Search Results (96)

Human–computer interaction (HCI) is central to wearable technology; however, traditional interaction methods face constraints from environmental noise, privacy risks, and operational inconveniences. With the convergence of flexible electronics and artificial intelligence, smart wearable systems equipped with biomimetic biointerfaces are evolving into “external organs” that augment human capabilities, establishing a new paradigm for natural and intelligent interaction. This narrative review provides a comprehensive overview of the research progress in seamless HCI driven by wearable biointerfaces and intelligent systems. From the input perspective, we elucidate how high-fidelity physiological and motion signals are captured through biocompatible electronic skins, and subsequently decoded via intelligent algorithms capable of robust noise decoupling, cross-user generalization, and multimodal data fusion, while emphasizing algorithmic trustworthiness including privacy and interpretability. From the output perspective, we explore adaptive closed-loop feedback mechanisms, spanning both non-visual multi-sensory rendering and biomimetic actuation-based physical interventions. Finally, we discuss key engineering and algorithmic bottlenecks—such as material durability, internal latency, system integration, and trustworthiness—offering future perspectives for the development of next-generation personalized and immersive HCI systems. Full article

Tea quality evaluation is increasingly moving from subjective sensory assessment and destructive laboratory analysis toward rapid, non-destructive, and data-driven approaches. This review summarizes recent advances in multimodal sensing integrated with deep learning for tea quality evaluation, with emphasis on sensor complementarity, data-fusion strategies, representative applications, and deployment-related limitations. Major sensing modalities, including machine vision, near- and mid-infrared spectroscopy, Raman and fluorescence spectroscopy, hyperspectral imaging, and electronic nose/electronic tongue systems, are discussed in relation to their ability to characterize appearance, chemical composition, aroma, flavor, processing status, and safety-related attributes. Applications are examined for quality grading, chemical composition prediction, aroma and flavor characterization, fermentation monitoring, and safety-related extensions across representative tea products, including green tea, black tea, dark tea, matcha, and jasmine tea. Overall, multimodal approaches can outperform single-sensor systems only when the selected modalities provide complementary, rather than redundant, information layers. However, practical translation remains constrained by small and weakly standardized datasets, insufficient external validation, sensor instability, limited model transferability, high computational cost, and insufficient interpretability. Future research should prioritize standardized datasets, leakage-free validation protocols, interpretable multimodal modeling, truly independent external validation, interoperable multi-sensor platforms, and lightweight deployable models. Full article

Background/Objectives: Self-mutilation behavior is often triggered by neuropathic pain associated with peripheral nerve injuries (PNIs). Polyethylene glycol (PEG)-fusion is a repair method that rapidly joins/fuses the open ends of closely apposed severed axons, greatly reduces Wallerian degeneration, and restores sensorimotor behavior much more rapidly than current clinical procedures. Here, we examined whether the improved sensorimotor behavior recovery following PEG-fusion repair of sciatic nerve injuries compared to Negative Controls (NC) correlated with self-mutilation. We also examined six variables (repair method, behavioral tests, sex, injury type, strain, and surgical experience) that could influence self-mutilation outcomes. Methods: The Sciatic Functional Index (SFI) and the Von Frey (VF) behavioral tests were performed and analyzed. Regression and other analyses were performed to determine the independent effect of six variables on self-mutilation rates and severity. Results: PEG-fused rats that had no self-mutilation had significantly better SFI scores than those that had self-mutilation. More rapid VF sensory recovery in PEG-fused rats was also associated with less self-mutilation. Self-mutilation rates and severity were: (1) significantly reduced following PEG-fusion repairs compared to NCs; (2) significantly increased following weekly VF tests; (3) not different between female and male rats or (4) between simple transection and segmental-loss PNIs; (5) non-existent in Lewis rats and significantly less severe in Sprague Dawley rats than Long Evans rats; and (6) significantly reduced in rats operated on by experienced PEG-fusion surgeons who historically achieved better SFI outcomes than trainee surgeons. Conclusions: Our data suggest potential clinical benefits of PEG-fusion repair to produce more rapid and better sensorimotor recoveries and reductions of self-mutilation behaviors. Full article

In millimeter wave V2I communication systems, accurate beam prediction is crucial for optimizing network performance and improving signal transmission efficiency. Traditional beam prediction methods mainly rely on single-modal data, which often fails to capture the comprehensive environmental information required for high accuracy prediction. In contrast, multi-modal approaches leverage complementary information from different data sources and offer a more promising solution. However, many existing fusion methods primarily depend on real-time sensory inputs and do not fully exploit stable environmental features in V2I scenarios, limiting the effective use of each modality. To address these limitations, this paper proposes a environment-aware proactive beam prediction method based on a multi-modal prior mask map (MMPMM), which integrates offline mapping with an online beam prediction network. Specifically, the method fuses information from images, point clouds, positions, and the MMPMM to predict the optimal beam index. The MMPMM provides channel-related prior information by extracting static V2I scene features offline without incurring any additional online measurement overhead. Experimental results on real-world datasets demonstrate that the proposed method achieves a Top-3 beam prediction accuracy of up to 71.23% while maintaining stable performance under the evaluated dynamic and degraded conditions, demonstrating its effectiveness in the considered scenarios. Full article

Traditional sensory evaluation of traditional Chinese medicine (TCM) and medicinal and food homologous products has long relied on human observation of appearance, color, aroma, and taste. However, this approach is highly subjective, difficult to quantify, and often lacks reproducibility across evaluators. Intelligent sensory systems, including the electronic nose, electronic tongue, and machine vision, provide objective and digitized sensory information for TCM quality evaluation. Nevertheless, these platforms generate high-dimensional and heterogeneous datasets, creating a strong demand for efficient artificial intelligence (AI)-based analytical tools. This review summarizes recent advances in the application of machine learning and deep learning methods, such as support vector machine, random forest, convolutional neural network, and long short-term memory networks, for intelligent sensory evaluation of TCM. Particular emphasis is placed on how AI supports feature extraction, pattern recognition, classification, regression, and multisource data fusion across electronic nose, electronic tongue, and machine vision systems. Representative applications in raw material authentication, geographical origin discrimination, processing monitoring, and quality grading are also discussed. In addition, the current challenges related to data standardization, sensor drift, model robustness, and interpretability are highlighted. Overall, this review provides an integrated overview of AI-enabled intelligent sensory technologies and clarifies their potential to advance TCM quality evaluation toward a more objective, efficient, and holistic framework. Full article

Effectively fusing the rich semantic information from camera images with the high-precision geometric measurements provided by LiDAR point clouds is a key challenge in mobile robot environmental perception. To address this problem, this paper proposes a highly extensible instance-aware fusion framework designed to achieve efficient alignment and unified modeling of heterogeneous sensory data. The proposed approach adopts a modular processing pipeline. First, semantic instance masks are extracted from RGB images using an instance segmentation network, and a projection mechanism is employed to establish spatial correspondences between image pixels and LiDAR point cloud measurements. Subsequently, three-dimensional bounding boxes are reconstructed through point cloud clustering and geometric fitting, and a reprojection-based validation mechanism is introduced to ensure consistency across modalities. Building upon this representation, the system integrates a data association module with a Kalman filter-based state estimator to form a closed-loop multi-object tracking framework. Experimental results on the KITTI dataset demonstrate that the proposed system achieves strong 2D and 3D detection performance across different difficulty levels. In multi-object tracking evaluation, the method attains a MOTA score of 47.8 and an IDF1 score of 71.93, validating the stability of the association strategy and the continuity of object trajectories in complex scenes. Furthermore, real-world experiments on a mobile computing platform show an average end-to-end latency of only 173.9 ms, while ablation studies further confirm the effectiveness of individual system components. Overall, the proposed framework exhibits strong performance in terms of geometric reconstruction accuracy and tracking robustness, and its lightweight design and low latency satisfy the stringent requirements of practical robotic deployment. Full article

Independent vector analysis (IVA) has emerged as a powerful tool for fusing and analyzing functional magnetic resonance imaging (fMRI) data. Applying IVA to multi-task fMRI data enhances analytical power by capturing the relationships across different tasks in order to discover their underlying multivariate relationship to one another. Incorporation of prior information into IVA enhances the separability and interpretability of estimated components. In this paper, we demonstrate successful fusion of multi-task fMRI feature data under two settings: constrained IVA and constrained transposed IVA (tIVA). We show that using these methods for fusing multi-task fMRI feature data offers novel ways to improve the quality and interpretability of the analysis. While constrained IVA extracts components linked to distinct brain networks, tIVA reverses the roles of spatial components and subject profiles, enabling flexible analysis of behavioral effects. We apply both methods to a multi-task fMRI dataset of 247 subjects. We demonstrate that for task-based fMRI, structural MRI (sMRI) references provide a better match for task data than resting-state fMRI (rs-fMRI) references, and using sMRI priors improves identification of group differences in task-related networks, such as the sensory-motor network during the Auditory Oddball (AOD) task. Additionally, constrained tIVA allows for targeted investigation of the effects of behavioral variables by applying them individually during the analysis. For instance, by using the letter number sequence subtest, a measure of working memory, as a behavioral constraint in tIVA, we observed significant group differences in the auditory and sensory-motor networks during the AOD task. Results show that the use of two constrained approaches, guided by well-aligned structural and behavioral references, enables a more comprehensive analysis of underlying brain function as modulated by task. Full article

Purpose: To evaluate short-term motor and sensory–motor outcomes following postoperative OT in children with IXT after strabismus surgery. Methods: This prospective before–after observational study included children with IXT who underwent bilateral lateral rectus recession and were referred for postoperative OT based on predefined clinical criteria. A structured 12-week OTplan was initiated approximately six months after surgery. Outcome measures included angle of deviation (prism diopters, PD), near point of convergence (cm), positive fusional vergence amplitudes (PD), and convergence amplitudes at distance and near (PD). Pre- and post-therapy changes were analysed using paired-samples t-tests with effect sizes calculated using Cohen’s d. Final postoperative alignment was additionally compared cross-sectionally between children who underwent OT and those managed without OT. Results: Eighty-eight children had complete paired motor and sensory–motor data and were included in the analyses. Changes in static ocular alignment were small, with mean residual deviation improving from −7.02 ± 6.91 PD to −5.22 ± 6.60 PD after OT (mean change +1.80 PD; p < 0.01; d ≈ 0.30). No significant difference in final postoperative alignment was observed between the OT and non-OT groups (p = 0.827). In contrast, marked improvements were observed in sensory–motor outcomes. Positive fusional vergence amplitude increased from 7.30 ± 8.33 PD to 22.19 ± 9.26 PD (p < 0.001; d ≈ 1.5). Distance convergence amplitude improved from 7.30 ± 8.33 PD to 22.19 ± 9.26 PD, and near convergence amplitude from 10.95 ± 12.50 PD to 33.29 ± 13.89 PD (both p < 0.001; d ≈ 1.5). Near point of convergence showed a modest but significant improvement. Conclusions: Postoperative OT was associated with substantial short-term improvements in sensory–motor function, particularly fusional and convergence capacities, while changes in static ocular alignment were small and of limited clinical relevance. These findings support the role of OT as a functional adjunct to surgery, aimed at enhancing binocular control and postoperative sensory–motor stability in children with IXT. Full article

Eruca sativa Mill. (rocket; Fam. Brassicaceae) is widely appreciated for its peculiar flavour and beneficial effects on human health. Glucosinolates (GSLs) and their enzymatic hydrolysis products, isothiocyanates (ITCs), are considered to be responsible for health-promoting effects and for sensory relevance in rocket, respectively. This study aimed at evaluating and comparing the metabolite profiles of rocket leaves collected at different phenological stages, to investigate the content evolution during cultivation. To minimise metabolic variability induced by environmental factors, plants were cultivated in an innovative growing system equipped with precision lighting and ventilation. A multi-platform metabolomics approach combining liquid chromatography–high-resolution mass spectrometry (LC–HRMS) and headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC–MS) was carried out for comprehensive coverage of non-volatile and volatile organic compounds (VOCs). To integrate data from both platforms, a multivariate data fusion strategy was used. Higher GSLs content was detected in the microgreens stage. In particular, glucoraphanin, glucoiberverin, glucoerucin, DMB-GLS, and 1,4-dimethoxyglucobrassicin were identified as biological markers of rocket microgreens. ITCs levels were found to increase in mature leaves. These findings suggest a dynamic modulation of secondary metabolism during the plant life cycle, possibly in response to different adaptation needs to environmental conditions. Our findings confirm the potential of microgreens as a functional food in promoting health and preventing chronic diseases and can also tailor rocket cultivation to maximise the production of beneficial metabolites and to improve selected sensorial features. Full article

Biomimetic olfactory and taste biosensors replicate human sensory functions by coupling selective biological recognition elements (such as receptors, binding proteins, or synthetic mimics) with highly sensitive transducers (including electrochemical, transistor, optical, and mechanical types). This review summarizes recent progress in olfactory and taste biosensors focusing on three key areas: (i) materials and device design, (ii) artificial intelligence (AI) and data fusion for real-time decision-making, and (iii) pathways for practical application, including hybrid platforms, Internet of Things (IoT) connectivity, and regulatory considerations. We provide a comparative analysis of smell and taste sensing methods, emphasizing cases where integrating both modalities enhances sensitivity, selectivity, detection limits, and reliability in complex environments like food, environmental monitoring, healthcare, and security. Ongoing challenges are addressed with emerging solutions such as antifouling/self-healing interfaces, modular cartridges, machine learning (ML)-assisted calibration, and manufacturing-friendly approaches using scalable microfabrication and sustainable materials. The review concludes with a practical roadmap advocating for the joint development of receptors, materials, and algorithms; establishment of open standards for long-term stability; implementation of explainable/edge AI with privacy-focused analytics; and proactive collaboration with regulatory bodies. Collectively, these strategies aim to advance biomimetic smell and taste biosensors from experimental prototypes to dependable, commercially viable tools for continuous chemical sensing in real-world applications. Full article

Maize (Zea mays L.) silage’s forage quality significantly impacts dairy animal performance and the profitability of the livestock industry. Recently, using uncrewed aerial vehicles (UAVs) equipped with advanced sensors has become a research frontier in maize high-throughput phenotyping (HTP). However, extensive existing studies only consider a single sensor modality and models developed for estimating forage quality are single-task ones that fail to utilize the relatedness between each quality trait. To fill the research gap, we propose MUSTA, a MUlti-Sensory feature fusion model that utilizes MUlti-Task learning and the Attention mechanism to simultaneously estimate dry matter yield and multiple nutritive values for silage maize breeding hybrids in the field environment. Specifically, we conducted UAV flights over maize breeding sites and extracted multi-temporal optical- and LiDAR-based features from the UAV-deployed hyperspectral, RGB, and LiDAR sensors. Then, we constructed an attention-based feature fusion module, which included an attention convolutional layer and an attention bidirectional long short-term memory layer, to combine the multi-temporal features and discern the patterns within them. Subsequently, we employed multi-head attention mechanism to obtain comprehensive crop information. We trained MUSTA end-to-end and evaluated it on multiple quantitative metrics. Our results showed that it is capable of practical quality estimation results, as evidenced by the agreement between the estimated quality traits and the ground truth data, with weighted Kendall’s tau coefficients (τ_w) of 0.79 for dry matter yield, 0.74 for MILK2006, 0.68 for crude protein (CP), 0.42 for starch, 0.39 for neutral detergent fiber (NDF), and 0.51 for acid detergent fiber (ADF). Additionally, we implemented a retrieval-augmented method that enabled comparable prediction performance, even without certain costly features available. The comparison experiments showed that the proposed approach is effective in estimating maize silage yield and nutritional values, providing a digitized alternative to traditional field-based phenotyping. Full article

The Gas Metal Arc Welding (GMAW) process is widely utilized in industrial production, requiring careful selection of appropriate procedures to ensure the highest quality. A key area of study closely related to GMAW quality is the control of process stability. This research presents a methodology for analyzing welding data to identify instability, thus enabling the development of a stability indicator. Our approach focuses on sensory fusion by integrating multiple sources of information, including sound signals, images, and current signals captured during the welding process. This work explores various configurations of variables to analyze the three primary transfer modes. Additionally, a comprehensive statistical analysis of the results obtained is conducted. Image processing techniques, sound analysis, and artificial intelligence methodologies are employed to enhance the analysis process. Full article

Sweet taste plays a pivotal role in human dietary behavior and metabolic regulation. With the increasing incidence of metabolic disorders linked to excessive sugar intake, the development and accurate evaluation of new sweeteners have become critical topics in food science and public health. However, the structural diversity of sweeteners and their complex interactions with sweet taste receptors present major challenges for standardized sweetness detection. This review offers a comprehensive and up-to-date overview of sweet taste transmission mechanisms and current detection methods. It outlines the classification and sensory characteristics of both conventional and emerging sweeteners, and explains the multi-level signaling pathway from receptor binding to neural encoding. Key detection techniques, including sensory evaluation, electronic tongues, and biosensors, are systematically compared in terms of their working principles, application scope, and limitations. Special emphasis is placed on advanced biosensing technologies utilizing receptor–ligand interactions and nanomaterials for highly sensitive and specific detection. Furthermore, an intelligent detection framework integrating molecular recognition, multi-source data fusion, and artificial intelligence is proposed. This interdisciplinary approach provides new insights and technical solutions to support precise sweetness evaluation and the future development of healthier food systems. Full article

The fermentation of oolong tea is a critical process that determines its quality and flavor. Current fermentation control relies on tea makers’ sensory experience, which is labor-intensive and time-consuming. In this study, using Tieguanyin oolong tea as the research object, features including the tea water loss rate, aroma, image color, and texture were obtained using weight sensors, a tin oxide-type gas sensor, and a visual acquisition system. Support vector regression (SVR), random forest (RF) machine learning, and long short-term memory (LSTM) deep learning algorithms were employed to establish models for assessing the fermentation degree based on both single features and fused multi-source features, respectively. The results showed that in the test set of the fermentation degree models based on single features, the mean absolute error (MAE) ranged from 4.537 to 6.732, the root mean square error (RMSE) ranged from 5.980 to 9.416, and the coefficient of determination (R²) values varied between 0.898 and 0.959. In contrast, the data fusion models demonstrated superior performance, with the MAE reduced to 2.232–2.783, the RMSE reduced to 2.693–3.969, and R² increased to 0.982–0.991, confirming that feature fusion enhanced characterization accuracy. Finally, the Sparrow Search Algorithm (SSA) was applied to optimize the data fusion models. After optimization, the models exhibited a MAE ranging from 1.703 to 2.078, a RMSE from 2.258 to 3.230, and R² values between 0.988 and 0.994 on the test set. The application of the SSA further enhanced model accuracy, with the Fusion-SSA-LSTM model demonstrating the best performance. The research results enable online real-time monitoring of the fermentation degree of Tieguanyin oolong tea, which contributes to the automated production of Tieguanyin oolong tea. Full article

The Advanced Insect Detection Network (AIDN), which represents a significant advancement in the application of deep learning for ecological monitoring, is specifically designed to enhance the accuracy and efficiency of insect detection from unmanned aerial vehicle (UAV) imagery. Utilizing a novel architecture that incorporates advanced activation and normalization techniques, multi-scale feature fusion, and a custom-tailored loss function, the AIDN addresses the unique challenges posed by the small size, high mobility, and diverse backgrounds of insects in aerial images. In comprehensive testing against established detection models, the AIDN demonstrated superior performance, achieving 92% precision, 88% recall, an F1-score of 90%, and a mean Average Precision (mAP) score of 89%. These results signify a substantial improvement over traditional models such as YOLO v4, SSD, and Faster R-CNN, which typically show performance metrics approximately 10–15% lower across similar tests. The practical implications of AIDNs are profound, offering significant benefits for agricultural management and biodiversity conservation. By automating the detection and classification processes, the AIDN reduces the labor-intensive tasks of manual insect monitoring, enabling more frequent and accurate data collection. This improvement in data collection quality and frequency enhances decision making in pest management and ecological conservation, leading to more effective interventions and management strategies. The AIDN’s design and capabilities set a new standard in the field, promising scalable and effective solutions for the challenges of UAV-based monitoring. Its ongoing development is expected to integrate additional sensory data and real-time adaptive models to further enhance accuracy and applicability, ensuring its role as a transformative tool in ecological monitoring and environmental science. Full article