Search Results (110)

Traditional acupoint localization methods rely heavily on manual operation, resulting in high subjectivity and limited accuracy. To improve the precision and stability of acupoint detection, this study integrates machine vision technology with in situ projection to achieve automated recognition and real-time visualization of human acupoints. First, an automatic calibration method based on image processing is proposed for back acupoints. Spinal features are extracted from the blue channel, enhanced using adaptive histogram equalization, and processed through region of interest extraction, minimum-threshold binarization, and morphological operations. Key spinal curve points are then fitted using Bézier functions. Canny edge detection is used to extract the human silhouette, locate the acromion, and derive the pixel scale of the “cun” measurement, enabling coordinate computation for 141 back acupoints. In the deep learning component, an improved YOLOv8-Pose model is developed for acupoint localization. Unlike existing methods that use local attention or the original Object Keypoint Similarity (OKS) loss, we introduce two innovations: a non-local attention module for global dependency modeling, and a novel Efficient Object Keypoint Similarity (EOKS) loss function that incorporates geometric constraints—namely, width, height, and center distance—in addition to Euclidean distance. A non-local attention mechanism is incorporated into the backbone to enhance global feature extraction, and the EOKS loss function is designed to improve spatiogeometric regression accuracy. An inference mechanism is further introduced to derive the remaining acupoints from 49 detected keypoints; experiments demonstrate that the improved model achieves 95.0% detection accuracy, outperforming the baseline by 2.62%, with an inference time of 14.5 ms. Finally, an in situ projection platform is constructed, combining camera calibration, four-point proportional scaling, and an OpenCV 4.5.4-based interactive interface. The system supports real-time translation, rotation, and scaling, enabling accurate projection of detected acupoints onto the human body. Full article

Titanium alloys are widely used in aerospace, marine engineering, and biomedical fields owing to their excellent specific strength, corrosion resistance, and biocompatibility. As an important surface modification technique, sandblasting combined with acid pickling can not only eliminate the machining defects in Ti-6Al-4V but also improve its surface morphology, thereby playing a crucial role in enhancing its service performance. By employing advanced characterization equipment, including scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), a 3D profilometer, and a friction and wear tester, combined with wetting theoretical models and morphological evolution analysis, this study systematically investigated the comprehensive effects of different pressure sandblasting followed by acid pickling on the surface microstructure, wetting behavior, and tribological properties of forged Ti-6Al-4V alloy. The results indicated that the combined application of sandblasting and acid pickling exerted a significant regulatory effect on the surface and interface characteristics of the Ti-6Al-4V alloy. After the combined sandblasting and acid pickling treatment, a distinct micro-pit network structure was formed on the surface of the Ti-6Al-4V alloy, and its hydrophilicity and roughness showed a positive correlation with the increase in sandblasting pressure. Notably, the microstructural evolution exhibited a high degree of internal consistency with the macroscopic wear resistance: the hierarchical micro-pit network exposed after acid pickling exerted an excellent “debris capture” effect, thereby suppressing the severe third-body abrasive wear observed in the solely sandblasted state. This study aims to enhance the surface roughness, wear resistance, and hydrophilicity of the Ti-6Al-4V alloy, providing a cost-effective and industrially applicable method for the surface texturing of titanium alloys. Full article

With the growing trend toward insulator hybridization, higher requirements are imposed on the synergistic improvement of interfacial durability and pollution flashover performance. Machining annular grooves at the green-body stage and embedding silicone rubber enables the construction of an embedded structure with improved durability, forming hydrophilic/hydrophobic alternating surfaces. However, the outdoor insulation characteristics of such hybrid surfaces remain insufficiently investigated, and their engineering feasibility requires further validation. In this study, a series of hydrophilic/hydrophobic alternating surfaces were fabricated, and artificial pollution tests were conducted. The results show that the AC pollution flashover voltage exhibits a saturated increasing trend as the hydrophobic interfaces become more dispersed. When twenty 4 mm wide hydrophobic interfaces were distributed along a 16 cm creepage distance, the flashover voltage was 12.4% higher than that of a fully hydrophobic surface. These results indicate that appropriate design of hydrophobic interface distribution can achieve excellent pollution flashover performance even at relatively low hydrophobic coverage (≤50%). High-speed imaging combined with infrared thermography reveals the discharge mechanism governed by hydrophobic interface distribution from an electro–thermal coupling perspective. The coexistence of multiple dry bands induced by discrete hydrophobic interfaces is identified as the key factor enhancing flashover withstand capability. A static pollution flashover model was established to quantitatively estimate the AC flashover voltage, confirming the external insulation feasibility of the embedded hybrid concept. Full article

Continuous monitoring of sitting posture is crucial for ergonomic assessment and fatigue prevention, yet many existing approaches rely on vision-based systems or single-modality sensing that are limited in capturing spatial and temporal biomechanical dynamics. This paper presents a multimodal smart-skin sensing system for spatial and temporal ergonomic fatigue analysis in sitting postures. The proposed platform integrates 42 distributed pressure, temperature, and vibration sensors arranged in 14 trimodal sensing nodes embedded across anatomical seating and back regions to enable real-time multimodal acquisition of human–chair interaction patterns. The study introduces an analytical framework combining anatomical heatmap visualization, temporal evolution analysis, delta pressure mapping, fatigue intensity estimation, and hotspot detection to characterize dynamic pressure redistribution during prolonged sitting. Experimental evaluations were conducted using a biomechanical mannequin and a single human participant with identical anthropometric characteristics (165 cm height and 62 kg body mass) across nine seated conditions, including neutral sitting, reclining, leaning, periodic shifting, and vibration-induced motion. Each posture condition was recorded as a time-series session and segmented into temporal phases to analyze fatigue evolution during prolonged sitting. Statistical analysis of pressure redistribution dynamics indicates significantly higher pressure drift in human measurements compared with the mechanically stable mannequin baseline (p < 0.001). The proposed framework provides a scalable sensing approach for ergonomic monitoring, intelligent seating systems, and human–machine interface applications. Full article

Ultrasound patches represent a transformative advancement beyond conventional ultrasonography, evolving into intelligent theranostic systems for personalized healthcare. This evolution is propelled by synergistic innovations in flexible piezoelectric materials and integrated designs. The development of piezoelectric polymers, lead-free ceramics, and bio-composite materials has laid the foundation for long-term, conformal, and biosafe interfacing with the human body. Structurally, miniaturized transducer arrays (e.g., CMOS-integrated arrays achieving ~200 μm focal spots and 100 kPa focal pressure), multimodal integration, and bioinspired interfaces have enabled high-precision deep-tissue sensing and spatiotemporally controlled energy delivery—exemplified by strain-sensing feedback improving the signal-to-noise ratio by 5 dB for precise neuromodulation. These capabilities are converging to create closed-loop platforms, as demonstrated in continuous cardiovascular monitoring (up to 164 mm depth for 12 h), image-guided neuromodulation for neurological disorders, on-demand drug delivery (achieving 100% higher plasma concentration than ultrasound alone), and integrated tumor therapy with real-time feedback. Despite persistent challenges in material biocompatibility, energy efficiency, and clinical standardization, the future of ultrasound patches lies in their deep integration with multimodal sensing, machine learning, and adaptive control algorithms. This path will ultimately realize their potential for intelligent, closed-loop theranostics in chronic disease management, telemedicine, and personalized therapy. Full article

The Copernicus Sentinel-2 (S2) data are a crucial resource for urban policymakers in land-cover classification, offering a freely accessible alternative to expensive commercial data sources. While medium spatial resolution often limits the applicability of data-intensive machine learning approaches, the Geographic Object-Based Image Analysis (GEOBIA) framework could be an effective, operational alternative for urban land-cover classification using S2 data. This study applies the Geographic Object-Based Image Analysis (GEOBIA) approach to classify land cover in Hue, Vietnam, using Sentinel-2 data processed through the eCognition interface. The study’s findings emphasize the potential of GEOBIA and S2 data in enhancing decision-making processes for city authorities, ensuring better resource allocation, environmental protection, and infrastructure development. The results indicate that the method performs reliably for mesoscale and spatially continuous classes, such as vegetation and built-up surfaces, while accuracy is lower for small or spectrally heterogeneous features, particularly shallow water bodies and fragmented rice paddies, due to mixed-pixel effects inherent in 10–20 m resolution imagery. The results demonstrate an Overall Accuracy (OA) of 91%, highlighting the method’s effectiveness in extracting and classifying urban land-cover classes. This study demonstrates a replicable model for urban land monitoring that can be adapted across various geographic contexts. Furthermore, this approach fosters a more data-driven governance model, where urban expansion and land-use changes can be monitored in real time, allowing for proactive interventions. With urbanization accelerating worldwide, particularly in rapidly developing regions, such a cost-effective and accessible classification method can significantly aid in achieving long-term urban sustainability. The findings illustrate the relevance of GEOBIA as a feasible tool for supporting data-driven urban governance, enabling systematic tracking of land-use change, informed infrastructure planning, and sustainable urban management in both developed and rapidly urbanizing regions. Full article

Flexible skin electronics are increasingly sought after for their potential in sensing and drug delivery within wearable human–machine interfaces. However, developing multifunctional applications that maintain biocompatibility and stable electrical performance under various mechanical deformations remains a challenge. Here, we introduce tattoo paper-based graphene–gold conductors that are approximately 0.04 mm thick and feature a dual conductive pathway within the graphene–gold film. By integrating a folding structure with this dual conductive pathway, we can mitigate the strain effects on the electrical resistance of film-based conductors, resulting in wider areas of stable resistance. In addition, we have designed film conductors with a kirigami structure, which achieves a high initial conductivity of 1.5 × 10³ S cm⁻¹ and exhibits negligible resistance changes across a broad strain range of 0 to 130%. We utilize these conductors to develop waterproof on-skin patches that incorporate electrically and optically active heaters for body heating and drug delivery. Furthermore, we have created an on-skin dialing interface using these conductors, which enables users to make telephone calls based on triboelectric nanogenerators. Full article

The bond strength (τ) of the interface between the anchor bolt and grouting body (or rebar–concrete) is a key indicator used to evaluate the bearing capacity of anchorage engineering. And when rebars are subject to corrosion, τ also serves as an important durability metric. However, traditional experimental measurement of τ is complex, time-consuming and labor-intensive. In this study, based on pullout test data from 429 rebar–concrete specimens, we develop a machine learning method to construct a prediction model with strong generalization ability. Fundamental features—including specimen geometry, dimensions, material strengths, and corrosion rate—are used as inputs. The Sparrow Search Algorithm (SSA) and Particle Swarm Optimization (PSO) are used to fine-tune the hyperparameters of three machine learning models which are Random Forest (RF), Least Squares Boosting (LSBoost), and Generalized Additive Model (GAM). We perform a comparative error analysis of each model and benchmark them against three empirical formulas for τ. The unoptimized models exhibit low predictive accuracy and clear overfitting. After optimization using SSA and PSO algorithms, the prediction accuracy and overfitting issues are significantly improved, with the PSO-LSBoost model achieving the best performance (R² = 0.93). The PSO-LSBoost model’s prediction accuracy for τ far exceeds that of the three empirical formulas. SHAP analysis reveals that the corrosion rate (C_w) contributes most to τ, while the rebar type (ST) contributes least. This work introduces a novel, efficient approach for predicting anchorage bond strength and assessing bolt durability, thereby enhancing the reliability of anchorage structures. Full article

Bacterial infections and the lack of bioactivity of titanium implants and their alloys remain critical challenges for the long-term performance and clinical success of these devices. These issues arise from the undesirable combination of early microbial adhesion and the limited ability of metallic surfaces to form a bioactive interface capable of supporting osseointegration. To address these limitations simultaneously, this study employed electrical discharge machining (EDM), which enables surface topography modification and in situ incorporation of bioactive ions from the dielectric fluid. Ti-6Al-4V ELI surfaces were modified using two dielectric fluids, a fluorine/phosphorus-based solution (DF1-F) and a calcium/phosphorus-based solution (DF2-Ca), under positive and negative polarities. The recast layer was characterized by SEM and EDS, while bioactivity was evaluated through immersion in simulated body fluid (SBF) for up to 21 days. Antibacterial performance was assessed against Staphylococcus aureus at 6 h and 24 h of incubation. The results demonstrated that dielectric composition and polarity strongly influenced ionic incorporation and the structural stability of the modified layers. The DF2-Ca(+) condition exhibited the most favorable bioactive response, with Ca/P ratios closer to hydroxyapatite and surface morphologies typical of mineralized coatings. In antibacterial assays, Ca/P-containing surfaces significantly decreased S. aureus attachment (>80–90%). Overall, EDM with Ca/P-containing dielectrics enables the fabrication of Ti-6Al-4V surfaces with enhanced mineralization capacity and anti-adhesive effects against Gram-positive bacteria, reinforcing their potential for multifunctional biomedical applications. Full article

The emerging paradigm of “fusion of lifeforms” represents a transformative shift from conventional human–machine interfaces toward deeply integrated symbiotic systems, where biological and artificial components co-adapt structurally, energetically, informationally, and cognitively. This review systematically classifies multi-level perception systems within fusion of lifeforms into four functional categories: sensory and functional restoration, beyond-natural sensing, endogenous state sensing, and cognitive enhancement. We survey recent advances in neuroprosthetics, sensory augmentation, closed-loop physiological monitoring, and brain–computer interfaces, highlighting the transition from substitution to fusion. Despite significant progress, critical challenges remain, including multi-source heterogeneous integration, bandwidth and latency limitations, power and thermal constraints, biocompatibility, and system-level safety. We propose future directions such as layered in-body communication networks, sustainable energy strategies, advanced biointerfaces, and robust safety frameworks. Ethical considerations regarding self-identity, neural privacy, and legal responsibility are also discussed. This work aims to provide a comprehensive reference and roadmap for the development of next-generation fusion of lifeforms, ultimately steering human–machine integration from episodic functional repair toward sustained, multi-level symbiosis between biological and artificial systems. Full article

To better serve human life with smart and harmonic communication between the real and digital worlds, wearable human–machine interfaces (HMIs) with edge computing capabilities indicate the path to the next revolution of information technology. In this review, we focus on wearable HMIs and highlight several key aspects which are worth investigating. Firstly, we review wearable HMIs powered by commercial-ready technologies, highlighting some limitations. Next, to establish a dual-way interaction for exchanging comprehensive information, sensing and feedback functions on the human body need to be customized based on specific scenarios. Power consumption is another primary issue that is critical to wearable applications due to limited space, one that is possible to be solved by energy harvesting techniques and self-powered data transmission approaches. To further improve the data interpretation with higher intelligence, machine learning (ML)-assisted analysis is preferred for multi-dimensional data. Eventually, with the presence of edge computing systems, those data can be pre-processed locally for downstream applications. Generally, this review offers an overview of the development of intelligent wearable HMIs with edge computing capabilities and self-sustainability, which can greatly enhance the user experience in healthcare, industrial productivity, education, etc. 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

Stress, workload, and uncertainty characterize occupational tasks across sports, healthcare, military, and transportation domains. Emerging theory and empirical research suggest that coordinated whole-body movements may reflect these transient mental states. Wearable sensors and optical motion capture offer opportunities to quantify such movement dynamics and classify mental states that influence occupational performance and human–machine interaction. We tested this possibility in a small pilot study (N = 10) designed to test feasibility and identify preliminary movement features linked to mental states. Participants performed a perceptual decision-making task involving facial emotion recognition (i.e., deciding whether depicted faces were happy versus angry) with variable levels of stress (via a risk of electric shock), workload (via time pressure), and uncertainty (via visual degradation of task stimuli). The time series of movement trajectories was analyzed both holistically (full trajectory) and by phase: lowered (early), raising (middle), aiming (late), and face-to-face (sequential). For each epoch, up to 3844 linear and non-linear features were extracted across temporal, spectral, probability, divergence, and fractal domains. Features were entered into a repeated 10-fold cross-validation procedure using 80/20 train/test splits. Feature selection was conducted with the T-Rex Selector, and selected features were used to train a scikit-learn pipeline with a Robust Scaler and a Logistic Regression classifier. Models achieved mean ROC AUC scores as high as 0.76 for stress classification, with the highest sensitivity during the full movement trajectory and middle (raise) phases. Classification of workload and uncertainty states was less successful. These findings demonstrate the potential of movement-based sensing to infer stress states in applied settings and inform future human–machine interface development. Full article

Implantable technologies targeting the cerebral cortex and deeper brain structures are increasingly utilised in human–machine interfacing, advanced neuroprosthetics, and clinical interventions for neurological conditions. These systems require highly efficient and low-power methods for exchanging information between the implant and external electronics. Traditional approaches often rely on inductively coupled data transfer (ic-DT), where the same coils used for wireless power are modulated for communication. Other designs use high-frequency antenna-based radio systems, typically operating in the 401–406 MHz MedRadio band or the 2.4 GHz ISM band. A promising alternative is intrabody communication (IBC), which leverages the bioelectrical characteristics of body tissue to enable signal propagation. This work presents a theoretical investigation into two schemes—inductive coupling and galvanically coupled IBC (gc-IBC)—as applied to cortical data links, considering frequencies from 1 to 10 MHz and implant depths of up to 7 cm. We propose a hybrid solution where gc-IBC supports data transmission and inductive coupling facilitates wireless power delivery. Our findings indicate that gc-IBC can accommodate wider bandwidths than ic-DT and offers significantly reduced path loss, approximately 20 dB lower than those of conventional RF-based antenna systems. Full article

With the proliferation of the Internet of Things (IoT), gesture recognition has attracted attention as a core technology in human–computer interaction (HCI). In particular, mmWave frequency-modulated continuous-wave (FMCW) radar has emerged as an alternative to vision-based approaches due to its robustness to illumination changes and advantages in privacy. However, in real-world human–machine interface (HMI) environments, hand gestures are inevitably accompanied by torso- and arm-related reflections, which can also contain gesture-relevant variations. To effectively capture these variations without discarding them, we propose a preprocessing method called Adaptive Top-K Selection, which leverages vector entropy to summarize and preserve informative signals from both hand and body reflections. In addition, we present a Multi-Stream EfficientNetV2 architecture that jointly exploits temporal range and Doppler trajectories, together with radar-specific data augmentation and a training optimization strategy. In experiments on the publicly available FMCW gesture dataset released by the Karlsruhe Institute of Technology, the proposed method achieved an average accuracy of 99.5%. These results show that the proposed approach enables accurate and reliable gesture recognition even in realistic HMI environments with co-existing body reflections. Full article