Search Results (725)

Background: Automated pain assessment aims to enable objective measurement of patients’ individual pain experiences for improving health care and conserving medical staff. This is particularly important for patients with a disability to communicate caused by mental impairments, unconsciousness, or infantile restrictions. When operating in the critical domain of health care, where wrong decisions harbor the risk of reducing patients’ quality of life—or even result in life-threatening conditions—multimodal pain assessment systems are the preferred choice to facilitate robust decision-making and to maximize resilience against partial sensor outages. Methods: Hence, we propose the MultiModal Supervised Contrastive Adversarial AutoEncoder (MM-SCAAE) pretraining framework for multi-sensor information fusion. Specifically, we implement an application-specific model to accomplish the task of pain recognition using biopotentials from the publicly available heat pain database BioVid. Results: Our model reaches new state-of-the-art performance for multimodal classification regarding all pain recognition tasks of ‘no pain’ versus ‘pain intensity’. For the most relevant task of ‘no pain’ versus ‘highest pain’, we achieve $84.22\%$ accuracy ($F_1$-score: $83.72\%$), which can be boosted in practice to an accuracy of ≈$95\%$ through grouped-prediction estimates. Conclusions: The generic MM-SCAAE framework offers promising perspectives for multimodal representation learning. Full article

In cervical dystonia (CD), balance and gait impairments can compromise daily activities and negatively affect quality of life. However, interventions addressing these deficits remain poorly investigated. A 54-year-old woman with CD, presenting balance and gait difficulties that interfered with work-related motor tasks, underwent a four-month training program. Sessions (40 min, three times per week) combined lower-limb strengthening, proprioceptive and balance exercises, and integrated motor–cognitive tasks. Pre- and post-intervention assessments included gait speed (GS), stride length (SL), and stance time (ST) under usual (UW), fast (FW), and dual-task (DT) walking conditions, measured with an inertial sensor (BTS G-Walk). DT cost was calculated for GS and SL. Balance was evaluated with the Mini-BEST and Four-Square Step Test (FSST), while fear of falling was measured with the Falls Efficacy Scale-International (FES-I). Of note, both assessment sessions were conducted in the absence of botulinum toxin effects, whereas the training was performed, at least in part, under its influence. After training, increase were observed in GS and SL, with reductions in ST across all gait conditions. DT cost decreased for both GS and SL. Balance performance increased, and fear of falling was reduced. Importantly, the patient reported a marked improvement in work-related performance. This case suggests that a specific training program may effectively ameliorate balance and gait in CD, with positive effects on functional mobility. Further studies on larger samples are warranted to confirm efficacy. Full article

The absent in melanoma 2 (AIM2) inflammasome is a cytosolic DNA sensor that links genomic instability, mitochondrial dysfunction, and chronic inflammation. Unlike the nucleotide-binding domain, leucine-rich repeat (NLR) family pyrin domain-containing protein 3 (NLRP3) inflammasome, AIM2 is activated directly by double-stranded Deoxyribonucleic Acid (dsDNA), including mitochondrial DNA (mtDNA) released under stress conditions. This positions AIM2 at the intersection of oxidative stress, impaired mitophagy, and innate immune dysregulation. Current therapies for ankylosis spondylitis (AS), such as anti-tumor necrosis factor (TNF), anti-interleukin 17 (IL-17), and Janus kinase (JAK) inhibitors, improve clinical outcomes; however, they do not address upstream mitochondrial dysfunction or DNA-driven inflammasome activation. By contrast, other inflammasomes, such as AIM2, remain comparatively less studied. Since autoimmune diseases, including AS, are frequently accompanied by uncontrolled innate immune responses to self-DNA, these findings provide a framework for comprehending the mechanisms of AIM2 activation and its interaction with inflammation, mitophagy, and oxidative stress. Here, we review the current evidence on AIM2 inflammasome involvement in AS pathogenesis and its potential as a therapeutic target. This approach offers new insight into disease control through re-establishing the balance between mitochondrial dysfunction and autoimmunity. Full article

Accurate vibration measurement is crucial for maintaining the performance, reliability, and safety of automated manufacturing environments. Abnormal vibrations caused by faults in gears or bearings can degrade positional accuracy, reduce productivity, and, over time, significantly impair production efficiency and product quality. Such vibrations may also disrupt supply chains, cause financial losses, and pose safety risks to workers through collisions, falling objects, or other operational hazards. Conventional vibration measurement techniques, such as wired accelerometers and strain gauges, are typically limited to a few discrete measurement points. Achieving multi-point measurements requires numerous sensors, which increases installation complexity, wiring constraints, and setup time, making the process both time-consuming and costly. The integration of high-frame-rate (HFR) cameras with Digital Image Correlation (DIC) enables non-contact, multi-point, full-field vibration measurement of robot manipulators, effectively addressing these limitations. In this study, HFR cameras were employed to perform non-contact, full-field vibration measurements of industrial robots. The HFR camera recorded the robot’s vibrations at 1000 frames per second (fps), and the resulting video was decomposed into individual frames according to the frame rate. Each frame, with a resolution of 1920 × 1080 pixels, was divided into 128 × 128 pixel blocks with a 64-pixel stride, yielding 435 sub-images. This setup effectively simulates the operation of 435 virtual vibration sensors. By applying mask processing to these sub-images, eight key points representing critical robot components were selected for multi-point DIC displacement measurements, enabling effective assessment of vibration distribution and real-time vibration visualization across the entire manipulator. This approach allows simultaneous capture of displacements across all robot components without the need for physical sensors. The transfer function is defined in the frequency domain as the ratio between the output displacement of each robot component and the input excitation applied by the shaker mounted on the end-effector. The frequency–domain transfer functions were computed for multiple robot components, enabling accurate and full-field vibration analysis during operation. Full article

Pharmacological compounds can disrupt glucose homeostasis, leading to impaired glucose tolerance, hyperglycemia, or newly diagnosed diabetes, as well as worsening glycemic control in patients with pre-existing diabetes. Traditional risk factors alone cannot explain the rapidly growing global incidence of diabetes. Therefore, prevention of insulin resistance could represent an effective strategy. Achieving this goal requires a deeper understanding of the mechanisms underlying the development of insulin resistance, with particular attention to the aryl hydrocarbon receptor (AhR). AhR, a transcription factor functioning as a xenobiotic sensor, plays a key role in various molecular pathways regulating normal homeostasis, organogenesis, and immune function. Activated by a range of exogenous and endogenous ligands, AhR is involved in the regulation of glucose and lipid metabolism as well as insulin sensitivity. However, current findings remain contradictory regarding whether AhR activation exerts beneficial or detrimental effects. This narrative review summarizes recent studies exploring the role of the AhR pathway in insulin secretion and glucose homeostasis across different tissues, and discusses molecular mechanisms involved in this process. Considering that several drugs act as AhR ligands, the review also compares how these ligands affect metabolic pathways of glucose and lipid metabolism and insulin sensitivity, producing either positive or negative effects. Full article

This study presents an integrated micro-system solution to address the challenges of gait instability in patients with impaired hip motor function. We developed a novel wearable hip exoskeleton, where a flexible support unit and a parallel drive mechanism achieve self-alignment with the biological hip joint to minimize parasitic forces. The system is driven by an active brain–computer interface (BCI) that synergizes an augmented reality visual stimulation (AR-VS) paradigm for enhanced motor intent recognition with a high-performance decoding algorithm, all implemented on a real-time embedded processor. This integration of micro-sensors, control algorithms, and actuation enables the establishment of a gait phase-dependent hybrid controller that optimizes assistance. Online experiments demonstrated that the system assisted subjects in completing 10 gait cycles with an average task time of 37.94 s, a correlated instantaneous rate of 0.0428, and an effective output ratio of 82.17%. Compared to traditional models, the system achieved an 18.64% reduction in task time, a 28.31% decrease in instantaneous rate, and a 7.36% improvement in output ratio. This work demonstrates a significant advancement in intelligent micro-system platforms for human-centric rehabilitation robotics. Full article

Conductive hydrogels are ideal for flexible strain sensors, yet their practical use is often limited by water evaporation, signal hysteresis, and structural instability, which impair linearity, durability, and long-term reliability. To overcome these challenges, we developed a robust multiple-network hydrogel composed of poly(vinyl alcohol) (PVA), polyacrylic acid (PAA), in situ polymerized polyaniline (PANi), and the ionic liquid [EMIM][TFSI]. The resulting composite exhibits an exceptional linear piezoresistive response across its entire working range—from rest to fracture strain of 290%—together with high conductivity (0.68 S/cm), fast response/recovery (0.34 s/0.35 s), and a maximum gauge factor of 2.78. Mechanically robust (tensile strength ≈ 3.7 MPa, modulus ≈ 1.3 MPa), the hydrogel also demonstrates outstanding cyclic durability, withstanding over 12,000 stretching–relaxation cycles, and markedly improved dehydration resistance, retaining about 60% of its mass after 3 days at room temperature. This work provides a holistic material solution for developing high-performance, reliable strain sensors suitable for wearable electronics and soft robotics. Full article

The Timed Up and Go (TUG) test is a widely used clinical tool for assessing mobility and fall risk in older adults and individuals with neurological or musculoskeletal conditions. While it provides a quick measure of functional independence, traditional stopwatch-based timing offers only a single completion time and fails to reveal which movement phases contribute to impairment. This study presents a smartphone-based system that automatically segments the TUG test into distinct phases, delivering objective and low-cost biomarkers of lower-limb performance. This approach enables clinicians to identify phase-specific impairments in populations such as individuals with Parkinson’s disease, and older adults, supporting precise diagnosis, personalized rehabilitation, and continuous monitoring of mobility decline and neuroplastic recovery. Our method combines adaptive preprocessing of accelerometer and gyroscope signals with supervised learning models (Random Forest, Support Vector Machine (SVM), and XGBoost) using statistical features to achieve continuous phase detection and maintain robustness against slow or irregular gait, accommodating individual variability. A threshold-based turn detection strategy captures both sharp and gradual rotations. Validation against video ground truth using group K-fold cross-validation demonstrated strong and consistent performance: start and end points were detected in 100% of trials. The mean absolute error for total time was 0.42 s (95% CI: 0.36–0.48 s). The average error across phases (stand, walk, turn) was less than 0.35 s, and macro F₁ scores exceeded 0.85 for all models, with the SVM achieving the highest score of 0.882. Combining accelerometer and gyroscope features improved macro F1 by up to 12%. Statistical tests (McNemar, Bowker) confirmed significant differences between models, and calibration metrics indicated reliable probabilistic outputs (ROC-AUC > 0.96, Brier score < 0.08). These findings show that a single smartphone can deliver accurate, interpretable, and phase-aware TUG analysis without complex multi-sensor setups, enabling practical and scalable mobility assessment for clinical use. Full article

Subclinical hypocalcemia (SCH) is one of the most prevalent metabolic disorders in early-lactation dairy cows, yet its multifaceted physiological effects are often overlooked due to the absence of clinical symptoms. This study aimed to characterize SCH through an integrative assessment of blood biochemical markers, in-line milk composition, and sensor-derived behavioral traits. Seventy-five Holstein cows between 2 and 21 days in milk were classified into hypocalcemic (group 1) (Ca < 2.0 mmol/L; n = 20) and healthy (group 2) groups (n = 55). Blood samples, milk data, and rumination metrics were evaluated, and group differences were analyzed using Welch’s t-test and Pearson correlations. Cows with SCH exhibited significantly lower concentrations of Ca, PHOS, Mg, ALB, TP, GLUC, and Fe, indicating disruptions in mineral balance, protein metabolism, and energy status. Hepatic indicators (AST, ALT, GGT) did not differ between groups, whereas CREA was significantly lower in hypocalcemic cows, suggesting altered muscle metabolism rather than impaired renal function. Although differences in milk yield, composition, and rumination time did not reach statistical significance, hypocalcemic cows showed consistent biological tendencies toward reduced milk components and lower milk temperature. Correlation analysis revealed strong physiological linkages among Ca, ALB, P, TP, and Fe, underscoring the interconnected nature of mineral and protein metabolism in early lactation. These findings demonstrate that SCH is associated with coordinated biochemical and behavioral changes even in the absence of clinical signs. Integrating blood biomarkers with real-time sensor data provides a more comprehensive understanding of calcium-related metabolic challenges and highlights the potential of precision-livestock technologies for early detection. Future studies incorporating ionized calcium and longitudinal sampling are needed to refine diagnostic thresholds and improve predictive monitoring of SCH in dairy herds. Full article

Visual impairment (VI) affects over two billion people globally, with prevalence increasing due to preventable conditions. To address mobility and navigation challenges, this study presents a multi-platform, multi-sensor Electronic Travel Aid (ETA) integrating a combination of ultrasonic, LiDAR, and vision-based sensing across head-, torso-, and cane-mounted nodes. Grounded in orientation and mobility (OM) principles, the system delivers context-aware haptic and auditory feedback to enhance perception and independence for users with VI. The ETA employs a hardware–software co-design approach guided by proxemic theory, comprising three autonomous components—Glasses, Belt, and Cane nodes—each optimized for a distinct spatial zone while maintaining overlap for redundancy. Embedded ESP32 microcontrollers enable low-latency sensor fusion providing real-time multi-modal user feedback. Static and dynamic experiments using a custom-built motion rig evaluated detection accuracy and feedback latency under repeatable laboratory conditions. Results demonstrate millimetre-level accuracy and sub-30 ms proximity-to-feedback latency across all nodes. The Cane node’s dual LiDAR achieved a coefficient of variation at most 0.04%, while the Belt and Glasses nodes maintained mean detection errors below 1%. The validated tri-modal ETA architecture establishes a scalable, resilient framework for safe, real-time navigation—advancing sensory augmentation for individuals with VI. Full article

Images captured by vision sensors in outdoor environments often suffer from haze-induced degradations, including blurred details, faded colors, and reduced visibility, which severely impair the performance of sensing and perception systems. To address this issue, we propose a haze-removal algorithm for hazy images using multiple variational constraints. Based on the classic atmospheric scattering model, a mixed variational framework is presented that incorporates three regularization terms for the transmission map and scene radiance. Concretely, an ${\ell }_p$ norm and an ${\ell }_2$ norm were constructed to jointly enforce the transmissions for smoothing the details and preserving the structures, and a weighted ${\ell }_1$ norm was devised to constrain the scene radiance for suppressing the noises. Furthermore, our devised weight function takes into account both the local variances and the gradients of the scene radiance, which adaptively perceives the textures and structures and controls the smoothness in the process of image restoration. To address the mixed variational model, a re-weighted least square strategy was employed to iteratively solve two separated subproblems. Finally, a gamma correction was applied to adjust the overall brightness, yielding the final recovered result. Extensive comparisons with state-of-the-art methods demonstrated that our proposed algorithm produces visually satisfactory results with a superior clarity and vibrant colors. In addition, our proposed algorithm demonstrated a superior generalization to diverse degradation scenarios, including low-light and remote sensing hazy images, and it effectively improved the performance of high-level vision tasks. Full article

From an evolutionary perspective, smell and taste are the oldest human senses. Despite this, other than chemical senses—particularly vision—are commonly regarded as the most powerful tools for interacting with our environment. Within such a frame, it has become a common belief that blind individuals, especially those who are congenitally blind, develop a compensatory sensory pattern, enhancing the power of their sense of smell. However, the literature results are unclear, mainly due to the heterogeneity of the study population and of the investigation methods. Emotional reactions to olfactory stimuli in blind individuals remain underexplored, primarily due to challenges in delivering stimuli in a standardized and unbiased manner suitable for quantitative assessment. In such a framework, the present pilot study sought to indirectly discover the emotional responses of blind individuals to a specific class of sensory stimuli through the application of wearable sensors for capturing electrocardiographic (ECG) signals and galvanic skin response (GSR). Tonic GSR varied in blind individuals (p < 0.001), but not in controls. Notably, variations were observed between Baseline and Odor 1 (p = 0.002), Odors 1 and 2 (p = 0.003), Odors 2 and 3 (p = 0.003), and on the GSR phasic peak between Baseline and Odor 1 (p = 0.001). No differences were observed for ECG; however, blind individuals’ heart rate correlated with reported pleasantness (r = 0.436, p = 0.005). In light of the different patterns retrieved across stimulus responses, particularly in the GSR signal features, the comparison with a group of non-visually impaired peers shed light on the peculiarities in the psychophysiological responses of blind individuals, with potential use for tailored treatments for the improvement of well-being or, in some cases, for practical applications fostering social inclusion for affected subjects. Full article

Sepsis is a clinical syndrome caused by abnormal host response to infection. Thrombocytopenia and platelet dysfunction are common findings in sepsis and associated with worse outcomes. The innate immune single-stranded RNA sensor, Toll-like Receptor-7 (TLR7), plays a key role in thrombocytopenia in sepsis. This study investigated whether TLR7 signaling also contributes to platelet dysfunction in sepsis, and whether the bioactivity of downstream inflammatory mediators, specifically extracellular vesicles (EVs), is impacted by the TLR7 signaling pathway. Sepsis was induced in wild-type (WT) and TLR7-deficient (TLR7^−/−) mice by cecal ligation and puncture. Blood was collected at twenty-four hours for platelet and plasma isolation, and platelet function was assessed using aggregation, adhesion, and calcium flux assays. EVs were isolated from plasma and used in vitro to evaluate their impact on platelet–leukocyte aggregate (PLA) formation. We found that septic platelets are highly activated and more adhesive, yet show markedly impaired aggregation and reduced calcium signaling, indicating functional exhaustion despite activation. Notably, mice lacking TLR7 maintained stronger platelet aggregation, enhanced adhesion, and preserved calcium release in the septic state compared to wild-type controls, suggesting a protective effect of TLR7 deficiency. Plasma EVs increased in abundance and size during sepsis and promoted clot and PLA formation in vitro. Notably, EV-mediated platelet activation was reduced with EVs derived from TLR7-deficient mice. Our results demonstrate that while sepsis drives persistent platelet activation and dysfunction, TLR7 deficiency preserves platelet function and modulates the pathogenic activity of EV-mediated platelet activation, highlighting TLR7 as a key regulator and potential therapeutic target in sepsis-induced platelet dysfunction. Full article

Most existing virtual reality exergames rely on generic VR devices, which can limit the physical exertion in VR-based exercises. In contrast, physical props can enhance exercise intensity, yet their impact on users’ performance and experience remains understudied, particularly in skill-based tasks. Meanwhile, physical props offer richer tactile and kinesthetic feedback, which, combined with the visual effects of head-mounted displays, presents a potential solution for improving user experience in VR. To explore this, this study developed a sensor-driven experimental framework for investigating high-skill VR tasks. By integrating vision sensors with standard VR devices, we constructed a VR archery system that enables objective quantification of motor performance. Leveraging the sensor-driven framework, we investigate the effects of physical props and physics-associated visual feedback on players’ performance and experience in VR tasks through an experiment involving 33 participants. By objectively quantifying performance, we reveal a dual-pathway mechanism: physical props significantly increased hand tremor, which in turn impaired aiming accuracy, but this negative effect was effectively moderated by time and physics-associated visual feedback that enabled real-time sensorimotor compensation. While complex physical props reduced task performance, they substantially enhanced enjoyment and presence, particularly demonstrating a synergistic effect on users’ flow experience when combined with physics-associated visual feedback. These findings elucidate the complex interplay between physical prop interfaces and visual feedback in high-skill VR tasks, providing valuable insights for designing VR experiences which balance performance requirements and engagement enhancement. Full article

Gait impairment in post-stroke patients increases the risk of falls, but the role of ground reaction force variability (GRF variability) in controlling gait stability remains unclear. The objectives of this study were (1) to clarify the differences in GRF variability during walking between post-stroke patients and age-matched controls and (2) to identify the differences in GRF variability between post-stroke patient fallers and non-fallers. Sixteen post-stroke patients (age: 72.19 ± 8.54, six female, four fallers: age: 71.75 ± 11.32, twelve non-fallers: age: 72.33 ± 8.03) and nineteen age-matched controls (age: 68.63 ± 5.73, nine female) participated. GRF variability was measured using shoe sensors during walking. After adjusting for walking speed, the anterior–posterior (AP) GRF variability on the paretic side in the 91–100% stance phase was significantly lower in the post-stroke patients (F = 3.721, p = 0.038). This phase’s AP GRF variability was not correlated with Berg Balance Scale scores. Furthermore, the faller group in stroke patients showed the AP GRF variability on the paretic side was lower in the 41–50% (W = 17, p = 0.045), 51–60% (W = 16, p = 0.045), 61–70% (W = 16, p = 0.045), and 91–100% (W = 23, p = 0.045) sub-stance phases. After adjusting for sex and orthosis, the sensitivity analysis showed no significant intergroup difference. This suggested an adaptive control mechanism for maintaining gait and avoiding falls in post-stroke patients. Full article