Search Results (4,225)

Background: Autosomal recessive inherited pathogenetic variants in the histidine triad nucleotide-binding protein 1 (HINT1) gene are responsible for an axonal Charcot-Marie-Tooth neuropathy associated with neuromyotonia, a phenomenon resulting from peripheral nerve hyperexcitability that causes a spontaneous muscle activity such as persistent muscle contraction, impaired relaxation and myokymias. Methods: Herein, we describe two brothers in whom biallelic HINT1 variants were identified following a multidisciplinary approach. Results: The younger brother came to our attention for clinical evaluation of moderate intellectual disability, language developmental delay, and some behavioral issues. His elder brother presented mild intellectual disability, hyperactivity, tiptoe walking, and gait ataxia. At first evaluation, motor impairment with frequent falls, pes cavus, and distal hyposthenia with reduced osteotendinous reflexes were found in both. Grip myotonic phenomenon was also noted. Blood tests revealed mildly elevated creatine kinase, and neurophysiology investigations revealed predominantly axonal polyneuropathy. Muscle MRI highlighted fibro-adipose infiltration, prevalent in the lower limbs. Gene panel testing detected a heterozygous HINT1 variant (c.355C>T/p.(Arg119Trp)) on the paternal allele. A further in-depth analysis using Integrative Genomics Viewer and Optical Genome Mapping led us to identify an additional variant in HINT1 represented by a complex rearrangement located in the region 5′UTR-exon 1-intron 1, not previously described. Conclusions: This complex rearrangement could have been overlooked if the clinical picture had not been evaluated as a whole (from a clinical, neurophysiological, and neuroimaging point of view). Neuropsychiatric manifestations (intellectual disability, hyperactivity, etc.) are part of the picture of HINT1-related neuromyotonia. Full article

Unstable gait increases the risk of falls, posing a significant danger, particularly for frail older adults. The margin of stability (MoS) is a quantitative index that reflects the risk of falling due to postural imbalance in both the anterior-posterior and mediolateral directions during walking. Although MoS is a reliable indicator, its computation typically requires specialized equipment, such as motion capture systems, limiting its application to laboratory settings. To address this limitation, we propose a method for estimating MoS using time-series data from the translational and angular velocities of a single body segment—the pelvis. By applying principal motion analysis to process the multivariate time-series data, we successfully estimated MoS. Our results demonstrate that the estimated MoS in the mediolateral direction achieved an RMSE of 0.88 cm and a correlation coefficient of 0.72 with measured values, while in the anterior-posterior direction, the RMSE was 0.73 cm with a correlation coefficient of 0.87. These values for the mediolateral direction are better than those obtained in previous studies using only the three translational velocity components of the pelvis, whereas the values for the anterior direction are comparable to previous approaches. Our findings suggest that MoS can be reliably estimated using six-axial kinematic data of the pelvis, offering a more accessible method for assessing gait stability. Full article

Background: The aim of the present study was to analyse the association between neuroticism (one of the Big Five personality traits) and the most common secondary sensorimotor complications occurring in patients after spinal cord injury (SCI), i.e., muscle spasticity (hypertonia) and pain, and to investigate the associations between neuroticism and the effects of conventional rehabilitation (dynamic parapodium) and those using robotic-assisted gait training (RAGT) in this group of patients. In addition, the association of neuroticism with self-efficacy, personal beliefs about pain control, and adopted coping strategies among SCI patients was analysed. These data can be used as a reference for designing effective forms of therapy and support dedicated to this group of patients. Methods and procedures: Quantitative analysis included 110 patients after SCI. The participants were divided by simple randomisation into a rehabilitation group with RAGT and a rehabilitation group with dynamic parapodium therapy (DPT). The following survey instruments were used for data collection: Revised NEO Personality Inventory (NEO-PI-R); Ashworth Scale; the Spinal Cord Independence Measure III (SCIM III); the Walking Index for Spinal Cord Injury II (WISCI-II); the American Spinal Injury Association Impairment Scale (AIS); the Pain Coping Strategies Questionnaire—CSQ; and the Beliefs about Pain Control Questionnaire—BPCQ. Outcomes and results: analyses showed a positive association between neuroticism and spastic tension (rho = 0.39; p < 0.001). Conclusions and implications: the study showed that a high level of neuroticism correlates with a higher level of spasticity, but no such correlation was observed for pain. Additionally, the study did not show a significant correlation between neuroticism and rehabilitation outcome depending on the rehabilitation modality (RAGT vs. DPT). The results underline the importance of carrying out a psychological diagnosis of patients to provide therapeutic support in the rehabilitation process. Full article

Patients with Parkinson’s disease (PD) can present several biomechanical alterations, such as tremors, rigidity, bradykinesia, postural instability, and gait alterations. The Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) has a good reputation for uniformly evaluating motor and non-motor aspects of PD. However, motor clinical assessment depends on visual observations, which are mostly qualitative, with subtle differences not recognized. Many works have examined evaluations and analyses of these biomechanical alterations. However, there are no reviews on this topic. This paper presents a scoping review of computer models based on expert knowledge and machine learning (ML). The eligibility criteria and sources of evidence are represented by papers in journals indexed in the Journal Citation Report (JCR), and this paper analyzes the data, methods, results, and application opportunities in clinical environments or as support for new research. Finally, we analyze the results’ explainability and the acceptance of such systems as tools to help physicians, both now and in future contributions. Many researchers have addressed PD biomechanics by using explainable artificial intelligence or combining several analysis models to provide explainable and transparent results, considering possible biases and precision and creating trust and security when using the models. Full article

Background/Objectives: Dementia involves the loss of cognitive abilities and impairs functional abilities in daily life. In motor imagery (MI) techniques, motor acts are mentally rehearsed without any overt body movements. The purpose of the randomized controlled trial was to examine the effects of MI on the motor function of older adults with dementia. Methods: Overall, 160 participants (43 men, 117 women, MMSE M = 23.20, SD = 0.15) from an Athens Day Care Center of the Alzheimer Association were randomized to (a) the MI and exercise group (experimental group) (n = 55), (b) the only exercise group (1st control group) (n = 52) and (c) the neither MI nor exercise group (2nd control group) (n = 53). The exercise session comprised 24 physiotherapy exercise sessions, lasting 45 min each, twice a week for 12 weeks. The exercises were selected from the Otago Exercise Program. Three assessments were performed: (a) one week prior to the program, (b) at one and a half months and (c) after the program. The experimental group performed a 30-minute MI with exercise program content after the end of every physiotherapy exercise session. The Multidirectional Reach Test, Five Times Sit-to-Stand Test (FTSST), Timed Up and Go test (TUG), Functional Gait Assessment (FGA) and Berg Balance Scale (BBS) were used to assess participants’ balance and functional status. Results: In the intention to treat analysis (18 participants dropped out), the 3 × 3 repeated measures ANOVA indicated statistically significant results between the three groups on (a) the TUG (F = 3.06, df (2), p = 0.04), (b) the FTSST (F = 3.00, df (2), p = 0.05), (c) the forward direction test (F = 4.14 df (2), p = 0.02), the lateral right and the lateral left direction tests (F = 3.90, df (2), p = 0.02 and F = 7.87, df (2), p = 0.00, respectively), and (d) the FGA (F = 4.35, df (2), p = 0.01). The Friedman test showed significant statistical significant differences among the three groups for BBS (X² = 7.62, df = 2, p = 0.22), and an effect size of partial η² coefficient for F-tests was found. Post hoc comparisons using a Bonferroni test for ANOVA and Wilcoxon test for Friedman indicated that the mean scores for the experimental group and the 1st control were significantly better than the 2rd control group in many dependent variables. Conclusions: The study showed a positive effect of MI on balance and the functional status of older adults with early stages of dementia with possible beneficial effects on maintaining independence and reducing physical decline. Full article

Crouch gait, also referred to as flexed knee gait, is an abnormal walking pattern, characterized by an excessive flexion of the knee, and sometimes also with anomalous flexion in the hip and/or the ankle, during the stance phase of gait. Due to the fact that the amount of clinical data related to crouch gait are scarce, it is difficult to find studies addressing this problem from a data-based perspective. Consequently, in this paper we propose a gait recognition strategy using synthetic data that have been obtained using a polynomial based-generator. Furthermore, though this study, we consider datasets that correspond to different levels of crouch gait severity. The classification of the elements of the datasets into the different levels of abnormality is achieved by using different algorithms like k-nearest neighbors (KNN) and Naive Bayes (NB), among others. On the other hand, to evaluate the classification performance we consider different metrics, including accuracy (Acc) and F measure (FM). The obtained results show that the proposed strategy is able to recognize crouch gait with an accuracy of more than 92%. Thus, it is our belief that this recognition strategy may be useful during the diagnosis phase of crouch gait disease. Finally, the crouch gait recognition approach introduced here may be extended to identify other gait abnormalities. Full article

To suppress pedestrian positioning drift, a velocity constraint commonly known as zero-velocity update (ZUPT) is widely used. However, it cannot correct the error in the non-zero velocity interval (non-ZVI) or observe heading errors. In addition, the positioning accuracy will be further affected when a velocity error occurs in the ZVI (e.g., foot tremble). In this study, the foot, ankle, and shank were regarded as a triangular structure. Consequently, an angle constraint was established by utilizing the sum of the internal angles. Moreover, in contrast to the traditional ZUPT algorithm, a velocity constraint method combined with Coriolis theorem was constructed. Magnetometer measurements were used to correct heading. Three groups of experiments with different trajectories were carried out. The ZUPT method of the single inertial measurement unit (IMU) and the distance constraint method of dual IMUs were employed for comparisons. The experimental results showed that the proposed method had high accuracy in positioning. Furthermore, the constraints built by the lower limb structure were applied to the whole gait cycle (ZVI and non-ZVI). Full article

Background/Purpose: Fractal gait patterns have been shown to be modifiable, but the extent to which they are retained and transferred to new contexts is relatively unknown. This study aimed to close those gaps by enrolling participants (N = 23) in a seven-day fractal gait training program. Methods: Building on related work, the fractal gait training occurred on a treadmill over a 10-min period. Before and after the treadmill training, each participant walked for 10 min overground without the fractal stimulus used during training. The daily post-test was used to examine immediate retention and transfer of the fractal gait patterns from the treadmill to overground. The pre-tests in days 2–7 were used to examine the extent to which the fractal gait patterns from the preceding day were retained 24 h later. Inertial measurement units were used to measure stride time so a consistent measurement method could be employed in the treadmill and overground phases of the study. Results: Our results showed that multiple days of treadmill training led to elevated fractal patterns, indicating a positive training effect. However, the positive training effect observed on the treadmill did not transfer to overground walking. Conclusions: Collectively, the data show that fractal patterns in gait are modifiable across multiple days of training, but the transferability of these patterns to new contexts needs to be further explored. Full article

Postbiotics are emerging as potential functional ingredients for companion animal diets. This study aimed to determine if a Saccharomyces cerevisiae-based postbiotic can alter cytokine and stress responses to exercise and transport stress in adult Labrador Retrievers. Dogs received 15 g ground corn germ (Control, n = 12), 7.5 g postbiotic (Low, n = 12), or 15 g postbiotic (High, n = 12), daily for 63 days. Exercise was twice weekly for 7 weeks, and a single transport per dog occurred in week 8. Fecal inflammatory biomarkers, serum chemistries, and complete blood counts were assessed at the beginning and end of the study. Serum cytokines were quantified before and 18–20 h after the first and last exercise runs. Gait analysis was assessed before and 24 h after the first and final runs. Saliva cortisol was measured before and after transportation. Treatment did not affect blood chemistries, gait, fecal biomarkers, or saliva cortisol (p ≥ 0.19). Eosinophils increased slightly in Controls (p = 0.01), though remained below 0.80 × 10⁹ cells/L. Most cytokines were unaffected by treatment (p ≥ 0.15), but there were minor changes in circulating monocyte chemoattractant protein-1 (p = 0.01) and IL-8 over time at the initial run (p = 0.03) and IL-10 in males (p = 0.02) in the Low dose dogs. The High dose decreased Blautia (p = 0.04) slightly and tended to decrease Fusobacterium abundances (p = 0.07). The Low dose tended to increase Clostridium hiranonis (p = 0.07) slightly. The tested S. cerevisiae postbiotic produced small changes in immune function and gut microbial species in dogs. Full article

Reduced walking endurance is common in people with multiple sclerosis (PwMS), leading to reduced social participation and increased fall risk. This highlights the importance of identifying which gait aspects should be mostly targeted by rehabilitation to maintain/increase walking endurance in this population. A total of 56 PwMS and 24 healthy subjects (HSs) executed the 6 min walk test (6 MWT), a clinical measure of walking endurance, wearing three inertial sensors (IMUs) on their shanks and lower back. Five IMU-based digital metrics descriptive of different gait domains, i.e., double support duration, trunk sway, gait regularity, symmetry, and local dynamic instability, were computed. All metrics demonstrated moderate–high ability to discriminate between HSs and PwMS (AUC: 0.79–0.91) and were able to detect differences between PwMS at minimal (PwMS_mFR) and moderate–high fall risk (PwMS_FR). Compared to PwMS_mFR, PwMS_FR walked with a prolonged double support phase (+100%), larger trunk sway (+23%), lower stride regularity (−32%) and gait symmetry (−18%), and higher local dynamic instability (+24%). Normative cut-off values were provided for all metrics to help clinicians in detecting abnormal scores at an individual level. The five metrics, entered into a multiple linear regression model with 6 MWT distance as the dependent variable, showed that gait regularity and the three metrics most related to dynamic balance (i.e., double support duration, trunk sway, and local dynamic instability) were significant independent contributors to 6 MWT distance, while gait symmetry was not. While double support duration and local dynamic instability were independently associated with walking endurance in both PwMS_mFR and PwMS_FR, gait regularity and trunk sway significantly contributed to 6 MWT distance only in PwMS_mFR and PwMS_FR, respectively. Taken together, the present results allowed us to provide hints for tailored rehabilitation exercises aimed at specifically improving walking endurance in PwMS. Full article

Background: Patellofemoral pain is one of the most common injuries in recreational runners, with significant implications for dynamic knee valgus. The knee valgus angle can be corrected surgically or with a more conservative non-operative approach. Increasing running cadence may be an effective biomechanical gait retraining intervention to reduce knee valgus and thus patellofemoral pain. The primary purpose of this study was to examine if an increase in cadence could change the knee valgus angle. Methods: Ten asymptomatic recreational runners were recorded running on a treadmill during control and experimental intervals. Each interval lasted five minutes, and participants ran at 100% and 110% of their baseline cadence. Peak angles of knee valgus were compared between both intervals using the video analysis software application Dartfish Express. A paired sample, a two-tailed t-test, was used to determine the significant difference between bilateral frontal plane knee angle measurements during both intervals. Results: The average decrease in knee valgus measured in control versus experimental intervals was 2.23° for the right leg and 2.05° for the left leg, with a significance of p < 0.001 and p < 0.001, respectively. Conclusion: The results indicated a statistically significant decrease in angles of dynamic knee valgus, attributable to increased cadence. These changes in knee valgus angle are likely to have a positive impact on preventing and reducing pain associated with PFP. Full article

Background: Osteoarthritis (OA) is a common degenerative joint condition caused by an imbalance between cartilage synthesis and degradation, which disrupts joint homeostasis. This study investigated the anti-inflammatory and joint-improving effects of Pinus densiflora root extract powder (PDREP) in both in vitro and in vivo OA models. Methods/Results: In an in vitro OA model, in which SW1353 human chondrosarcoma cells were treated with interleukin (IL)-1β, PDREP treatment significantly reduced the mRNA levels of matrix metalloproteinase (MMP)-1, MMP-3, and MMP-13 while enhancing collagen type II alpha 1 (Col2a1) mRNA level, and decreased IL-6 and prostaglandin E2 (PGE2) levels. In addition, PDREP inhibited the phosphorylation of extracellular signal-regulated kinases (ERK), c-Jun N-terminal kinase (JNK), p38, nuclear factor-kappa B (NF-κB), and the expression of inducible nitric oxide synthase (iNOS). In a monosodium iodoacetate (MIA)-induced OA rat model, the administration of PDREP resulted in decreased OA clinical indices, improved weight-bearing indices and gait patterns, reduced histological damage, and lowered serum inflammatory cytokine and MMPs expression. Furthermore, PDREP downregulated the phosphorylation of ERK, JNK, p38, and NF-κB, as well as the expression of iNOS, consistent with the in vitro findings. Conclusions: These results suggest that PDREP exhibits anti-inflammatory and joint-improving effects and has potential as a therapeutic strategy or functional food for the treatment of OA. Full article

Background/Objectives: This paper proposes a method for managing gait imbalances by integrating the Internet of Things (IoT) and machine learning technologies. Ankle–foot orthosis (AFO) devices are crucial medical braces that align the lower leg, ankle, and foot, offering essential support for individuals with gait imbalances by assisting weak or paralyzed muscles. This research aims to revolutionize medical orthotics through IoT and machine learning, providing a sophisticated solution for managing gait issues and enhancing patient care with personalized, data-driven insights. Methods: The smart ankle–foot orthosis (AFO) is equipped with a surface electromyography (sEMG) sensor to measure muscle activity and an Inertial Measurement Unit (IMU) sensor to monitor gait movements. Data from these sensors are transmitted to the cloud via fog computing for analysis, aiming to identify distinct walking phases, whether normal or aberrant. This involves preprocessing the data and analyzing it using various machine learning methods, such as Random Forest, Decision Tree, Support Vector Machine (SVM), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Transformer models. Results: The Transformer model demonstrates exceptional performance in classifying walking phases based on sensor data, achieving an accuracy of 98.97%. With this preprocessed data, the model can accurately predict and measure improvements in patients’ walking patterns, highlighting its effectiveness in distinguishing between normal and aberrant phases during gait analysis. Conclusions: These predictive capabilities enable tailored recommendations regarding the duration and intensity of ankle–foot orthosis (AFO) usage based on individual recovery needs. The analysis results are sent to the physician’s device for validation and regular monitoring. Upon approval, the comprehensive report is made accessible to the patient, ensuring continuous progress tracking and timely adjustments to the treatment plan. Full article

Background: Three-dimensional gait analysis, supported by advanced sensor systems, is a crucial component in the rehabilitation assessment of post-stroke hemiplegic patients. However, the sensor data generated from such analyses are often complex and challenging to interpret in clinical practice, requiring significant time and complicated procedures. The Gait Deviation Index (GDI) serves as a simplified metric for quantifying the severity of pathological gait. Although isokinetic dynamometry, utilizing sophisticated sensors, is widely employed in muscle function assessment and rehabilitation, its application in gait analysis remains underexplored. Objective: This study aims to investigate the use of sensor-acquired isokinetic muscle strength data, combined with machine learning techniques, to predict the GDI in hemiplegic patients. This study utilizes data captured from sensors embedded in the Biodex dynamometry system and the Vicon 3D motion capture system, highlighting the integration of sensor technology in clinical gait analysis. Methods: This study was a cross-sectional, observational study that included a cohort of 150 post-stroke hemiplegic patients. The sensor data included measurements such as peak torque, peak torque/body weight, maximum work of repeated actions, coefficient of variation, average power, total work, acceleration time, deceleration time, range of motion, and average peak torque for both flexor and extensor muscles on the affected side at three angular velocities (60°/s, 90°/s, and 120°/s) using the Biodex System 4 Pro. The GDI was calculated using data from a Vicon 3D motion capture system. This study employed four machine learning models—Lasso Regression, Random Forest (RF), Support Vector regression (SVR), and BP Neural Network—to model and validate the sensor data. Model performance was evaluated using mean squared error (MSE), the coefficient of determination (R²), and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Results: The RF model outperformed others in predicting GDI, with an MSE of 16.18, an R² of 0.89, and an MAE of 2.99. In contrast, the Lasso Regression model yielded an MSE of 22.29, an R² of 0.85, and an MAE of 3.71. The SVR model had an MSE of 31.58, an R² of 0.82, and an MAE of 7.68, while the BP Neural Network model exhibited the poorest performance with an MSE of 50.38, an R² of 0.79, and an MAE of 9.59. SHAP analysis identified the maximum work of repeated actions of the extensor muscles at 60°/s and 120°/s as the most critical sensor-derived features for predicting GDI, underscoring the importance of muscle strength metrics at varying speeds in rehabilitation assessments. Conclusions: This study highlights the potential of integrating advanced sensor technology with machine learning techniques in the analysis of complex clinical data. The developed GDI prediction model, based on sensor-acquired isokinetic dynamometry data, offers a novel, streamlined, and effective tool for assessing rehabilitation progress in post-stroke hemiplegic patients, with promising implications for broader clinical application. Full article

Human locomotion contains innate variability which may provide health insights. Detrended fluctuation analysis (DFA) has been used to quantify the temporal structure of variability for treadmill running, although it has been less commonly applied to uncontrolled overground running. This study aimed to determine how running gait complexity changes in response to gradient and elapsed exercise duration during uncontrolled overground running. Sixty-eight participants completed an overground, mass-participation five-kilometre run (a parkrun). Stride times were recorded using an inertial measurement unit mounted on the distal shank. Data were divided into four consecutive intervals (uphill lap 1, downhill lap 1, uphill lap 2, downhill lap 2). The magnitude (SD) and structure (DFA) of stride time variability were compared across elapsed exercise duration and gradient using a repeated-measures ANOVA. Participants maintained consistent stride times throughout the run. Stride time DFA-α displayed a moderate decrease (d = |0.39| ± 0.13) during downhill running compared to uphill running. DFA-α did not change in response to elapsed exercise duration, although a greater stride time SD was found during the first section of lap 1 (d = |0.30| ± 0.12). These findings suggest that inter- and intra-run changes in gait complexity should be interpreted in the context of course elevation profiles before conclusions on human health are drawn. Full article