Search Results (89)

Auditory cueing has become an increasingly practical tool in gait rehabilitation; however, the specific sound features that modulate gait performance remain unclear. This study investigated how tempo and auditory continuity, two fundamental acoustic features, influence spatiotemporal gait parameters in healthy adults. Thirty-five participants walked under six auditory conditions combining discrete, continuous, and hybrid feedback at slow (60 BPM) and fast (120 BPM) tempi, with gait metrics captured via a pressure-sensor walkway and subjective responses gathered through questionnaires. Compared with the silent baseline, auditory cueing significantly affected cadence [F(1.88, 63.75) = 8.95, p < 0.001, η_p² = 0.21]; velocity [F(1.69, 57.49) = 10.15, p < 0.001, η_p² = 0.23]; and stride length [F(1.74, 59.26) = 6.87, p = 0.003, η_p² = 0.17]. Slower tempi reduced gait parameters, while the combined continuous and discrete conditions produced the greatest modulation. Participants reported that they had attempted to synchronize their steps with the auditory cues, which may have led to small adjustments in their natural walking speed and stride patterns, especially during the slower tempo. This suggests that rhythmic structure and sound continuity affect both perceptual and motor processes. Overall, sound continuity exerted a stronger influence on gait than tempo alone. These findings advance understanding of sensorimotor synchronization and highlight the potential of designing tailored auditory feedback systems to enhance movement awareness and inform clinical gait-rehabilitation strategies. Full article

Background: Eye-tracking technology enables the objective quantification of oculomotor behavior, providing key insights into visuocognitive performance. This study presents a comparative analysis of visual attention patterns between rhythmic gymnasts and school-aged students using an optical eye-tracking system combined with machine learning algorithms. Methods: Eye movement data were recorded during controlled visual tasks using the DIVE system (sampling rate: 120 Hz). Spatiotemporal metrics—including fixation duration, saccadic amplitude, and gaze entropy—were extracted and used as input features for supervised models: Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Decision Tree (CART), Random Forest, XGBoost, and a one-dimensional Convolutional Neural Network (1D-CNN). Data were divided according to a hold-out scheme (70/30) and evaluated using accuracy, F1-macro score, and Receiver Operating Characteristic (ROC) curves. Results: XGBoost achieved the best performance (accuracy = 94.6%; F1-macro = 0.945), followed by Random Forest (accuracy = 94.0%; F1-macro = 0.937). The neural network showed intermediate performance (accuracy = 89.3%; F1-macro = 0.888), whereas SVM and k-NN exhibited lower values. Gymnasts demonstrated more stable and goal-directed gaze patterns than students, reflecting greater efficiency in visuomotor control. Conclusions: Integrating eye-tracking with artificial intelligence provides a robust framework for the quantitative assessment of visuocognitive performance. Ensemble algorithms demonstrated high discriminative power, while neural networks require further optimization. This approach shows promising applications in sports science, cognitive diagnostics, and the development of adaptive human–machine interfaces. Full article

Brain function is reliant upon maintaining a constant internal environment; however, the methods employed to maintain this environment have historically been viewed as largely passive in nature, relying on diffusion and vascular pulsations to create the conditions necessary for continued brain activity. This review seeks to provide an overview of current data suggesting that brain clearance is in fact an active process that is dependent upon both the current regulatory state of the brain and the presence of noradrenergic slow vasomotion, which is generated by rhythmic output from the locus coeruleus (LC). The LC-generated output has been found to influence the degree of contraction exhibited by pericytes, the geometric shape of astrocytic end-feet, and vascular tone, ultimately impacting the rate of exchange between cerebrospinal fluid (CSF), interstitial fluid (ISF), and the blood–brain barrier through aquaporin-4 (AQP4) channels. These LC-generated rhythmic changes are thought to provide the mechanical forces necessary for sustaining the metabolic clearance of waste products within the parenchyma. This review seeks to synthesize several recent studies which indicate that LC-generated vasomotion correlates with both the structure and progression of sleep states, neuronal oscillation patterns, and metabolic states, and that dysfunction of this LC-generated rhythm may contribute to pathological features associated with Alzheimer’s disease, Parkinson’s disease, and small-vessel disease. Understanding the mechanisms of clearance within the brain as a physiologically tunable system will allow researchers to view brain clearance as an adaptive neuro-modulatory function rather than merely as a passive event. Therefore, the focus of this review is on identifying the potential applications of advancements in the field of physiological imaging, molecular biomarkers, and neuro-modulatory or vascular-based therapies for early detection and therapeutic manipulation of clearance processes. Understanding these mechanisms will potentially lead to enhanced cognitive resilience and immune regulation, and promote healthy brain aging. Full article

Music data exhibits numerous distinct symmetric and asymmetric patterns—ranging from symmetric pitch sequences and rhythmic cycles to asymmetric phrase structures and dynamic shifts. These varied and often subjective patterns present notable challenges for data analysis, such as distinguishing meaningful structural features from noise and adapting analytical methods to accommodate both regularity and irregularity. To tackle this challenge, we present a novel uncertain hypothesis test, referred to as the conservative hypothesis test, which is designed to assess the validity of statistical hypotheses associated with the symmetric and asymmetric patterns exhibited by two multivariate normal uncertain populations. Specifically, we extend the uncertain hypothesis test for the mean difference between two single-characteristic normal uncertain populations to the multivariate case, filling a research gap in uncertainty theory. Building on this two-population multivariate hypothesis test, we propose the conservative hypothesis test—a feasible uncertain hypothesis testing method for multivariable scenarios, developed based on multiple comparison procedures. To demonstrate the practical utility of these methods, we apply them to music-related statistical data, assessing whether two groups of evaluators use consistent criteria to score music. In essence, the hypothesis tests proposed in this paper hold significant value for social sciences, particularly music statistics, where data inherently contains ambiguity and uncertainty. Full article

This work presents a mixed reality application for drum learning that combines the PocketDrum virtual drumming device with the Meta Quest 3 headset, integrating hand tracking to provide an immersive, responsive experience without the need for a physical drum set. The system features a modular architecture for real-time strike detection, visual guidance synchronized with music, spatial calibration, and audio rendering. The system additionally makes use of the headset’s color Passthrough during the calibration stage to align the virtual drum kit with the player’s position. To evaluate the system’s performance, a technical analysis was conducted to measure latency, jitter, and sampling rate across the technologies involved. Additionally, a functional validation experiment assessed how spatial hand tracking from Meta Quest 3 improved PocketDrum’s classification accuracy. Results showed that the fused system corrected 19.1% of drum assignment errors made by the inertial-only setup, enhancing consistency in complex rhythmic patterns. These findings demonstrate the effectiveness of sensor fusion for immersive percussion training and support its potential use in accessible, feedback-rich musical learning environments. Full article

Spontaneous rhythmic performance is a fundamental feature of human movement, well established in biomechanical models (EBMs) but less understood in complex physical fitness exercises (PFEs). This study examined the task dependency of spontaneous rhythmic performance across three EBMs (walking, hopping, finger tapping) and seven PFEs (hip abduction, back extension, sit-up, push-up, shoulder abduction, squat, lunge). A total of 15 men and 15 women performed each task at a self-selected pace while wearing inertial sensors. Measures included spontaneous motor tempo (SMT), temporal structure metrics, and their within- and between-trial individual variability (%CV) (ANOVA, SPSS 28.0, p ≤ 0.05). SMT was task-dependent, with EMB tasks being near ~2 Hz (walking: 1.82 ± 0.10 Hz; hopping: 2.08 ± 0.22 Hz; finger tapping: 1.89 ± 0.43 Hz) and PFEs being slower (0.36–0.68 Hz). Temporal structure mirrored these differences with shorter cycle and phase durations in EBM than PFE tasks, with relative phase durations consistently at about a 1:1 ratio. Τhe overall low %CV indicated stable performance (within-trial: 1.4–7.5%; between-trial: 0.5–7.8%). The results highlight the task dependency of SMT and temporal structure, as well as the robustness of an overarching internal timing framework supporting rhythmic motor control across diverse movement contexts. Full article

Hierarchical Reinforcement Learning (HRL) is effective for long-horizon and sparse-reward tasks by decomposing complex decision processes, but its real-world application remains limited due to instability between levels, inefficient subgoal scheduling, delayed responses, and poor interpretability. To address these challenges, we propose Timed and Bionic Circuit Hierarchical Reinforcement Learning (TBC-HRL), a biologically inspired framework that integrates two mechanisms. First, a timed subgoal scheduling strategy assigns a fixed execution duration $\tau$ to each subgoal, mimicking rhythmic action patterns in animal behavior to improve inter-level coordination and maintain goal consistency. Second, a Neuro-Dynamic Bionic Circuit Network (NDBCNet), inspired by the neural circuitry of C. elegans, replaces conventional fully connected networks in the low-level controller. Featuring sparse connectivity, continuous-time dynamics, and adaptive responses, NDBCNet models temporal dependencies more effectively while offering improved interpretability and reduced computational overhead, making it suitable for resource-constrained platforms. Experiments across six dynamic and complex simulated tasks show that TBC-HRL consistently improves policy stability, action precision, and adaptability compared with traditional HRL, demonstrating the practical value and future potential of biologically inspired structures in intelligent control systems. Full article

Electrocardiogram (ECG) waveform delineation is fundamental to cardiac disease diagnosis. This task requires precise localization of key fiducial points, specifically the onset, peak, and offset positions of P-waves, QRS complexes, and T-waves. Current methods exhibit significant performance degradation in noisy clinical environments (baseline drift, electromyographic interference, powerline interference, etc.), compromising diagnostic reliability. To address this limitation, we introduce ECG-SCFNet: a novel dual-stream architecture employing selective context fusion. Our framework is further enhanced by a consistency training paradigm, enabling it to maintain robust waveform delineation accuracy under challenging noise conditions.The network employs a dual-stream architecture: (1) A temporal stream captures dynamic rhythmic features through sequential multi-branch convolution and temporal attention mechanisms; (2) A morphology stream combines parallel multi-scale convolution with feature pyramid integration to extract multi-scale waveform structural features through morphological attention; (3) The Selective Context Fusion (SCF) module adaptively integrates features from the temporal and morphology streams using a dual attention mechanism, which operates across both channel and spatial dimensions to selectively emphasize informative features from each stream, thereby enhancing the representation learning for accurate ECG segmentation. On the LUDB and QT datasets, ECG-SCFNet achieves high performance, with F1-scores of 97.83% and 97.80%, respectively. Crucially, it maintains robust performance under challenging noise conditions on these datasets, with 88.49% and 86.25% F1-scores, showing significantly improved noise robustness compared to other methods and demonstrating exceptional robustness and precise boundary localization for clinical ECG analysis. Full article

Pilot visual behavior safety assessment is a cross-disciplinary technology that analyzes pilots’ gaze behavior and neurocognitive responses. This paper proposes a multimodal analysis method for pilot visual behavior safety, specifically for cognitive state decoding. This method aims to achieve a quantitative and efficient assessment of pilots’ observational behavior. Addressing the subjective limitations of traditional methods, this paper proposes an observational behavior detection model that integrates facial images to achieve dynamic and quantitative analysis of observational behavior. It addresses the “Midas contact” problem of observational behavior by constructing a cognitive analysis method using multimodal signals. We propose a bidirectional long short-term memory (LSTM) network that matches physiological signal rhythmic features to address the problem of isolated features in multidimensional signals. This method captures the dynamic correlations between multiple physiological behaviors, such as prefrontal theta and chest-abdominal coordination, to decode the cognitive state of pilots’ observational behavior. Finally, the paper uses a decision-level fusion method based on an improved Dempster–Shafer (DS) evidence theory to provide a quantifiable detection strategy for aviation safety standards. This dual-dimensional quantitative assessment system of “visual behavior–neurophysiological cognition” reveals the dynamic correlations between visual behavior and cognitive state among pilots of varying experience. This method can provide a new paradigm for pilot neuroergonomics training and early warning of vestibular-visual integration disorders. Full article

Vibrotactile feedback synchronized with audio through haptic music players (HMPs) creates a synergistic effect that has been shown to improve the music listening experience. However, current HMPs are still unable to efficiently retrieve multiple music features, decelerating app scalability and jeopardizing long-term user engagement. This study introduces a wearable HMP that utilizes piezoelectric actuators and a novel audio-tactile rendering algorithm that uses YIN to extract pitch and spectral flux for rhythm. Building upon prior work, the system additionally features a modified discretization step and software optimization to improve multi-feature extraction and tactile display of music. The pitch, melody/timbre, and rhythm displays, respectively, were validated using Mean Average Error (MAE), Dynamic Time Warping (DTW) distance, and accuracy, yielding normalized averages of MAE = 0.1020 and DTW = 0.1518, and a rhythmic pattern accuracy of 97.56%. The Yin algorithm was shown to greatly improve the tactile display of vocals, with slight improvements for bass and accompaniments, while spectral flux and software optimizations significantly improved rhythm display. The wearable HMP effectively communicates multiple music features without the pitfalls of prior approaches. Future research can improve the system’s audio-tactile signal fidelity and explore the qualitative merits of multi-feature extraction in HMPs. Full article

Background: The coordination between the upper and lower limbs is essential for athletic performance. However, the structural features that influence coordination difficulty remain insufficiently understood. Few studies have systematically analyzed how task components such as the directional congruence or rhythm structure affect inter-limb coordination. Objective: This study aimed to clarify the structural factors that influence the difficulty of upper–lower limb coordination tasks under rhythmic constraints and to explore the feasibility of applying such tasks in future coordination assessments. Methods: Eighty-six male high school baseball players performed six Rhythm Jump tasks combining fixed upper limb movements with varying lower limb patterns. The task performance was analyzed using three indices: full task success, partial success, and average successful series. One year later, a follow-up test involving 27 participants was conducted to evaluate the reproducibility and sensitivity to the performance change. Results: The task difficulty was significantly affected by structural features, including directional incongruence, upper limb static holding, and rhythmic asynchrony. The tasks that exhibited these features had lower success rates. Some tasks demonstrated moderate reproducibility and captured subtle longitudinal changes in the performance. Conclusions: The results highlight the key structural factors contributing to coordination difficulty and support the potential applicability of Rhythm Jump tasks as a basis for future assessment tools. Although further validation is necessary, this study provides foundational evidence for the development of practical methods for evaluating inter-limb coordination. Full article

Background and Objectives: Essential tremor (ET) is a common neurological disorder, typically presenting as bilateral, rhythmic, and symmetric kinetic or postural tremors. In contrast, Parkinson’s disease (PD) is a progressive neurodegenerative disorder, characterized by resting tremor, rigidity, bradykinesia, and postural instability. Although both disorders involve tremor, ET and PD differ in clinical presentation and pathophysiology: ET generally involves action tremor and has a strong familial component, while PD more commonly presents with resting tremor and a weaker family history. A subset of ET patients may develop Parkinsonian features over time, although the relationship between ET and subsequent PD remains unclear. Genetic studies have identified only a few pathogenic variants in ET, suggesting it develops as a result of multifactorial genetic and environmental influences rather than simple Mendelian inheritance. ET is also recognized as a risk factor for developing PD, although the underlying mechanisms remain poorly understood. This study aimed to clarify potential genetic overlaps and distinctions in patients diagnosed with both ET and PD. Materials and Methods: We retrospectively analyzed 40 patients with a family history of ET or PD who were initially diagnosed with ET and later developed PD. Genetic screening and clinical assessments were conducted to investigate associated variants and clinical features. Results: Among these 40 patients, 17 different mutations were detected in 16 individuals. Three pathogenic or likely pathogenic variants were identified. The clinical characteristics and treatment responses of these patients were reviewed in relation to their genetic findings. Notably, none of the identified variants had previously been reported in association with PD following ET. Conclusions: A comprehensive clinical and genetic evaluation of ET patients who develop PD may offer insights into the underlying pathophysiology and inform future therapeutic strategies. Our findings support the need for further studies to explore the genetic landscape of patients with overlapping ET and PD features. Full article

Deciphering the structural hierarchy of musical compositions is indispensable for a range of music analysis applications, encompassing feature extraction, data compression, interpretation, and visualization. In this paper, we introduce a quantitative model grounded in fractal theory to evaluate the significance of individual notes within a musical piece. To analyze the quantized note importance, we adopt a rhythm-based approach and propose a series of detection operators informed by fundamental rhythmic combinations. Employing the Mamba model, we carry out recursive detection operations that offer a hierarchic understanding of musical structures. By organizing the composition into a tree data structure, we achieve an ordered layer traversal that highlights the music piece’s multi-dimensional features. Musical compositions often exhibit intrinsic symmetry in their temporal organization, manifested through repetition, variation, and self-similar patterns across scales. Among these symmetry properties, fractality stands out as a prominent characteristic, reflecting recursive structures both rhythmically and melodically. Our model effectively captures this property, providing insights into the fractal-like regularities within music. It also proves effective in musical phrase boundary detection tasks, enhancing the clarity and visualization of musical information. The findings illustrate the model’s potential to advance the quantitative analysis of music hierarchy, promoting novel methodologies in musicological research. Full article

This study explores rhythm metrics as a sociolinguistic marker in Philippine English (PhE), addressing gaps in understanding rhythmic variation in Southeast Asian Englishes. It aims to uncover how rhythmic patterns reflect sociolectal identities within a multilingual context. Using acoustic data from 30 participants in Manila, rhythm metrics (%V, ΔV, ΔC, nPVI, and rPVI) were analyzed to examine rhythmic tendencies. The findings reveal distinct patterns: PhE acrolect aligns with stress-timed rhythms of general American English, PhE basilect reflects syllable-timed features similar to Spanish, and PhE mesolect occupies a hybrid position blending elements of both. By emphasizing rhythm as a key identifier of sociolectal variation, this study advances the understanding of linguistic diversity in World Englishes and provides a novel framework for exploring identity in multilingual settings. Full article

Arabic poetry follows intricate rhythmic patterns known as ‘arūḍ’ (prosody), which makes its automated categorization particularly challenging. While earlier studies primarily relied on conventional machine learning and recurrent neural networks, this work evaluates the effectiveness of transformer-based models—an area not extensively explored for this task. We investigate several pretrained transformer models, including Arabic Bidirectional Encoder Representations from Transformers (Arabic-BERT), BERT base Arabic (AraBERT), Arabic Efficiently Learning an Encoder that Classifies Token Replacements Accurately (AraELECTRA), Computational Approaches to Modeling Arabic BERT (CAMeLBERT), Multi-dialect Arabic BERT (MARBERT), and Modern Arabic BERT (ARBERT), alongside deep learning models such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU). This study uses half-verse data across 14 m. The CAMeLBERT model achieved the highest performance, with an accuracy of 90.62% and an F1-score of 0.91, outperforming other models. We further analyze feature significance and model behavior using the Local Interpretable Model-Agnostic Explanations (LIME) interpretability technique. The LIME-based analysis highlights key linguistic features that most influence model predictions. These findings demonstrate the strengths and limitations of each method and pave the way for further advancements in Arabic poetry analysis using deep learning. Full article