Search Results (1,167)

Falls are a prominent issue in society and the second leading cause of unintentional death globally. Traditional gait analysis is a process that can aid in identifying factors that increase a person’s risk of falling through determining their gait parameters in a controlled environment. Advances in wearable sensor technology and analytical methods such as deep learning can enable remote gait analysis, increasing the quality of the collected data, standardizing the process between centers, and automating aspects of the analysis. Real-world gait analysis requires two problems to be solved: high-accuracy Human Activity Recognition (HAR) and high-accuracy terrain classification. High accuracy HAR has been achieved through the application of powerful novel classification techniques to various HAR datasets; however, terrain classification cannot be approached in this way due to a lack of suitable datasets. In this study, we present the Context-Aware Human Activity Recognition (CAHAR) dataset: the first activity- and terrain-labeled dataset that targets a full range of indoor and outdoor terrains, along with the common gait activities associated with them. Data were captured using Inertial Measurement Units (IMUs), Force-Sensing Resistor (FSR) insoles, color sensors, and LiDARs from 20 healthy participants. With this dataset, researchers can develop new classification models that are capable of both HAR and terrain identification to progress the capabilities of wearable sensors towards remote gait analysis. Full article

Neuromuscular diseases are biologically diverse, clinically heterogeneous, and often difficult to diagnose and treat, highlighting the need for computational tools that can help resolve overlapping phenotypes and support timely, mechanism-informed interventions. This narrative review synthesizes recent advances in artificial intelligence (AI) and machine learning applied to neuromuscular diseases across diagnosis, outcome modeling, biomarker development, and therapeutics. AI-based approaches may assist clinical and genetic diagnosis from phenotypic data; however, early phenotype-driven tools have seen limited clinician adoption due to modest accuracy, usability challenges, and poor workflow integration. Electrophysiological studies remain central to diagnosing neuromuscular diseases, and AI shows promise for accurate classification of electrophysiological signals. Predictive models for disease outcome and progression—particularly in amyotrophic lateral sclerosis—are under active investigation, but most remain at an early stage of development and are not yet ready for routine clinical use. Digital biomarkers derived from imaging, gait, voice, and wearable sensors are emerging, with MRI-based quantification of muscle fat replacement representing the most mature and widely accepted application to date. Efforts to apply AI to therapeutic discovery, including drug repurposing and optimization of gene-based therapies, are ongoing but have thus far yielded limited clinical translation. Persistent barriers to broader adoption include disease rarity, data scarcity, heterogeneous acquisition protocols, inconsistent terminology, limited external validation, insufficient model explainability, and lack of seamless integration into clinical workflows. Addressing these challenges is essential to moving AI tools from the laboratory into clinical practice. We conclude with a practical checklist of considerations intended to guide the development and adoption of AI tools in neuromuscular disease care. Full article

Recent reports have revealed that the number of lower limb amputees worldwide has increased as a result of war, accidents, and vascular diseases and that transfemoral amputation accounts for 39% of cases, highlighting the need to develop an improved functional prosthetic knee joint that improves the amputee’s ability to resume activities of daily living. To enable transfemoral prosthesis users to walk on level ground, accurate prediction of the intended knee joint angle is critical for transfemoral prosthesis control. Therefore, the purpose of this research was to develop a technique for estimating knee joint angle utilizing a long short-term memory (LSTM) network and kinematic data collected from inertial measurement units (IMUs). The proposed LSTM network was trained and tested to estimate the contralateral knee angle using data collected from twenty able-bodied subjects using a lab-developed sensory gadget, which included four IMUs. Accordingly, the present work represents a feasibility investigation conducted on able-bodied individuals rather than a clinical validation for amputee gait. This study contributes to the field of bionics by mimicking the natural biomechanical behavior of the human knee joint during gait cycle to improve the control of artificial prosthetic knees. The proposed LSTM model learns the contralateral knee’s motion patterns in able-bodied gait and demonstrates the potential for future application in prosthesis control, although direct generalization to amputee users is outside the scope of this preliminary study. The contralateral LSTM models exhibited a real-time RMSE range of 2.48–2.78° and a correlation coefficient range of 0.9937–0.9991. This study proves the effectiveness of LSTM networks in estimating contralateral knee joint angles and shows their real-time performance and robustness, supporting its feasibility while acknowledging that further testing with amputee participants is required. Full article

Modern battlefield operations require wearable technologies to operate reliably under harsh physical, environmental, and security conditions. This review looks at today and tomorrow’s potential for ready field-grade wearables embedded with biometric authentication systems. It details physiological, kinematic, and multimodal sensor platforms built to withstand rugged, high-stress environments, and reviews biometric modalities like ECG, PPG, EEG, gait, and voice for continuous or on-demand identity confirmation. Accuracy, latency, energy efficiency, and tolerance to motion artifacts, environmental extremes, and physiological variability are critical performance drivers. Security threats, such as spoofing and data tapping, and techniques for template protection, liveness assurance, and protected on-device processing also come under review. Emerging trends in low-power edge AI, multimodal integration, adaptive learning from field experience, and privacy-preserving analytics in terms of defense readiness, and ongoing challenges, such as gear interoperability, long-term stability of templates, and common stress-testing protocols, are assessed. In conclusion, an R&D plan to lead the development of rugged, trustworthy, and operationally validated wearable authentication systems for the current and future militaries is proposed. Full article

Gait analysis is a non-invasive, cost-effective method for detecting subtle motor changes in neurodegenerative disorders. This study uses an exploratory approach to identify temporal–kinetic gait feature relationships specific to amyotrophic lateral sclerosis (ALS) and Huntington (HUNT) and Parkinson (PARK) disease versus healthy controls (CONTROL) using recent advances in InterCriteria Analysis (ICrA). The novelty lies in the (i) comprehensive temporal–kinetic feature set, (ii) use of ICrA to characterize inter-feature coordination patterns at population and disease-group levels and (iii) interpretation in a neuromechanical context. Forty-one temporal/kinetic features were extracted from left/right leg ground reaction force and rate-of-force-development signals, considering laterality, gait phase (stance, swing, double support), magnitudes, waveform correlations, and inter-/intra-limb asymmetries. The analysis included 14,580 steps from 64 recordings in the Gait in Neurodegenerative Disease Database: 16 CONTROL (4054 steps), 13 ALS (2465), 20 HUNT (4730), 15 PARK (3331). Sensitivity analysis identified strict consonance thresholds (μ ≥ 0.75, ν ≤ 0.25), selecting <5% strongest inter-feature relations from 820 feature pairs: population level (16 positive, 14 negative), group-level (15–25 positive, 9–14 negative). ICrA identified group-specific consonances—present in one group but absent in others—highlighting disease-related alterations in gait coordination: ALS (15/11 positive/negative, disrupted bilateral stride coordination, prolonged stance/double-support, decoupled stride/cadence, desynchronized force-generation patterns—reflecting compensatory adaptations to muscle weakness and instability), HUNT (11/7, severe temporal–kinetic breakdown consistent with gait instability—loss of bilateral coordination, reduced swing time, slowed force development), PARK (1/2, subtle localized disruptions—prolonged stance and double-support intervals, reduced force during weight transfer, overall coordination remained largely preserved). Benchmarking vs. Pearson correlation showed strong linear agreement (R² = 0.847, p < 0.001), confirming that ICrA captures dominant dependencies while moderating the correlation via uncertainty. These results demonstrate that ICrA provides a quantitative, interpretable framework for characterizing gait coordination patterns and can guide principled feature selection in future predictive modeling. Full article

The accurate prediction of human movement direction plays a crucial role in fields such as rehabilitation monitoring, sports science, and intelligent military systems. Based on plantar pressure and inertial sensor data, this study developed a hybrid deep learning model integrating a Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BiLSTM) network to enable joint spatiotemporal feature learning. Systematic comparative experiments involving four distinct deep learning models—CNN, BiLSTM, CNN-LSTM, and CNN-BiLSTM—were conducted to evaluate their convergence performance and prediction accuracy comprehensively. Results show that the CNN-BiLSTM model outperforms the other three models, achieving the lowest RMSE (0.26) and MAE (0.14) on the test set, with an R² of 0.86, which indicates superior fitting accuracy and generalization ability. The superior performance of the CNN-BiLSTM model is attributed to its ability to effectively capture local spatial features via CNN and model bidirectional temporal dependencies via BiLSTM, thus demonstrating strong adaptability for complex motion scenarios. This work focuses on the optimization and comparison of deep learning algorithms for spatiotemporal feature extraction, providing a reliable framework for real-time human motion prediction and offering potential applications in intelligent gait analysis, wearable monitoring, and adaptive human–machine interaction. Full article

Background and Clinical Significance: Giant sphenoid wing meningiomas are generally viewed as skull base masses that compress frontal centers and their respective pathways gradually enough to cause a dysexecutive–apathetic syndrome, which can mimic primary neurodegenerative disease. The aim of this report is to illustrate how bedside phenotyping and multimodal imaging can disclose similar clinical presentations as surgically treatable network lesions. Case Presentation: An independent, right-handed older female developed an incremental, two-year decline of her ability to perform executive functions, extreme apathy, lack of instrumental functioning, and a frontal-based gait disturbance, culminating in a first generalized seizure and a newly acquired left-sided upper extremity pyramidal sign. Standardized neuropsychological evaluation revealed a predominant frontal-based dysexecutive profile with intact core language skills, similar to behavioral-variant frontotemporal dementia (bvFTD). MRI demonstrated a large, right fronto-temporo-basal extra-axial tumor attached to the sphenoid wing with homogeneous postcontrast enhancement, significant vasogenic edema within the frontal projection pathways, and a marked midline displacement of structures with an open venous pathway. With the use of a skull-base flattening pterional craniotomy with early devascularization followed by staged internal debulking, arachnoid preserving dissection, and conservative venous preservation, the surgeon accomplished a Simpson Grade I resection. Sequential improvements in the patient’s frontal “re-awakening” were demonstrated through postoperative improvements on standardized stroke, cognitive and functional assessment scales that correlated well with persistent decompression and symmetric ventricles on follow-up images. Conclusions: This case illustrates the possibility of a non-dominant sphenoid wing meningioma resulting in a pseudo-degenerative frontal syndrome and its potential for reversal if recognized as a network lesion and treated with tailored, venous-sparing skull-base surgery. Contrast-enhanced imaging and routine frontal testing in atypical “dementia” presentations may aid in identifying additional patients with potentially surgically remediable cases. Full article

Tendon injuries, whether resulting from trauma, repetitive strain, or degenerative conditions, present a considerable clinical challenge. The natural healing process, which involves inflammatory, proliferative, and remodeling phases, is often inefficient and leads to excessive scar tissue formation, ultimately compromising the mechanical properties of the tendon compared to its native state. This highlights the critical need for innovative approaches to enhance tendon repair and regeneration. Leveraging the regenerative properties of human amniotic membrane (HAM) and electrospun PCL/gelatin nanofibers, this study aims to develop and assess a novel composite scaffold in a rodent model to facilitate improved tendon healing. This prospective experimental study involved 12 male Sprague Dawley rats (250–300 g), randomly assigned to three groups: Group A (No Treatment/No HAM), Group B (HAM-treated), and Group C (HAM with electrospun nanofibers, HAM-NF). A surgically induced tendon injury was created in the left hind limb, while the right limb served as a control. Following surgery, HAM and HAM-NF (0.5 cm²) were applied to the respective treatment groups, and tendon healing was assessed after six weeks. Gait analysis, including stride length and toe-out angle, was conducted both pre-operatively and six weeks post-operatively. Macroscopic and microscopic evaluations were performed on harvested tendons to assess regeneration, comparing treated groups to the controls. Gait analysis demonstrated that the HAM-NF group showed a significant increase in stride length from 11.70 ± 1.50 cm to 12.79 ± 1.71 cm (p < 0.05), with only a modest change in toe-out angle (14.58 ± 2.96° to 16.27 ± 2.20°). In contrast, the No Treatment group exhibited reduced stride length (10.27 ± 2.17 cm to 8.40 ± 1.67 cm) and a marked increase in toe-out angle (16.33 ± 4.51° to 26.47 ± 5.81°, p < 0.05), while the HAM-only group showed mild changes in both parameters. Macroscopic evaluation showed a significant difference in tendon healing. HAM-NF group had the highest score that indicates more rapid tissue regeneration. Histological analysis after 6 weeks showed that tendons treated with HAM-NF achieved a mean histological score of 5.54 ± 4.14, closely resembling the uninjured tendon (6.67 ± 1.63), indicating substantial regenerative potential. The combination of human amniotic membrane (HAM) and electrospun nanofibers presents significant potential as an effective strategy for tendon regeneration. The HAM/NF group exhibited consistent improvements in gait parameters and histological outcomes, closely mirroring those of uninjured tendons. These preliminary results indicate that this biomaterial-based approach can enhance both functional recovery and structural integrity, providing a promising pathway for advanced tendon repair therapies. Full article

Sarcopenia is highly prevalent among patients with chronic kidney disease (CKD) and chronic heart failure (CHF), yet the underlying immunometabolic mechanisms remain insufficiently understood. Short-chain fatty acids (SCFAs), inflammatory cytokines, and body-composition alterations may jointly contribute to the development of muscle dysfunction in this population. In this cross-sectional study, 80 patients with CKD and CHF underwent comprehensive clinical, biochemical, bioimpedance, inflammatory, and SCFA profiling. Sarcopenia was diagnosed according to EWGSOP2 criteria. Multivariable logistic regression, LASSO feature selection, correlation analysis, PCA, and Random Forest modeling were used to identify key determinants of sarcopenia. Sarcopenia was present in 39 (49%) participants. Patients with sarcopenia exhibited significantly lower body fat percentage, reduced ASM, and slower gait speed. Hexanoic acid (C6) showed an independent positive association with sarcopenia (OR = 2.24, 95% CI: 1.08–5.37), while IL-8 showed an inverse association with sarcopenia (OR = 0.38, 95% CI: 0.13–0.94), indicating that lower IL-8 levels were more frequently observed in individuals with sarcopenia. Correlation heatmaps revealed distinct SCFA–cytokine coupling patterns depending on sarcopenia status, with stronger pro-inflammatory clustering in C6-associated networks. The final multivariable model integrating SCFAs, cytokines, and body-composition metrics achieved excellent discrimination (AUC = 0.911) and good calibration. Sarcopenia in CKD–CHF patients represents a systemic immunometabolic disorder characterized by altered body composition, chronic inflammation, and dysregulated SCFA signaling. Hexanoic acid (C6) and IL-8 may serve as informative biomarkers of muscle decline. These findings support the use of multidimensional assessment and highlight potential targets for personalized nutritional, microbiota-modulating, and rehabilitative interventions. Full article

Background: Benign Paroxysmal Positional Vertigo (BPPV) is the most common vestibular disorder. Although canalith repositioning procedures (CRPs) typically resolve positional vertigo, several patients still report imbalance or residual dizziness, which remain difficult to quantify with standard tests. Wearable inertial sensors now allow high-resolution, objective gait analysis and may detect subtle vestibular-related impairments. Objectives: This study evaluates the clinical usefulness of sensor-based gait metrics, enhanced by the newly developed φ-bonacci index framework to quantify gait changes and residual dizziness in BPPV before and after CRPs. Methods: Fifteen BPPV patients (BPPV-P) and fifteen age-matched controls completed walking tests under eyes-open (EO) and eyes-closed (EC) conditions using wearable inertial measurement units (IMU). φ-bonacci index components—self-similarity (A1), swing symmetry (A2), and double-support consistency (A4)—were calculated to assess gait harmonicity, symmetry and stability. Results: Before treatment, BPPV-P exhibited significantly higher A1 values than healthy controls (p = 0.038 EO; p = 0.011 EC), indicating impaired gait harmonicity. After CRPs, A1 values normalized to control levels, suggesting restored gait self-similarity. Under visual deprivation, both A1 and A4 showed pronounced increases across all groups, reflecting the contribution of vision to balance control. Among post-treatment patients, those reporting residual dizziness demonstrated persistently elevated A4 values—particularly under EC conditions—indicating incomplete sensory reweighting despite clinical recovery. Conclusions: Wearable sensor–derived φ-bonacci metrics offer sensitive, objective markers of gait abnormalities and residual dizziness in BPPV, supporting their use as digital biomarkers for diagnosis, rehabilitation, and follow-up. Full article

This paper presents the design, development, and evaluation of a proof-of-concept arm swing facilitator device (ASFD) to promote proper arm swing during gait training. Although coordinated arm swing plays a critical role in human locomotion and neurorehabilitation, few wearable systems have been developed to integrate it into gait training. The ASFD was designed to test the feasibility of generating torque at the shoulder joint to initiate arm flexion–extension motion while allowing other shoulder degrees of freedom to move freely. The device induced cyclic arm motion at 1 Hz, producing sufficient torque while maintaining ergonomic criteria, such as a large workspace and back-mounted actuation to minimize arm load. The system incorporated a double-parallelogram mechanism to expand the workspace and a two-stage pulley–belt transmission to amplify torque. Testing showed that the ASFD produced up to 15 N·m and 11 N·m torques in static and dynamic load tests, respectively. Kinematic and experimental analyses confirmed sufficient motion freedom, except for some constraints in rotation. Human subject experiment demonstrated that the ASFD successfully induced arm swing within the 0.8–1.2 Hz frequency range and torques below 11 N·m. The ASFD met its design objectives, establishing a foundation for future development aimed at gait rehabilitation applications. Full article

Human gait exhibits stable contralateral coupling, making healthy-side motion a viable predictor for affected-limb kinematics. Leveraging this property, this study develops FusionTCN–Attention, a causality-preserving temporal model designed to forecast contralateral hip and knee trajectories from unilateral IMU measurements. The model integrates dilated temporal convolutions with a lightweight attention mechanism to enhance feature representation while maintaining strict real-time causality. Evaluated on twenty-one subjects, the method achieves hip and knee RMSEs of 5.71° and 7.43°, correlation coefficients over 0.9, and a deterministic phase lag of 14.56 ms, consistently outperforming conventional sequence models including Seq2Seq and causal Transformers. These results demonstrate that unilateral IMU sensing supports low-latency, stable prediction, thereby establishing a control-oriented methodological basis for unilateral prediction as a necessary engineering prerequisite for future hemiparetic exoskeleton applications. Full article

Background/Objectives: Managing large midline olfactory groove meningiomas is especially difficult in elderly patients who have limited physiological reserves. Here we describe a unique and dangerous geriatric case where we used new quantifiable anatomical measurements and developed a structured multidisciplinary preoperative and postoperative protocol to assist in all aspects of surgery. Case Presentation: A 68-year-old male with fronto-lobe syndrome and disability (astasia-abasia; Tinetti Balance Score of 4/16 and Gait Score of 0/12) as well as cognitive dysfunction (MoCA score of 12/30) and blindness bilaterally. Imaging prior to surgery demonstrated a very large olfactory groove meningioma which severely compressed both optic pathways at the level of the optic canals (up to 71% reduction in cross-sectional area of the optic nerves) and had complex vascular relationships with the anterior cerebral artery complex (210° contact surface). Due to significant cardiovascular disease and liver disease, his care followed a coordinated optimization protocol for the perioperative period. He underwent bifrontal craniotomy, initial early devascularization and then staged ultrasonic internal decompression (approximately 70% reduction in tumor volume) and finally microsurgical dissection of the tumor under multi-modal monitoring of neurophysiology. Discussion: We analyzed his imaging data prior to surgery using a standardized measurement protocol to provide quantitative measures of the degree of compression of the optic pathways (traction-stretch index = 1.93; optic angulation = 47.3°). These quantitative measures allowed us to make a risk-based evaluation of the anatomy and to guide our choices of corridors through which to dissect and remove the tumor. Following surgery, imaging studies demonstrated complete removal of the tumor with significant relief of the frontal lobe and optic apparatus from compression. His pathology showed that he had a WHO Grade I meningioma with an AKT1(E17K) mutation identified on molecular profiling. Conclusions: This case is intended to demonstrate the feasibility of integrating quantitative anatomical measurements into a multidisciplinary, protocol-based perioperative pathway to maximize the safety and effectiveness of the surgical removal of a complex and high-risk skull-base tumor. While the proposed quantitative indices are experimental and require additional validation, the use of a systematic approach such as this may serve as a useful paradigm for other complex skull-base cases. Full article

Pathogenic variants in the PAK1 gene are linked to neurodevelopmental and neurodegenerative disorders by disrupting neuronal signaling and function. Despite increasing recognition, the mechanisms underlying these conditions remain incompletely understood, limiting therapeutic options. Here, we report a novel de novo PAK1 variant, c.396C>A (p.Asn132Lys), in a 5-year-old girl with Intellectual Developmental Disorder with Macrocephaly, Seizures, and Speech Delay (IDDMSSD). The patient presented with mild intellectual disability, delayed speech, macrocephaly, hypotonia, gait ataxia, autism-like behaviors, and focal epileptiform activity. Trio exome sequencing confirmed the variant as likely pathogenic, absent in her parents and population databases. This finding expands the phenotypic spectrum of PAK1-related disorders and underscores the critical role of the autoinhibitory domain in neurodevelopment. In addition, we performed a comprehensive literature review of PAK1 variants affecting both the autoregulatory and kinase domains, summarizing associated clinical features and pathogenic mechanisms. Our study highlights the importance of identifying PAK1 pathogenic variants for accurate diagnosis, refined genotype-phenotype correlations, and the development of potential targeted therapeutic strategies. By integrating novel case data with existing literature, this work advances understanding of PAK1-related neurodevelopmental disorders and supports the application of genetic analysis in rare pediatric NDD cases. Full article

(1) Background: Navigating surfaces during walking can alter gait patterns. This study aims to develop tools for automatic walking condition classification using inertial measurement unit (IMU) and foot pressure sensors. We compared sensor modalities (IMUs on lower-limbs, IMUs on feet, IMUs on the pelvis, pressure insoles, and IMUs on the feet or pelvis combined with pressure insoles) and evaluated whether gait cycle segmentation improves performance compared to a sliding window. (2) Methods: Twenty participants performed flat, stairs up, stairs down, slope up, and slope down walking trials while fitted with IMUs and pressure insoles. Machine learning (ML; Extreme Gradient Boosting) and deep learning (DL; Convolutional Neural Network + Long Short-Term Memory) models were trained to classify these conditions. (3) Results: Overall, a DL model using lower-limb IMUs processed with gait segmentation performed the best ($F1=0.89$). Models trained with IMUs outperformed those trained on pressure insoles ($p<0.01$). Combining sensor modalities and gait segmentation improved performance for ML models ($p<0.01$). The best minimal model was a DL model trained on IMU pelvis + pressure insole data using sliding window segmentation ($F1=0.83$). (4) Conclusions: IMUs provide the most discriminative features for automatic walking condition classification. Combining sensor modalities may be helpful for some model architectures. DL models perform well without gait segmentation, making them independent of gait event identification algorithms. Full article