Search Results (346)

Significant improvements in the forest-based industrial sector are expected due to increased digitalization; however, examples of practical implementations remain limited. This study explores the use of an automated algorithm to estimate truckload volumes based on 3D point cloud data acquired using two different LiDAR scanning platforms. This research compares the performance of a professional mobile laser scanning (MLS GeoSLAM) platform and a smartphone-based iPhone LiDAR system. A total of 48 truckloads were measured using a combination of manual, factory-based, and digital approaches. Accuracy was evaluated using standard error metrics, including the mean absolute error (MAE) and root mean square error (RMSE), with manual or factory references used as benchmarks. The results showed a strong correlation and no significant differences between the algorithmic and manual measurements when using the MLS platform (MAE = 2.06 m³; RMSE = 2.46 m³). For the iPhone platform, the results showed higher deviations and significant overestimation compared to the factory reference (MAE = 3.29 m³; RMSE = 3.60 m³). Despite these differences, the iPhone platform offers real-time acquisition and low-cost deployment. These findings highlight the trade-offs between precision and operational efficiency and support the adoption of automated measurement tools in timber supply chains. Full article

With the rapid proliferation of Android smartphones, mobile malware threats have escalated significantly, underscoring the need for more accurate and adaptive detection solutions. This work proposes an innovative deep learning hybrid model that combines Convolutional Neural Networks (CNNs) with Bidirectional Long Short-Term Memory (BiLSTM) networks for learning both local features and sequential behavior in Android applications. To improve the relevance and clarity of the input data, Mutual Information is applied for feature selection, while Bayesian Optimization is adopted to efficiently optimize the model’s parameters. The designed system is tested on standard Android malware datasets and achieves an impressive detection accuracy of 99.3%, clearly outperforming classical approaches such as Support Vector Machines (SVMs), Random Forest, CNN, and Naive Bayes. Moreover, it delivers strong outcomes across critical evaluation metrics like F1-score and ROC-AUC. These findings confirm the framework’s high efficiency, adaptability, and practical applicability, making it a compelling solution for Android malware detection in today’s evolving threat landscape. Full article

The COVID-19 pandemic and recent viral outbreaks have highlighted the need for viral diagnostics that balance accuracy with accessibility. While traditional laboratory methods remain essential, point-of-care solutions are critical for decentralized testing at the population level. However, a gap persists between academic proof-of-concept studies and clinically viable tools, with novel technologies remaining inaccessible to clinics due to cost, complexity, training, and logistical constraints. Recent advances in surface functionalization, assay simplification, multiplexing, and performance in complex media have improved the feasibility of both optical and non-optical sensing techniques. These innovations, coupled with scalable manufacturing methods such as 3D printing and streamlined hardware production, pave the way for practical deployment in real-world settings. Additionally, software-assisted data interpretation, through simplified readouts, smartphone integration, and machine learning, enables the broader use of diagnostics once limited to experts. This review explores improvements in viral diagnostic approaches, including colorimetric, optical, and electrochemical assays, showcasing their potential for democratization efforts targeting the clinic. We also examine trends such as open-source hardware, modular assay design, and standardized reporting, which collectively reduce barriers to clinical adoption and the public dissemination of information. By analyzing these interdisciplinary advances, we demonstrate how emerging technologies can mature into accessible, low-cost diagnostic tools for widespread testing. Full article

Augmented Reality Smart Glasses (ARSGs) allow users to engage in picture-taking and video recording, as well as real-time storage and sharing of pictures and videos through cloud services. Unlike smartphones, newer ARSGs resemble ordinary sunglasses, allowing for unobtrusive recording. As these devices become available on an international market, it is important to understand how different cultural attitudes towards privacy and the recording and sharing of images of bystanders could impact the acceptance and adoption of ARSGs. South Korea and the United States have vastly different culturally based perceptions of photography and recording in public. S. Korea has cultural and legal restrictions in place, while the U.S.’s values of freedom of expression and individual rights are reflected in limited restrictions. Accordingly, drawing upon the Technology Acceptance Model (TAM), this paper explored the impact of privacy concerns on key constructs of the TAM for U.S. and S. Korean participants. This paper examined how Americans’ (U.S. = 402) and S. Koreans’ (S. Korea = 898) perceived usefulness, perceived ease of use, attitude toward using, and behavioral intention to use ARSGs were impacted by privacy concerns. The results of this study found that S. Korean respondents had significantly greater privacy concerns about using ARSGs than U.S. respondents. However, they also had significantly more positive attitudes and greater behavioral intentions to use ARSGs. Path analyses examining ARSGs’ acceptance revealed that privacy concerns impacted attitudes towards ARSGs, but that these had a greater impact on U.S. participants than on Koreans. The results highlight the importance of considering nuanced cultural perspectives, specifically privacy concerns, in examining the development and adoption of new technologies. Raw data and scripts for this study are available to ensure reproducibility. Full article

A millimeter-wave multi-layer and miniaturized multibeam antenna fed by an E-plane Butler matrix (BM) in substrate integrated waveguide (SIW) technology is proposed. For the beam-forming network (BFN), a folded E-plane 4 × 4 BM is proposed, whose basic components are stacked up along the vertical direction aiming to reduce the horizontal size by more than 75% compared with a single-layer BM. For the radiation portion, an unconventional slot antenna array arranged in a ladder type is adopted. The slot antenna elements are distributed in separate layers, making them more compatible with the presented BM and are arranged in the longitudinal direction to suppress the mutual coupling effect. Furthermore, the BM has been adjusted to accommodate the slot antenna array and obtain further miniaturization. The overall dimension of the designed multibeam antenna, taking the BFN into account, is 12 mm × 45 mm × 2 mm (1.2 λ × 4.5 λ × 0.2 λ), which is preferable for future 6G smartphone applications. The impacts of the air gap in fabrication are also taken into consideration to alleviate the error between simulated model and fabricated prototype. Full article

The challenges and increasing number of elderly individuals requiring remote monitoring at home highlight the need for technological innovations. This study devised an eHealth mobile application designed to detect abnormal movement behavior and alert caregivers when a lack of movement is detected for an abnormal period. By utilizing the built-in accelerometer of a conventional mobile phone, an application was developed to accurately record movement patterns and identify active and idle states. Fuzzy logic, an artificial intelligence (AI)-inspired paradigm particularly effective for real-time reasoning under uncertainty, was integrated to analyze activity data and generate timely alerts, ensuring rapid response in emergencies. The approach reduced development costs while leveraging the widespread familiarity with mobile phones, facilitating easy adoption. The approach involved collecting real-time accelerometry data, analyzing movement patterns using fuzzy logic-based inferencing, and implementing a rule-based decision system to classify user activity and detect inactivity. This pilot study primarily validated the devised fuzzy logic method and the functional prototype of the mobile application, demonstrating its potential to leverage universal smartphone accelerometers for accessible remote monitoring. Using fuzzy logic, temporal and behavioral symmetry in movement patterns were adapted to detect asymmetric anomalies, e.g., abnormal inactivity or falls. The study is particularly relevant considering lonely individuals found deceased in their homes long after dying. By providing real-time monitoring and proactive alerts, this eHealth solution offers a scalable, cost-effective approach to improving elderly care, enhancing safety, and reducing the risk of unnoticed deaths through fuzzy logic. Full article

Advances in information technology and the widespread adoption of smartphones have improved human convenience and quality of life by facilitating extensive information sharing. However, the increasing frequency and duration of smartphone use is linked to a high risk of musculoskeletal disorders, particularly manifesting as changes in posture and gait. These alterations can lead to various physical issues, including spinal deformities, reduced gait stability, and increased muscle fatigue. Furthermore, excessive smartphone use can negatively affect mental health, contributing to depression, anxiety, and cognitive impairment. This narrative review primarily aims to systematically examine the effects of smartphone-related posture and gait alterations on physical function and identify associated problems. This study systematically summarized individual studies published between 2009, when smartphones first became widespread, and 2024 that investigated the effects of smartphone-induced posture and gait alterations. Through identifying issues related to these alterations, we aim to propose preventive strategies to avoid further complications. Full article

An aging society increases the demand for solutions that enable quick reactions, such as calling for help in response to events that may threaten life or health. One of such events is a fall, which is a common cause (or consequence) of injuries among the elderly, that can lead to health problems or even death. Fall may be also a symptom of a serious health problem, such as a stroke or a heart attack. This study addresses the fall detection problem. We propose a fall detection solution based on accelerometer data from smartphone devices. The proposed model is based on a Recurrent Neural Network employing a Gated Recurrent Unit (GRU) layer. We compared the results with the state-of-the-art solutions available in the literature using the UniMiB SHAR dataset containing accelerometer data collected using smartphone devices. The dataset contains the validation dataset prepared for evaluation using the Leave-One-Subject-Out (LOSO-CV) and 5-Fold Cross-Validation (CV) strategies; consequently, we used them for evaluation. Our solution achieves the highest result for Leave-One-Subject-Out and a comparable result for the k-Fold Cross-Validation strategy, achieving 98.99% and 99.82% accuracy, respectively. We believe it has the potential for adoption in production devices, which could be helpful, for example, in nursing homes, improving the provision of assistance especially when combined into a multimodal system with other sensors. We also provide all the data and code used in our experiments publicly, allowing other researchers to reproduce our results. Full article

Digital applications for supporting health management often fail to achieve large-scale adoption. Costs related to purchasing, maintaining, and using medical or sensor devices, such as smartwatches, currently hinder uptake and sustained engagement, particularly in the prevention and monitoring of lifelong conditions. As an alternative, smartphone-based passive monitoring could provide a viable strategy for lifelong use, removing hardware-related costs and exploiting the synergies between mobile health (mHealth) and ambient assisted living (AAL). However, smartphone sensor toolkits are not designed for diagnostic purposes, and their quality varies depending on the model, maker, and generation. This narrative overview of recent reviews (narrative meta-review) on the current state of smartphone-based passive monitoring highlights the strengths, weaknesses, opportunities, and threats (SWOT analysis) of this approach, which pervasively encompasses digital health, mHealth, and AAL. The results are then consolidated into a newly defined concept of a mobile biomarker, that is, a general model of medical indices for diagnostic tasks that can be computed using smartphone sensors and capabilities. Full article

In the increasingly competitive mobile ecosystem, understanding user behavior is essential to improve targeted sales and the effectiveness of advertising. With the widespread adoption of smartphones and the increasing variety of mobile applications, predicting user behavior has become more complex. This study presents a comprehensive framework for predicting mobile payment behavior by integrating demographic, situational, and behavioral factors, focusing on patterns in mobile application usage. To address the complexity of the data, we use a combination of machine-learning models, including extreme gradient boosting, light gradient boosting machine, and CatBoost, along with Shapley additive explanations (SHAP) to improve interpretability. An analysis of extensive panel data from Korean Android users reveals that incorporating application usage behavior in such models considerably improves the accuracy of mobile payment predictions. The study identifies key predictors of payment behavior, indicated by high Shapley values, such as using social networking services (e.g., KakaoTalk and Instagram), media applications (e.g., YouTube), and financial and membership applications (e.g., Toss and OK Cashbag). Moreover, the results of the SHAP force analysis reveal the individual session-level drivers of mobile purchases. These findings advance the literature on mobile payment prediction and offer practical insights for improving targeted marketing strategies by identifying key behavioral drivers of mobile transactions. Full article

Recently, technologies monitoring users’ physiological signals in consumer electronics such as smartphones or kiosks with cameras and displays are gaining attention for their potential role in diverse services. While many of these technologies focus on photoplethysmography for the measurement of blood flow changes, respiratory measurement is also essential for assessing an individual’s health status. Previous studies have proposed thermal camera-based and body movement-based respiratory measurement methods. In this paper, we adopt an approach to extract respiratory signals from RGB face videos using photoplethysmography. Prior research shows that photoplethysmography can measure respiratory signals, due to its correlation with cardiac activity, by setting arterial vessel regions as areas of interest for respiratory measurement. However, this correlation does not directly reflect real-time respiratory components in photoplethysmography. Our new approach measures the respiratory rate by capturing changes in skin brightness from motion artifacts. We utilize these brightness factors, including facial movement, for respiratory signal measurement. We applied the wavelet transform and smoothing filters to remove other unrelated motion artifacts. In order to validate our method, we built a dataset of respiratory rate measurements from 20 individuals using an RGB camera in a facial movement-aware environment. Our approach demonstrated a similar performance level to the reference signal obtained with a contact-based respiratory belt, with a correlation above 0.9 and an MAE within 1 bpm. Moreover, our approach offers advantages for real-time measurements, excluding complex computational processes for measuring optical flow caused by the movement of the chest due to respiration. Full article

Background/Objectives: This study is a pilot evaluation of the applicability of wearable digital healthcare devices for Korean male seafarers. Seafarers are exposed to health risks due to unstable and confined living conditions, and their access to healthcare services becomes significantly challenging, especially with the substantial decrease in physical activity onboard. This study aimed to monitor the physical activity of these seafarers through wearable devices and evaluate the potential of managing their health using these technologies. Methods: During the 12-week study, which included 11 participants, it was confirmed that monitoring physical activity using wearable devices and smartphone applications was effective. Results: Over the 12-week period, the average systolic blood pressure decreased from 137.09 ± 13.05 mmHg to 124.36 ± 5.66 mmHg, and the average diastolic blood pressure decreased from 86.45 ± 10.24 mmHg to 77.45 ± 5.26 mmHg, showing a statistically significant reduction (p = 0.011). Additionally, participants experienced an average weight reduction of 1.19 kg. Satisfaction with the use of wearable devices was reported to be moderate. Conclusions: Such digital healthcare can encourage the maintenance of healthy habits by continuously monitoring physical activity and providing feedback. Considering the difficulties seafarers face in accessing medical services, the adoption of digital healthcare through wearable devices is essential, contributing to the prevention of chronic diseases and overall health improvement of seafarers. Future research should explore the long-term benefits and potential challenges of these digital healthcare solutions on a larger scale. Full article

Riding comfort and safety are essential requirements for any form of transportation but particularly for electric bicycles (e-bikes), which are highly affected by varying road conditions. These factors largely depend on the effectiveness of the e-bike’s control strategy. While several studies have proposed control approaches that address comfort and safety, vibration—an influential factor in both structural integrity and rider experience—has received limited attention during the design phase. Moreover, many commercially available e-bikes provide manual assistance-level settings, leaving comfort and safety management to the rider’s experience. This study proposes a Road-Adaptive Vibration Reduction System (RAVRS) that can be deployed on an e-bike rider’s smartphone to automatically maintain riding comfort and safety using manual assistance control. A fuzzy-based control algorithm is adopted to dynamically select the appropriate assistance level, aiming to minimize vibration while maintaining velocity and acceleration within thresholds associated with comfort and safety. This study presents a vibration analysis to highlight the significance of vibration control in improving electronic reliability, reducing mechanical fatigue, and enhancing user experience. A functional prototype of the RAVRS was implemented and evaluated using real-world data collected from experimental trips. The simulation results demonstrate that the proposed system achieves effective control of speed and acceleration, with success rates of 83.97% and 99.79%, respectively, outperforming existing control strategies. In addition, the proposed RAVRS significantly enhances the riding experience by improving both comfort and safety. Full article

Achieving the mainstream adoption of circular packaging is essential for mitigating the environmental impacts of plastic waste. Its widespread adoption, however, remains hindered by significant user barriers. This study investigates the barriers to user adoption of upstream packaging solutions in Indonesia with the aim of reducing plastic packaging waste. Through a mixed-methods approach including case studies, expert workshops, and focus group discussions, nine key barriers were identified and analysed. These include inconvenience, resistance to changing habits and behaviours, higher costs and deposit schemes, contamination and hygiene concerns, wear and tear, functional and performance limitations, a lack of awareness about the environmental impacts, limited availability and variety, and a lack of trust. This research advances the literature by offering a detailed analysis of these barriers, categorising them into sociocultural, economic, contextual, and regulatory aspects. Additionally, barriers specific to Indonesia were identified such as a shift from being served to self-service refilling, some people not having smartphones, poor cellular signals in rural areas, a preference for plastic packaging due to its resale value, and a preference for cash payments due to limited access to credit or bank cards. The findings highlight the need for tailored, multidisciplinary strategies to overcome these barriers and promote the adoption of circular packaging solutions. This research provides valuable insights for researchers studying circular design, businesses seeking to innovate upstream packaging solutions, and policymakers aiming to develop regulations that support the adoption of circular packaging practices. Full article

With the widespread adoption of smartphones, concerns about increased exposure to non-ionizing radiofrequency have emerged. This scoping review examines the effects of mobile phone exposure on neural oscillations and cortical excitability, focusing on both motor and non-motor regions of the cerebral cortex. A scoping review identified seventy-eight studies that involved healthy individuals and employed electroencephalography and only two studies that investigated transcranial magnetic stimulation as primary technical tools. The findings suggest that mobile phone exposure may affect brain oscillations and cortical excitability. However, inconsistencies in experimental methods across studies make it difficult to draw definitive conclusions. Additionally, research on fifth-generation technology, particularly mmWave exposure from next-generation mobile networks, remains limited and needs further exploration. These gaps highlight the need for more in-depth studies on how mobile phone exposure impacts brain function. Full article