Field Communication (NFC) tags on the contralateral foot. A Force Sensitive Resistor (FSR) in the intoeing shoe sets a time window for sniffing during gait and enables step counting. Data are stored in 15 min epochs. Laboratory testing and an IRB-approved human participant study validated system performance and identified the need for improved mechanical robustness, prompting a redesign of the monitor. A custom Python (version 3.10.13)-based Graphical User Interface (GUI) lets clinicians initiate recording sessions and view time records of THEs and steps. The monitor’s flexible design supports broader applications to real-world activity detection. Full article

This paper presents a lower-limb hip exoskeleton system integrated with an adjustable-stiffness flexible energy-storage module for three-dimensional gait correction. This system features a modular flexible mechanical design and a stiffness-gain scheduled PID control strategy for dynamic, personalized assistance. Based on biomechanical analysis of the hip joint, a 3D gait correction model was constructed targeting impairments in flexion, abduction, and adduction. The control strategy adjusts system stiffness in real-time according to gait phase and user-specific parameters. Experimental results demonstrated that the exoskeleton effectively reduced joint trajectory variability (22% decrease in standard deviation of hip flexion angle) and improved muscle activation patterns (21.4% increase in rectus femoris activity), thereby enhancing gait symmetry and stability. This study offers a feasible mechatronic solution for pathological gait correction with promising clinical applicability. Full article

Wearable sensors have become valuable tools for assessing gait in both laboratory and free-living environments. However, detection of walking in free-living environments remains challenging, especially in clinical populations. Machine learning models may offer more robust gait identification, but most are trained on healthy participants, limiting their generalizability to other populations. To extend a previously validated machine learning model, an updated model was trained using an open dataset (PAMAP2), before progressively including training datasets with additional healthy participants and a clinical osteoarthritis population. The performance of the model in identifying walking was also evaluated using a frequency-based gait detection algorithm. The results showed that the model trained with all three datasets performed best in terms of activity classification, ultimately achieving a high accuracy of 96% on held-out test data. The model generally performed on par with the heuristic, frequency-based method for walking bout identification. However, for patients with slower gait speeds (<0.8 m/s), the machine learning model maintained high recall (>0.89), while the heuristic method performed poorly, with recall as low as 0.38. This study demonstrates the enhancement of existing model architectures by training with diverse datasets, highlighting the importance of dataset diversity when developing more robust models for clinical applications. Full article

Improvements in exoskeletons and robotic systems are gaining increasing attention because of their potential to improve neuromuscular rehabilitation and assist people in their daily activities, significantly improving their quality of life. However, the high cost and complexity of current devices limit their accessibility to many patients and rehabilitation centers. This work presents the design and development of a low-cost walking exoskeleton, conceived to offer an affordable and simple alternative. The system uses a compact eight-bar mechanism with only one degree of freedom per leg, drastically simplifying motorization and control. The exoskeleton is customized for each patient using a synthesis process based on evolutionary algorithms to replicate a predefined gait. Despite the reduced number of degrees of freedom, the resulting mechanism perfectly matches the desired ankle and knee trajectories. The device is designed to be lightweight and affordable, with components fabricated using 3D printing, standard aluminum bars, and one actuator per leg. A working prototype was fabricated, and its functionality and gait accuracy were confirmed. Although limited to a predefined gait pattern and requiring crutches for balance and steering, this exoskeleton represents a promising solution for rehabilitation centers with limited resources, offering accessible and effective gait assistance to a wider population. Full article