Search Results (443)

The prevention of diabetic foot ulcers remains a critical challenge. This study evaluates a smart compression sock designed to address this issue by integrating temperature, plantar pressure, and blood oxygen sensors and monitoring data recorded by these sensors. The smart sock, developed with input from a certified Pedorthist, was tested on 20 healthy adult participants aged 16 to 53. It includes four temperature sensors and pressure sensors at common ulcer sites (first and fifth metatarsal heads, calcaneus, and hallux), and a blood oxygen sensor on the hallux. The sensors are monitored, and their transduced data are collected and stored through an app installed on a personal cell phone. The mobile app interface is user-friendly, providing intuitive navigation and easy access to the sensors’ data. Using repeated measures ANOVA and post hoc tests, we analyzed the impact of various physical activities on physiological changes in the foot. The device effectively detected significant variations in blood oxygen, temperature, and pressure across six activities. Statistical analyses revealed significant differences based on activity type and sensor location. These results highlight the smart sock’s sensitivity and accuracy, suggesting its potential to prevent diabetic foot ulcers. Further clinical trials are needed to evaluate its efficacy in a larger, more diverse population. Full article

The Consumer-Oriented Trusted Service Manager (CO-TSM) model has been recognised as a significant advancement in managing applications on Near Field Communication (NFC)-enabled mobile devices and multi-application smart cards. Traditional Trusted Service Manager (TSM) models, while useful, often result in market fragmentation and limit widespread adoption due to their centralised control mechanisms. The CO-TSM model addresses these issues by decentralising management and offering greater flexibility and scalability, making it more adaptable to the evolving needs of embedded systems, particularly in the context of the Internet of Things (IoT) and Radio Frequency Identification (RFID) technologies. This paper provides a comprehensive analysis of the CO-TSM model, highlighting its application in various technological domains such as smart cards, HCE-based NFC mobile phones, TEE-enabled smart home IoT devices, and RFID-based smart supply chains. By evaluating the CO-TSM model’s architecture, implementation challenges, and practical deployment scenarios, this paper demonstrates how CO-TSM can overcome the limitations of traditional TSM approaches. The case studies presented offer practical insights into the model’s adaptability and effectiveness in real-world scenarios. Through this examination, the paper aims to underscore the CO-TSM model’s role in enhancing scalability, flexibility, and user autonomy in secure embedded device management, while also identifying areas for future research and development. Full article

The development of smart agriculture has become a critical issue for the future of smart cities, with large-scale management of aquaculture posing numerous challenges. Particularly in the fish farming industry, producing single-sex fingerlings (especially male fingerlings) is crucial for enhancing rearing efficiency and could even provide key support in addressing future global food demands. However, traditional methods of manually selecting the gender of broodfish rely heavily on experienced technicians, are labor-intensive and time-consuming, and present significant bottlenecks in improving production efficiency, thus limiting the capacity and sustainable development potential of fish farms. In response to this situation, this study has developed an intelligent identification system based on the You Only Look Once (YOLO) artificial intelligence (AI) model, specifically designed for analyzing secondary sexual characteristics and gender screening in farmed fish. Through this system, farmers can quickly photograph the fish’s cloaca using a mobile phone, and AI technology is then used to perform real-time gender identification. The study involved two phases of training with different sample sets: in the first phase, the AI model was trained on a single batch of images with varying parameter conditions. In the second phase, additional sample data were introduced to improve generalization. The results of the study show that the system achieved an identification accuracy of over 95% even in complex farming environments, significantly reducing the labor costs and physical strain associated with traditional screening operations and greatly improving the production efficiency of breeding facilities. This research not only has the potential to overcome existing technological bottlenecks but also may become an essential tool for smart aquaculture. As the system continues to be refined, it is expected to be applicable across the entire life cycle management of fish, including gender screening during the growth phase, thereby enabling a more efficient production and management model. This not only provides an opportunity for technological upgrades in the aquaculture industry but also promotes the sustainable development of aquaculture. The smart aquaculture solution proposed in this study demonstrates the immense potential of applying AI technology to the aquaculture industry and offers strong support for global food security and the construction of smart cities. Full article

This paper introduces a novel deep learning model for grape disease detection that integrates multimodal data and parallel heterogeneous activation functions, significantly enhancing detection accuracy and robustness. Through experiments, the model demonstrated excellent performance in grape disease detection, achieving an accuracy of 91%, a precision of 93%, a recall of 90%, a mean average precision (mAP) of 91%, and 56 frames per second (FPS), outperforming traditional deep learning models such as YOLOv3, YOLOv5, DEtection TRansformer (DETR), TinySegformer, and Tranvolution-GAN. To meet the demands of rapid on-site detection, this study also developed a lightweight model for mobile devices, successfully deployed on the iPhone 15. Techniques such as structural pruning, quantization, and depthwise separable convolution were used to significantly reduce the model’s computational complexity and resource consumption, ensuring efficient operation and real-time performance. These achievements not only advance the development of smart agricultural technologies but also provide new technical solutions and practical tools for disease detection. Full article

This work examines swipe-based interactions on smart devices, like smartphones and smartwatches, that detect vibration signals through defined swipe surfaces. We investigate how these devices, held in users’ hands or worn on their wrists, process vibration signals from swipe interactions and ambient noise using a support vector machine (SVM). The work details the signal processing workflow involving filters, sliding windows, feature vectors, SVM kernels, and ambient noise management. It includes how we separate the vibration signal from a potential swipe surface and ambient noise. We explore both software and human factors influencing the signals: the former includes the computational techniques mentioned, while the latter encompasses swipe orientation, contact, and movement. Our findings show that the SVM classifies swipe surface signals with an accuracy of 69.61% when both devices are used, 97.59% with only the smartphone, and 99.79% with only the smartwatch. However, the classification accuracy drops to about 50% in field user studies simulating real-world conditions such as phone calls, typing, walking, and other undirected movements throughout the day. The decline in performance under these conditions suggests challenges in ambient noise discrimination, which this work discusses, along with potential strategies for improvement in future research. Full article

With the rapid consumer adoption of mobile devices such as tablets and smart phones, tele-traffic has experienced a tremendous growth, making low-power technologies highly desirable for future communication networks. In this paper, we consider an ambient backscatter (AB)-based user cooperation (UC) scheme for mmWave wireless-powered communication networks (WPCNs) with lens antenna arrays. Firstly, we formulate an optimization problem to maximize the minimum rate of two users by jointly designing power and time allocation. Then, we introduce auxiliary variables and transform the original problem into a convex form. Finally, we propose an efficient algorithm to solve the transformed problem. Simulation results demonstrate that the proposed AB-based UC scheme outperforms the competing schemes, thus improving the fairness performance of throughput in WPCNs. Full article

Suicidal behavior and Non-Suicidal Self-Injuries (NSSIs) are a major health problem in the adolescent population. New technologies can contribute to the development of innovative interventions in suicide prevention. Here, we present the SmartCrisis-Teen study protocol. The study consists of a randomized clinical trial which aims to evaluate the effectiveness of a digital safety plan to prevent suicidal behavior and NSSIs in adolescents. This is a multicentric study which will be conducted among the adolescent population, both in clinical and student settings, with a target sample of 1080 participants. The intervention group will receive an Ecological Momentary Intervention (EMI) consisting of a digital safety plan on their mobile phone. All participants will receive their Treatment As Usual (TAU). Participants will be followed for six months, with weekly and monthly telephone visits and face-to-face visits at three and six months. Participants will be assessed using traditional questionnaires as well as Ecological Momentary Assessment (EMA) and Implicit Association Tests (IATs). With this intervention, we expect a reduction in NSSIs through the acquisition of coping strategies and a decrease in suicidal behavior over the course of follow-up. This study provides a novel, scalable digital intervention for preventing suicidal behavior and NSSIs in adolescents, which could contribute to improving adolescent mental health outcomes globally. Full article

Buildings worldwide are becoming more thermally insulated, and air circulation is being reduced to a minimum. As a result, measuring indoor air quality is important to prevent harmful concentrations of various gases that can lead to safety risks and health problems. To measure such gases, it is necessary to produce low-cost and low-power-consuming sensors. Researchers have been focusing on semiconducting metal oxide (SMOx) gas sensors that can be combined with intelligent technologies such as smart homes, smart phones or smart watches to enable gas sensing anywhere and at any time. As a type of SMOx, p-type gas sensors are promising candidates and have attracted more interest in recent years due to their excellent electrical properties and stability. This review paper gives a short overview of the main development of sensors based on copper oxides and their composites, highlighting their potential for detecting CO₂ and the factors influencing their performance. Full article

Wearable smart devices have gradually become indispensable devices in people’s lives. Their security and privacy have gained increasing popularity among the public due to their ability to monitor and record various aspects of users’ daily activities and health data. These devices maintain a wireless connection with mobile phones through periodic signal transmissions, which can be intercepted and analyzed by external observers. While these signal packets contain valuable information about the device owner, the identity of the actual user remains unknown. In this study, we propose two approaches to link wearable smart devices with users’ mobile phones, which serve as electronic identities, to enable novel applications such as multi-device authentication and user-device graph construction for targeted advertising. To establish this linkage, we propose two approaches: a passive-sniffing-based linking approach and an active-interference-based linking approach, which solve the problem of sniffing Bluetooth Low Energy broadcast packets in two stages of Bluetooth Low Energy communication. Through experiments conducted across three scenarios, we demonstrate that seven wearable devices can be successfully linked with an accuracy rate exceeding 80%, with accuracy rates approaching 100% when a device is recorded more than 11 times. Additionally, we find that four wearable devices can be linked via an active-interference-based linking approach with an accuracy rate exceeding 70%. Our results highlight the potential of wearable devices and mobile phones as a means of establishing user identities and enabling the development of more sophisticated applications in the field of wearable technology. Full article

Some of the barriers preventing virtual reality (VR) from being widely adopted are the cost and unfamiliarity of VR systems. Here, we propose that in many cases, the specialized controllers shipped with most VR head-mounted displays can be replaced by a regular smartphone, cutting the cost of the system, and allowing users to interact in VR using a device they are already familiar with. To achieve this, we developed SmartVR Pointer, an approach that uses smartphones to replace the specialized controllers for two essential operations in VR: selection and navigation by teleporting. In SmartVR Pointer, a camera mounted on the head-mounted display (HMD) is tilted downwards so that it points to where the user will naturally be holding their phone in front of them. SmartVR Pointer supports three selection modalities: tracker based, gaze based, and combined/hybrid. In the tracker-based SmartVR Pointer selection, we use image-based tracking to track a QR code displayed on the phone screen and then map the phone’s position to a pointer shown within the field of view of the camera in the virtual environment. In the gaze-based selection modality, the user controls the pointer using their gaze and taps on the phone for selection. The combined technique is a hybrid between gaze-based interaction in VR and tracker-based Augmented Reality. It allows the user to control a VR pointer that looks and behaves like a mouse pointer by moving their smartphone to select objects within the virtual environment, and to interact with the selected objects using the smartphone’s touch screen. The touchscreen is used for selection and dragging. The SmartVR Pointer is simple and requires no calibration and no complex hardware assembly or disassembly. We demonstrate successful interactive applications of SmartVR Pointer in a VR environment with a demo where the user navigates in the virtual environment using teleportation points on the floor and then solves a Tetris-style key-and-lock challenge. Full article

Flutter has become popular for providing a uniform development environment for user interfaces (UIs) on smart phones, web browsers, and desktop applications. We have developed the Flutter programming learning assistant system (FPLAS) to assist its novice students’ self-study. We implemented the Docker-based Flutter environment with Visual Studio Code and three introductory exercise projects. However, the correctness of students’ answers is manually checked, although automatic checking is necessary to reduce teachers’ workload and provide quick responses to students. This paper presents an image-based user interface (UI) testing method to automate UI testing by the answer code using the Flask framework. This method produces the UI image by running the answer code and compares it with the image made by the model code for the assignment using ORB and SIFT algorithms in the OpenCV library. One notable aspect is the necessity to capture multiple UI screenshots through page transitions by user input actions for the accurate detection of changes in UI elements. For evaluations, we assigned five Flutter exercise projects to fourth-year bachelor and first-year master engineering students at Okayama University, Japan, and applied the proposed method to their answers. The results confirm the effectiveness of the proposal. Full article

This paper aims to map the daily functional characteristics of people diagnosed with essential tremor (ET) based on their subjective self-reports. In addition, we provide objective measurements of a cup-drinking task. This study involved 20 participants diagnosed with ET who completed the Columbia University Assessment of Disability in Essential Tremor (CADET) questionnaire that included five additional tasks related to digital equipment operation we wrote. Participants also described task-performance modifications they implemented. To create objective personal performance profiles, they performed a cup-drinking task while being monitored using a sensor measurement system. The CADET’s subjective self-report results indicate that the most prevalent tasks participants reported as having difficulty with or requiring modifications were writing, threading a needle, carrying a cup, using a spoon, pouring, and taking a photo or video on a mobile phone. Analysis of participants’ modifications revealed that holding the object with two hands or with one hand supporting the other were the most prevalent types. No significant correlation was found between the CADET total scores and the cup drinking objective measures. Capturing patients’ perspectives on their functional disability, alongside objective performance measures, is envisioned to contribute to the development of custom-tailored interventions aligned with individual profiles, i.e., patient-based/smart healthcare. Full article

Signaling data are records of the interactions of users’ mobile phones with their nearest cellular stations, which could provide long-term and continuous-time location data of large-scale citizens, and therefore have great potential in intelligent transportation, smart cities, and urban sensing. However, utilizing the raw signaling data often suffers from two problems: (1) Low positioning accuracy. Since the signaling data only describes the interaction between the user and the mobile base station, they can only restore users’ approximate geographical location. (2) Poor data quality. Due to the limitations of mobile signals, user signaling may be missing and drifting. To address the above issues, we propose a graph-enhanced trajectory recovery network, GenTrajRec, to recover precise trajectories from signaling data. GenTrajRec encodes signaling data through spatiotemporal encoders and enhances the traveling semantics by constructing a signaling transition graph. In fusing the spatiotemporal information as well as the deep traveling semantics, GenTrajRec can well tackle the challenge of poor data quality, and recover precise trajectory from raw signaling data. Extensive experiments have been conducted on two real-world datasets from Mobile Signaling and Geolife, and the results confirm the effectiveness of our approach, and the positioning accuracy can be improved from 315 m per point to 82 m per point for signaling data using our network. Full article

In the industrial era, production equipment serves as an essential mother machine. In the global manufacturing industry, components such as laptop computers, mobile phones, and automotive parts all strive for aesthetic appearance. Taiwan’s machine tool industry plays a significant role globally. Faced with the constantly changing market environment, the development and competitive advantage of CNC machines are crucial topics for manufacturers. Domestic manufacturers of computer numerical control machines should move towards the integration of automated equipment to accommodate various advanced parts processing procedures. Smart manufacturing will become the trend of the industry in the future. This study invited experts from academia, industry, and research institutions to conduct expert interviews. Their opinions were compiled and analyzed, supplemented by fuzzy Delphi analysis to establish the development trends of various modules. The feasibility and demand of the product’s functional technology for industrial development were analyzed under three research dimensions and eight technical items. A total of 26 key sub-technical items were identified, achieving an expert consensus level of over 80. Furthermore, the importance ranking was analyzed using the fuzzy analytic hierarchy process, and the consistency tests were passed with C.I. < 0.1 and C.R. < 0.1. Finally, the obtained importance ranking of the hierarchical structure was used to predict the future development of computer numerical control machines through a technology roadmap, helping manufacturers use it as a reference model for future development trends to enhance market competitiveness. Full article

Traditional underwater rigid robots have some shortcomings that limit their applications in the ocean. In contrast, because of their inherent flexibility, soft robots, which have gained popularity recently, offer greater adaptability, efficiency, and safety than rigid robots. Among them, the soft actuator is the core component to power the soft robot. Here, we propose a class of soft electrohydraulic bending actuators suitable for underwater robots, which realize the bending motion of the actuator by squeezing the working liquid with an electric field. The actuator consists of a silicone rubber film, polydimethylsiloxane (PDMS) films, soft electrodes, silicone oils, an acrylic frame, and a soft flipper. When a square wave voltage is applied, the actuator can generate continuous flapping motions. By mimicking Haliclystus auricula, we designed an underwater robot based on six soft electrohydraulic bending actuators and constructed a mechanical model of the robot. Additionally, a high-voltage square wave circuit board was created to achieve the robot’s untethered motions and remote control using a smart phone via WiFi. The test results show that 1 Hz was the robot’s ideal driving frequency, and the maximum horizontal swimming speed of the robot was 7.3 mm/s. Full article