Search Results (322)

Paper-based analytical devices have emerged as a versatile and cost-effective platform for on-site chemical and biological analysis. The integration of fluorescence detection with smartphone imaging has significantly enhanced the analytical performance and portability of these systems, enabling sensitive, rapid, and user-friendly detection of diverse analytes. This review highlights recent advancements in paper-based fluorescence sensing technologies, focusing on their design principles, materials, and detection strategies. Emphasis is placed on the use of nanomaterials, quantum dots, and carbon-based fluorophores that improve sensitivity and selectivity in food, bioanalytical, and environmental applications. The role of smartphones as optical detectors and data processing tools is explored, underscoring innovations in image analysis, calibration algorithms, and app-based quantification methods. Full article

Mobile technologies are rapidly growing and shaping the tourism industry. Nonetheless, remote locations have specific characteristics that could restrain the deployment and use of technologies and jeopardize the sense of safety, affecting tourism experiences. There is a lack of empirical research that studies the importance of mobile technologies and security networks in remote destinations. A survey based on the Technology Acceptance Model (TAM) was conducted on 738 tourists during their stay in the Autonomous Region of the Azores, a nine-island Portuguese archipelago, to analyze the importance and impact of mobile technologies and security services. Since tourists have a high intensity of smartphone usage during their stay (86% use mobile internet and almost 50% use smartphones once per hour), mobile communication services and technologies need to be in place. Internet access and Wi-Fi are highly important for tourists for browsing and messaging, especially in urban areas, but also in rural and maritime areas. The availability of emergency and security networks is critical for destination selection and to engage in tourism activities. This paper contributes to the study of mobile tourism in remote destinations, with inputs regarding tourists’ behavior, and has implications for governance and industry stakeholders regarding destination management and the creation of meaningful and sustainable experiences with a high value for digital and smart tourists in the post-pandemic era. Full article

This paper proposes a robust convolutional neural network (CNN) architecture for human activity recognition (HAR) using smartphone accelerometer data, evaluated on the WISDM dataset. We introduce two novel pooling mechanisms—Pooling A (Extrema Contrast Pooling (ECP)) and Pooling B (Center Minus Variation (CMV))—that enhance feature discrimination and noise robustness. ECP emphasizes sharp signal transitions through a nonlinear penalty based on the squared range between extrema, while CMV Pooling penalizes local variability by subtracting the standard deviation, improving resilience to noise. Input data are normalized to the [0, 1] range to ensure bounded and interpretable pooled outputs. The proposed framework is evaluated in two separate configurations: (1) a 1D CNN applied to raw tri-axial sensor streams with the proposed pooling layers, and (2) a histogram-based image encoding pipeline that transforms segment-level sensor redundancy into RGB representations for a 2D CNN with fully connected layers. Ablation studies show that histogram encoding provides the largest improvement, while the combination of ECP and CMV further enhances classification performance. Across six activity classes, the 2D CNN system achieves up to 96.84% weighted classification accuracy, outperforming baseline models and traditional average pooling. Under Gaussian, salt-and-pepper, and mixed noise conditions, the proposed pooling layers consistently reduce performance degradation, demonstrating improved stability in real-world sensing environments. These results highlight the benefits of redundancy-aware pooling and histogram-based representations for accurate and robust mobile HAR systems. Full article

Smartphones are ubiquitous and continuously carried high-performance devices equipped with speech recognition capabilities that enable the analysis of surrounding conversations. When leveraged for public purposes, networks of smartphones can function as a large-scale sensing infrastructure capable of detecting and reporting early signs of serious crimes or terrorist activities. This paper proposes the concept of “Smartphone as Societal Safety Guard” as an approach to substantially enhancing public safety through relatively low additional cost and the combination of existing technologies (first pillar). At the same time, this concept entails serious risks of privacy infringement, as exemplified by the potential introduction of always-on eavesdropping through operating system updates. The originality of this study lies in redefining smartphones not merely as personal tools but as public safety infrastructure within democratic societies, and in systematizing the conditions for their social acceptability from the perspective of institutional design. This research presents a reference architecture and a regulatory framework, and organizes six key challenges that must be addressed to reconcile public safety with privacy protection: external attacks, mitigation of privacy information, false positives, expansion of the scope of application, discriminatory use, and misuse by authorized insiders. In particular, misuse by authorized insiders is positioned as the core challenge. As a necessary condition for acceptance in democratic societies (second pillar), this paper proposes a privacy-protective infrastructure centered on the Verifiable Record of AI Output (VRAIO). By combining on-device two-stage urgency classification with the review and recording of AI outputs by independent third-party entities, the proposed framework aims to provide a mechanism that can ensure, as a design requirement, that information unrelated to emergencies is not released outside the device. In summary, this paper presents a design framework for reconciling enhanced public safety with the protection of privacy. Full article

Background/Objectives: Smartphone-based leisure reservation platforms increasingly shape how individuals participate in leisure, yet little is known about how such technology-mediated engagement influences users’ awareness of multidimensional health benefits. The purpose of this study is to investigate how regular users of smartphone-based leisure reservation platforms perceive multidimensional health benefits associated with their leisure activities. Methods: Based on a constructivist/interpretivist approach, this study applied Interpretative Phenomenological Analysis (IPA). Ten participants with at least one year of platform use completed semi-structured interviews. Data were analyzed through iterative coding and theme development, with trustworthiness ensured through member checking, peer debriefing, and triangulation. Results: Participants reported three dimensions of health awareness. (1) App-enabled accessibility as a catalyst for physical health awareness (i.e., physical health benefits) included improved vitality and increased motivation to maintain exercise routines. (2) App-based planning and anticipation in supporting mental well-being (i.e., mental health benefits) involved stress reduction, emotional recovery, enjoyment, and heightened self-care awareness. (3) Platform-mediated social encounters and the construction of social health (i.e., social health benefits) reflected expanded social networks, strengthened interpersonal relationships, and a greater sense of belonging fostered through shared leisure experiences. Conclusions: Smartphone-based leisure platforms play a meaningful role in enhancing users’ awareness of multidimensional health benefits. By improving accessibility, diversifying leisure options, and facilitating social interaction, these platforms support holistic well-being. The findings contribute to understanding technology-mediated leisure and offer practical implications for designing digital leisure services that promote physical, mental, and social health. Full article

Perfluorooctanoic acid (PFOA) is a persistent and bioaccumulative hazardous pollutant, presenting substantial threats to the environment and human health. The dual-mode, portable, sensitive, low-background, and cost-effective detection methods for PFOA were developed by integrating up-conversion fluorescent silicon quantum dot–molecularly imprinted polymer (MIPs) with a smartphone-based sensing system. The interaction between PFOA and MIPs resulted in a fluorescence quenching with a range of 2–20 µmol/L and a limit of detection (LOD) of 37.5 nmol/L for the low-background up-conversion fluorescence detection of PFOA, whereas the portable smartphone sensing platform enabled the detection of PFOA with a linear range of 0–5 µmol/L and a LOD of 73.9 nmol/L. Furthermore, the established methods were successfully applied to the detection of PFOA in environmental waters and food samples. This study provides the dual-mode, portable, novel, practical and low-background approaches for the detection of PFOA in the environment and food products. Full article

Spatial cognition refers to the mental processing, perception, and interpretation of spatial information. It is often operationalized through self-assessments like sense of direction and mental rotation ability or field-based real-world tasks like pointing to a specific building and wayfinding; however, the former and latter entail unclear ecological validity and high participant burdens, respectively. Since the advent of smartphones, this repertoire has been extended substantially through the use of sensors or apps. This study used a large longitudinal experience sampling method (ESM) in two different countries (Canada and Australia, N = 217) and analyzed spatial cognition both conventionally (i.e., sense of direction and speeded mental rotation test) and through new techniques like self-rated and objectively assessed daily Google Maps usage, movement patterns throughout the 14-day assessment phase (using H3 tiles for geolocation), and a Point North task. The Point North task objectively assessed deviation from the celestial direction, North, by using smartphone compass sensors. In both countries, spatial orientation was found to be associated only with the Point North task, while no significant associations were found for daily Google Maps usage (subjectively and objectively measured) and moving distance throughout the assessment phase. Although further validation is required, the Point North task shows promise as an objective, ecologically valid, and easily employable smartphone-based measure for assessing spatial cognition in real-world contexts. Full article

Maintaining the structural integrity of conveyor belts is essential for safe and reliable mining operations. However, these belts are susceptible to longitudinal tearing and surface degradation from material impact, fatigue, and deformation. Many computer vision-based inspection methods are inefficient and unreliable in harsh mining environments characterized by dust and variable lighting. This study introduces a smartphone-driven defect detection system for the cost-effective, geometric inspection of conveyor belt surfaces. Using Apple’s iPhone 12 Pro Max (Apple Inc., Cupertino, CA, USA), the system captures 3D point cloud data from a moving belt with induced damage via the integrated TrueDepth camera. A key innovation is a 3D-to-2D projection pipeline that converts point cloud data into structured representations compatible with standard 2D Convolutional Neural Networks (CNNs). We then propose a hybrid deep learning and machine learning model, where features extracted by pre-trained CNNs (VGG16, ResNet50, InceptionV3, Xception) are classified by ensemble methods (Random Forest, XGBoost, LightGBM). The proposed system achieves high detection accuracy exceeding 0.97 F1 score in the case of all proposed model implementations with TrueDepth F1 score over 0.05 higher than RGB approach. Applied cost-effective smartphone-based sensing platform proved to support near-real-time maintenance decisions. Laboratory results demonstrate the method’s reliability, with measurement errors for defect dimensions within 3 mm. This approach shows significant potential to improve conveyor belt management, reduce maintenance costs, and enhance operational safety. Full article

Mobile Global Positioning System (GPS) data offer a promising approach to inferring mental health status through behavioural analysis. Whilst previous research has explored location-based behavioural indicators including location clusters, entropy, and variance, persistent GPS measurement errors have compromised data reliability, limiting the practical deployment of smartphone-based digital phenotyping systems. This study develops and validates an algorithmic preprocessing method designed to mitigate inherent GPS measurement limitations in mobile health applications. We conducted comprehensive evaluation through controlled experimental protocols and naturalistic field assessments involving 38 participants over a seven-day period, capturing GPS data across diverse environmental contexts on both Android and iOS platforms. The proposed preprocessing algorithm demonstrated exceptional precision, consistently detecting major activity centres within an average 50-metre margin of error across both platforms. In naturalistic settings, the algorithm yielded robust location detection capabilities, producing spatial patterns that reflected plausible and behaviourally meaningful traits at the individual level. Cross-platform analysis revealed consistent performance regardless of operating system, with no significant differences in accuracy metrics between Android and iOS devices. These findings substantiate the potential of mobile GPS data as a reliable, objective source of behavioural information for mental health monitoring systems, contingent upon implementing sophisticated error-mitigation techniques. The validated algorithm addresses a critical technical barrier to the practical implementation of GPS-based digital phenotyping, enabling the more accurate assessment of mobility-related behavioural markers across diverse mental health conditions. This research contributes to the growing field of mobile health technology by providing a robust algorithmic framework for leveraging smartphone sensing capabilities in healthcare applications. Full article

In this work, a 3D microfluidic paper-based analytical device (3D-µPAD) was developed for the smartphone-based colorimetric determination of phosphate in environmental samples. The assay relied on the formation of a blue-colored product (molybdenum blue) in the detection area of the 3D-µPAD upon reduction of the heteropolyacid H₃PMo₁₂O₄₀ formed in the presence of phosphate. A number of experimental parameters were optimized, including geometric aspects of 3D-µPADs, digitization and image processing conditions, the amount of chemicals deposited in specific areas of the 3D-µPAD, and the reaction time. In addition, the stability of the device was evaluated at three different storage temperatures. Under optimal conditions, the working range was found to be from 4 to 25 mg P/L (12–77 mg PO₄⁻³/L). The limits of detection (LOD) and quantification (LOQ) were 0.015 mg P/L and 0.05 mg P/L, respectively. The repeatability and intermediate precision of a 5 mg P/L standard were 4.8% and 7.1%, respectively. The proposed colorimetric assay has been successfully applied to phosphorous determination in various waters, soils, and sediments, obtaining recoveries in the range of 94 to 107%. The ready-to-use 3D-µPAD showed a greener profile than the standard method for phosphate determination, being affordable, easy-to-use, and suitable for citizen science applications. Full article

The rapid global expansion of genetically modified (GM) crops requires fast, on-site detection methods. Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated (CRISPR/Cas) systems offer a promising platform for decentralized GM organism (GMO) monitoring. This review focuses specifically on the application of this technology in agriculture and food supply chains, diverging from previous reviews centered on clinical diagnostics. We examine the mechanisms of key CRISPR effectors (e.g., Cas12a, Cas13a) and their integration into diagnostic platforms (e.g., DETECTR, SHERLOCK) for detecting transgenic elements (e.g., CaMV35S promoter). A dedicated comparison of signal readout modalities, including fluorescence, lateral flow, and electrochemical sensing, highlights their suitability for different GMO detection scenarios, from field screening to laboratory confirmation. Finally, we discuss current challenges, including multiplexing and standardization, and outline future directions, such as the engineering of novel Cas variants and integration with smartphone technology. CRISPR-based diagnostics are poised to become indispensable tools for decentralized, efficient, and reliable GMO detection. Full article

Smart assistive technologies such as sensor-based footwear and walking aids offer promising opportunities for gait rehabilitation through real-time feedback and patient-centered monitoring. While biofeedback applications show great potential, current research rarely explores integrated closed-loop systems with device- and modality-specific feedback. In this work, we present a modular sensor-based system combining a smart foot orthosis and an instrumented forearm crutch to deliver real-time vibrotactile biofeedback. The system integrates plantar pressure and motion sensing, vibrotactile feedback, and wireless communication via a smartphone application. We conducted a user study with eight participants to validate the system’s feasibility for mobile gait detection and app usability, and to evaluate different vibrotactile feedback types across the orthosis and forearm crutch. The results indicate that pattern-based vibrotactile feedback was rated as more useful and suitable for regular use than simple vibration alerts. Moreover, participants reported clear perceptual differences between feedback delivered via the orthosis and the forearm crutch, indicating device-dependent feedback perception. The findings highlight the relevance of feedback strategy design beyond hardware implementation and inform the development of user-centered haptic biofeedback systems. Full article

Many countries around the world repeatedly suffer from natural disasters such as earthquakes, tsunamis, floods, and hurricanes due to geographical factors, including plate boundaries, tropical cyclone zones, and coastal regions. Representative examples include Hurricane Katrina, which struck the United States in 2005, and the Great East Japan Earthquake in 2011. Both were large-scale disasters that occurred in developed countries and caused enormous human and economic losses regardless of disaster type or location. As the occurrence of such catastrophic events remains inevitable, establishing effective preparedness and rapid response systems for large-scale disasters has become an urgent global challenge. One of the critical issues in disaster response is the rapid estimation of the number of affected individuals required for effective rescue operations. During large-scale disasters, terrestrial communication infrastructure is often rendered unusable, which severely hampers the collection of situational information. If the population within a disaster-affected area can be estimated without relying on ground-based communication networks, rescue resources can be more appropriately allocated based on the estimated number of people in need, thereby accelerating rescue operations and potentially reducing casualties. In this study, we propose a population-estimation system that remotely senses radio signals emitted from smartphones in disaster areas using Low Earth Orbit (LEO) satellites. Through numerical analysis conducted in MATLAB R2023b, the feasibility of the proposed system is examined. The numerical results demonstrate that, under ideal conditions, the proposed system can estimate the number of smartphones within the observation area with an average error of 2.254 devices. Furthermore, an additional evaluation incorporating a 3D urban model demonstrates that the proposed system can estimate the number of smartphones with an average error of 19.03 devices. To the best of our knowledge, this is the first attempt to estimate post-disaster population using wireless LAN signals sensed by LEO satellites, offering a novel remote-sensing-based approach for rapid disaster response. Full article

Forestry is essential for environmental sustainability, biodiversity conservation, carbon sequestration, and renewable resource management. Traditional methods for forest inventory, particularly the manual measurement of diameter at breast height (DBH), are labor-intensive and prone to error. Recent advancements in proximal sensing, including lidar and photogrammetry, have paved the way for more efficient approaches, yet high costs remain a barrier to widespread adoption. This study investigates the potential of close-range photogrammetry (CRP) using low-cost devices, such as smartphones, cameras, and specialized handheld laser scanners (Stonex and LIVOX prototype), to generate 3D point clouds for accurate DBH estimation. We compared these devices by assessing their agreement and efficiency when compared to conventional methods in diverse forest conditions across multiple tree species. Additionally, we analyze factors influencing measurement errors and propose a comprehensive decision-making framework to guide technology selection in forest inventory. The results show that the lowest-cost devices and photogrammetric methods achieved the highest agreement with the conventional (caliper-based) measurements, while mobile applications were the fastest and least expensive but also the least accurate. Photogrammetry provided the most accurate DBH estimates (error ≈ 0.7 cm) but required the highest effort; handheld laser scanners achieved an average accuracy of about 1.5 cm at substantially higher cost, while mobile applications were the fastest and least expensive but also the least accurate (3–3.5 cm error). The outcomes of this research aim to facilitate more accessible, reliable, and sustainable forest management practices. Full article

This study introduces a hybrid sensor fusion approach that integrates Schreiber’s nonlinear filter with traditional filtering methods to enhance the performance of IMU-based systems in autonomous vehicles. As autonomous vehicles grow more dependent on Inertial Measurement Unit (IMU) data for real-time stability and control, the need for resilient and accurate sensor fusion becomes critical. This research addresses that need by introducing a method capable of maintaining robustness under highly dynamic and uncertain conditions. Accelerometer and gyroscope data from an IMU are first fused using a complementary filter. The fused signals are then refined by phase-space reconstruction and local manifold projection, improving noise resilience and maintaining system dynamics. Two datasets are used to assess the methodology: one was collected indoors with a smartphone, and another was captured outdoors using a Bosch sensor in various environmental settings. The proposed method demonstrates superior noise reduction, greater resistance to outliers, and improved signal consistency compared to conventional complementary and Kalman filters. The findings demonstrate how chaos-based nonlinear filtering may improve the reliability of sensor fusion on a variety of sensing platforms in highly dynamic environments. Given the importance of IMU data for maintaining vehicle stability, this study seeks to support the development of more stable autonomous transportation systems. Full article