Search Results (3,461)

Background: Pumping in Laser-class sailing is a dynamic propulsion technique used in marginal wind conditions and characterized by repetitive, coordinated oscillations of the sailor–sail system. Despite its practical relevance, its biomechanical and ergonomic demands remain insufficiently characterized. Methods: A mixed-methods framework was applied combining questionnaire data, kinematic analysis, ergonomic assessment, and musculoskeletal modelling. Thirty-six competitive Laser sailors completed a Borg CR-10-based questionnaire on perceived discomfort/fatigue across body regions at predefined time points (during pumping, immediately after training, and the following day). A controlled land-based multi-angle video acquisition was used to reconstruct a standardized pumping posture and parameterize a digital human model in DELMIA^® for postural/kinematic analysis. Ergonomic risk was assessed using REBA, and muscle activity was estimated using the AnyBody^® Modeling System (simulation-derived normalized muscle activity across 129 muscles). Results: the simulation identified high neuromuscular demand in the trunk and shoulder complex, with several deep trunk stabilizers and the left latissimus dorsi reaching 100% modeled normalized muscle activity. Marked lateral asymmetry was observed, with right-sided trunk dominance and left-sided shoulder dominance. Kinematic analysis showed substantial joint excursions, with large lumbar motion amplitudes, while REBA yielded a score of 11 (Very-High Risk). Questionnaire data indicated a high prevalence of pumping-related musculoskeletal discomfort (72.2%), most frequently involving the lower back, shoulders, and knees. A dissociation was observed between modeled muscle activity and perceived fatigue, with the lower limbs rated as most fatigued despite lower modeled activation than the trunk. Conclusions: Findings identify the deep trunk stabilizers, latissimus dorsi, and lower extremities as key regions involved in pumping, with marked lateral asymmetry and high ergonomic risk. They support targeted training, injury-prevention, and ergonomic strategies to improve performance and reduce injury risk in competitive sailing. Full article

Multimodal human–AI systems generally consider facial expressions and body motions as separate input streams, leading to disjointed interpretations and diminished emotional coherence. To overcome this issue, we offer the Engagement-Safe Expressive Alignment (ESEA) paradigm and the Unified Visual Synchrony (UVS) framework as its computational implementation. UVS models the coherence between facial expressions and gestures, offering an interpretable visual synchrony signal that can function as adaptive feedback in human–AI interactions. The framework’s key component is the Consistency Index for Affective Synchrony ($CIAS$), which correlates brief visual segments with scalar synchrony scores through a common latent representation. Facial and gestural signals are processed by modality-specific projection networks into a unified latent space, and $CIAS$ is derived from the similarity and short-term temporal consistency of these latent trajectories. The synchrony index is regarded as an estimation of affective visual coherence within the ESEA paradigm. We formalize the UVS/$CIAS$ framework and conduct a comparative experimental evaluation utilizing matched and mismatched face–gesture segments derived from rendered dialog footage. Utilizing ROC analysis, score distribution comparisons, temporal visualizations, and negative control tests, we illustrate that $CIAS$ effectively captures structured face–gesture alignment that surpasses similarity-based baselines, while also delivering a persistent, time-resolved synchronization signal. These findings establish $CIAS$ as a principled and interpretable feedback signal for future affect-aware, engagement-focused multimodal agents. Full article

Talking Head Generation (THG) is a rapidly advancing field at the intersection of computer vision, deep learning, and speech synthesis, enabling the creation of animated human-like heads that can produce speech and express emotions with high visual realism. The core objective of THG systems is to synthesize coherent and natural audio–visual outputs by modeling the intricate relationship between speech signals, facial dynamics, and emotional cues. These systems find widespread applications in virtual assistants, interactive avatars, video dubbing for multilingual content, educational technologies, and immersive virtual and augmented reality environments. Moreover, the development of THG has significant implications for accessibility technologies, cultural preservation, and remote healthcare interfaces. This survey paper presents a comprehensive and systematic overview of the technological landscape of Talking Head Generation. We begin by outlining the foundational methodologies that underpin the synthesis process, including generative adversarial networks (GANs), motion-aware recurrent architectures, and attention-based models. A taxonomy is introduced to organize the diverse approaches based on the nature of input modalities and generation goals. We further examine the contributions of various domains such as computer vision, speech processing, and human–robot interaction, each of which plays a critical role in advancing the capabilities of THG systems. The paper also provides a detailed review of datasets used for training and evaluating THG models, highlighting their coverage, structure, and relevance. In parallel, we analyze widely adopted evaluation metrics, categorized by their focus on image quality, motion accuracy, synchronization, and semantic fidelity. Operating parameters such as latency, frame rate, resolution, and real-time capability are also discussed to assess deployment feasibility. Special emphasis is placed on the integration of generative artificial intelligence (GenAI), which has significantly enhanced the adaptability and realism of talking head systems through more powerful and generalizable learning frameworks. Full article

Hollow hydrogels are promising for flexible electronics and bioengineering, yet their fabrication is limited by sacrificial templates, specialized equipment, and complex engineering processes. Herein, a facile wet-spinning strategy is developed to fabricate sodium alginate (SA) hollow hydrogels. Extruding SA/CaCO₃ precursor suspension into an acidic coagulation bath induces simultaneous ionic cross-linking and in situ CO₂ generation, driving the self-formation of hollow tubular architectures with tunable morphologies, mechanical performance, macroscopic architecture, and functional properties. Moreover, the introduction of secondary cross-linking enhances the SA hydrogels’ water retention and resistance to freezing conditions. Utilizing their intrinsic ionic conductivity, the hollow hydrogels demonstrate outstanding sensing performance, enabling reliable detection of both large-amplitude limb motions and subtle muscle activity in the human body. Furthermore, hollow hydrogel tubes with diverse geometries can be readily fabricated by simply modifying the spinning mold, thereby broadening their potential applications. In vitro cytotoxicity assessments further confirm that the SA hollow hydrogels exhibit excellent biocompatibility with minimal cytotoxicity, satisfying the fundamental criteria for bioengineering applications. The combination of a simple yet controllable fabrication strategy with the intrinsic multifunctionality of the SA hollow tubes confers substantial potential for their deployment in bioengineering and flexible electronic applications. Full article

Resistance training machines are designed to provide either constant or variable resistance, with the latter intended to generate a machine resistive torque (MRT) that mirrors the natural fluctuations in human torque capability (HTC) across joint angles. Yet, achieving a precise match between MRT and HTC remains a persistent challenge. This study aimed to validate a novel variable-cam resistance system, the Variable Moment Arm Cam^® (VMAC^®), by examining torque output and muscle activation during leg extension across the full range of motion (100–0°), using repeated testing and direct comparison with an isokinetic dynamometer. Twenty-two young men completed four randomized sessions, two on the variable-cam system and two on the dynamometer, each separated by 72–96 h. Torque and muscle activity were recorded during six isometric contractions at 100°, 80°, 60°, 40°, 20°, and 0°. The variable-cam system produced torque and activation patterns broadly comparable to the dynamometer, with acceptable agreement across angles. Validity was highest at 60°, aligning with the region of peak torque, whereas greater variability emerged at the extremes of flexion and extension. Muscle activation profiles were similar between devices, though more variable than torque, underscoring the inherent complexity of neuromuscular assessment. Full article

As virtual reality technologies evolve toward widespread adoption in education, industry, and social communication, their increasing complexity exposes new and often overlooked security challenges. Immersive environments collect continuous multimodal data, including motion tracking, gaze, voice, and biometric indicators that extend far beyond traditional computing attack surfaces. This paper synthesizes recent research (2023–2025) on cybersecurity, privacy, and behavioral safety in virtual reality (VR) systems, identifies the main vulnerabilities, and proposes a unified defense architecture: the three-layer VR Security Framework (TVR-Sec). Through comparative review and conceptual integration of 31 peer-reviewed studies, three interdependent protection domains emerged: (1) System Integrity, securing hardware, firmware, and network communications against spoofing and malware; (2) User Privacy, ensuring the ethical management of biometric and behavioral data through federated learning and consent-based control; and (3) Socio-Behavioral Safety, addressing harassment, manipulation, and psychological exploitation in shared virtual spaces. The framework situates VR security as a multidimensional adaptive process that combines technical hardening with human-centered defense and ethical design. By aligning cyber–human protections through an AI-driven monitoring and policy engine, TVR-Sec advances a holistic paradigm for securing future immersive ecosystems. Full article

Background: Lumbar spondylosis is a frequent cause of chronic low back pain, often associated with radiculopathy. Although imaging evaluation is widely used, it does not always reflect the degree of functional impairment of the nerve roots. Electrophysiological assessments, such as nerve conduction studies (NCS) and surface electromyography (sEMG), can provide additional information on neuromuscular function under conservative treatment. Methods: This quasi-experimental study included 60 patients with lumbar spondylosis and 25 healthy subjects, who underwent clinical, imaging, and electrophysiological assessments. NCS and sEMG parameters were assessed in the patient group before and six months after rehabilitation treatment. The control group was assessed only once, at baseline. We analyzed the nerve conduction velocity of the tibial and peroneal nerves and the sEMG activity of the tibialis anterior muscle bilaterally. Statistical analysis used nonparametric tests, Spearman’s coefficient, and Hodges–Lehmann estimates. Results: Compared to the control group, patients presented increased residual latencies and reduced CMAP amplitude and motor conduction velocity values (p < 0.001). After rehabilitation treatment, significant improvements in NCS parameters were observed, with decreased latencies and increased CMAP amplitude and motor conduction velocity bilaterally (p < 0.001). Also, sEMG amplitude and recruitment pattern scores increased significantly at the 6-month follow-up (p ≤ 0.004). Correlations between electrophysiological parameters and the severity of imaging changes were limited, with modest associations for left tibial latencies (ρ = 0.401–0.467; p < 0.050). Conclusions: In patients with lumbar spondylosis, rehabilitation treatment was associated with functional improvements in nerve conduction velocity parameters and muscle activity. Full article

The flexible foam piezoresistive sensor demonstrates significant potential for wearable strain-sensing applications due to its substantial deformation capacity, excellent flexibility, and cost effectiveness. However, conventional flexible foam piezoresistive sensors often struggle to simultaneously achieve high sensitivity, a wide pressure detection range, fast response and long-term stability. This paper employed a glucose-based sugar-templating method to fabricate a fine-pore (50 μm) foam structure complemented by a dual-filler strategy to enhance overall performance. A robust porous conductive network was constructed by embedding zinc oxide (ZnO) and multi-walled carbon nanotubes (MWCNTs) into a polydimethylsiloxane (PDMS) matrix. The resulting sensor exhibits outstanding piezoresistive properties, featuring a wide linear detection range (0–80% strain) and a high sensitivity of 9.02 kPa⁻¹ within the 0–10 kPa pressure range. It demonstrates rapid response/recovery times of 50/70 ms and maintains stable output performance even after 5000 compression cycles at 300 kPa. The sensor also exhibits negligible environmental interference and excellent long-term stability. When attached to finger joints, feet soles, or the throat, the sensor enables functions such as finger bending recognition, race-walking violation discrimination, gait analysis, and vocal fold vibration recognition, thereby demonstrating its considerable potential for application in human–computer interaction and human motion detection. Full article

Reverse parking maneuvering of a vehicle with a trailer system is a difficult task to complete for human drivers due to the multi-body nature of the system and the unintuitive controls required to orientate the trailer properly. The problem is complicated with the presence of other vehicles that the trailer and its connected vehicle must avoid during the reverse parking maneuver. While path-planning methods in reverse motion for vehicles with trailers exist, there is a lack of results that also offer collision avoidance as part of the algorithm. This paper hence proposes a modified Hybrid A*-based algorithm that can accommodate the vehicle–trailer system as well as collision avoidance considerations with the other vehicles and obstacles in the parking environment. One of the novelties of this proposed approach is its adaptability to the vehicle with trailer system, where limits of usable steering input that prevent the occurrence of jackknife incidents vary with respect to system configuration. The other contribution is the addition of the collision avoidance functionality which the standard Hybrid A* algorithm lacks. The method is developed and presented first, followed by simulation case studies to demonstrate the efficacy of the proposed approach. Full article

Breaststroke performance is highly dependent on lower-limb biomechanics and the coordination of movement during the kick cycle. Recent advances in wearable inertial sensor technology enable objective analysis of human motion in real training environments. This study presents an exploratory pilot investigation aimed at evaluating the feasibility of using wearable inertial sensors for biomechanical analysis of the breaststroke kick in young swimmers. Five male children (aged 8–10 years) with basic breaststroke proficiency participated in a single-group pre–post exploratory study conducted over a three-month period. Lower-limb motion was monitored using wearable inertial measurement units attached bilaterally to the shanks and feet, allowing real-time kinematic feedback and data recording during training sessions. The intervention consisted of five structured training sessions integrating drill-based breaststroke kick exercises with sensor-assisted feedback. Outcome measures included time-based swimming performance tests (40 m breaststroke kick with kickboard and 40 m breaststroke without kickboard) and qualitative biomechanical evaluations of the passive and active phases of the breaststroke kick. Additionally, selected IMU-derived kinematic variables (peak ankle dorsiflexion and external foot rotation angles) were analyzed to provide quantitative biomechanical insight. Following the intervention, improvements were observed across all outcome measures, including reduced swimming times and increased technique scores assigned by two independent evaluators. These findings support the feasibility of integrating wearable IMUs for technique monitoring and simple kinematic quantification of breaststroke kick mechanics in young swimmers; larger controlled studies are required to assess efficacy. Full article

In autonomous driving, motion sickness (MS) arises from physical or visual stimuli, or a combination of both. However, objective quantification of MS level (MSL) remains limited beyond questionnaire-based assessments. Using multimodal human signals (physiological and behavioral) collected in an autonomous driving simulator, this study addresses the association between these signals and MSL, across these MS types, by (i) screening and curating a decade of human-signal MS studies (HS-Set) to establish a data-driven foundation for selecting target sensor domains and features, (ii) constructing a dataset with subjective measures of MSL (fast motion sickness scale and simulator sickness questionnaire (SSQ)), alongside human signals (electroencephalogram (EEG), photoplethysmogram (PPG), electrodermal activity (EDA), skin temperature, and head/eye movement), (iii) conducting a correlation analysis between MSL and the identified features from HS-Set, and (iv) quantifying multivariable contributions at the feature and sensor domains through an explainable boosting machine (EBM). Key correlations include head amplitude/energy (pitch/surge) with SSQ total/oculomotor, eye entropy with nausea/oculomotor (positive), and EDA with nausea (negative). The EBM-based contribution analysis highlights EEG connectivity and head kinematics as dominant contributors; excluding EEG, the interpretability of single-domain models remains limited. Additionally, a combination of Head, PPG, and EDA domains retains over 80% of the full model’s interpretability. Full article

Machine learning (ML) is increasingly influencing the assessment and delivery of movement and exercise, yet its role within adapted physical activity (APA) for individuals with chronic conditions has not been comprehensively synthesized. ML-based approaches have the potential to enhance functional assessment, support individualized exercise prescription, and facilitate scalable monitoring across preventive, community-based, and long-term adapted exercise settings, particularly in populations characterized by functional heterogeneity and variable responses to exercise. The aim of this narrative review is to synthesize and critically discuss current ML applications relevant to the core professional processes of APA practice. A structured narrative review was conducted using searches in PubMed/MEDLINE, Scopus, and Web of Science, complemented by targeted searches in engineering-oriented sources to capture ML methods not consistently indexed in biomedical databases. The search covered the period in which contemporary ML approaches have been increasingly applied to human movement and exercise research and was last updated in January 2026. Evidence was synthesized thematically into application-oriented domains relevant to APA practice. ML applications in APA include markerless motion and gait analysis, wearable-sensor data processing, balance and fall-risk assessment, and functional classification. Predictive and adaptive models support individualized regulation of exercise intensity, progression, and workload, including remote and hybrid delivery models. Applications span oncology, cardiometabolic, respiratory, neuromuscular conditions, and adapted sport contexts. Ethical, legal, and governance issues, such as algorithmic bias, data privacy, and professional accountability, emerge as central considerations for safe and equitable implementation. ML represents a promising decision-support layer for APA, complementing professional expertise through enhanced assessment, personalization, and monitoring. Its effective integration requires robust validation, interpretability, and responsible governance to ensure that ML augments, rather than replaces, professional judgment in APA practice. Full article

Biarticular actuators can enhance efficiency and stability in legged locomotion by transferring energy between joints. Their effectiveness depends strongly on the lever arm ratio—the ratio of the actuator’s moment arm at one joint to its moment arm at another—which governs how torque is distributed across joints during movement. Inspired by biomechanics, early robotic studies implemented biarticular actuators to improve energy efficiency, joint coordination, and positional control, primarily in planar or single-joint systems, leaving a gap in fully 3D robotic legs. Here, we present a geometry optimization framework for a robotic leg incorporating both biarticular and monoarticular actuators. Using human motion capture and joint torque data, we optimized the linkage mechanisms so that the system can maintain the required joint torques while keeping biarticular actuator moment arm ratios near their optimal values during walking and running. The optimized leg achieved a minimum achievable cost of transport of approximately $0.41 \mathrm{J}/(\mathrm{kg}·\mathrm{m})$ for walking and $0.62 \mathrm{J}/(\mathrm{kg}·\mathrm{m})$ for running. Full article

Background: Microgravity exposure causes muscle atrophy and bone density loss in astronauts. Traditional motion analysis provides estimations of external kinematics and muscle activation, but cannot resolve internal load. OpenSim closes this gap by applying musculoskeletal modeling to estimate internal joint mechanics. Methods: In this study, we aimed to develop an OpenSim workflow to estimate joint angles and moments using datasets from two publicly available gait studies: the Politecnico di Milano study (Dataset 1), which includes level-floor walking, walking on heels, walking on toes, and step-down-from-stairs tasks, and Maclean et al.’s walking study in reduced gravities (Dataset 2), which includes four simulated gravity levels (1.0 G, 0.76 G, 0.54 G, and 0.31 G). Marker and ground reaction force (GRF) data, along with participants’ mass, were used to prepare the first three steps of OpenSim’s workflow, including scaling, inverse kinematics (IK), and inverse dynamics (ID). Scripts using MATLAB R2025a (The MathWorks, Inc., Natick, MA, USA) were created to store, normalize, and compare OpenSim outputs with reference data on the right leg. Pearson’s correlation coefficient (PCC) was used to quantify agreement between OpenSim-derived joint angles and moments and the reference data, and root mean square error (RMSE) was used to characterize accuracy. Results: Hip and knee angles showed excellent correlation across both datasets (PCC > 0.974). Ankle angles were more variable, particularly in Dataset 1 (PCC = 0.833; RMSE = 19.797°) compared to Dataset 2 (PCC = 0.995; RMSE = 8.73°). Joint moment correlations were strong for hip and knee (PCC > 0.85), though ankle moments in Dataset 1 exhibited lower correlation (PCC = 0.677) and higher error (0.30 Nm/kg) compared to the high accuracy observed across all joints in Dataset 2. Discussion: We speculate that the lower PCC values and higher RMSE observed for ankle dorsi/plantar flexion angle and moment in Dataset 1 are mainly attributable to differences in shank segment frame definitions between the OpenSim model and the human body model used in Dataset 1. Higher ankle angle RMSEs in Dataset 2 may be due to lower weights assigned to ankle markers in the scaling and IK setup files, resulting in different ankle joint center definitions. Conclusion: In the future, we plan to improve this OpenSim workflow by including additional participants and datasets collected in simulated reduced-gravity environments and by implementing a residual reduction algorithm (RRA) and computed muscle control (CMC) to enable muscle activation estimation. Full article

In the era of Industry 4.0, artificial intelligence technology is experiencing rapid development, and the integration of artificial intelligence (AI) with flexible sensors has emerged as a transformative approach for human motion and posture sensing. This paper explores the advancements in AI-enhanced flexible sensors, focusing on the application of flexible sensors on various parts of the human body. Flexible sensors, due to their conformability and sensitivity, are ideal for capturing the dynamic and subtle movements of the human body. AI algorithms, particularly machine learning and deep learning techniques are employed to process the complex data streams from these sensors, enabling the accurate recognition and prediction of various human postures and motions. The combination of these technologies overcomes the limitations of traditional sensing systems, offering higher precision, adaptability, and real-time feedback. It can be applied to healthcare for rehabilitation monitoring, sports for performance enhancement, and human–computer interaction for intuitive control. This review also discusses the challenges such as sensor reliability, data privacy, and power management. The future outlook emphasizes more sophisticated AI models and deeper technology integration, promising a seamless integration into everyday life for enhanced human–machine interaction and health monitoring. Full article