Search Results (219)

The rapid growth of AI-enabled medical wearables and home-based diagnostic devices has opened new pathways for preventive care, chronic disease management and user-driven health insights. Despite significant technological progress, many solutions face adoption hurdles, often due to usability challenges, episodic measurements and poor alignment with daily life. This review surveys the current landscape of at-home healthcare technologies, including wearable vital sign monitors, digital diagnostics and body composition assessment tools. We synthesize insights from the existing literature for this narrative review, highlighting strengths and limitations in sensing accuracy, user experience and integration into daily health routines. Special attention is given to the role of AI in enabling real-time insights, adaptive feedback and predictive monitoring across these devices. To examine persistent adoption challenges from a user-centered perspective, we reflect on the Pi-CON methodology, a conceptual framework previously introduced to stimulate discussion around passive, non-contact, and continuous data acquisition. While Pi-CON is highlighted as a representative methodology, recent external studies in multimodal sensing, RFID-based monitoring, and wearable–ambient integration confirm the broader feasibility of unobtrusive, passive, and continuous health monitoring in real-world environments. We conclude with strategic recommendations to guide the development of more accessible, intelligent and user-aligned smart healthcare solutions. Full article

Visual impairment affects millions worldwide, creating significant barriers to environmental interaction and independence. Existing assistive technologies often rely on cloud-based processing, raising privacy concerns and limiting accessibility in resource-constrained environments. This paper explores the integration and potential of open-source AI models in developing a fully offline assistive system that can be locally set up and operated to support visually impaired individuals. Built on a Raspberry Pi 5, the system combines real-time object detection (YOLOv8), optical character recognition (Tesseract), face recognition with voice-guided registration, and offline voice command control (VOSK), delivering hands-free multimodal interaction without dependence on cloud infrastructure. Audio feedback is generated using Piper for real-time environmental awareness. Designed to prioritize user privacy, low latency, and affordability, the platform demonstrates that effective assistive functionality can be achieved using only open-source tools on low-power edge hardware. Evaluation results in controlled conditions show 75–90% detection and recognition accuracies, with sub-second response times, confirming the feasibility of deploying such systems in privacy-sensitive or resource-constrained environments. Full article

The evolution of Human–Computer Interaction (HCI) has laid the foundation for more immersive and dynamic forms of communication between humans and machines. Building on this trajectory, this work introduces a significant advancement in the domain of Human–Robot Manipulation (HRM), particularly in the remote operation of humanoid robots in complex scenarios. We propose the Advanced Manipulation Assistant System (AMAS), a novel manipulation method designed to be low cost, low latency, and highly efficient, enabling real-time, precise control of humanoid robots from a distance. This method addresses critical challenges in current teleoperation systems, such as delayed response, expensive hardware requirements, and inefficient data transmission. By leveraging lightweight communication protocols, optimized sensor integration, and intelligent motion mapping, our system ensures minimal lag and accurate reproduction of human movements in the robot counterpart. In addition to these advantages, AMAS integrates multimodal feedback combining visual and haptic cues to enhance situational awareness, close the control loop, and further stabilize teleoperation. This transition from traditional HCI paradigms to advanced HRM reflects a broader shift toward more embodied forms of interaction, where human intent is seamlessly translated into robotic action. The implications are far-reaching, spanning applications in remote caregiving, hazardous environment exploration, and collaborative robotics. AMAS represents a step forward in making humanoid robot manipulation more accessible, scalable, and practical for real-world deployment. Full article

The confluence of computer vision and natural language processing has yielded powerful vision language models (VLMs) capable of multimodal understanding. We applied state-of-the-art VLMs for quality monitoring of the shoe assembly industry. By leveraging the ability of VLMs to jointly process visual and textual data, we developed a system for automated defect detection and contextualized feedback generation to enhance the efficiency and consistency of quality assurance processes. We conducted an empirical evaluation by evaluating the effectiveness of the developed VLM system in identifying standard procedures for assembly, using the video data from a shoe assembly line. The experimental results validated the potential of the VLM system in detecting the quality of footwear assembly, highlighting the feasibility of future practical deployment in industrial quality control scenarios. Full article

This paper explores the integration of computing and multimodal technologies into personal reading practices to enhance student engagement and knowledge assimilation in higher education. In response to a documented decline in voluntary academic reading, we investigated how technology-enhanced reading environments can re-engage students through interactive and personalized experiences. Central to this research is SmartRead, a proposed multimodal eReading platform that incorporates gamification, adaptive content delivery, and real-time feedback mechanisms. Drawing on empirical data collected from students at a higher education institution, we examined how features such as progress tracking, motivational rewards, and interactive comprehension aids influence reading behavior, engagement levels, and information retention. Results indicate that such multimodal interventions can significantly improve learner outcomes and user satisfaction. This paper contributes actionable insights into the design of innovative, accessible, and pedagogically sound digital reading tools and proposes a framework for future eReading technologies that align with multimodal interaction principles. Full article

Phishing, especially brand impersonation attacks, is a critical cybersecurity threat that harms user trust and organization security. This paper establishes a lightweight model for real-time detection that relies on URL-only sequences, addressing limitations for multimodal methods that leverage HTML, images, or metadata. This approach is based on a Temporal Convolutional Network with Attention (TCNWithAttention) that utilizes character-level URLs to capture both local and long-range dependencies, while providing interpretability with attention visualization and Shapley additive explanations (SHAP). The model was trained and tested on the balanced GramBeddings dataset (800,000 URLs) and validated on the PhiUSIIL dataset of real-world phishing URLs. The model achieved 97.54% accuracy on the GramBeddings dataset, and 81% recall on the PhiUSIIL dataset. The model demonstrated strong generalization, fast inference, and CPU-only deployability. It outperformed CNN, BiLSTM and BERT baselines. Explanations highlighted phishing indicators, such as deceptive subdomains, brand impersonation, and suspicious tokens. It also affirmed real patterns in the legitimate domains. To our knowledge, a Streamlit application to facilitate single and batch URL analysis and log feedback to maintain usability is the first phishing detection framework to integrate TCN, attention, and SHAP, bridging academic innovation with practical cybersecurity techniques. Full article

This paper presents a real-time smart mirror system combining multiple AI modules for multimodal health monitoring. The proposed platform integrates three core components: facial expression analysis, remote photoplethysmography (rPPG), and conversational AI. A key innovation lies in transforming the Moving Average rPPG (MA-rPPG) model—originally developed for offline batch processing—into a real-time, continuously streaming setup, enabling seamless heart rate and peripheral oxygen saturation (SpO₂) monitoring using standard webcams. The system also incorporates the DeepFace facial analysis library for live emotion, age detection, and a Generative Pre-trained Transformer 4o (GPT-4o)-based mental health chatbot with bilingual (English/Korean) support and voice synthesis. Embedded into a touchscreen mirror with Graphical User Interface (GUI), this solution delivers ambient, low-interruption interaction and real-time user feedback. By unifying these AI modules within an interactive smart mirror, our findings demonstrate the feasibility of integrating multimodal sensing (rPPG, affect detection) and conversational AI into a real-time smart mirror platform. This system is presented as a feasibility-stage prototype to promote real-time health awareness and empathetic feedback. The physiological validation was limited to a single subject, and the user evaluation constituted only a small formative assessment; therefore, results should be interpreted strictly as preliminary feasibility evidence. The system is not intended to provide clinical diagnosis or generalizable accuracy at this stage. Full article

Respiratory infectious diseases, such as COVID-19, influenza, and tuberculosis, continue to impose a significant global health burden, underscoring the urgent demand for rapid, sensitive, and cost-effective diagnostic technologies. Integrated microfluidic platforms offer compelling advantages through miniaturization, automation, and high-throughput processing, enabling “sample-in, answer-out” workflows suitable for point-of-care applications. However, their clinical deployment faces challenges, including the complexity of sample matrices, low-abundance target detection, and the need for reliable multiplexing. The convergence of artificial intelligence (AI) with microfluidic systems has emerged as a transformative paradigm, addressing these limitations by optimizing chip design, automating sample pre-processing, enhancing signal interpretation, and enabling real-time feedback control. This critical review surveys AI-enabled strategies across each functional layer of respiratory pathogen diagnostics: from chip architecture and fluidic control to amplification analysis, signal prediction, and smartphone/IoT-linked decision support. We highlight key areas where AI offers measurable benefits over conventional methods. To transition from research prototypes to clinical tools, future systems must become more adaptive, data-efficient, and clinically insightful. Advances such as sensor-integrated chips, privacy-preserving machine learning, and multimodal data fusion will be essential to ensure robust performance and meaningful outputs across diverse scenarios. This review outlines recent progress, current limitations, and future directions. The rapid development of AI and microfluidics presents exciting opportunities for next-generation pathogen diagnostics, and we hope this work contributes to the advancement of intelligent, point-of-care testing (POCT) solutions. Full article

Effectively managing visual search tasks across multiple spatial regions during daily activities such as driving, cycling, and navigating complex environments often overwhelms visual processing capacity, increasing the risk of errors and missed critical information. This study investigates an integrated approach that combines an Ambient Display system utilizing peripheral vision cues with traditional Head-Mounted Displays (HMDs) to enhance spatial search efficiency while minimizing cognitive burden. We systematically evaluated this integrated HMD-Ambient Display system against standalone HMD configurations through comprehensive user studies involving target search scenarios across multiple spatial regions. Our findings demonstrate that the combined approach significantly improves user performance by establishing a complementary visual system where peripheral stimuli effectively capture initial attention while central HMD cues provide precise directional guidance. The integrated system showed substantial improvements in reaction time for rear visual region searches and higher user preference ratings compared with HMD-only conditions. This integrated approach represents an innovative solution that efficiently utilizes dual visual channels, reducing cognitive load while enhancing search efficiency across distributed spatial areas. Our contributions provide valuable design guidelines for developing assistive technologies that improve performance in multi-region visual search tasks by strategically leveraging the complementary strengths of peripheral and central visual processing mechanisms. Full article

To address the limitations of low fusion efficiency and insufficient personalization in multimodal perception for educational intelligence, a novel deep learning framework is proposed that integrates image sensor data with textual and contextual information through a cross-modal attention mechanism. The architecture employs a cross-modal alignment module to achieve fine-grained semantic correspondence between visual features captured by image sensors and associated textual elements, followed by a personalized feedback generator that incorporates learner background and task context embeddings to produce adaptive educational guidance. A cognitive weakness highlighter is introduced to enhance the discriminability of task-relevant features, enabling explicit localization and interpretation of conceptual gaps. Experiments show the proposed method outperforms conventional fusion and unimodal baselines with 92.37% accuracy, 91.28% recall, and 90.84% precision. Cross-task and noise-robustness tests confirm its stability, while ablation studies highlight the fusion module’s +4.2% accuracy gain and the attention mechanism’s +3.8% recall and +3.5% precision improvements. These results establish the proposed method as a transferable, high-performance solution for next-generation adaptive learning systems, offering precise, explainable, and context-aware feedback grounded in advanced multimodal perception modeling. Full article

Breast cancer continues to be a serious concern for global health, especially when proper treatment is time-sensitive. This research contributes a novel method to improve breast cancer detection in ultrasound images by employing a deep learning technique that integrates UNet and Convolution Neural Networks(CNN) architectures. For tumor segmentation within breast ultrasound images, UNet has been used, alongside a CNN that classifies the resulting tumor as benign or malignant and performs feature extraction. When evaluated on the ‘Dataset_BUSI_with_GT’, the model was found to be reliable across varying conditions, achieving high sensitivity (97.44%) and accuracy (95.24%), scores better than those ofexisting approaches. The developed system is composed of an imaging module, image upload, preprocessing, inference, result display, and feedback, providing non-interrupted service and enhancing user-centered functionalities. Continuous improvement capabilities allow the system to redefine new image changes, sustaining reliability in examinations and clinical settings. Compared to other methodologies, the proposed model demonstrates superior accuracy alongside less computational resources, translating to reduced diagnostic human error while optimizing the workflow in primary healthcare. Future work could includethe application of multimodal imaging, deploy real-time imaging, and increase its interpretability to strengthen its use in medical diagnosis. Full article

Contact-force perception is a critical component of safe robotic grasping. With the rapid advances in embodied intelligence technology, humanoid robots have enhanced their multimodal perception capabilities. Conventional force sensors face limitations, such as complex spatial arrangements, installation challenges at multiple nodes, and potential interference with robotic flexibility. Consequently, these conventional sensors are unsuitable for biomimetic robot requirements in object perception, natural interaction, and agile movement. Therefore, this study proposes a sensorless external force detection method that integrates SuperPoint-Scale Invariant Feature Transform (SIFT) feature extraction with finite element analysis to address force perception challenges. A visual analysis method based on the SuperPoint-SIFT feature fusion algorithm was implemented to reconstruct a three-dimensional displacement field of the target object. Subsequently, the displacement field was mapped to the contact force distribution using finite element modeling. Experimental results demonstrate a mean force estimation error of 7.60% (isotropic) and 8.15% (anisotropic), with RMSE < 8%, validated by flexible pressure sensors. To enhance the model’s reliability, a dual-channel video comparison framework was developed. By analyzing the consistency of the deformation patterns and mechanical responses between the actual compression and finite element simulation video keyframes, the proposed approach provides a novel solution for real-time force perception in robotic interactions. The proposed solution is suitable for applications such as precision assembly and medical robotics, where sensorless force feedback is crucial. Full article

Recent developments in large language models allow for real time, context-aware tutoring. AI Gem, presented in this article, is a layered architecture that integrates personalization, adaptive feedback, and curricular alignment into transformer based tutoring agents. The architecture combines retrieval augmented generation, Bayesian learner model, and policy-based dialog in a verifiable and deployable software stack. The opportunities are scalable tutoring, multimodal interaction, and augmentation of teachers through content tools and analytics. Risks are factual errors, bias, over reliance, latency, cost, and privacy. The paper positions AI Gem as a design framework with testable hypotheses. A scenario-based walkthrough and new diagrams assign each learner step to the ten layers. Governance guidance covers data privacy across jurisdictions and operation in resource constrained environments. Full article

This paper presents a comprehensive analysis of artificial intelligence-powered automated grading systems (AI AGSs) in STEM education, systematically examining their algorithmic foundations, mathematical modeling approaches, and quantitative evaluation methodologies. AI AGSs enhance grading efficiency by providing large-scale, instant feedback and reducing educators’ workloads. Compared to traditional manual grading, these systems improve consistency and scalability, supporting a wide range of assessment types, from programming assignments to open-ended responses. This paper provides a structured taxonomy of AI techniques including logistic regression, decision trees, support vector machines, convolutional neural networks, transformers, and generative models, analyzing their mathematical formulations and performance characteristics. It further examines critical challenges, such as user trust issues, potential biases, and students’ over-reliance on automated feedback, alongside quantitative evaluation using precision, recall, F1-score, and Cohen’s Kappa metrics. The analysis includes feature engineering strategies for diverse educational data types and prompt engineering methodologies for large language models. Lastly, we highlight emerging trends, including explainable AI and multimodal assessment systems, offering educators and researchers a mathematical foundation for understanding and implementing AI AGSs into educational practices. Full article

This paper presents the design and evaluation of a sound-driven robot quiz system that enhances fairness and engagement in educational human–robot interaction (HRI). The system integrates a real-time sound-based first-responder detection mechanism with gamified multimodal feedback, including verbal cues, music, gestures, points, and badges. Motivational design followed the Octalysis framework, and the system was evaluated using validated scales from the Technology Acceptance Model (TAM), the Intrinsic Motivation Inventory (IMI), and the Godspeed Questionnaire. An experimental study was conducted with 32 university students comparing the proposed multimodal system combined with sound-driven first quiz responder detection to a sequential turn-taking quiz response with a verbal-only feedback system as a baseline. Results revealed significantly higher scores for the experimental group across perceived usefulness (M = 4.32 vs. 3.05, d = 2.14), perceived ease of use (M = 4.03 vs. 3.17, d = 1.43), behavioral intention (M = 4.24 vs. 3.28, d = 1.62), and motivation (M = 4.48 vs. 3.39, d = 3.11). The sound-based first-responder detection system achieved 97.5% accuracy and was perceived as fair and intuitive. These findings highlight the impact of fairness, motivational feedback, and multimodal interaction on learner engagement. The proposed system offers a scalable model for designing inclusive and engaging educational robots that promote active participation through meaningful and enjoyable interactions. Full article