Association rule mining is a fundamental data mining technique for uncovering latent relationships among variables in large-scale datasets. However, conventional approaches rely on single-metric filtering strategies, which are insufficient for capturing the inherent multi-criteria nature of rule quality. To address this limitation, this study formulates ARM as a many-objective optimization problem and proposes a hybrid algorithm, NSGA-II/DE-ARM, that simultaneously optimizes four rule-quality measures: support, confidence, lift, and NetConf. The proposed algorithm enhances the NSGA-II framework by integrating binary differential evolution operators, an adaptive operator selection mechanism, lift-weighted tournament selection, and a constraint-domination principle combined with a dynamic minimum support threshold. Its performance was evaluated using two datasets: a SIPRI-World Bank panel dataset consisting of defense industry and macroeconomic indicators covering 46 items over the 2002-2023 period, and the UCI Mushroom benchmark dataset consisting of 118 items. Across 30 independent runs on the SIPRI-World Bank dataset, NSGA-II/DE-ARM outperformed the Apriori baseline in all four metrics (mean lift = 4.748, confidence = 0.853, support = 0.146, NetConf = 0.789), with large effect sizes (Cohen's d = 1.77-5.77, p < 0.001 in each case). On the Mushroom benchmark dataset, the proposed method also achieved substantial improvements, with Cohen's d values ranging from 0.93 to 6.16. NSGA-II/DE-ARM generated 68 Pareto-optimal rules in a representative run and achieved the highest hypervolume values on both datasets, with HV = 3.231 for SIPRI-World Bank and HV = 6.262 for Mushroom. These results suggest that NSGA-II/DE-ARM offers decision-makers a broader and more balanced multi-criteria solution set than single-metric filtering approaches.
Optimizing flapping-foil kinematics for underwater propulsion is challenging due to strong temporal coupling and nonlinear fluid-structure interactions. Most existing approaches rely on parameterized motion profiles, which restrict the accessible design space. A non-parametric kinematic optimization framework based on the discrete adjoint method is developed, enabling direct optimization of time-resolved motions without predefined functional forms. A Morison-based low-order hydrodynamic model, calibrated against Computational Fluid Dynamics (CFD), is employed for efficient evaluation within a validated regime. Results show that optimized motions substantially enhance propulsion performance over conventional sinusoidal motions, yielding non-sinusoidal, high-efficiency kinematics. In thrust-maximization cases, the optimized kinematics achieve a 50.29% increase in mean thrust by redistributing heave and pitch amplitudes and timing. Under balanced thrust-power conditions, the optimized motions consistently outperform sinusoidal counterparts. In power-minimization cases, a "generator-like" regime emerges, indicating a reversal of net energy transfer enabled by the non-parametric formulation. These results demonstrate that non-parametric optimization provides enhanced design flexibility and improved propulsion performance, offering a practical framework for biomimetic underwater propulsion design.
Trust in embodied intelligence is dynamic, contextual, and interaction-dependent, but many existing computational approaches still model trust using static similarity structures. This study proposes and evaluates a compositional trust modeling approach based on quantum natural language processing (QNLP). Using open-ended survey responses about human trust in embodied agents, we compared classical NLP clustering and QNLP-based clustering in terms of dimension coverage, semantic coherence, contextual sensitivity, and robustness. The QNLP pipeline captured richer latent structure, producing ten clusters and identifying eight trust dimensions, including two emergent dimensions: calibrated trust and predictive reliability. Compared with classical approaches, QNLP clusters showed improved semantic separation and stronger context retention under preprocessing variation. These findings support a temporally structured view of trust in embodied AI and demonstrate that compositional quantum-inspired representations can reveal nuanced trust dynamics that are difficult to detect with conventional methods. This study contributes both a methodological framework for trust-sensitive text modeling and a theoretical account linking trust formation to retrospective calibration and prospective expectation in human-agent interaction.
Resolving the kinematic redundancy of 7-DoF humanoid arms to generate natural, human-like motions remains a fundamental challenge in biomimetic robotics. This paper presents a hybrid inverse kinematics (IK) framework that learns a pose-dependent redundancy parameter and integrates it into a differential IK solver. Specifically, we employ the stereographic Shoulder-Elbow-Wrist (SEW) angle as a well-conditioned geometric parameterization. This formulation transforms the algorithmic singularity into a unidirectional half-line, which can be oriented outside the typical reachable workspace. To specify the optimal configuration within the self-motion manifold, a motion dataset was collected by teleoperating a humanoid arm via an anthropomorphic wearable exoskeleton. This approach translates operator-specific postural preferences into the robot's joint space. A lightweight neural network was then trained to learn the mapping from end-effector poses to these operator-specific SEW angles. By incorporating the predicted SEW angle as a dynamic secondary objective in the null space of the primary tracking task, the proposed framework enables natural redundancy resolution while preserving end-effector tracking accuracy. Both simulations and real-robot experiments were conducted to validate the approach. Results show that, compared to the average performance of static fixed-parameter strategies, the proposed method improves the Joint Configuration Quality Index (CQI) by 22.5% and reduces energy costs by 11.3%. Moreover, the sub-millisecond inference latency (0.44 ms) facilitates seamless integration into real-time control pipelines.
Effective diabetes management heavily relies on appropriate insulin administration, which strongly depends on the correct administration strategy. In this sense, insulin administration plays a fundamental role, as its use depends on the patient’s clinical condition and diabetes type. Traditional syringe-based methods require proper training to ensure that insulin is successfully delivered into the subcutaneous tissue, where it can be absorbed and metabolized; however, it is desirable to develop an insulin applicator that does not require training for its appropriate use. Aiming to provide support solutions that help patients to develop a correct administration technique, a biodesign-based methodology, coupled with biomimetic concepts, is employed to design a device that assists the user in creating a stable skin fold and guiding needle orientation during injection without requiring exhaustive training for device usage. A three-step approach is employed for the design, where computational fluid dynamics (CFD) and finite element analysis (FEA) methods are employed to ensure that the device produces a laminar insulin flow and the device strength is tested. It should be pointed out both methods are required since complications produced by sudden flows must be avoided, with CFD allowing assessment of the device mechanical properties in terms of the device strength. Initial functional evaluation indicates that the proposed approach does not require extensive training or complex operational procedures, facilitating its integration into everyday use. The device design is validated from the results obtained for the CFD analysis, as no turbulent flow is produced, whereas the FEA indicates that the geometrical form can handle the stresses produced by the folding generation without generating excessive deformations. Moreover, an infrared thermography analysis is also carried out to find out if the folding force generation is located in the zone of interest, the results of which indicate that the device operates in the desired physical zone.
Background/Objectives: Patient-specific subperiosteal implants are increasingly used to treat severely atrophic ridges due to advances in digital planning and additive manufacturing. This study aimed to evaluate the effects of material type and implant configuration on stress distribution in subperiosteal implant systems and to compare their overall biomechanical performance using a multi-criteria decision framework. Methods: A three-dimensional model of a severely atrophic maxilla was reconstructed to simulate four clinical scenarios combining two configurations (one-piece and two-piece) and two materials (titanium and 60% carbon fiber-reinforced polyetheretherketone). Finite element analysis was conducted to assess stress distribution within the implant body, fixation screws, prosthetic framework, and surrounding bone under vertical and oblique loading conditions. Maximum and minimum principal stresses were evaluated in bone, whereas von Mises stresses were calculated for implant components. The resulting biomechanical indicators were subsequently integrated using an entropy weight-TOPSIS multi-criteria decision analysis. Results: Principal stresses in the surrounding bone showed minimal variation between titanium and 60% carbon fiber-reinforced polyetheretherketone across all configurations. Implant configuration had a more pronounced effect on implant body stress. Under oblique loading, the two-piece configuration demonstrated substantially higher implant stresses than the one-piece design, whereas under vertical loading, lower implant stresses were observed in the two-piece configuration. The multi-criteria analysis ranked the one-piece titanium model highest under oblique loading and the two-piece titanium model highest under vertical loading. Conclusions: Implant configuration and loading direction influenced biomechanical behavior more than material selection in patient-specific subperiosteal implants.
Agricultural cold chain logistics is characterized by inherent challenges—product perishability, high carbon emissions, and stringent time windows—which are further exacerbated by dynamic disruptions. Existing methods suffer from slow adaptability, unstable multi-objective convergence, and severe cold-start issues. This work falls within the broad scope of biomimetics—the science of emulating nature’s time-tested strategies to solve complex engineering problems—and bio-inspired data-driven methods and their applications in engineering control, optimization, and artificial intelligence. The proposed H-MODRL framework embodies core biomimetic principles: the Genetic Algorithm (GA) mimics Darwinian natural selection and genetic inheritance, the Sparrow Search Algorithm (SSA) abstracts the cooperative foraging and anti-predation behaviors of sparrow populations in nature, and the Arrhenius-based freshness-decay model captures the biochemical kinetics governing perishable biological products. By synergistically integrating these biological evolution principles, swarm intelligence, and deep learning, the framework tackles real-world logistics complexity in a manner directly inspired by living systems. This study presents a well-organized hybrid optimization framework (H-MODRL) that couples a three-stage hybrid evolutionary mechanism, synergistically integrating heuristic warm-start, evolutionary policy guidance, and deep reinforcement learning decision-making. First, an improved genetic algorithm combined with the earliest deadline first strategy constructs a feasible initial population satisfying hard time-window constraints. Second, a large neighborhood search-enhanced chaotic sparrow search algorithm builds a high-quality elite guidance set for policy learning. Third, a physics-based multi-objective proximal policy optimization model embedded with Arrhenius equation-derived freshness-decay kinetics performs online decision-making. Experiments demonstrate that pre-computed all-pairs shortest paths and an O(1) hash-based dynamic-disruption indexing mechanism support fast online replanning. On heterogeneous simulated terrains based on real Chinese geospatial data, H-MODRL outperforms state-of-the-art algorithms across four objectives—logistics cost, carbon emissions, terminal freshness, and delivery time—while exhibiting compact, low-variance performance distributions, thereby validating its engineering robustness and practical value in complex agricultural cold chain environments.
Flexible pressure sensors with a porous architecture are highly desirable for wearable health monitoring and intelligent human-machine interaction, owing to their excellent comfort and conformability to human motion. However, conventional porous sensors often suffer from poor signal accuracy and unstable output, which limit their capability for precision sensing. To address these challenges, we designed and fabricated a flexible pressure sensor with exceptional linearity by mimicking the unique surface structure of Iron Cross Begonia (Begonia masoniana) leaves. The sensor is constructed using a readily available melamine foam as the backbone: a porous sensing scaffold is first obtained via a simple dip-coating process, and a film featuring bioinspired protrusions is fabricated by repeated replica molding. Lamination of these two components yields a stacked sensor device. Characterization demonstrates that the sensor achieves a broad pressure detection range of up to 350 kPa, with a minimum resolvable pressure of 250 Pa, and exhibits an excellent linearity of 0.999 over its entire working range (0-350 kPa). Moreover, the sensor shows stable responses under varying loading frequencies, is capable of detecting low-frequency signals, and retains its performance without notable degradation even after 5000 repeated loading-unloading cycles. In practical applications, the sensor accurately monitors flexion and extension movements of the wrist, finger, neck, and knee, capturing human motion signals with high fidelity. Furthermore, it enables information encoding and transmission through finger gestures. The proposed bioinspired structural design strategy effectively enhances the overall performance of porous pressure sensors, offering a new paradigm for the development of flexible sensing devices with promising applications in wearable health monitoring, human motion detection, and human-machine interaction.
Periodontal disease remains one of the leading causes of tooth loss worldwide, highlighting the need for effective regeneration of alveolar bone, periodontal ligament, and cementum. The structural complexity and unique biological behavior of these tissues have historically posed significant challenges for clinical regeneration strategies. The primary therapeutic approach used is guided bone regeneration; however, it has certain limitations, such as morbidity, low structural integrity and dimensional stability. Recent advances in 3-dimensional (3D) bioprinting have made it possible to fabricate customized scaffolds with precise architecture and spatial organization that closely mimic normal periodontal structures. The incorporation of multifunctional nanocomposite biomaterials and nanoparticles further enhances the performance of the scaffolds by increasing mechanical strength, bioactivity and controlling degradation rates. These advanced scaffolds function as dynamic microenvironments that support cell adhesion, proliferation and differentiation, ultimately promoting tissue regeneration. Furthermore, their multifunctional properties allow for the controlled release of growth factors, anti-inflammatory and antimicrobial agents, as well as the incorporation of stem cells and bioactive molecules that facilitate angiogenesis. This review investigates and critically evaluates modern approaches for the regeneration of periodontal tissues through scaffolds, biomaterials and 3D bioprinting technologies, as well as to assess their effectiveness compared to established clinical practices.
Empirical logic (EL) is a bio-inspired soft computing approach to rule-based decision-making that emphasizes intuitive, experience-based reasoning. While its theoretical foundations have been established in previous work, its practical applicability and accessibility have so far received less attention. This paper addresses this gap by providing two representative application examples from distinct domains: control engineering and cluster analysis. The first example demonstrates the use of EL for the speed control of a DC drive, highlighting its ability to achieve competitive dynamic performance with a small number of intuitive rules. The second example introduces a novel approach to cluster analysis, where cluster structures emerge from the collective interaction of EL rules rather than from the optimization of a predefined objective function. In addition, the paper emphasizes the availability of publicly accessible software realizations of EL, including a Maple-based prototype and a Python framework, which enable direct experimentation and practical use. By combining illustrative applications with executable tools, the paper aims to facilitate the transition from conceptual understanding to practical deployment and to support further exploration of EL in applied soft computing contexts.
Conventional thrust vector control nozzles are severely constrained by a single-pivot deflection paradigm, which induces asymmetric shock reflections and adverse boundary layer separation at large angles. Multi-segmented serial configurations offer a promising alternative to overcome these limitations by distributing the total deflection across multiple joint interfaces, thereby achieving large terminal angles and smooth flow-path curvatures. To realize such a configuration, this study draws inspiration from the abdominal bending mechanism of the damselfly Ischnura elegans during mating wheel formation. Real-time video recording and morphological characterizations identified abdominal segments VI and VII as critical for high-amplitude bending under load. Finite element analysis under muscular actuation elucidated the biomechanical synergy, which was rigorously verified through mesh convergence and material property sensitivity checks. Inspired by this biological system, a multi-segmented nozzle configuration incorporating discrete elastic elements and a centralized cable-driven layout was designed and evaluated using multibody dynamics and computational fluid dynamics. The nozzle achieved a continuous 61.20° deflection within 8 s under subsonic exhaust conditions, successfully stabilizing periodic supersonic shock structures and completely suppressing adverse boundary layer separation. These findings turn biological bending into a thrust vectoring method, giving insights for next-generation agile aerospace propulsion systems.
Sustainability challenges in the built environment demand a shift in architectural education from form-based approaches toward adaptive, systems-oriented, and performance-driven thinking. This paper examines an integrated pedagogical model combining biomimicry, parametric thinking, and modular design to enhance sustainable design learning in architectural studios. Using a qualitative case study approach, this research investigates Architectural Design Studio 4 at the American University of Ras Al Khaimah (AURAK), where third-year students followed a three-stage discovery-based process. Students first analyzed biological systems to identify transferable principles, then translated these principles into parametric modules using computational tools such as Dynamo and Revit, and finally applied the systems to high-rise architectural design. The findings indicate that integrating biomimicry with parametric workflows encouraged optimization, adaptability, and reusable design strategies rather than fixed outcomes. Modular design approaches helped students manage architectural complexity, while computational tools supported performance-based exploration and informed decision-making. The absence of a predetermined final design fostered critical thinking, creativity, and problem-solving skills. This study contributes empirical evidence to architectural education research by demonstrating that process-based, discovery-oriented studios can strengthen students’ understanding of sustainability, systems logic, and adaptability, preparing future architects for contemporary environmental and technological challenges.
Hypophosphites (also known as phosphinates) are the salts of hypophosphorous acid (also known as phosphinic acid), H3PO2. Various hypophosphite salts are known such as calcium hypophosphite (Ca(H2PO2)2) and sodium hypophosphite (Na(H2PO2)). Hypophosphites were proposed as a potential treatment for tuberculosis as early as the 1850s; however, they were found to be ineffective against this disease. Following this, there was a period of around 100 years during which no new studies on hypophosphites were published. Subsequent in vitro studies have shown that hypophosphites can be used as potential antibacterial food preservatives. Currently, calcium hypophosphite is used in commercial food supplements for children, as this compound is a suitable source of calcium ions. It also has other advantageous properties, including exceptionally high water solubility (154 g/L at 25 °C), a neutral taste, a high mass fraction of calcium per molecule (23.6%), and an excellent safety profile. Recent studies have shown its potential as an active ingredient in the field of oral care. Since biological mechanisms such as tooth and bone formation and natural remineralization due to saliva rely on calcium ions, calcium hypophosphite can be regarded as a biomimetic agent. Upon contact with phosphate from saliva, calcium hypophosphite forms hydroxyapatite; this imitation of physiological mineralization and crystallization processes in the human body further underlines its biomimetic character. This review summarizes and discusses the available literature on hypophosphites in human health and related fields.
This study investigates the bio-inspired photocatalytic degradation of humic acids using TiO2-functionalized clinoptilolite (C-TiO2) and Ag-doped TiO2 (C-TiO2/Ag) under UV irradiation. TiO2 acts as an artificial analogue of naturally occurring photoactive mineral phases, while clinoptilolite provides a biomimetic scaffold mimicking mineral-organic interfaces. Ag doping enhances charge separation and promotes reactive oxygen species formation, accelerating degradation. The effects of pH and catalyst composition were evaluated over a range of conditions, including the native pH of the humic solution. Degradation was monitored via changes in UV254 absorbance, VIS436 absorbance, and COD values, revealing a multistage pathway: rapid decolorization of chromophoric groups, slower breakdown of aromatic structures, and final mineralization. Acidic conditions further enhanced performance through increased adsorption and ROS (reactive oxygen species) generation, while measurable activity persisted at near-natural pH values. Kinetic analysis indicated pseudo-first-order behavior, with the highest apparent rate constants obtained for VIS436 removal under C-TiO2/Ag at pH 3 (k = 0.0166 min-1), followed by COD1 (k = 0.0190 min-1), confirming faster oxidation of labile fractions and slower mineralization of recalcitrant intermediates. Therefore, the results demonstrate that semiconductor-mineral hybrid systems can serve as biomimetic platforms that reproduce and accelerate natural self-purification processes, providing mechanistic insights into nature-inspired pathways for water treatment.
The extracellular matrix (ECM) provides a dynamic microenvironment that regulates cell proliferation, migration, and tissue remodeling during wound healing. However, replicating the structural and functional complexity and ECM heterogeneity of native skin ECM remains challenging with conventional single-material hydrogels. Recent advances in multimaterial 3D bioprinting have enabled the spatial integration of diverse biomaterials within a single construct. Lignocellulose has attracted increasing attention as a promising biomaterial for recreating key structural features of the native ECM because of its fibrous architecture, mechanical strength, and biocompatibility. This review offers a comprehensive and integrated perspective on the use of lignocellulose-based multimaterial printing to recreate ECM-mimicking architectures, an underexplored area at the intersection of biomaterials and biofabrication. The roles of cellulose, hemicellulose, and lignin in printability, scaffold stability, porosity, bioactivity, and wound-healing performance are discussed. Representative studies have demonstrated that lignocellulose-based multimaterial bioinks provide porous architectures that support cell adhesion, proliferation, and tissue regeneration. These benefits are accompanied by improved mechanical performance, as cellulose nanofibers exhibit elastic moduli exceeding 100 GPa, and lignin-containing hydrogels have achieved compressive moduli of up to 135 kPa. Such mechanical advantages make lignocellulosic materials particularly attractive for fabricating ECM-mimicking scaffolds that require long-term structural integrity. Finally, key design considerations and current limitations associated with lignocellulose-based multimaterial bioprinting are critically discussed. A framework for the rational design of lignocellulose-based multimaterial bioinks is presented, together with future directions toward gradient and adaptive scaffolds, smart wound dressings, and advanced wound-healing applications.
The deep classifiers used for chest X-ray diagnosis can be sensitive to the visual frame around the evidence, producing correct labels for the wrong reasons by relying on the background, borders, text marks, or acquisition artifacts. This framing sensitivity reduces their trustworthiness, and they can fail under a distribution shift. Inspired by biological vision, especially figure-ground segregation and selective attention, we propose a bio-inspired adversarial attention alignment training process that encourages evidence-centered decisions without changing the classifier structure. A classifier is first trained with image-level labels. Class activation mapping (CAM) is then used to produce a differentiable heatmap that indicates where the model attends. We treat this heatmap as a generated localization map and train a discriminator to distinguish the generated heatmaps from ground truth masks. The classifier is updated using a joint objective that preserves the classification performance while pushing its CAM heatmap toward a mask-like structure, reducing the reliance on background cues. We also introduce evaluation measures for the test phase, including augmentation inconsistency (prediction flip rate under angle-based augmentations) and framing sensitivity (CAM energy outside the mask). The experiments show improved lung-focused attention and robustness, while requiring masks only during training and no additional inputs at inference.
High-dimensional small-sample data are widely encountered in medical diagnosis, bioinformatics, and industrial inspection, where traditional feature selection methods often suffer from premature convergence and local optima. To address these issues, this paper proposes a Hybrid Breeding-based Co-evolutionary Ant Colony Optimization method (HBACO) for feature selection. Inspired by the principle of hybrid breeding, in which individuals with distinct traits produce superior offspring through cross recombination, inheritance of desirable genes and continuous evolution, the proposed algorithm establishes a three-population collaborative framework. It consists of an ACO-based search population, an HRO-based evolutionary population and a cooperative feedback population that evolve iteratively together. Furthermore, we devise a heuristic strategy integrating correlation and genetic characteristics to help mine high-value feature subsets. Meanwhile, a collaborative pheromone updating mechanism is adopted to realize efficient knowledge sharing among populations. Experiments conducted on 13 high-dimensional datasets, including Colon and Lung, demonstrate that HBACO achieves superior classification accuracy, feature reduction performance, and convergence behavior compared with 10 representative algorithms. Specifically, HBACO improves the average classification accuracy by 3.9% and achieves an average feature dimensionality reduction rate of 91.4%. Statistical tests further confirm the significance of the proposed method. The results indicate that HBACO provides an effective and robust solution for high-dimensional feature selection problems.
Biomimetic optimizers are increasingly used to solve complex engineering problems, yet their performance depends strongly on how effectively they preserve diversity while maintaining selection pressure toward promising regions. In this study, the Starfish Optimization Algorithm (SFOA) is enhanced through fitness-distance-aware selection control, leading to two improved variants: Fitness-Distance Balance Starfish Optimization Algorithm (FDBSFOA) and Dynamic Fitness-Distance Balance Starfish Optimization Algorithm (dFDBSFOA). The proposed framework guides candidate selection using both solution quality and spatial diversity relative to the current best solution, while the dynamic variant further adapts this balance over the course of the search to improve exploration in early iterations and exploitation near convergence. The proposed methods are evaluated on the IEEE CEC2017, CEC2020, and CEC2022 benchmark suites under a unified maximum function evaluation budget, MaxFEs = 10,000 × D, with 21 independent runs, and are further validated on constrained engineering design problems. Performance is assessed using convergence behavior, robustness indicators, computational overhead, and nonparametric statistical tests. The results show that the proposed variants improve the robustness and search efficiency of baseline SFOA, with dFDBSFOA providing the most consistent overall performance while introducing a controlled and interpretable computational overhead. These findings suggest that diversity-aware selection can serve as an effective design principle for strengthening biomimetic optimization frameworks. The current study focuses mainly on continuous, single-objective, and stationary benchmark problems, while the engineering-design validation also includes constrained and discrete/integer-coded cases. Extending the proposed strategy to dynamic, noisy, large-scale mixed-integer, or multi-objective settings remains future work.
With the development of Internet of Things (IoT) technology, fire detection systems based on multi-sensor fusion have become critical infrastructure to ensure public safety. Due to environmental noise and sensor heterogeneity, these systems often suffer from high rates of false alarms and missed detections. Although existing machine learning approaches have partially improved classification accuracy, their overall performance remains limited. Inspired by the cognitive mechanisms of the human brain, we developed an improved mind-linked continuous-coupled neural network (ML-CCNN) based on the existing continuous-coupled neural network (CCNN). We propose a parameter adaptation mechanism that modulates neural activations through a global threshold. We utilized the synthetic minority oversampling technique (SMOTE) to mitigate data imbalance and transformed sample feature vectors into matrices for training. Our model achieved an accuracy of 99.96% on our own dataset and 99.97% on the public Smoke Detection Dataset (SDD), which highlights ML-CCNN’s potential for fire detection.
Wearable pressure-sensing orthoses are increasingly used in neurorehabilitation to support gait recovery, monitor plantar pressure distribution, and improve patient mobility during repetitive therapy sessions. The performance of these devices depends strongly on the materials used in the skin-contact layer, since material properties influence comfort, flexibility, durability, and force transmission during daily use. This study proposes a hybrid multi-criteria decision-making framework based on the Analytic Hierarchy Process (AHP) and the VIKOR method for material selection in sensor-integrated plantar orthoses. Five candidate materials, ethylene vinyl acetate (EVA), polyethylene (PE), polyurethane (PU), cobalt-chromium-molybdenum alloy (CoCrMo), and polypropylene (PP), were evaluated using five criteria: comfort and skin compatibility, elasticity, fatigue resistance, density, and energy dissipation. AHP was applied to determine the relative importance of the evaluation criteria using expert judgment, while VIKOR was used to rank the material alternatives and identify the compromise solution. The results showed that polyurethane achieved the best overall performance due to its balanced behavior in comfort, elasticity, and fatigue resistance, which are essential properties for long-term wearable neurorehabilitation devices. A sensitivity analysis confirmed that moderate variations in expert weighting did not modify the final ranking. Compared with conventional selection approaches based mainly on isolated material properties, the proposed framework offers a clear and reproducible method for integrating mechanical and user-related requirements into the material selection process for wearable orthoses.
