Search Results (1,829)

Background/Objectives: White–Sutton syndrome (WHSUS; OMIM 616364) is a rare neurodevelopmental disorder caused by pathogenic variants in the POGZ gene and characterized by developmental delay, intellectual disability, speech impairment, autism spectrum features, and dysmorphic traits. Although most reported cases are sporadic, inherited forms are exceptionally rare. We describe a familial case of WHSUS involving an affected mother and two children carrying a heterozygous POGZ nonsense variant, highlighting marked intra-familial phenotypic variability and expanding the clinical spectrum of the disorder. Methods: Clinical evaluation included multidisciplinary assessments. Genetic testing was performed using clinical exome sequencing (CES) with a virtual neurodevelopmental disorder (NDD) gene panel, followed by Sanger confirmation and segregation analysis in family members. The POGZ transcript reference NM_015100.3 was used for variant nomenclature and verified with the Mutalyzer tool. CNV detection from NGS data was performed using the Alissa CNV caller (Agilent) and visualized via IGV; the Xp11.22 microduplication was confirmed by chromosomal microarray (aCGH) and parental segregation analyses. Results: CES identified the heterozygous pathogenic POGZ variant c.1522C>T (p.Arg508*) in the female proband (III₆), an infant presenting with global developmental delay, hypotonia, speech impairment, gait abnormalities, and characteristic dysmorphic features. Segregation analysis demonstrated maternal inheritance and confirmed the presence of the variant in her affected brother (III₄), who also carries a de novo 1.79 kb microduplication at Xp11.22, while the maternal grandparents tested negative, indicating a de novo origin in the mother. The mother exhibited an attenuated phenotype, including mild neuropsychiatric and gastrointestinal manifestations. The variant is predicted to undergo nonsense-mediated decay (NMD), consistent with a moderate clinical presentation; however, experimental validation was not performed. Conclusions: This report documents a rare familial occurrence of WHSUS with highly variable expressivity. Our findings broaden the phenotypic and molecular characterization of POGZ-related disorders and emphasize the importance of comprehensive segregation studies and early genomic diagnosis. While experimental data link POGZ deficiency to DNA repair defects, no longitudinal clinical studies have demonstrated increased cancer risk in WHSUS; therefore, formal malignancy screening guidelines cannot be established at present, and this issue deserves future study in larger cohorts or registries. Full article

Background/Objectives: Falls have devastating consequences for older adults. The Functional Ambulation and Stair Test (FAST) was developed to characterize older adult mobility and eventual fall risk. This project aimed to determine the criterion validity of the FAST assessment by comparing the relationship between FAST outcomes and existing gold-standard clinical assessments of mobility and fall risk. A secondary aim was assessing the FAST’s capacity to elicit dual-task effects in older adults. Methods: The FAST is a multi-faceted mobility assessment combining stair navigation, turning and level-ground walking; total time and time spent in each phase are the calculated outcomes. Data from 199 older adults completing the FAST, Berg Balance Scale (BBS), Timed Up and Go (TUG), and Ten Meter Walk Test (10MWT) at comfortable and fast speed were evaluated. Relationships between the FAST and clinical outcomes were evaluated with Spearman’s correlations. The FAST and TUG were assessed under single- and dual-task conditions; linear mixed models evaluated the dual-task effects for overall FAST time and each phase. Results: Spearman’s correlations between the FAST and the BBS, TUG, 10MWT comfortable and 10MWT fast were −0.65, 0.88, −0.79, and −0.83, respectively. Participants experienced an 8.6% and 13.2% dual-task cost in the FAST and TUG, respectively. The greatest dual-task cost during the FAST was in the gait initiation, walking, and wide turn phases. Conclusions: Agreement between the FAST and gold-standard clinical mobility assessments confirms the criterion validity of the FAST. Delineation of mobility phases via the FAST offers insight into specific mobility deficits. Future work is ongoing to evaluate the FAST as a fall risk assessment in older adults. Full article

Falls among older adults remain a major public health concern, yet scalable and interpretable sensing approaches for functional fall-risk stratification remain limited. This study presents a lightweight contactless framework for five-level Short Physical Performance Battery (SPPB)-aligned functional fall-risk stratification using monocular RGB video. A total of 688 community-dwelling older adults completed SPPB-aligned assessments, including balance, five-times sit-to-stand, and 3 m gait tasks. Because prospective fall-event outcomes were unavailable, supervised labels were constructed from a pre-specified SPPB-aligned functional risk index rather than observed future falls. BlazePose-based two-dimensional keypoints were extracted, normalized using pelvis-centered and height-scaled transformations, and represented as temporal skeletal trajectories. Biomechanical descriptors were fused with embeddings from the proposed Temporal Convolutional Artificial Intelligence Fall-Risk Network (TCAI-FallNet). Participant-level data partitioning was used to reduce data leakage. TCAI-FallNet achieved a macro-averaged area under the curve of 0.91 and an overall accuracy of 81.3%. The trained model had a footprint under 3 MB, and TCN inference latency was below 20 ms per sequence under workstation-based evaluation. These findings suggest that TCAI-FallNet may support contactless SPPB-aligned functional mobility risk stratification, while prospective fall-event validation remains necessary. Full article

An electroencephalogram (EEG) signals provide vital insights for stroke rehabilitation, yet analyzing these complex, high-dimensional data to detect gait anomalies remains challenging. Artificial intelligence offers a promising solution to precisely identify abnormal movements, assisting physicians in optimizing personalized treatments. This exploratory pilot study aims to evaluate multi-class deep learning frameworks for classifying eight distinct normal and abnormal motor activities in stroke patients using EEG data. EEG signals from eight stroke patients were utilized to train and evaluate a customized Convolutional Neural Network (CNN), DeepConvNet, and EEGNet. Furthermore, channel reduction configurations (32, 22, and 15 channels) were investigated to determine optimal clinical setups. In the Leave-One-Out Cross-Validation (LOOCV) evaluation involving seven patients, EEGNet attained the highest descriptive average F1-score of 0.810. Moreover, when assessed independently on an unseen patient, it achieved an F1-score of 0.915, indicating its potential in accommodating individual differences within this limited cohort. Moreover, EEGNet exhibited a low false positive rate of 0.175, minimizing false alarms. While the 32-channel setup yielded the highest consistency, reduced configurations served as hypothesis-generating for specific tasks. In conclusion, EEGNet demonstrated superior average performance in differentiating complicated gait patterns in this exploratory pilot study, underscoring its promise for real-time, non-invasive monitoring in stroke neurorehabilitation. Full article

Background: Basal ganglia calcifications (BGCs) are frequently detected on brain CT scans in older adults, but their clinical relevance for mobility and fall risk is unclear. This study investigated the association of BGCs with impaired mobility and recurrent falls. Methods: In this cross-sectional study, all consecutive patients referred to the mobility clinic of a regional teaching hospital between 2019 and 2021 were included. Mobility was assessed using the Performance-Oriented Mobility Assessment (POMA) for balance, gait and overall mobility, and the Timed Up and Go (TUG) test for functional mobility. All assessments were performed by a trained physiotherapist. Recurrent falls were defined as self-reported occurrence of more than one fall in the past 12 months. Brain CT scans were evaluated for BGCs by a trained senior radiologist and were scored by severity. Univariable and multivariable logistic regression analyses were performed, adjusting for age, sex, and history of cardiovascular events. Results: A total of 253 participants were included (median age 82 years; 58% female), of whom 31% had BGCs. Falls data were available for 246 participants, and 70% reported recurrent falls. In both univariable and multivariable analyses, there was no evidence of a statistically significant association between the presence of BGCs and impaired balance, gait, overall mobility, functional mobility, or recurrent falls. Conclusions: No evidence of a statistically significant association was found between incidentally detected BGCs and impaired mobility or recurrent falls in older adults. Further longitudinal research is needed to confirm these findings and clarify whether BGCs are clinically relevant for mobility and fall risk assessment. Full article

Background and Objectives: Robotic-assisted total knee arthroplasty (rTKA) is being increasingly used to improve surgical precision, soft-tissue balancing, and functional recovery. However, evidence comparing rTKA with conventional manual TKA (mTKA) across functional, patient-reported, neuromotor, and safety outcomes remains heterogeneous. Materials and Methods: This narrative (non-systematic) review synthesises studies evaluating functional outcomes, patient-reported outcome measures (PROMs), joint awareness, range of motion (ROM), neuromotor recovery, and complications following rTKA versus mTKA. Study inclusion was based on author judgement and data accessibility. The reviewed evidence included five randomised controlled trials, 9 retrospective studies, six prospective non-randomised studies, two meta-analyses, one cross-sectional study, and one umbrella review, covering CT-based and imageless robotic platforms, including semi-active and active systems such as MAKO, NAVIO, CORI, ROSA, ROBODOC, CUVIS Joint, SkyWalker, TSolution One, AKEC, JIANJIA, and YUANHUA. Results: rTKA consistently demonstrated outcomes comparable to mTKA in PROMs (OKS, KOOS, WOMAC, KSS), with some studies reporting modest early improvements in pain and function. Joint awareness and patient satisfaction showed the most consistent early advantages for rTKA. Early postoperative ROM and neuromotor recovery, including balance and gait symmetry, were improved with rTKA, likely due to enhanced alignment and soft-tissue balancing; however, mid- and long-term outcomes were similar. Complication rates were low and comparable, with robotic-specific issues being rare and self-limited. Conclusions: rTKA provides small but reproducible early benefits in joint awareness, neuromotor function, and patient satisfaction, without clear long-term superiority. These early advantages may translate into meaningful population-level benefits, including faster recovery and potential healthcare cost reduction. Further high-quality studies are needed to assess long-term clinical and economic outcomes. Full article

Gait analysis is an essential tool in clinical domains for diagnosing musculoskeletal disorders and evaluating rehabilitation, yet traditional marker-based systems are limited by high costs and spatial constraints. To overcome these challenges, this study proposes and evaluates a joint-level hybrid framework that integrates a single RGB camera with two shoe-mounted inertial measurement units (IMUs) to leverage their complementary strengths. The camera-based module estimates hip and knee sagittal joint angles using 3D pose estimation, where the DEAS optimization algorithm aligns estimated coordinates with a humanoid model, and an LSTM-based refinement network corrects hip angles by referencing more accurately estimated knee data. Simultaneously, the IMU-based module estimates sagittal ankle angles through kinematic chain relationships that combine camera-derived proximal joint information with IMU-measured foot orientation. Experimental validation with 11 healthy participants in a controlled laboratory environment demonstrates promising estimation performance, achieving an average mean absolute error (MAE) of 7.89° and RMSE of 10.09° on the held-out test set across sagittal hip, knee, and ankle angles. Leave-one-subject-out (LOSO) cross-validation of the LSTM correction model further confirmed its generalizability, yielding an average MAE of 6.40° across bilateral hip angles. By accurately mitigating the trunk-inclination-induced overestimation of hip angles with a minimal sensor configuration (one camera and two IMUs), the proposed framework provides a practical and interpretable approach for portable lower limb gait analysis. Full article

Continuous monitoring of lower-limb kinematics in natural environments is essential for gait analysis and rehabilitation but remains challenging due to the limitations of optical systems and the inaccuracy of sparse inertial sensor methods. To address this, we propose a high-precision, minimalist wearable system utilizing only three inertial measurement units placed on the pelvis and shanks. In the data preprocessing stage, engineering modifications are made based on the traditional gradient descent algorithm to implement adaptive channel adjustment on the acceleration and magnetic data of a single IMU, aiming to alleviate the impact of motion acceleration and external magnetic interference on the temporal feature manifold. Subsequently, a pure Transformer neural network is utilized to capture long-range temporal dependencies, reconstructing full lower-limb kinematics without relying on rigid biomechanical assumptions. The model was optimized and deployed on an STM32N647 microcontroller to achieve real-time edge inference with a low latency of approximately 17 ms. Experimental results demonstrate that the proposed method achieves a mean absolute error of 2.41° for level walking, significantly outperforming traditional constrained Kalman filter approaches. Furthermore, it maintains high tracking robustness during complex nonlinear movements such as squatting and lunging. In conclusion, this edge-computing-enabled framework provides an accurate, comfortable, and real-time solution for unconstrained human motion capture in daily scenarios. Full article

Flexible strain sensors have attracted considerable attention in gait recognition owing to their ability to adhere directly to the skin near joints and transduce local deformation. In existing work, however, sensor placement and orientation are largely determined by anatomical experience, while multi-channel classification still relies on back-end digital processors, whose power consumption and latency constrain system practicality in wearable scenarios. This paper presents an integrated design path that proceeds from skin-mechanics theory through sensor-layout optimization to analog-domain front-end inference. On the layout side, the lines-of-non-extension (LoNE) theory is employed to convert the selection of sensor attachment angles from empirical judgment into a calculable mechanics problem; guided by the spatial course of LoNE in the ankle and knee regions, the positions and angles of the nine sensors are determined individually—channels perpendicular to the LoNE capture maximum strain, channels offset by 45 degrees supplement non-sagittal-plane information, and a channel aligned along the LoNE provides a near-zero-strain reference. On the circuit side, the mathematical equivalence between the weighted summation of a linear classifier and Kirchhoff’s current law (KCL) nodal current superposition is exploited to map the classification operation onto current aggregation in an analog circuit, yielding an in-sensor computing (ISC) front end in which the nine-channel weighted summation is completed in a single analog step. The sensors are fabricated by screen-printing a liquid-metal–polymer composite conductive ink onto a TPU film substrate, with a gauge factor RSD of $6.8\%$ and a tensile linearity $R^2>0.99$. Using walking, running, and stair descent as verification targets, the analog classifier reaches 99% accuracy at the circuit-level functional-verification stage. On real multi-subject data, it achieves $87.0\%\pm 8.4\%$ accuracy under intra-subject cross-session validation, with an analog-domain inference response faster than $100\mathsf{\mu }\mathrm{s}$. This design path is not bound to a specific joint or sensor material; when the layout methodology is extended to additional joint regions and the circuit architecture incorporates multiple outputs to cover more classification categories, the same workflow remains applicable, offering a promising low-power, lightweight technical solution for wearable motion monitoring. Full article

Gait symmetry in healthy children is frequently interpreted as a marker of biomechanical balance; however, its developmental trajectory and measurement stability remain incompletely defined. This systematic review with structured quantitative synthesis aimed to evaluate (i) the reliability of instrumented symmetry measures and (ii) the magnitude of physiological asymmetry across childhood. Following PRISMA guidelines, 500 records were identified, of which 297 met inclusion criteria for qualitative synthesis. Forty-six studies provided extractable numerical data for quantitative integration, representing approximately 9420 participants. Reliability aggregation demonstrated strong repeatability of plantar pressure symmetry measures (ICC = 0.88), whereas stabilometric indices showed lower consistency (ICC = 0.54), highlighting the importance of instrument-aware interpretation. Quantitative synthesis revealed small but consistent asymmetry in healthy children (Hedges’ g = 0.31 for plantar pressure; g = 0.18 for temporal–spatial parameters), with the magnitude decreasing progressively from early childhood to adolescence. These findings indicate that complete bilateral equivalence is not a normative standard in pediatric gait. Instead, symmetry represents a development-dependent continuum shaped by neuromotor maturation and measurement context. Age-specific reference ranges and standardized asymmetry indices are essential for clinical interpretation and future methodological harmonization. Full article

Gait is a behavioral biometric trait that enables non-invasive person recognition based on individual walking patterns. Camera-based gait acquisition is convenient, but silhouette sequences often contain substantial motion-irrelevant appearance information, such as body shape, clothing, and carried objects. To address this problem, a multi-scale time series differencer is proposed to acquire tensor difference data between adjacent frames, so as to extract dynamic feature information in motion gait image sequences. Experiments on the CASIA-B dataset show that the proposed method achieves Rank-1 accuracies of 97.7%, 94.6%, and 80.0% under NM, BG, and CL conditions, respectively. Ablation results further demonstrate that MTDU improves the mean accuracy from 84.7% to 90.8% compared with single-scale temporal differencing. The multi-scale time series differencer shows potential for fields including sports motion gait detection and recognition, surveillance security motion gait identity authentication, and medical motion gait recovery assessment for sports injuries, demonstrating practical application value. 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

Background and Objectives: Persistent quadriceps weakness, muscle atrophy, and functional deficits are common following anterior cruciate ligament (ACL) reconstruction and may compromise return to sport and increase the risk of reinjury. Eccentric-oriented strength training has been widely used to enhance muscle strength and hypertrophy in various musculoskeletal conditions; however, its specific application within ACL rehabilitation remains insufficiently explored. The aim of this scoping review was to map the existing evidence on the use of eccentric-oriented strength training in ACL rehabilitation, identify gaps in the current literature, and provide suggestions for future research. Materials and Methods: A scoping review search was conducted in PubMed, Scopus, Web of Science, and PEDro from inception to February 2026 using the following keywords and Boolean operators: (“anterior cruciate ligament”, “ACL”, “anterior cruciate ligament reconstruction”, “ACLR”) AND (“eccentric training”, “eccentric exercise”, “eccentric loading”, “flywheel training”, “isoinertial training”). Eligible studies included studies that investigated eccentric exercises as part of ACL rehabilitation and reported outcomes related to muscle strength, muscle morphology, functional performance, or return to sport. Data were extracted and synthesized descriptively in accordance with the PRISMA-ScR extension for Scoping Reviews guidelines. Methodological quality and risk of bias were evaluated using the PEDro scale (RCTs) and the ROBINS-I tool (non-randomized studies). Results: Fifteen studies met the inclusion criteria. The included literature primarily examined isokinetic eccentric exercise, eccentric cycling, early progressive eccentric resistance training, Nordic hamstring exercise, eccentric ergometry, and flywheel strength training. Most studies reported improvements in quadriceps strength and muscle morphology, with additional benefits observed in functional performance measures (i.e., hop tests), gait mechanics, and limb symmetry. Evidence was unevenly distributed across rehabilitation phases, with relatively few studies focusing on the mid-phase of ACL rehabilitation. Conclusions: Eccentric-oriented strength training represents a promising but underexplored component of ACL rehabilitation. However, the existing literature lacks standardized protocols, comprehensive outcome measures, and phase-specific guidance, particularly during the mid and late stages of rehabilitation. Further high-quality studies are needed to clarify the optimal timing, dosage, and integration of eccentric training across rehabilitation phases. Full article

Background: Traditional gait assessments in adult degenerative scoliosis (ADS) often rely on prohibitively expensive, laboratory-bound optoelectronic systems that lack clinical accessibility. This research aims to independently evaluate both lower limbs using a wearable Inertial Measurement Unit (IMU) system, in contrast to studies that employ a unilateral reference, thereby elucidating the unique bilateral asymmetries and dynamic stability patterns exhibited in ADS. Methods: Gait patterns of 20 ADS patients and 15 healthy controls were analyzed using the Rokoko Smartsuit Pro. Segmental kinematic data were integrated with anthropometric mass distribution models to calculate the total body center of mass (CoM). Spatiotemporal parameters, joint range of motion (RoM), and CoM excursions in three planes were statistically compared between the groups. Results: ADS patients exhibited a cautious gait strategy characterized by significantly reduced step speed, shortened step lengths, and increased step width ($p<0.05$). Temporal analysis showed prolonged stride, stance, and double support time ($p<0.001$), while cadence remained comparable to healthy controls. A triple-joint deficit, including hip, knee, and ankle, was identified in the sagittal plane, especially with peak flexion reductions reaching up to 55% in the left knee and 38% in the right knee, highlighting profound functional asymmetry ($p<0.001$). Additionally, the CoM analysis reflected these stability restrictions, showing increased horizontal excursion and reduced vertical oscillation. Conclusions: Our findings suggest that ADS is associated with distinct, bilateral alterations in the lower limb kinematic chain and notable adaptations in dynamic balance parameters, characterized by a cautious gait strategy and profound sagittal triple-joint asymmetries. These findings highlight the feasibility of full-body wearable IMU technology in capturing objective, bilateral gait alterations, providing a foundational baseline that could complement standard static radiography in future clinical evaluations. Full article

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