Stroke is a leading cause of long-term disability, and severe upper-limb paresis often persists despite conventional rehabilitation. Brain–computer interfaces (BCIs) have been proposed to support motor recovery by pairing motor imagery with contingent sensory feedback, although clinical evidence remains heterogeneous, particularly for robot-assisted systems. This pilot study evaluates the feasibility of a noninvasive motor-imagery (MI)-based BCI delivering simultaneous multimodal feedback (embodied visual avatar and robot-assisted hand movement), and characterizes motor, cognitive, and functional outcomes in stroke patients receiving it alongside conventional therapy. Sixteen individuals with first-ever stroke were allocated to a control group (CG, n=6) receiving conventional rehabilitation or an experimental group (EG, n=10) receiving the same therapy plus 23 one-hour BCI sessions. Motor, cognitive, and functional outcomes were assessed pre- and post-intervention and reported descriptively, consistent with guidance for pilot and feasibility trials. The EG showed pre-post point estimates whose 95% confidence intervals excluded zero across several motor (Fugl–Meyer Assessment total score: 25.0 to 33.6), cognitive (episodic memory, visuoconstructive performance), and functional (general health, functional independence) measures. The CG showed a similar pattern for some measures, including functional independence and physical and social health perceptions, indicating these gains were not exclusive to the EG; between-group effect sizes favored the EG over the CG for episodic memory and semantic verbal fluency. Usability was positively rated (mean 76.3/100; 95% CI: 64.8–87.7). These preliminary findings support the feasibility of the proposed therapy and motivate larger, adequately powered, ideally sham-controlled trials to confirm added clinical benefit over conventional rehabilitation.
Background: Metabolic simulators help accelerate the development of diabetes technologies, but existing mechanistic models for type 2 diabetes (T2D) are difficult to individualize and provide limited explanation of the complex interplay between physiology and available treatments. Methods: First, we trained NeuralOGTT, a neural network state-space model that replicates glucose-insulin dynamics during the oral glucose tolerance test (OGTT) in T2D. Next, we determined the minimal model architecture using a variance-based sensitivity analysis. Finally, we created digital twins (DT-NeuralOGTT) by adding auxiliary sub-networks to the NeuralOGTT trained using individuals data from a free-living observational study (N=38, age: 50 ± 10 years; 27 females; 1 high risk of diabetes, 9 pre-T2D, and 28 T2D). We simulated study scenarios and assessed the digital twins ability to replicate real-world glucose outcomes: time in range (TIR,70−180 mg/dL), time in tight range (TITR,70−140 mg/dL), time below range (TBR, < 70 mg/dL), and time above range (TAR, > 180 mg/dL). Results: NeuralOGTT replicated glucose dynamics during OGTTs. Personalized DT-NeuralOGTT enabled generalization under free-living conditions across 12-h simulations, achieving simulated glucose outcomes that are statistically identical to observed outcomes: TIR: 91.8 (95% CI:82.2–96.0)% vs. 92.1 (95% CI:83.5–95.8)%, TITR: 56.0 (95% CI:36.6–77.4)% vs. 59.3 (95% CI:48.7–76.4)%, TAR: 6.3 (95% CI:1.6–15.8)% vs. 7.4 (95% CI:3.8–15.7)%, and TBR: 0.5 (95% CI:0.0–1.0)% vs. 0.1 (95% CI:0.1–0.2)%. Conclusion: NeuralOGTT models glucose–insulin dynamics using a structure that preserves explainability while requiring small amounts of training data. DT-NeuralOGTT incorporates individual variability to better capture dynamics in pre-T2D and T2D. Code available: https://github.com/mosqueralopez/T2DSim_AI
The assessment of cardiovascular dynamics from heart period (RR) and systolic arterial pressure (SAP) time series requires analytical tools capable of capturing the complexity and nonlinearity of the underlying regulatory mechanisms and the directionality of their interactions. This work presented and tested a methodological framework combining Mutual Information Rate (MIR) with multiple surrogate data analysis to quantify and statistically validate complexity, causality, and nonlinearity in cardiovascular dynamics. Specifically, MIR was estimated through a model-free nearest-neighbor approach and decomposed into measures of complexity (i.e., entropy rate) and causality (i.e., transfer entropy). The capability of the method to characterize cardiovascular regulation was evaluated across young normotensive healthy subjects, older healthy individuals, and post-acute myocardial infarction (AMI) patients, undergoing an orthostatic stress test. In healthy young subjects, orthostatic stress induced the expected reduction in cardiovascular entropy rate and nonlinear coupling, reflecting a regulated baroreflex response. In contrast, older adults and post-AMI patients exhibited altered dynamics already at rest and lacked the adaptive modulation observed in the young group. These results demonstrated the potential of MIR decomposition, supported by surrogate analysis, as a robust framework to characterize the modulation of cardiovascular control mechanisms in response to orthostatic stress and their alterations across pathophysiological conditions.
This paper is focused on the Heart Rate Asymmetry (HRA) issue – a still insufficiently understood feature of Heart Rate Variability (HRV). We compared the conventional methods of HRA metrics (Porta Index, Guzik Index, Ehlers Index and Slope Index) and introduced a new direct measure of HRA: the Deceleration Input (DI) into the transitions between heart rate accelerations and decelerations. The study conducted on 151 healthy males undergoing head-up tilt allowed deepened cognition of HRA, HRV and relations in between them. 60.3% of the studied heart rate signals were asymmetric in supine (assessed by DI). Moreover, different types of time irreversibility in human heart rate was observed by analyzing the distance between consecutive points on Poincaré plot. Non-linear relations between time- and frequency domain HRV metrics have been measured. The correlation between asymmetry indices gives deeper insight into this phenomenon and indicates that the nature of the HRA is observable not only in the global count of heart rate accelerations and decelerations, but also in the specific dynamics of transitions between them. The DI index outperforms most of other HRA metrics in resistance to the outliers in signal.
This study aims to simulate the process of disc degeneration induced by endplate lesions, quantify the effect of lesion type and severity on disc biomechanical response, and explore the potential of traction intervention in mitigating degenerative changes. A finite element model of the lumbar disc was constructed based on cell-activity coupled mechano-electrochemical mixture theory. Nutrient concentrations, cell density, glycosaminoglycan (GAG) content, water content, and tissue deformation within the disc were predicted under different endplate lesions (endplate calcification and Schmorl's nodes). The impact of traction loadings (100 N, 200 N, and 300 N) on the disc was investigated. The results showed that endplate lesion worsened nutrient concentrations, triggering cell death, which over time led to decreased GAG content, ultimately resulting in tissue shrinkage and a reduction in water content. With increasing endplate lesion severity, GAG content, water content, and volume ratio decreased, while disc height loss and disc radial deformation increased. Moreover, traction intervention increased the minimum glucose concentration, reduced the relative critical volume, and enhanced GAG content in a load-dependent linear manner (R2 > 0.93). This study characterizes the process of disc degeneration induced by endplate lesions, highlighting the significance of endplate integrity in maintaining disc health. A dose-response relationship exists between the severity of endplate lesions and the degree of disc degeneration, with endplate calcification appearing more detrimental than Schmorl's nodes. Furthermore, traction intervention may be effective in mitigating the progression of endplate lesion-induced disc degeneration.
The synergistic mechanical and biomechanical mechanisms of mini-implant anchorage combined with T-loops for interdental space closure remain insufficiently explored, with most researches focusing on isolated components. To address this, this research established micro-implant assisted T-loop (MIATL) models with variable parameters based on a clinically derived, complete mandibular reconstruction. An indirect finite element analysis (FEA) strategy was developed to decouple the analysis, overcoming the inherent limitation of directly simulating the dynamic interaction between the deforming appliance and the biological system. Mechanical FEA revealed that T-loop restoring force is positively correlated with archwire elastic modulus, cross-sectional dimensions, and horizontal arm length, but negatively correlated with vertical arm length and gap distance. MIATL provided enhanced anchorage, with its force positively correlated with implant height and trigonometrically related to the traction hook distance. Biomechanical FEA confirmed that tooth movement directly correlates with the applied force, with MIATL introducing a crown-root intrusive component. Safety was validated by periodontal ligament (PDL) stress analysis: the maximum stress concentration at the mesial root of No. 46 remained below the critical 26 kPa threshold. This PDL stress was positively correlated with micro-implant diameter and length, and negatively correlated with implantation depth. This work provides a quantitative framework for optimizing personalized orthodontic design, advances digital treatment planning, and resolves a key FEA limitation in simulating the complete orthodontic force pathway from appliance deformation to tooth movement.
Sonothrombolysis is a technique that utilises ultrasound waves and microbubbles to remove blood clots. When insonated with high-pressure ultrasound waves, microbubbles undergo inertial cavitation and collapse violently to form high-speed jets. If this phenomenon occurs near the clot, the shear stress resulting from the jet impact can lead to considerable mechanical erosion of the clot surface. Efforts to enhance shear stress by optimising treatment parameters are limited by the trade-off between jet velocity and the distance travelled by the microbubble. Increasing jet velocity usually shortens the distance travelled, while extending the distance travelled often reduces jet velocity. In this study, a two-stage insonation scheme via a pressure ramping strategy was proposed to address this issue. In the first stage, low-pressure insonation enables stable oscillation and translation of the microbubble towards the clot. The pressure was then increased in the following stage to trigger inertial cavitation. Numerical findings indicated that the pressure ramping strategy can produce shear stress of 702 kPa with a ramped-up pressure of only 400 kPa. This surpasses the 487 kPa achieved without pressure ramping at a pressure magnitude of 1000 kPa. The elevated shear stress resulting from pressure ramping is due to the significant movement of the microbubble towards the clot during the initial insonation phase that utilises low pressure. In addition to enhancing shear stress, pressure ramping also facilitates drug penetration as the jet penetrates the clot. Therefore, pressure ramping may serve as an effective strategy to improve the effectiveness of sonothrombolysis.
Acoustic human–computer interaction remains vulnerable to environmental noise, constrained in privacy-sensitive settings, and often inaccessible to users with impaired speech production. Silent speech interfaces (SSIs) address these limitations by decoding communicative intent from non-acoustic physiological or articulatory signals. Among the available SSI modalities, surface electromyography (sEMG) is particularly attractive because it provides non-invasive access to speech-related neuromuscular activity while remaining compatible with wearable implementation. This review examines sEMG-based SSIs from a deployment-oriented perspective across the full processing pipeline, spanning physiological foundations, signal acquisition, feature representation, speech-related modeling, and cross-modality comparison. Particular attention is given to the evolution of decoding paradigms from early probabilistic recognition systems to recent neural approaches, including modern sequence modeling, generative EMG-to-speech reconstruction, and language-assisted EMG-to-text decoding. To situate sEMG within the broader SSI landscape, representative competing modalities are compared through a structured deployment-oriented framework covering silent-operation capability, clinical viability, wearability, economic scalability, and practical usability. The reviewed evidence suggests that the importance of sEMG lies not in universal superiority over alternative SSI technologies, but in its comparatively balanced combination of silent-operation capability, physiological relevance, non-invasive wearability, and realistic paths toward scalable deployment. At the same time, major challenges remain in sensing stability, calibration burden, cross-session and cross-subject robustness, benchmark realism, and clinically meaningful evaluation. The review concludes that further progress will depend on coordinated advances in wearable sensing hardware, shared benchmark infrastructure, robust adaptation and generative modeling, system-level integration, and closed-loop human–machine co-adaptation.
To address the limitations in localization accuracy of optically pumped magnetometer magnetoencephalography (OPM-MEG) systems arising from low channel counts or mismatches between head size and a rigid-helmet OPM array, this study systematically evaluates a Virtual MEG Helmet (VMH) approach. By applying quantitative translations or rotations to the OPM array and performing repeated measurements, the acquired datasets are combined to effectively increase spatial sampling density. Using an adult head model, we constructed 16-, 32-, and 64-channel OPM-MEG standard systems and their corresponding VMH systems, and based on a unified forward-field model, computed and compared the F-norm, the quantity of undetectable voxels, and localization error. We further validated the feasibility of the VMH approach through experiments using a triangular dry phantom. The phantom tests indicate that VMH can, to a certain extent, improve localization performance for OPM-MEG systems operating with rigid-helmet arrays. The experimental results are consistent with the simulations and support the feasibility of using VMH to enhance detectability and localization accuracy at relatively low hardware cost, while also mitigating head-size mismatch between a fixed sensor array and the participant’s head.
In aging societies, the high medical and nursing care costs associated with fragility hip fractures among the elderly have become a global concern. Shock-absorbing mats are sometimes used as an alternative to hip protectors; however, scientific evidence regarding their efficacy in preventing hip fractures remains insufficient. Furthermore, their low stiffness can compromise stability during movement and gait. In Japan, a super-aging society, a novel mechanical metamaterial flooring material (MM-floor) has been developed to balance stability with shock absorption through its unique structural design, and it is expected to be adopted for governmentled fragility fracture prevention initiatives. This study constructed a CT-based finite element (CT/FE) model to evaluate the efficacy of three flooring materials: a 2-mm vinyl sheet, a 40-mm sponge mat, and the 22-mm MMfloor. Using CT DICOM data from an elderly Asian woman's femur, we simulated a backward fall in which the greater trochanter impacts the floor. Static analysis identified a fracture threshold at a reaction force of 1,400 N. Dynamic analysis, replicating the impact energy of a fall, showed that peak reaction forces for both the sponge mat and MM-floor remained at 1,200 N, preserving bone geometry through elastic rebound. Conversely, the vinyl sheet resulted in the complete destruction of the proximal femur. Our CT/FE model demonstrated the clinical and economic potential of the novel MM-floor. Furthermore, this model enables rapid product optimization by evaluating arbitrary material properties without posing risks to human subjects or incurring excessive development costs.
Background and objective: Fetal growth restriction (FGR) is associated with impaired oxygen delivery to vital fetal organs, yet the regulatory mechanisms linking ductus venosus-inferior vena cava (DV-IVC) anastomotic morphology and intra-abdominal umbilical vein (IUV) blood flow patterns to blood oxygen polarization remain unclear. This study aimed to systematically investigate the effects of DV-IVC anastomotic angles (axial angle (3, radial angle alpha) and IUV flow patterns (constant vs. pulsatile) on oxygen distribution polarization in the fetal IVC. Methods: Computational fluid dynamics (CFD) and multiphase flow methods were employed to construct parametric models of fetal venous systems. Sixteen geometric configurations with varying (3 (30 degrees-75 degrees) and alpha (0 degrees-30 degrees) angles were analyzed. Oxygenated and deoxygenated blood phases were modeled using the Euler-Euler approach. Hemodynamic parameters, including oxygen distribution ratio (ODR), wall shear stress (WSS), oscillatory shear index (OSI), and relative residence time (RRT), were evaluated under pulsatile and constant flow conditions. Results: Decreasing (3 significantly enhanced oxygen polarization (ODR decreased by 24.32% as (3 increased from 30 degrees to 75%). Smaller (3 angles reduced low WSS areas (<2 Pa) and mitigated abnormal OSI (>0.02) and RRT (>20) risks. Larger alpha angles marginally improved polarization (ODR increased by 6.93% at alpha = 30 degrees vs. alpha = 0 degrees). Pulsatile IUV flow outperformed constant flow, increasing mean ODR by 4.09% and stabilizing oxygen distribution. Conclusions: The DV-IVC anastomotic angle and IUV flow pattern critically influence fetal blood oxygen polarization. Smaller (3 angles and pulsatile flow enhance oxygen delivery efficiency while reducing endothelial dysfunction risks.
The rhythmic propagation of intestinal electrophysiological activity is crucial for maintaining normal peristalsis, and its disruption is often linked to functional disorders. This study proposes an electro-magnetic coupling modeling and inversion framework that integrates the FitzHugh-Nagumo (FHN) electrophysiological model with frequency-domain magnetic-field inversion, establishing a complete simulation pipeline from electrophysiological activation to the resulting magnetic-field distribution and the reconstruction of current density from measured magnetic signals. A 3D local intestinal segment was constructed using six connected curved ellipsoids, and the simulation reproduces the spatiotemporal propagation of slow waves and typical dipolar magnetic patterns, demonstrating strong physiological-electromagnetic consistency. The reconstructed current density distribution, obtained via frequency-domain Fourier inversion, aligns well with membrane potential dynamics. Simulations under three typical functional conditions - normal conduction, local blockage, and multi-source pacing - confirm that the proposed method effectively identifies closed-loop current paths, detects conduction disruptions, and resolves multiple activation sources. This modeling and inversion framework offers a new approach for noninvasive intestinal function imaging and abnormality detection, showing promising application potential.
Most authentication models are vulnerable to security breaches when personal data is exposed. This study introduces a novel hybrid visual computer interface integrating event-related potentials (ERPs) and steady-state visually evoked potentials (SSVEPs) to develop an authentication system that enhances both performance and personalization in neural interfaces. Our model utilizes distinctive neural patterns elicited by a range of visual stimuli based on 4-digit numbers, such as familiar numbers (personal birthdates, excluding targets), standard targets, and non-targets. The results revealed a distinct P300 response to familiar numbers when compared to both non-target and target stimuli. Incorporating these stimuli into our Transformer-based authentication system, coupled with personalized electroencephalogram (EEG) data segmentation, resulted in high accuracy in authenticating users and demonstrated remarkable robustness against security breaches. Additionally, a 10 Hz grow/shrink background image successfully elicited SSVEP. Furthermore, the comparison of harmonic and fundamental frequencies aids in optimizing neural interfaces.
A phantom is an essential tool for evaluating the localization accuracy of magnetoencephalography (MEG) systems. However, the structures of the phantom coils can introduce additional measurement errors during MEG localization testing. Despite its significance, optimal strategies for designing dry phantoms that are specifically adapted to different MEG systems and capable of minimizing such errors have not been fully explored. This study employed a simulation model based on an optically pumped magnetometer MEG (OPM-MEG) system to quantitatively characterize the spatial distribution of localization errors and to guide the design of coil placement. To assess the influence of reference location accuracy on source localization, a comprehensive analysis of error sources in phantom-based MEG testing was conducted, focusing on the role of phantom design in localization outcomes. The impact of reference location coordinate accuracy on localization performance was further validated through experimental testing. Based on the simulation and experimental results, a systematic design methodology for a dry phantom system using triangular coils is proposed. This approach includes both a coil placement strategy adapted to the OPM array and a structural design framework for the triangular coil assembly. The methodology improves the accuracy of MEG calibration tests and establishes a reproducible quality-control framework for pre-clinical OPM-MEG deployment and cross-site comparability.
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) are widely recognized for their high information transfer rates (ITRs), yet their practical application remains constrained by the challenge of balancing performance and user experience. To address this issue, this study proposes a design framework for individualized spatial-phase-amplitude modulation stimuli aimed at enhancing response strength at frequencies above the critical flicker fusion threshold. In this framework, phase parameters are allocated to subregional stimuli to alleviate cancellation effects induced by the cortical cruciform organization, while amplitude parameters selectively determine whether a given subregion should be stimulated, thereby avoiding redundant use of visual resources. A single-target experiment was first conducted to record SSVEP responses to 60 Hz stimuli from 16 subregions across two eccentricities and eight polar angle positions. Pronounced differences were observed in both amplitude and phase distributions across subregions, and substantial inter-individual variability further underscored the necessity of subject-specific parameter optimization. A first-choice hill climbing strategy was subsequently introduced to enable efficient optimization of spatial-phase-amplitude parameters within the large space generated by the numerous possible combinations. Finally, the superiority of individualized stimuli was validated in a four-target BCI experiment. Within the 0-7 degrees eccentricity range, individualized stimuli (offline peak: 156.58 f 8.53 bpm, online: 77.83 f 1.23 bpm) achieved a significantly higher ITR than unmodulated stimuli (offline peak: 123.16 f 7.45 bpm, online: 69.29 f 2.97 bpm) while maintaining high subjective userexperience ratings. These results demonstrate the feasibility of individualized spatial-phase-amplitude modulation as a pathway toward high-performance, visually comfortable, flicker-free SSVEP-BCIs.
Background and motivation: MicroRNAs (miRNAs) regulate gene expression and are critical to disease development and progression. Accurate miRNA sequence classification remains a key challenge due to short lengths, conserved motifs, and species variability. Although transformer models have shown promise, they often depend on generic tokenizers (e.g., k-mers, BPE) that disrupt biologically meaningful subsequences. Method: We propose GenAI 4.0, which implements a novel technique called BioBPE, a biologically-informed extension of Byte-Pair Encoding that prioritizes the preservation of conserved miRNA motifs during vocabulary construction. Unlike frequency-only schemes, BioBPE incorporates a domain-specific weighting function into the merge scoring process to align tokenization with the biological signal. To evaluate its efficacy, we conducted a comprehensive benchmark across six transformer models using five different tokenization. Experiments were performed on binary and multiclass classification datasets from MirGeneDB v3.0. Results: BioBPE-tokenized models achieved up to 8.6% improvement in classification accuracy and approximately 18-45% faster convergence to standard tokenizers. Statistical tests (paired t-tests, Wilcoxon) confirmed significance, and BioBPE achieved a mean motif preservation rate of approximately 92% across vocabulary sizes. Conclusions: Our findings demonstrate that tokenization is a central determinant of success in biological sequence classification tasks, and not just a preprocessing step. By explicitly aligning token boundaries with biological structures, BioBPE bridges the gap between generic NLP tokenizers and domain-specific sequence modeling.
Reliable in vitro models are used for optoelectronic device development, such as fluorescence detection devices for fluorescence-guided surgery of gliomas. A common approach involves inducing gliomas in animal models, followed by a dosage of 5-ALA which metabolises to Protoporphyrin IX (PpIX) in the glioma, resulting in fluorescence. Although these approaches excel in capturing key biomolecular and physiological features of the tumour, they are inherently indeterministic. This limits the scope of their use for preclinical device development, where consistent and controllable tumour reproduction across multiple animals, over time, is needed. Alternative approaches using fluorescence markers in gelatine provide a simple replication but may fail to capture the complexities of in vivo models. In this study, we introduce an exogenous brain tumour model for assessing PpIX fluorescence detection. The model was developed by injecting a PpIX solution into the cortical region of a resected adult rat brain, where the injection site simulated a tumoral region with elevated PpIX concentration. The created tumoral region had a gradient of concentrations, with a peak at the centre and a decrease towards the tumour margins, akin to glioma in in vivo conditions. The fluorescence profile was compared to in vivo conditions using 5-ALA and correlated well with other reported works, achieving a correlation of R-2 > 0.93. The model's validity was tested by examining the effect of the solvent, Dimethyl Sulfoxide, on the Autofluorescence (AF) of the brain sample. Additionally, the short-term effect of storage (over 20 h) on AF was analysed. Examinations confirmed that the solvent did not alter AF. The brain sample should be stored in Hank's Balanced Salt Solution (or an equivalent) and refrigerated to maintain moisture and preserve AF. The model accurately replicated surgical fluorescence conditions and offers a suitable alternative to glioma induction, benefiting the development of fluorescence detection devices across design iterations.
Individuals suffering from progressive neuromuscular diseases gradually lose all muscle control and therefore are forced to repeatedly adapt to new control interface technologies to maintain some level of independence. Consequently, ideal interface technology should adapt to the progression of paralysis. We propose an adaptive tongue-brain hybrid interface framework for the three-dimensional control of a robotic arm. The interface was tested with able-bodied individuals and individuals with amyotrophic lateral sclerosis. The experiments demonstrated the importance of flexible frameworks for cooperation between control modalities as this allows a critical optimization of the control performance relative to the disease stage. The hybrid framework allowed for a 4-32% stepwise decrease in performance while some tongue-functionality would exists, rather than a 200% decrease when moving directly from a tongue to a brain control interface. This hybrid framework is the first step towards a new concept of assistive robotic control with a higher focus on adapting to the functionality of disabled individuals.
Pain is a key factor in treatment and post-surgical recovery, evaluated in clinic for severity and experience to help inform diagnosis and treatment of the underlying condition. Attempts to quantify and add objectivity to pain assessment through physiologic response have so far primarily focused on pain severity while leaving the pain experience gap crucially unfilled. We investigate electrodermal activity (EDA), a non-invasive measure of skin conductance heavily linked to Sympathetic Nervous System response and a commonly studied method for pain severity assessment, as a method for detecting the characteristics of different nerve fiber stimulation schemes. Subjects were exposed to various intensities of transcutaneous electrical nerve stimulation (TENS) to elicit targeted nerve fiber activity. EDA was able to statistically differentiate mid-intensity stimulations (pain score 2.6 +/- 2.0 on a scale 0-10). In addition, we were able to differentiate between TENS regimes of C-fiber dominance, A delta-fiber dominance, A beta-fiber dominance, and no stimulation with a high degree of accuracy (>65% multiclass classification accuracy, AUROC > 0.85). When focused on pain-associated C-fiber dominant, A delta-fiber dominant, and no stimulation regimes, classification performance improves to over 87% multiclass accuracy and an AUROC of 0.95. These methods can help push quantitative pain sensing beyond the academic laboratory towards a usable clinical assessment tool.
This work investigates how progressive microdamage accumulates in intact human fourth ribs under bending loads and how acoustic emission (AE) signals reflect that deterioration. Twenty-four ex vivo ribs (eighteen under quasi-static (<0.0004s(-1) and six under dynamic (0.012-0.042 s(-1) were subjected to three-point bending while AE sensors recorded microcrack activity near regions of peak tensile stress. To accommodate large deformations and complex geometry, we applied finite strain theory and described the mechanical response with an orthotropic continuum damage model. Damage growth followed a Weibull distribution, and stiffness degradation closely tracked the damage variable. We then correlated AE event counts with damage progression by fitting an empirical relationship that captures both the gradual accumulation of low-damage events and the abrupt increases in events near failure. Our analyses reveal three key outcomes. First, the Hild-Lema & icirc;tre quasi-brittle damage model provides an excellent fit to stress-strain data across all strain-rate regimes. Second, cumulative AE counts increase monotonically with internal damage, confirming AE as a reliable real-time proxy for microcrack evolution. Third, AE-damage curves differ qualitatively with strain rate: quasi-static tests produce strongly convex profiles culminating in a near-vertical asymptote, whereas dynamic tests exhibit an initial concave segment followed by a more linear trend before ultimate failure. Furthermore, it was observed that increasing strain rate elevates both ultimate and damage strains, whereas subject age is associated with reductions in ultimate stress and stiffness. In contrast, BMI exerts only minor effects. Finally, b-value analysis did not yield predictive insight for human cortical bone fracture, unlike in concrete. Together, these findings establish AE monitoring coupled with continuum damage mechanics as a powerful framework for characterizing rate-dependent failure in rib cortical bone, which could inform real-time clinical monitoring during high-risk procedures.
