Search Results (3,401)

Background/Objectives: Parkinsonian gait is characterized by impaired motor adaptability, altered temporal organization, and reduced movement variability. While data augmentation is commonly used to mitigate class imbalance in gait-based machine learning models, conventional strategies often ignore physiological differences between healthy and pathological movements, potentially distorting meaningful motor dynamics. This study explores whether preserving healthy motor variability while selectively augmenting pathological gait signals can improve the robustness and physiological coherence of gait pattern classification models. Methods: Eight patients with Parkinsonian gait patterns and forty-eight healthy participants performed walking tasks on the Motigravity platform under hypogravity conditions. Full-body kinematic data were acquired using wearable inertial sensors. A selective augmentation strategy based on smooth time-warping was applied exclusively to pathological gait segments (×5, σ = 0.2), while healthy gait signals were left unaltered to preserve natural motor variability. Model performance was evaluated using a hybrid convolutional neural network–long short-term memory (CNN–LSTM) architecture across multiple augmentation configurations. Results: Selective augmentation of pathological gait signals achieved the highest classification performance (94.1% accuracy, AUC = 0.97), with balanced sensitivity (93.8%) and specificity (94.3%). Performance decreased when augmentation exceeded an optimal range of variability, suggesting that beneficial augmentation is constrained by physiologically plausible temporal dynamics. Conclusions: These findings demonstrate that physiology-informed, selective data augmentation can improve gait pattern classification under constrained data conditions. Rather than supporting disease-specific diagnosis, this proof-of-concept study highlights the importance of respecting intrinsic differences in motor variability when designing augmentation strategies for clinical gait analysis. Future studies incorporating disease-control cohorts and subject-independent validation are required to assess specificity and clinical generalizability. Full article

A device-free human detection system based on a received signal strength indicator (RSSI) monitors and analyzes the change of RSSI signals to detect human movements in a wireless network. This study proposes and implements a real-time, device-free human detection system based on an RSSI distribution-based detection method with an autonomous threshold. The novelty and contribution of our solution is that the RSSI distribution concept is considered and used to calculate the optimal threshold setting for human detection, while thresholds can be automatically determined from RSSI data streams gathered from test environments. The proposed system can efficiently work without requiring an offline phase, as introduced in many existing works in the research literature. Experiments using 2.4 GHz IEEE 802.15.4 technology have been carried out in indoor environments in two laboratory rooms with different numbers of wireless links, human movement patterns, and movement speeds. Experimental results show that, in all test scenarios, the proposed method can monitor and detect human movement in a wireless network in real time. It outperforms a comparative method and achieves high accuracy (i.e., 100% detection accuracy) with a low computational complexity requirement. Full article

This study utilizes Digital Elevation Models (DEMs) with different spatial resolutions (SRTM 90 m, ASTER DEM 30 m, and ALOS PALSAR 12.5 m), Landsat-8 satellite imagery, and the Bouguer WGM-12 gravity model to analyze morphotectonic features in the Upper Guajira region of Colombia, a desert area in northern South America, area that is composed by low-relief serranías of Cabo de la Vela, Carpintero, Cosinas, Simarua, Jarara, and Macuira. Three DEMs were used to extract and map morphotectonic lineaments, drainage networks, and morphological features. Lineaments were characterised by azimuth frequency, length, density, lithological distributions, and geological timeframes, with support from a digitized geological map from the Colombian Geological Service (SGC). The analysis of the east–west (E-W) Cuisa fault, using the Riedel shear model, suggests a transtensional/transpressional tectonic regime influenced by the Caribbean and South American plates, characterised by NE-SW and E-W fault orientations. Lineaments were grouped into five geochronological categories based on the geological map, revealing a shift from NE-SW to E-W orientations from the Cretaceous period onward, reflecting the ongoing movement of the Caribbean plate. Folds and faults from this tectonic activity were enhanced using Landsat-8 band combinations. The WGM-12 model was separated into regional and residual signals, with the latter highlighting the serranías subregions. Residual gravity analysis revealed significant negative anomalies, suggesting lower-density lithologies surrounded by higher-density blocks. This pattern aligns with the regional geological framework and may reflect a crustal root or terrain dragging linked to the tectonic processes that shaped the serranías. Derivative residual gravity data also revealed lineaments oriented NE–SW, whose distribution extends beyond the morphometric boundaries of the subregions. The study found a strong correlation between structural and drainage patterns, demonstrating structural control over geomorphology. This study establishes a solid morphotectonic and geophysical framework for the Upper Guajira region, demonstrating how multi-resolution DEM analysis combined with gravity data can resolve regional deformation patterns, crustal architecture, and tectonic development along the Caribbean–South American plate boundary. Full article

The rapid development of autonomous driving systems has increased the demand for scalable frameworks capable of modeling vehicle motion patterns in complex traffic environments. This paper proposes a big data spatio-temporal modeling architecture that integrates Apache Spark version 4.0.1 (SparkML) with Long Short-Term Memory (LSTM) networks to analyze and classify vehicle trajectory patterns. The proposed SparkML–LSTM framework exploits Spark’s distributed processing capabilities and LSTM’s strength in sequential learning to handle large-scale traffic trajectory data efficiently. Experiments were conducted using the DETRAC dataset, which is a large-scale benchmark for vehicle detection and multi-object tracking consisting of more than 10 h of video captured at 24 different locations. The videos were recorded at 25 frames per second with a resolution of 960 × 540 pixels and annotated across more than 140,000 frames, covering 8.250 vehicles and approximately 1.21 million bounding box annotations. The dataset provides detailed annotations, including vehicle categories (Car, Bus, Van, Others), weather conditions (Sunny, Cloudy, Rainy, Night), occlusion ratio, truncation ratio, and vehicle scale. Based on the extracted trajectory features, vehicle motion patterns were categorized into predefined movement classes derived from trajectory dynamics. The experimental results demonstrate strong classification performance. These findings suggest that the proposed SparkML–LSTM architecture is effective for large-scale spatio-temporal trajectory modeling and traffic behavior analysis, and can serve as a foundation for higher-level decision-making modules in intelligent transportation system. Full article

Background: Feigned restriction of shoulder joint movement for secondary gain is clinically relevant and may misdirect care, distort disability determinations, and inflate system costs. Distinguishing feigning from structural pathology and from functional or psychosocial presentations is difficult because pain is subjective, performance varies, and no single sign or test is definitive. This comprehensive review hypothesizes that the systematic integration of clinical examination, objective biomechanical and neurophysiological testing, and emerging technologies can substantially improve detection accuracy and provide defensible medicolegal documentation. Methods: PubMed and reference lists were searched within a prespecified time frame (primarily 2015–2025, with foundational earlier works included when conceptually essential) using terms related to shoulder movement restriction, malingering/feigning, symptom validity, effort testing, functional assessment, and secondary gain. Evidence was synthesized narratively, emphasizing objective or semi-objective quantification of motion and effort (goniometry, dynamometry, electrodiagnostics, kinematic sensing, and imaging). Results: Detection is best approached as a stepwise, multidimensional evaluation. First-line clinical assessment focuses on reproducible incongruence: non-anatomic patterns, internal inconsistencies, distraction-related improvement, and mismatch between claimed disability and observed function. Repeated examinations and documentation strengthen inference. Instrumented strength testing improves quantification beyond manual testing but remains effort-dependent; repeat-trial variability and atypical agonist–antagonist co-activation can indicate submaximal performance without proving intent. Imaging primarily tests plausibility by confirming lesions or highlighting discordance between claimed limitation and minimal pathology, while recognizing that normal imaging does not exclude pain. Diagnostic anesthetic injections and electrodiagnostics can clarify pain-mediated restriction or exclude neuropathic weakness but require cautious interpretation. Motion capture and inertial sensors can document compensatory strategies and context-dependent normalization, yet validated standalone thresholds are limited. Conclusions: Feigned shoulder impairment cannot be confirmed by any single test. The desirable strategy combines structured assessment of inconsistencies with objective biomechanical and neurophysiologic measurements, interpreted within the whole clinical context and rigorously documented; however, prospective validation is still needed before routine implementation. Full article

The aim of this study was to analyze lower limb muscle activation patterns and task-dependent asymmetries across selected badminton movement sequences using wearable electromyography (EMG). Twelve elite male badminton players (18.3 ± 3.3 years, 171.3 ± 6.8 cm, 67.7 ± 8.2 kg, and 13.1 ± 4.6% body fat) in the highest national league participated in the study. Surface EMG was recorded bilaterally from the quadriceps femoris, hamstring, and gluteus muscle groups using wearable EMG shorts during standardized badminton-specific movement sequences. Across all analyzed techniques, a pronounced dominance of quadriceps activation was observed compared to hamstrings and gluteus muscle groups (p < 0.001). Significant inter-limb asymmetries in quadriceps contribution were identified in most net and defensive movements, whereas hamstring activation remained relatively symmetrical across limbs. Gluteus muscles group contribution exhibited task-dependent asymmetry, particularly during defensive lunges. Badminton-specific movements are characterized by quadriceps-dominant neuromuscular strategies and technique-dependent inter-limb asymmetries. These findings are specific to elite, right-dominant male badminton players and should be interpreted within this performance context. Full article

Understanding how learners process data visualizations with seductive details is essential for improving comprehension and engagement. This study examined the influence of task-relevant and task-irrelevant seductive details on attentional distribution and comprehension in the context of data story learning, using COVID-19 data visualizations as experimental materials. A gaze-based methodology was applied, using eye-movement data and saliency maps to visualize learners’ attentional patterns while processing bar graphs with varying embellishments. Results showed that task-relevant seductive details supported comprehension for learners with higher visuospatial abilities by guiding attention toward textual information, while task-irrelevant details hindered comprehension, particularly for those with lower visuospatial abilities who focused disproportionately on visual elements. Working memory capacity emerged as a significant predictor of attentional distribution. Additionally, repeated exposure to data visualizations enhanced participants’ ability to recognize visualization types, improving efficiency and reducing reliance on legends and supplementary text. Overall, this study highlights the cognitive mechanisms underlying visualization processing in data story learning and provides practical implications for education, human–computer interaction, and adaptive technology design, emphasizing the importance of tailoring visualization strategies to individual learner differences. Full article

Reliable motion classification is essential for practical prosthetic-hand control. Unintended activations caused by ambiguous motions, unknown motions, or non-target body movements can degrade controllability and compromise user safety. Mechanical-sensing approaches are attracting attention as alternatives or complements to surface electromyography, and tactile-sensor-based methods represent one such direction. However, despite extensive studies on prosthetic control, systematic investigations of computationally lightweight motion-rejection strategies remain limited. This study investigates rejection mechanisms to improve the robustness of polyvinylidene fluoride (PVDF) tactile-sensor-based prosthetic control. The proposed approach selectively withholds outputs for misclassified and non-target inputs. We compare three mechanisms: (1) one-class support vector machine (OCSVM) outlier detection, (2) entropy-based rejection using a multilayer perceptron (BPNN-Entropy), and (3) a parameter-free decision-consistency check for one-vs-rest support vector machines (SVMs) that withholds classification when the output sign pattern is inconsistent (one-vs-rest reject option (OvR-RO); proposed). Performance is evaluated for three sources of unintended activation: ambiguous target trials (retrospectively defined), unknown motions excluded from training, and non-target body movements. The results show that OvR-RO achieves a favorable balance between rejection rate and rejection precision for ambiguous motions, while maintaining responsiveness. Overall, explicitly rejecting misclassified and non-target motions is effective for enhancing robustness in tactile-sensor-based prosthetic control. Full article

Visual search behavior, influenced by expertise, prior knowledge, training, and visual fatigue, is crucial in ophthalmic diagnostics. This study investigates differences in eye-tracking strategies between novice and experienced eye care practitioners during fundus image interpretation. Forty-seven participants, including 37 novices (first- to fourth-year optometry students) and 10 experienced optometrists (≥2 years of experience), viewed 20 fundus images (10 normal, 10 abnormal) while their eye movements were recorded using an Eyelink1000 Plus gaze tracker (2000 Hz). Diagnostic and laterality accuracy were assessed, and statistical analyses were conducted using Sigma Plot 12.0. Results showed that experienced practitioners had significantly higher diagnostic accuracy (83 ± 6.3%) than novices (70 ± 12.9%, p < 0.005). Significant differences in oculomotor behavior were observed, including median latency (p < 0.001), while no significant differences were found in median peak velocity (p = 0.11) or laterality accuracy (p = 0.97). Diagnostic accuracy correlated with fixation count in novices (r = 0.54, p < 0.001), while laterality accuracy correlated with total dwelling time (r = −0.62, p < 0.005). The experienced practitioners demonstrated systematic and focused visual search patterns, whereas the novices exhibited unorganized scan paths. Enhancing training with visual feedback could improve fundus image analysis accuracy in novice clinicians. Full article

Purpose: Orthognathic surgery is a cornerstone therapeutic approach for correcting dentofacial deformities; however, its Impact on neuromuscular adaptation remains incompletely understood, particularly regarding different surgical strategies. The aim of this study was to evaluate and compare neuromuscular changes in patients undergoing monomaxillary or bimaxillary orthognathic surgery. Methods: Eighty adult patients treated with combined orthodontic–surgical therapy were included (37 monomaxillary; 43 bimaxillary). A control group of 20 healthy adult subjects with physiological occlusion and no history of orthodontic or orthognathic treatment was included. Surface electromyography (sEMG) of the masseter and anterior temporalis muscles and mandibular kinesiography were performed using standardized protocols at five treatment phases. Electromyographic symmetry indices (Percent Overlapping Coefficient—POC), muscle activity (µV), IMPACT values, and mandibular movement parameters were analyzed. Results: During the presurgical orthodontic phase, both groups showed comparable reductions in neuromuscular activity. Postoperatively, monomaxillary patients exhibited earlier stabilization of sEMG symmetry and a faster increase in IMPACT values, approaching physiological reference ranges at the final follow-up. In contrast, bimaxillary patients showed greater variability and slower functional recovery. Mandibular opening and lateral movements improved in all patients, with more stable kinesiographic patterns observed in the monomaxillary group. Conclusions: Within the limitations of this study, neuromuscular adaptation following orthodontic–surgical treatment appears to be associated with the surgical approach adopted, rather than representing a direct effect of surgical extent. These findings support the role of functional assessment as a complementary component in the management of orthognathic patients. Full article

Chemical contaminants are increasingly detected in freshwater environments, yet the physiological and molecular responses of many non-model freshwater invertebrates to acute chemical stress remain poorly understood. In this study, we investigated the physiological and transcriptomic responses of the freshwater hydrozoan Craspedacusta sowerbii to two widespread aquatic pollutants: the antibiotic sulfamethoxazole (20 μM) and the heavy metal salt CdSO₄ (10 μM). Morphological and behavioral observations showed that sulfamethoxazole exposure led to reduced motility and body shrinkage, whereas cadmium exposure caused rapid loss of movement and complete mortality within 24 h. RNA sequencing revealed distinct transcriptional response patterns to the two stressors. Sulfamethoxazole exposure primarily induced the up-regulation of genes associated with oxidative stress, apoptosis, immune responses, and signaling pathways, suggesting an active but limited stress-adaptation response. In contrast, cadmium exposure resulted in extensive down-regulation of genes involved in metabolic pathways, cell cycle regulation, fatty acid metabolism, and anti-aging processes, suggesting severe disruption of core metabolic processes. Comparative pathway analyses identified both shared stress-related responses and pollutant-specific transcriptional signatures, with cadmium exerting markedly stronger inhibitory effects at both physiological and molecular levels. These results reveal clear thresholds of stress tolerance and response failure in C. sowerbii under chemical pollution, and highlight its ecological sensitivity to water quality deterioration. Together, these findings provide mechanistic insight into acute pollutant-induced stress responses in a freshwater Cnidarian and offer a useful reference for understanding how freshwater invertebrates respond to short-term chemical disturbances. Full article

This study presents a standardized method for multimodal monitoring of exercise execution in isolated, confined, and extreme (ICE) environments, addressing the need for reproducible assessment of neuromuscular and cardiovascular responses under space- and equipment-limited conditions. The method integrates wearable surface electromyography (sEMG), inertial measurement units (IMU), and electrocardiography (ECG) to capture muscle activation, movement, and cardiac dynamics during space-efficient exercise. Ten exercises suitable for confined habitats were implemented during analog missions conducted in the DeepLabH03 facility, with feasibility evaluated in a seven-day campaign involving three adult participants. Signals were synchronized using video-verified repetition boundaries, sEMG was normalized to maximum voluntary contraction, and sEMG amplitude- and frequency-domain features were extracted alongside heart rate variability indices. The protocol enabled stable real-time data acquisition, reliable repetition-level segmentation, and consistent detection of muscle-specific activation patterns across exercises. While amplitude-based sEMG indices showed no uniform main effect of exercise, robust exercise-by-muscle interactions were observed, and sEMG mean frequency demonstrated sensitivity to differences in movement strategy. Cardiac measures showed limited condition-specific modulation, consistent with short exercise bouts and small sample size. As a proof-of-concept feasibility study, the proposed protocol provides a practical and reproducible framework for multimodal physiological monitoring of exercise in ICE analogs and other constrained environments, supporting future studies on exercise quality, training load, and adaptive feedback systems. The protocol is designed to support near-real-time monitoring and forms a technical basis for future exercise-quality feedback in confined habitats. Full article

This study introduces a deterministic framework for optimising the path planning of autonomous drill rigs in open-pit mining operations. While prior research has primarily focused on automating drilling mechanics, this study addresses the essential but underexplored phase of tramming, defined as the rig’s non-productive movement between holes. The proposed approach integrates geometric pattern recognition and slope-based route alignment. It also incorporates practical maneuverability constraints to generate efficient, smooth, and safe paths. Unlike evolutionary algorithms, which suffer from variability and demand extensive computation, this method delivers fast and consistent results. These are well-suited to the dynamic conditions of real-world mining. Applied to a 1596-hole case study, the framework reduced tramming time by over 50%, shortening the total project duration by 8% compared with the actual project. The findings demonstrate its potential to improve both operational efficiency and commercial readiness for autonomous drilling systems. Full article

This study focuses on classifying electromyographic (EMG) signals to identify seven specific hand movements, including complete hand closure, individual finger closures, and a pincer grip. Accurately distinguishing these movements is challenging due to overlapping muscle activation patterns. To address this, a methodology structured in five stages was developed: placement of electrodes on specific forearm muscles to capture electrical activity during movements; acquisition of EMG signals from twelve participants performing the seven types of movements; preprocessing of the signals through filtering and rectification to enhance quality, followed by the extraction of features from three distinct types of preprocessed signals—filtered, rectified, and envelope signals—to facilitate analysis in the temporal, frequency, and time–frequency domains; extraction of relevant features such as amplitude, shape, symmetry, and frequency variance; and classification of the signals using eight machine learning algorithms: support vector machine (SVM), multiclass logistic regression, k-nearest neighbors (k-NN), Bayesian classifier, artificial neural network (ANN), random forest, XGBoost, and LightGBM. The performance of each algorithm was evaluated using different sets of features derived from the preprocessed signals to identify the most effective approach for classifying hand movements. Additionally, the impact of various signal representations on classification accuracy was examined. Experimental results indicated that some algorithms, especially when an expanded set of features was utilized, achieved improved accuracy in classifying hand movements. These findings contribute to the development of more efficient control systems for myoelectric prostheses and offer insights for future research in EMG signal processing and pattern recognition. Full article

Electroencephalography (EEG) has attracted significant attention as an effective modality for interaction between the physical and virtual worlds, with EEG-based person identification serving as a key gateway to such applications. Despite substantial progress in EEG-based person identification, several challenges remain: (1) how to design an end-to-end EEG-based identification pipeline; (2) how to perform automatic electrode selection for each user to reduce redundancy and improve discriminative capacity; (3) how to enhance the backbone network’s feature extraction capability by suppressing irrelevant information and better leveraging informative patterns; and (4) how to leverage higher-level information in EEG signals to achieve intent recognition (i.e., EEG-based task/activity recognition under controlled paradigms) on top of person identification. To address these issues, this article proposes, for the first time, a unified deep learning framework that integrates automatic electrode selection, person identification, and intent recognition. We introduce a novel backbone network, AES-MBE, which integrates automatic electrode selection (AES) and intent recognition. The network combines a channel-attention mechanism with a multi-scale bidirectional encoder (MBE), enabling adaptive capture of fine-grained local features while modeling global temporal dependencies in both forward and backward directions. We validate our approach using the PhysioNet EEG Motor Movement/Imagery Dataset (EEGMMIDB), which contains EEG recordings from 109 subjects performing 4 tasks. Compared with state-of-the-art methods, our framework achieves superior performance. Specifically, our method attains a person identification accuracy of 98.82% using only 4 electrodes and an average intent recognition accuracy of 91.58%. In addition, our approach demonstrates strong stability and robustness as the number of users varies, offering insights for future research and practical applications. Full article