Search Results (174)

Optimizing neuromuscular strength and balance is essential for performance and injury prevention in elite Paralympic sport. However, limited evidence describes how these parameters change over time during specific phases of the training season in athletes with lower limb deficiencies. This retrospective case series aimed to describe longitudinal changes in neuromuscular and balance performance during the fundamental preparation period in elite athletes using prosthetic devices. Routinely collected performance data from five international-level Paralympic athletes (Para-swimming and Para-athletics) were retrospectively analyzed across two preparatory observation windows conducted in consecutive competitive seasons. Neuromuscular performance was assessed using countermovement jump variables, while static balance was evaluated through Inertial Measurement Unit-derived sway metrics. Within-athlete changes were examined using descriptive and exploratory analyses. At the group level, changes were observed in selected neuromuscular and balance outcomes over time, including jump height and path length. Individual analyses revealed substantial inter-athlete variability in the magnitude and direction of changes across all outcomes. Overall, the findings indicate that neuromuscular and postural performance may fluctuate meaningfully during preparatory phases in elite athletes with lower limb deficiencies. This study provides exploratory insights derived from real-world training settings and highlights the value of longitudinal monitoring to support individualized performance management in Paralympic sport. Full article

Current internal dose assessments in nuclear emergencies rely on static, upright voxel phantoms, often neglecting realistic human postures and physiological factors—such as breathing rates specific to emergency scenarios—that influence radionuclide intake and biokinetics. We present a voxel deformation method based on an improved as-rigid-as-possible (ARAP) algorithm incorporating a novel smoothing term to generate anatomically consistent stooping and swivelling models. Coupled with Geant4 Monte Carlo simulations using the full decay spectra of radionuclides relevant to simulated nuclear accident scenarios (i.e., ¹³¹I and ¹³⁷Cs), and incorporating scenario-specific respiratory parameters into activity calculations, our results demonstrate that body posture significantly influences internal dose distributions: for ¹³⁷Cs, the specific absorbed fraction (SAF) of the liver increases by up to 24.9% in the stooping posture, while swivelling induces variations of up to 15.1%. In contrast, dose metrics for ¹³¹I show minimal sensitivity to posture (<5%). These findings highlight the importance of incorporating realistic postures and context-aware physiological parameters into emergency dosimetry. Our method enables behaviorally realistic internal dose reconstruction and provides a robust foundation for integrating human motion and respiratory data into rapid triage and risk assessment protocols. Full article

Soft pneumatic actuators (SPAs) are widely used in robotic systems due to their inherent compliance and safety during human–robot interaction. However, their intrinsic softness often leads to insufficient stiffness and a low load-bearing capacity, which limit their applicability. In this work, a novel soft–rigid hybrid pneumatic actuator incorporating a spine-like internal structure is proposed to enhance the effective stiffness while preserving bending flexibility. Inspired by the biomechanical structure of the human spine, the embedded spine-like structure consists of interconnected rigid vertebrae integrated along the central axis of a soft pneumatic actuator. Static bending experiments under different base orientations and external loads are conducted to evaluate the actuator’s performance. The experimental results demonstrate that the proposed actuator exhibits improved posture retention, enhanced load-bearing capacity, and higher robustness against gravitational loading compared to a soft pneumatic actuator without a spine-like structure. These results confirm that the spine-like internal structure effectively increases the actuator’s effective stiffness, enabling stable bending behavior under various working conditions. Full article

Soccer goalkeeper diving saves demand precise inter-segmental coordination to intercept shots under uncertainty, yet preparatory postures and kinematic adaptations between declared (D) and undeclared (ND) conditions remain underexplored in youth athletes. This study analyzed lower-limb kinematics and Continuous Relative Phase (CRP) in 10 elite youth male goalkeepers (14.3 ± 0.3 years) performing dives in different conditions using inertial sensors (Xsens MVN Awinda, 60 Hz) on a natural grass pitch. Data were time-normalized across the dive cycle and analyzed using Statistical Parametric Mapping 1D ANOVA to compare kinematic and coordination differences between conditions and preferred side. ND high dives showed significantly shorter total duration (1.02 ± 0.13 s vs. 1.09 ± 0.12 s) and take-off (0.19 ± 0.05 s vs. 0.21 ± 0.05 s) compared to the D condition. Pronounced laterality emerged in hip internal/external rotation (ipsilateral: 0–100%), with CRP alterations only in the ipsilateral ankle-hip/knee during preferred-side low dives (13–74%, p < 0.001), indicating tighter segmental coupling and reduced phase lag between joints from mid-stance to push-off. D condition appeared to favor mediolateral CoM shifts for reach optimization, while ND emphasized anteroposterior readiness. These findings highlight CRP’s sensitivity to coordination under uncertainty and reveal laterality effects in preferred-side low dives. Full article

This study examines the extent to which a firm’s business strategy shapes its strategic and audit risk profiles, and whether these risk characteristics ultimately manifest as measurable auditor–client disagreements. Auditor–client disagreement is operationalized using a direct, disclosure-based measure constructed as the scaled difference between unaudited preliminary net income—manually collected from mandatory timely filings disclosed through the Korea Financial Supervisory Service’s Electronic Disclosure System (DART)—and final audited net income reported in the audited financial statements. Using a sample of 6504 firm-year observations drawn from firms listed on the Korea Exchange (KOSPI and KOSDAQ) over the period 2020–2024, I find that a higher strategic score, reflecting a more innovation-oriented, prospector-type strategic posture, is consistently and significantly positively associated with the likelihood of auditor–client disagreement. Conversely, firms pursuing a cost-efficiency-oriented, defender-type strategy exhibit a significantly lower likelihood and smaller magnitude of disagreement. These findings suggest that business strategy functions as a fundamental, ex-ante determinant of inherent risk and audit risk, directly shaping auditors’ effort allocation and financial reporting outcomes. Collectively, this study contributes to the auditing literature by providing empirical evidence that a client’s strategic positioning constitutes a material, firm-level risk factor—consistent with the risk assessment framework mandated by International Standard on Auditing (ISA) 315—and should therefore be explicitly incorporated into auditors’ engagement risk assessments and the design of risk-based audit procedures. Full article

Compaction quality governs asphalt pavement durability, but conventional density checks are intermittent. Reliable compaction control of asphalt mixtures requires real-time information on internal responses rather than relying solely on endpoint density measurements. In this study, an embedded smart-particle framework is developed for in situ monitoring and index-based evaluation of vibratory compaction quality, integrating multi-source sensing, feature extraction, and compaction degree mapping. The smart particle integrates inertial/orientation sensing together with thermal–mechanical measurements, and its high-temperature survivability and calibratability are verified through thermal exposure and calibration tests. During laboratory vibratory compaction of representative asphalt mixtures, raw signals are converted into stable attitude responses via attitude estimation and filtering; posture-dominant descriptors are then extracted and used to establish a data-driven mapping from internal responses to compaction degree using regression models. Results show that the device remains stable under typical hot-mix asphalt conditions, with calibration exhibiting high linearity (temperature channel R² > 0.990; force channel R² > 0.980 in the relevant range). Filtering markedly enhances inertial-signal usability under strong vibration and improves the interpretability of attitude-response evolution during compaction. The evolution of attitude features is consistent with the “rapid-to-slow densification” process, yielding correlations of |r| ≈ 0.35–0.47 with compaction degree evolution. Nonlinear regressors outperform linear baselines, and the better-performing nonlinear models achieve strong predictive performance across all six specimens, with R² values reaching 0.740–0.960 and RMSE reaching 0.016–0.043. Moreover, machine-learning-based feature-importance analysis reveals distinct mixture-type-dependent characteristics, indicating that AC and SMA transmit compaction-state information through partly different dominant response features. These findings demonstrate the feasibility of embedded smart particles for online compaction-quality evaluation and provide a basis for real-time feedback in intelligent compaction. Full article

A capsule endoscope (CE) provides noninvasive access to the gastrointestinal tract, offering diagnostic information that cannot be obtained through external imaging alone. However, during the examination inside the stomach, the CE’s posture may change rapidly as it moves within a dynamically deforming organ, making it difficult to determine its orientation using only the onboard camera feedback. To address this problem, this study proposes a method that employs an external array of Hall Effect Sensors (HES) to estimate the capsule’s position and orientation in real time, based on the magnetic field generated by a permanent magnet (PM) embedded inside the capsule, without the need for any additional internal sensors. This approach introduces a unified magnetic actuation and localization framework that enables real-time 5-degree-of-freedom posture estimation using only the internal PM of the capsule. Furthermore, the proposed system features an integrated architecture capable of simultaneous actuation and localization. To enhance system practicality, the sensor module and communication board were combined into a single unit that employs a digital serial communication scheme, eliminating the need for analog to digital conversion of sensing signals. By avoiding additional onboard sensors and employing a PM-based actuation system, the proposed system simplifies hardware configuration by preserving capsule miniaturization and by eliminating the high power consumption and thermal issues associated with electromagnet-based actuation, while maintaining accurate real-time tracking performance. Through an optimization process, the system achieved a position error of less than 2 mm and an angular error within 2° over a sensing range of up to 60 mm. Repeated experiments further validated the system’s effectiveness and reliability under realistic operating conditions, demonstrating its feasibility for compact and clinically applicable active capsule endoscopy systems. Full article

Citrus production in Mexico relies predominantly on manual labor and traditional harvesting tools, which are often associated with physical overload, awkward postures, and reduced productivity. This study presents an exploratory, perception-based field evaluation of the BLIMPER, an early-stage ergonomic harvesting prototype designed for selective Persian lime collection. A total of 93 citrus harvesters participated through snowball sampling. A structured 33-item questionnaire was administered, covering five perception dimensions and open-ended comments. The instrument was expert-validated and demonstrated good internal consistency (Cronbach’s α = 0.85). Data analysis included descriptive statistics, Welch’s t-test for gender-based comparisons, and Hedges’ g to estimate the magnitude of the difference between groups. A modified Kano model was applied to classify perceived tool attributes and identify priorities for design refinement. The results indicated that 83–85% of respondents valued material strength, 64–70% approved of the unloading system, and 67–75% perceived reduced fatigue in the shoulders and lower back. The findings should be interpreted as an initial ergonomic validation based on user perceptions under real working conditions, rather than as evidence of readiness for large-scale deployment. The BLIMPER prototype shows potential to improve comfort and posture, while highlighting design aspects—weight distribution, mobility, and material selection—that require further optimization overall. Full article

A safe physical human–robot interaction (pHRI) in rehabilitation requires reliable perception and low-latency decision making under heterogeneous and unreliable sensor inputs. This paper presents a multimodal sensor-fusion-based safety framework that integrates physical state estimation, semantic information fusion, and an edge-deployed large language model (LLM) for real-time pHRI safety control. A dynamics-based virtual sensing method is introduced to estimate internal joint torques from external force–torque measurements, achieving a normalized mean absolute error of $18.5\%$ in real-world experiments. An asynchronous semantic state pool with a time-to-live mechanism is designed to fuse visual, force, posture, and human semantic cues while maintaining robustness to sensor delays and dropouts. Based on structured multimodal tokens, an instruction-tuned edge LLM outputs discrete safety decisions that are further mapped to continuous compliant control parameters. The framework is trained using a hybrid dataset consisting of limited real-world samples and LLM-augmented synthetic data, and evaluated on unseen real and mixed-condition scenarios. Experimental results show reliable detection of safety-critical events with a low emergency misdetection rate, while maintaining an end-to-end decision latency of approximately 223 ms on edge hardware. Real-world experiments on a rehabilitation robot demonstrate effective responses to impacts, user instability, and visual occlusions, indicating the practical applicability of the proposed approach for real-time pHRI safety monitoring. Full article

The increasing adoption of data-driven techniques in cybersecurity has introduced new opportunities to enhance detection, response, and automation capabilities within the cybersecurity ecosystem; however, cybersecurity auditing remains constrained by traditional compliance-oriented approaches that rely profoundly on binary, checklist-based evaluations. Such approaches often reinforce a policing or “sheriff-style” perception of auditing, emphasizing enforcement rather than enablement, risk insight, and organizational improvement. Of primary concern is that the “sheriff-style” cybersecurity audit approach often fails to accurately portray the true state of an organization’s cybersecurity posture, often providing a misleading sense of assurance based solely on formal compliance and controls existence. This study proposes an Anti-Sheriff Cybersecurity Audit Model, that moves beyond cybersecurity control checklists, by integrating intelligence-informed risk assessments with structured human judgment to support a more robust, adaptive, and risk-oriented auditing process. Grounded in design science research (DSR), the proposed approach combines conventional binary compliance verification with intelligence-derived risk indicators and governance-based maturity assessments to evaluate cybersecurity controls across technical, operational, and organizational dimensions. The approach aligns with established standards and frameworks, including International Organization for Standardization and the International Electrotechnical Commission (ISO/IEC) 27001, the National Institute of Standards and Technology (NIST), and the Center for Internet Security (CIS) benchmarks, while extending their application beyond static compliance validation. A fictional case study is used to demonstrate the model’s applicability and to illustrate how hybrid scoring can reveal residual risk not captured by conventional cybersecurity audits. The findings indicate that combining intelligence-informed analytics with structured human judgment enhances audit depth, interpretability, and business relevance. The proposed approach, therefore, provides a foundation for evolving cybersecurity auditing from just periodic compliance assessments, toward a continuous, risk-informed, and governance-aligned assurance system. Full article

The scientific study of affect has been historically characterized by a profound lack of terminological consensus, leading to a state of conceptual fragmentation that persists in psychology, neuroscience and many other fields. This ambiguity is not merely an academic concern; it has significant consequences for the development of artificial intelligence (AI) systems designed to recognize and respond to human “emotions”. In fact, it has an influence on the entire field of affective computing. The problem is obvious. Without a distinct definition of “emotion” it is difficult to train an algorithm to recognize it. The Walla Emotion Model, also known as the ESCAPE (Emotions Convey Affective Processing Effects) model, provides a potentially helpful and neurobiologically grounded framework to resolve this impasse and to improve any discourse about it, for businesses and even lawmakers aiming at healthy societies. By establishing clear, non-overlapping definitions for affective processing, feelings, and emotions, this model offers a path toward more precise research and more ethically sound affective computing including AI-driven “emotion” recognition. It introduces a concept that allows for the detection of incongruences between internal states and external signals with a very clear terminology supporting understandable communication. This is critical for identifying feigned or socially masked inner affective states, a challenge that traditional “face-reading” AI models frequently fail to address. Even tone of voice and body postures as well as gestures can be and are often voluntarily modified. Through the separation of subcortical affective processing (evaluation of valence; neural activity) from subjective experience (feeling) and external communication (emotion), the Walla model provides a helpful framework for AI-designs meant to have the capacity to infer an internal affective state from collected signals in the wild bypassing verbal self-report. This paper is purely theoretical; it does not provide any algorithm models or other distinct suggestions to train a software package. Its main purpose is the introduction of a new emotion model, particularly a new terminology that is considered helpful in order to proceed with this endeavor. It is considered important to first enable the clearest-possible form of communication about anything related to the term emotion across all disciplines dealing with it. Only then can progress be made. Full article

Purpose: This study aimed to quantify the severity of fear of falling (FOF) in people with low vision (LV) compared with age–gender-matched healthy individuals during gait initiation (GI). Methods: A total of 14 adults with LV and 14 age–gender-matched healthy adults were recruited from local communities. The Falls Efficacy Scale International was used to evaluate FOF. We compared temporal events between healthy and LV groups. For the healthy group, GI under normal vision was further compared to conditions using a low-vision sight simulator (SS) and an immersive virtual reality (VR) environment designed to simulate a fear-evoking experience. Independent t-test and one-way repeated measure ANOVA were conducted for statistical analysis (p < 0.05). Results: People with LV showed a significantly greater FOF than healthy individuals (p < 0.05). During GI, participants with LV exhibited significantly prolonged anticipatory postural adjustment (APA) durations compared to healthy normal and SS conditions (p < 0.05). While VR-evoked fear in healthy participants primarily prolonged the push-off (PO) phase, the delay in the LV group was characterized by a significantly extended initial anticipation (AP) phase. Notably, the APA duration in the LV group showed no significant difference compared to the healthy VR condition, indicating that the inherent fear in LV produces postural delays as severe as those induced by extreme VR-evoked fear of heights (p > 0.05). Conclusions: This study demonstrates that individuals with LV adopt a chronically conservative motor program during the transition from standing to walking. These postural hesitations are statistically comparable to those observed under fear-evoking, VR-induced environments. These findings suggest that LV is associated with a distinct biomechanical strategy that prioritizes static stability over dynamic movement. Accordingly, multidisciplinary rehabilitation approaches that emphasize sensory reweighting, including vestibular training, alongside interventions targeting FOF, may be essential for mitigating altered postural control and reducing fall risk in the LV population. Full article

The spinal angle is an important indicator of body balance. It is important to restore the 3D shape of the human body and estimate the spine center line. Existing multi-image-based body restoration methods require expensive equipment and complex procedures, and single image-based body restoration methods struggle to accurately estimate internal structures such as the spine center line due to occlusion and viewpoint limitation. This study proposes a method to compensate for the shortcomings of the multi-image-based method and to overcome the limitations of the single-image method. We propose a 3D body posture analysis system that integrates depth images from four directions to restore a 3D human model and automatically estimate the spine center line. Through hierarchical matching of global and fine registration, restoration to noise and occlusion is performed. In addition, adaptive vertex reduction is applied to maintain the resolution and shape reliability of the mesh, and the accuracy and stability of spinal angle estimation are simultaneously secured using the level of detail (LOD) ensemble. The proposed method achieves high-precision 3D spine registration estimation without relying on training data or complex neural network models, and the verification confirms the improvement in matching quality. Full article

Background/Objectives: Falls are a major global issue for older adults, and emotional stress may increase the risk due to its effects on postural control and balance. However, the immediate effects of a stressful stimulus on objective measures of balance and fall risk are unknown. The study aims to explore differences in older adults’ performance on the Timed Up and Go (TUG) test before and after such exposure. Methods: In this cross-sectional study, 31 older adults (71.6 ± 4.98 years) were exposed to an emotionally stressful stimulus using high-arousal images from the International Affective Picture System. Participants performed the TUG before (t1) and after (t2) exposure as the primary outcome measure. To assess the physiological and psychological impact of the stressful stimulus, heart rate variability (HRV) was recorded before and during image viewing. A visual analogue scale (VAS) of unease was completed both before and after the stimulus. Results: During the stressful stimulus, the HRV high-frequency (HF) band decreased significantly (p = 0.001), while the low-frequency (LF) band (p = 0.002) and the LF/HF ratio (p = 0.004) showed a significant increase. Similarly, after stressful stimulus, VAS scores demonstrated a statistically significant increase (p < 0.001). The time to complete the TUG showed a statistically significant increase at t2 (p < 0.001). Conclusions: The stressful stimulus triggered both physiological and subjective stress responses. Subsequently, TUG test performance declined (increased duration), suggesting that emotionally stressful stimuli could deteriorate functional balance performance in older adults, potentially increasing fall risk. Full article

Osteoarthritis (OA) is a progressive degenerative joint disease that fundamentally alters the mechanical environment of the knee. This study utilizes a finite element framework to evaluate the biomechanical response of the distal femur in healthy and osteoarthritic conditions across critical functional postures. To isolate the bone’s inherent structural stiffness and avoid numerical artifacts, a free-body computational approach was implemented, omitting external surface fixations. The distal femur was modeled as a linearly elastic domain with material properties representing healthy tissue and OA-induced degradation. Simulations were performed under passive gravitational loading at knee flexion angles of $0^{\circ },{60}^{\circ },$ and ${90}^{\circ }$. The results demonstrate that the mechanical response is highly sensitive to postural orientation, with peak von Mises stress consistently occurring at ${60}^{\circ }$ of flexion for both models. Quantitative analysis revealed that the stiffer Normal bone attracted significantly higher internal stress, with a reduction of over 30% in peak stress magnitude observed in the OA model at the most critical flexion angle. Total displacement magnitudes remained relatively stable across conditions, suggesting that OA-induced material softening primarily influences internal stress redistribution rather than global structural sag under passive loads. These findings provide a quantitative index of skeletal vulnerability, supporting the development of patient-specific orthopedic treatments and rehabilitation strategies. Full article