Search Results (117)

During the process of digital-system development from prototype to live implementation, differences in user interactions, perceived usability, and overall satisfaction can emerge. These differences often arise due to various factors, which may include the fidelity of the software prototype, the limitations of the prototyping tool, and the complexity of the live digital system. Recognizing these potential usability discrepancies between prototypes and live digital systems, assessment of how well user experience (UX) test approaches, such as usability testing and the System Usability Scale (SUS), reflect the UX in using the digital-system prototype and its counterpart deployed live system emerged as an important research gap. To address this gap, this study compares usability testing and SUS results among a Figma web prototype and its counterpart live web digital system, for the telecom service extension process as a representative digital-system case study. The research study involved a testing process with a total of 10 participants across the Figma prototype and live-web-system test environments, in which different sensing devices that included versatile types of mobile phones were utilized. The research study presents usability testing results related to the overlap in perceived usability issues for the same digital-product developments in both testing environments, which are experienced on different types of mobile sensing devices. The usability testing results are presented as reports on the frequency of occurrence of web system usability issues and corresponding severity levels. The obtained results demonstrated that prototype testing is highly effective for detecting a wide range of usability issues early in the digital-product development phase. The paper also evaluates the predictive capabilities of SUS assessment for the case of the Figma web prototype and its counterpart live web system in the phase of digital-product development. The results show that the SUS evaluation, when applied to digital-system prototype testing, can provide early in the development process a reliable indication of the perceived usability of its counterpart digital system, once it is developed and deployed. The findings presented in the paper offer valuable guidance for software designers and developers seeking to make prototypes and their counterpart real digital-product deployments with improved digital-product overall user experience. Full article

Driver distraction, particularly mobile phone use while driving, remains one of the leading causes of road traffic incidents worldwide. In response to this issue and leveraging recent technological advances and increased access to intelligent systems, this research presents the development of an application running on an intelligent embedded architecture for the automatic detection of mobile phone use by drivers, integrating computer vision, inertial sensing, and edge computing. The system, based on the YOLOv8n model deployed on a Jetson Xavier NX 16Gb—Nvidia, combines real-time inference with an inertial activation mechanism and cloud storage via Firebase Firestore, enabling event capture and traceability through a lightweight web-based HMI interface. The proposed solution achieved an overall accuracy of 81%, an inference rate of 12.8 FPS, and an average power consumption of 8.4 W, demonstrating a balanced trade-off between performance, energy efficiency, and thermal stability. Experimental tests under diverse driving scenarios validated the effectiveness of the system, with its best performance recorded during daytime driving—83.3% correct detections—attributed to stable illumination and enhanced edge discriminability. These results confirm the feasibility of embedded artificial intelligence systems as effective tools for preventing driver distraction and advancing intelligent road safety. Full article

Mobile networking in 4G and 5G remains vulnerable against fake base stations. A fake base station can inject and manipulate the radio resource control (RRC) communication protocol to disable the user equipment’s connectivity. To motivate our research, we empirically show that such a fake base station can cause an indefinite hold of the user equipment’s connectivity using our fake base station prototype against an off-the-shelf phone. To defend against such threat, we design and build an anomaly detection system to detect the fake base station threats. It detects any base station’s deviations from the 4G/5G RRC protocol, which supports both the connectivity provision case (all works well and the user receives connectivity) and the connection-release case (cannot provide connectivity at the time and thus releases connections). Our scheme based on unsupervised machine learning dynamically and automatically controls and sets the detection parameters, which vary with mobility and the communication channel, and utilizes greater information to improve its effectiveness. Using software-defined radios and srsRAN, we implement a prototype of our scheme from sensing to data collection to machine-learning-based detection processing. Our empirical evaluations demonstrate the detection effectiveness and adaptability; i.e., our scheme accurately detects fake base stations deviating from the set protocol in mobile scenarios by adapting its model parameters. Our scheme achieves 100% accuracy in static scenarios against the fake base station threats. If the dynamic control is disabled, i.e., not adapting to mobility and different channel environments, the accuracy drops to 65–76%, but our scheme adjusts the model via dynamic training to recover to 100% accuracy. Full article

Productive and healthy soils are essential in agriculture and other economic uses of land which depend on plant growth, and are under increasing pressure globally. The physical properties of soil, its porosity and pore structure, also have a significant impact on a wide range of environmental factors, such as surface water runoff and greenhouse gas exchange. Methods exist for evaluating soil porosity that are applied in a laboratory environment or by inserting sensors into soil in the field. However, such methods do not readily sample adequately in space or time and are labour-intensive. The purpose of the current study is to investigate the potential for estimation of soil porosity and pore size using the strength of reflection of audio pulses from natural soil surfaces. Estimation of porous material properties using acoustic reflections is well established. But because of the complex, viscous interactions between sound waves and pore structures, these methods are generally restricted to transmissions at low audio frequencies or at ultrasonic frequencies. In contrast, this study presents a novel design for an integrated broad band sensing system, which is compact, inexpensive, and which is capable of rapid, non-contact, and in situ sampling of a soil structure from a small, moving, farm vehicle. The new system is shown to have the capability of obtaining soil parameter estimates at sampling distances of less than 1 m and with accuracies of around 1%. In describing this novel design, special care is taken to consider the challenges presented by real agriculture soils. These challenges include the pasture, through which the sound must penetrate without significant losses, and soil roughness, which can potentially scatter sound away from the specular reflection path. The key to this new integrated acoustic design is an extension of an existing theory for acoustic interactions with porous materials and rigorous testing of assumptions via simulations. A configuration is suggested and tested, comprising seven audio frequencies and three angles of incidence. It is concluded that a practical, new operational tool of similar design should be readily manufactured. This tool would be inexpensive, compact, low-power, and non-intrusive to either the soil or the surrounding environment. Audio processing can be conducted within the scope of, say, mobile phones. The practical application is to be able to easily map regions of an agricultural space in some detail and to use that to guide land treatment and mitigation. Full article

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article

Visual impairment or even loss seriously affects quality of life. Benefited by the rapid development of sound/laser detection, Global Positioning System (GPS)/Beidou positioning, machine vision and other technologies, the quality of life of blind people is expected to be improved through visual substitution technology. The existing visual substitution devices still have limitations in terms of safety, robustness, and ease of operation. The remote companion system developed here fully utilizes multimodal navigation and remote communication technologies, and the positioning and interaction functions of commercial mobile phones. Together with the accumulated judgment of backend personnel, it can provide real-time, safe, and reliable navigation services for blind people, helping them complete daily activities such as independent travel, circulation, and shopping. The practical results show that the system not only has strong operability and is easy to use, but also can provide users with a strong sense of security and companionship, making it suitable for promotion. In the future, this system can also be promoted for other vulnerable groups such as the elderly. Full article

Block-level urban land use classification (BLULUC), like residential and commercial classification, is highly useful for urban planners. It can be achieved in the form of high-frequency full coverage without biases based on the data of high-spatial-resolution remote sensing images (HSRRSIs), which social sensing data like POI data or mobile phone data cannot provide. However, at present, the extraction of quantitative features from HSRRSIs for BLULUC primarily relies on computer vision or deep learning methods based on image signal characteristics rather than land cover patterns, like vegetation, water, or buildings, thus disconnecting existing knowledge between the landscape patterns and their functions as well as greatly hindering BLULUC by HSRRSIs. Well-known landscape metrics could play an important connecting role, but these also encounter the scale selection issue; i.e., the optimal spatial unit size is an image pixel or a segmented image object. Here, we use the task of BLULUC with 2 m satellite images in Beijing as a case study. The results show the following: (1) pixel-based classification can achieve higher accuracy than segmented object-based classification, with an average of 3% in overall aspects, while some land use types could reach 10%, such as commercial land. (2) At the pixel scale, if the quantity metrics at the class level, such as the number of patches, and the proportion metrics at the landscape level, such as vegetation proportion, are removed, the accuracy can be greatly reduced. Moreover, removing landscape-level metrics can lead to a more significant reduction in accuracy than removing class-level metrics. This indicates that in order to achieve a higher accuracy in BLULUC from HSRRSIs, landscape-level land cover metrics, including patch numbers and proportions at the pixel scale, can be used instead of object-scale metrics. Full article

Accurate intravenous (IV) fluid monitoring is critical in healthcare to prevent infusion errors and ensure patient safety. Traditional monitoring methods often depend on dedicated hardware, such as weight sensors or optical systems, which can be costly, complex, and challenging to scale across diverse clinical settings. This study introduces a software-defined sensing approach that leverages semantic segmentation using the pyramid scene parsing network (PSPNet) to estimate the remaining IV fluid volumes directly from images captured by standard smartphones. The system identifies the IV container (vessel) and its fluid content (liquid) using pixel-level segmentation and estimates the remaining fluid volume without requiring physical sensors. Trained on a custom IV-specific image dataset, the proposed model achieved high accuracy with mean intersection over union (mIoU) scores of 0.94 for the vessel and 0.92 for the fluid regions. Comparative analysis with the segment anything model (SAM) demonstrated that the PSPNet-based system significantly outperformed the SAM, particularly in segmenting transparent fluids without requiring manual threshold tuning. This approach provides a scalable, cost-effective alternative to hardware-dependent monitoring systems and opens the door to AI-powered fluid sensing in smart healthcare environments. Preliminary benchmarking demonstrated that the system achieves near-real-time inference on mobile devices such as the iPhone 12, confirming its suitability for bedside and point-of-care use. Full article

Using mobile crowd sourcing/sensing (MCS) noise monitoring can lead to false sound level reporting. The methods used for recruiting mobile phones in an area of interest vary from selecting full populations to randomly selecting a single phone. Other methods apply a clustering algorithm based on spatial or noise parameters to recruit mobile phones to MCS platforms. However, statistical t tests have revealed dissimilarities between these selection methods. In this paper, we assign these dissimilarities to (1) acoustic characteristics and (2) outlier mobile phones affecting the noise level. We propose two clustering phases for noise level monitoring in MCS platforms. The approach starts by applying spatial clustering to form focused clusters and removing spatial outliers. Then, noise level clustering is applied to eliminate noise level outliers. This creates subsets of mobile phones that are used to calculate the noise level. We conducted a real-world experiment with 25 mobile phones and performed a statistical t test evaluation of the selection methodologies. The statistical values indicated dissimilarities. Then, we compared our proposed method with the noise level clustering method in terms of properly detecting and eliminating outliers. Our method offers $4\%$ to $12\%$ higher performance than the noise clustering method. Full article

Earth science education can be enriched by adding technological knowledge to enable monitoring human earth impacts by using soil science as an example. Modern sensing technologies and a mobile mapping platform can enhance an existing field laboratory exercise to expand students’ knowledge beyond the core subject matter. This multi-year study’s objectives were to enrich laboratory exercise content on soil compaction using a soil penetration resistance (PR) tester (penetrometer) with the concepts of direct (soil PR) and proximal remote sensing (cellphone photos of the sample area), and crowdsourcing of field data using a GPS-enabled mobile phone application in an introductory soil science course at Clemson University, South Carolina (SC), United States of America (USA). Students from multiple Science, Technology, Engineering, and Mathematics (STEM) disciplines (forestry, wildlife biology, and environmental and natural resources) participated in the study. They completed a set of reusable learning objects (RLOs) in the following sequence: pre-testing questionnaire, laboratory video, quiz, and post-testing questionnaire. Students had increased familiarity with the concepts from this exercise, as demonstrated by the post-assessment survey. The quiz, which was taken by 113 students online, had an average total correct score of 9 out of a possible 10. A post-assessment survey indicated that the laboratory exercise was an effective way to learn about field soil PR data, direct and proximal remote sensing, and crowdsourcing with a GPS-enabled cellphone application. Results from the two study years (2022 and 2024) were consistent, indicating validity and confidence in the findings. Full article

Optimising outcomes after shoulder interventions requires objective shoulder range of motion (ROM) assessments. This narrative review examines video-based pose technologies and markerless motion capture, focusing on their clinical application for shoulder ROM assessment. Camera pose-based methods offer objective ROM measurements, though the accuracy varies due to the differences in gold standards, anatomical definitions, and deep learning techniques. Despite some biases, the studies report a high consistency, emphasising that methods should not be used interchangeably if they do not agree with each other. Smartphone cameras perform well in capturing 2D planar movements but struggle with that of rotational movements and forward flexion, particularly when thoracic compensations are involved. Proper camera positioning, orientation, and distance are key, highlighting the importance of standardised protocols in mobile phone-based ROM evaluations. Although 3D motion capture, per the International Society of Biomechanics recommendations, remains the gold standard, advancements in LiDAR/depth sensing, smartphone cameras, and deep learning show promise for reliable ROM assessments in clinical settings. Full article

Vibration sensors are integral to a multitude of engineering applications, yet the development of low-cost, easily assembled devices remains a formidable challenge. This study presents a highly sensitive flexible vibration sensor, based on the piezoresistive effect, tailored for the detection of high-dynamic-range vibrations and accelerations. The sensor’s design incorporates a polylactic acid (PLA) housing with cavities and spherical recesses, a polydimethylsiloxane (PDMS) membrane, and electrodes that are positioned above. Employing femtosecond laser ablation and template transfer techniques, a parallel groove array is created within the flexible polymer sensing layer. This includes conductive pathways, and integrates stainless-steel balls as oscillators to further amplify the sensor’s sensitivity. The sensor’s performance is evaluated over a frequency range of 50 Hz to 400 Hz for vibrations and from 1 g to 5 g for accelerations, exhibiting a linear correlation coefficient of 0.92 between the sensor’s voltage output and acceleration. It demonstrates stable and accurate responses to vibration signals from devices such as drills and mobile phone ringtones, as well as robust responsiveness to omnidirectional and long-distance vibrations. The sensor’s simplicity in microstructure fabrication, ease of assembly, and low cost render it highly promising for applications in engineering machinery with rotating or vibrating components. Full article

This work investigates the impact of rainfall on cellular communication links, leveraging smartphone-collected measurements. While existing studies primarily focus on line-of-sight (LoS) microwave propagation environments, this work explores the impact of rainfall on typical signal metrics over cellular links when the LoS path is not guaranteed. We examine both small-scale and large-scale variations in signal measurements across dry and rainy days, considering diverse locations and time windows. Through statistical and spectral analysis of a large dataset, we uncover novel insights into how rainfall influences cellular communication links. Specifically, we observe a consistent daily fluctuation pattern in key cellular metrics, such as the reference signal received quality. Additionally, spectral features of key mobile metrics show noticeable changes during rainfall events. These findings, consistent across three distinct locations, highlight the significant impact of rainfall on everyday cellular links. They also suggest that the widely available by-product signals from mobile phones could be leveraged for innovative rainfall-sensing applications. Full article

The optimization of a contact-less magnetoelastic sensing setup designed to detect substances/agents accumulating in its environment is presented. The setup is intended as a custom-built, low-cost yet effective magnetoelastic sensor for pest/bug detection in constrained places (small museums, labs, etc.). It involves a short, thin, and flexible polymer slab in a cantilever arrangement, with a short Metglas^® 2826 MB magnetoelastic ribbon attached on part of its surface. A mobile phone both supports and supplies low-amplitude vibration to the slab’s free end. When vibrating, the magnetoelastic ribbon generates variable magnetic flux, thus inducing voltage in a contact-less manner into a pick-up coil suspended above the ribbon. This voltage carries specific characteristic frequencies of the slab’s vibration. If substances/agents accumulate on parts of the (suitably coated) slab surface, its mass distribution and, hence, characteristic frequencies change. Then, simply monitoring shifts of such frequencies in the recorded voltage enables the detection of accumulating substances/agents. The current work uses extensive testing via various vibration profiles and load positions on the slab, for statistically evaluating the sensitivity of the mass detection of the setup. It is shown that, although this custom-built substance/agent detector involves limited (low-cost) hardware and a simplified design, it achieves promising results with respect to its cost. Full article

Background/Objectives: The aim of the narrative review is to identify information on the impact of technological interventions (such as telehealth and mobile health) on the health promotion of cardiac patients from diverse populations. Methods: The online databases of PubMed and the Cochrane Library were searched for articles in the English language regarding technological interventions for health promotion in cardiac patients. In addition, a methodological quality control process was conducted. Exclusion was based on first reading the abstract, and then the full manuscript was scanned to confirm that the content was not related to cardiac patients and technological interventions. Results: In all, 11 studies were included in this review after quality control analysis. The sample size reported in these studies ranged from 12 to 1424 subjects. In eight studies mobile phones, smartphones, and apps were used as mHealth interventions with tracking and texting components; two studies used videoconferencing as a digital intervention program, while three studies focused on using physical activity trackers. Conclusions: This review highlights the positive aspects of patient satisfaction with the technological interventions including, but not limited to, accessibility to health care providers, sense of security, and well-being. The digital divide becomes apparent in the articles reviewed, as individuals with limited eHealth literacy and lack of technological knowledge are not motivated or able participate in these interventions. Finding methods to overcome these barriers is important and can be solved to some extent by providing the technology and technical support. Full article