Mobile Handheld Devices Research Articles

Multi-Camera Lighting Estimation for Mobile Augmented Reality

Lighting estimation is an important and long-standing task in computer vision and graphics communities[1]. Over the past few years, we have observed a rising demand for creating immersive user experiences in mobile augmented reality (AR) applications. In particular, environment lighting estimation is essential to render visually coherent virtual objects that need to be overlaid on physical scenes in mobile AR. In this work, we explore the key research questions in lighting understanding for an emerging application domain, mobile AR on multi-camera handheld devices, particularly try-on applications where end users leverage handheld mobile devices, such as smartphones, to overlay products of interest on their faces. Mobile AR try-on apps promote online shopping and often require photorealism to provide user experiences on par with physical try-on.

Evaluating Distraction Safety Performance Indicators in an Urban Area of a Low- or Middle-Income Country: A Case Study of Yaoundé, Cameroon

Distracted driving is a major cause of road traffic crashes in Yaoundé. This is partly due to the scarcity of enforcement and a lack of evidence and investigation using the distraction safety performance indicator (SPI), hindering evidence-based interventions. This study aimed to address this evidence gap by evaluating the distraction SPI using a proven methodology. Data on distracted driving (handheld mobile device; interaction; eating/smoking/drinking) were collected from roadside observations on 36 randomly selected road sections carefully spread to cover the city. SPIs were computed and weighted with traffic volume to ensure the representativeness of the values. A total of 41,004 drivers were observed (38,248 in cars; 1116 in vans; 977 in trucks; 663 in buses). The prevalence of distracted driving in Yaoundé is 13.69% for the three distractions type combined. The prevalence is 7.84% for interaction, 4.89% for handled mobile device usage and 0.96% for eating/smoking/drinking. Leveraging these insights, a seven year (2024–2030) fighting strategy aimed at halving the prevalence was developed. The strategy contains interventions including legislation/enforcement, which have been proven to be effective. This study, pioneered in Yaoundé, provides stakeholders with evidence of the issue and measures to implement and can also be used when developing a road safety strategy. Future research should consider investigation at national level.

Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera

Background Diabetic retinopathy (DR) is a common complication of diabetes and may lead to irreversible visual loss. Efficient screening and improved treatment of both diabetes and DR have amended visual prognosis for DR. The number of patients with diabetes is increasing and telemedicine, mobile handheld devices and automated solutions may alleviate the burden for healthcare. We compared the performance of 21 artificial intelligence (AI) algorithms for referable DR screening in datasets taken by handheld Optomed Aurora fundus camera in a real-world setting. Patients and methods Prospective study of 156 patients (312 eyes) attending DR screening and follow-up. Both papilla- and macula-centred 50° fundus images were taken from each eye. DR was graded by experienced ophthalmologists and 21 AI algorithms. Results Most eyes, 183 out of 312 (58.7%), had no DR and mild NPDR was noted in 21 (6.7%) of the eyes. Moderate NPDR was detected in 66 (21.2%) of the eyes, severe NPDR in 1 (0.3%), and PDR in 41 (13.1%) composing a group of 34.6% of eyes with referable DR. The AI algorithms achieved a mean agreement of 79.4% for referable DR, but the results varied from 49.4% to 92.3%. The mean sensitivity for referable DR was 77.5% (95% CI 69.1–85.8) and specificity 80.6% (95% CI 72.1–89.2). The rate for images ungradable by AI varied from 0% to 28.2% (mean 1.9%). Nineteen out of 21 (90.5%) AI algorithms resulted in grading for DR at least in 98% of the images. Conclusions Fundus images captured with Optomed Aurora were suitable for DR screening. The performance of the AI algorithms varied considerably emphasizing the need for external validation of screening algorithms in real-world settings before their clinical application.

Handheld Devices Utilization for Cognitive Rehabilitation and Medical Assessment Based on Navstar Global Positioning System: Towards Brain-Rehabilitation

There is less than a third of clinically competent brain patients in developed and developing countries utilizing brain rehabilitation through exercise. The lack of hospital-based rehabilitation services and long travel times impede participation. As a consequence, calls have been made for the creation of more adaptable substitutes. The creation of a system that enables the transmission of a patient's unique EEG, sample rate, N-GPS based speed, and position, as well as walking-based brain rehabilitation. A programmed handheld mobile device (HMD) transmits these data to a secure server where an exercise scientist can view them in real-time. To evaluate the viability of this approach, 134 brain patients who were unable to engage in hospital-based rehabilitation underwent remotely supervised exercise assessment and exercise sessions. Completion rates, technical participation, the ability to spot EEG changes, and the six-minute walk test conducted both before and after the participation. The device's efficiency and speed were praised. The participants were able to finish a six-week exercise-based rehabilitation program while they were on the go or close to their homes or places of employment. The bulk of sessions went off without a hitch or technical issues, however sporadic signal loss in underserved areas occasionally caused issues. There were many post-workout and exercise-related EEG abnormalities found. In all countries with data available, less than a third of clinically competent patients use exercise-based brain rehabilitation.

A symmetric T-H shape wideband negative index metamaterial for 28-GHz millimeter-wave applications

One of the leading candidates for the next fifth generation of (5G) and body-centric wireless communications is millimeter-wave frequencies. In this study, a wideband tunable metamaterial (MTM) consisting of T-H shaped symmetric resonator is presented for body-centric applications operating in the millimeter-wave frequency band centered at 28 GHz. The characteristics of the proposed MTM are examined through wave propagation in both Z and X directions. It has a wide operating frequency range of 5.6 GHz for waves travelling along the Z-axis, with DNG characteristics at 26.2 GHz resonance frequency. However, it represents a negative epsilon metamaterial (ENG) range during X-axis wave propagation in the 25–29.4 GHz frequency range. The metamaterial is constructed on a Rogers (RT-5880) substrate and has a compact size, with overall dimensions of 0.420 λo × 0.420 λo. A proposed Y-shaped antenna is developed with a compact size of 1.36 λo × 1.82 λoto cover the 5G at 28 GHz. The single-Y-shaped antenna consists of aradiating patch, Rogers substrate, full ground plane, air bandgap (ABG), and array of DNG MTM.Thus, the array of MTM has been applied to validate the effect in the overall antenna performance such as gain which has been increased from 5.5 dBi to 8.5 dBi. The experimental, simulated results, and equivalent circuit model using Advanced Design Software (ADS) for validation functions showed that the completed results are comparable. The proposed mm-wave metamaterial is expected to be a good candidate for modern 5G wireless communication system components, especially to enhance the antenna gain and isolation. The proposed antenna loaded with MTM is a suitable contender for the upcoming 5G mm wave mobile handheld devices.

Usability Evaluation of Tablet-Based Electronic Medical Record Interface in Supporting Elderly Medical Doctors

Recently, tablet-based devices have become significantly more utilized platforms for electronic medical record (EMR) systems. EMR is the digital counterpart of the medical doctor’s office paper charts. EMR systems contain the medical and treatment histories of the patients in a unified practice. Nevertheless, statistics indicate that a considerable percentage of medical doctors are elderly, aged 60 and above. As using mobile handheld devices (including tablets) poses a well-recognized usability challenge for elderly users, the user interface (UI) usability of tablet-based EMR systems must be thoroughly assessed, considering the needs of elderly medical doctors. Accordingly, our objective is to address this need. Three expert evaluators implemented the heuristic evaluation (HE) approach to evaluate the UI usability of a commercial EMR system that is a tablet-based platform. Applying the HE approach helped identify usability problems that elderly medical doctors might encounter when utilizing a tablet-based EMR UI. In total, eight usability problems contributed to the seven heuristic violations discovered.

An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices

In recent years, we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state-of-the-art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This article aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.

Evaluating View Management for Situated Visualization in Web‐based Handheld AR

AbstractAs visualization makes the leap to mobile and situated settings, where data is increasingly integrated with the physical world using mixed reality, there is a corresponding need for effectively managing the immersed user's view of situated visualizations. In this paper we present an analysis of view management techniques for situated 3D visualizations in handheld augmented reality: a shadowbox, a world‐in‐miniature metaphor, and an interactive tour. We validate these view management solutions through a concrete implementation of all techniques within a situated visualization framework built using a web‐based augmented reality visualization toolkit, and present results from a user study in augmented reality accessed using handheld mobile devices.

Evaluation of ATSC 3.0 and 3GPP Rel-17 5G Broadcasting Systems for Mobile Handheld Applications

This paper provides performance evaluations of two state-of-the-art terrestrial broadcasting systems, ATSC 3.0 and 3GPP Rel-17 5G broadcast, in terms of physical layer capability, network deployment and operating costs. The physical layer performances are evaluated in the mobile broadcasting environment, considering practical implementations of the handheld terminals. Extensive simulation results demonstrate that ATSC 3.0 outperforms 5G broadcast, because of well-designed bit interleaved coded modulation (BICM) and time interleaver that can mitigate deep signal fades. The capital expenditures (CAPEX) and operating expenses (OPEX) of the two systems are analyzed based on physical layer performances. The evaluations could be used for the planning of terrestrial broadcast services to mobile handheld devices.

DynamicRead: Exploring Robust Gaze Interaction Methods for Reading on Handheld Mobile Devices under Dynamic Conditions

Enabling gaze interaction in real-time on handheld mobile devices has attracted significant attention in recent years. An increasing number of research projects have focused on sophisticated appearance-based deep learning models to enhance the precision of gaze estimation on smartphones. This inspires important research questions, including how the gaze can be used in a real-time application, and what type of gaze interaction methods are preferable under dynamic conditions in terms of both user acceptance and delivering reliable performance. To address these questions, we design four types of gaze scrolling techniques: three explicit technique based on Gaze Gesture, Dwell time, and Pursuit; and one implicit technique based on reading speed to support touch-free, page-scrolling on a reading application. We conduct a 20-participant user study under both sitting and walking settings and our results reveal that Gaze Gesture and Dwell time-based interfaces are more robust while walking and Gaze Gesture has achieved consistently good scores on usability while not causing high cognitive workload.

Investigating Privacy Perceptions and Subjective Acceptance of Eye Tracking on Handheld Mobile Devices

Although eye tracking brings many benefits to users of mobile devices and developers of mobile applications, it poses significant privacy risks to both: the users of mobile devices, and the bystanders that surround users, are within the front-facing camera's field of view. Recent research demonstrates that tracking an individual's gaze reveals personal and sensitive information. This paper presents an investigation of the privacy perceptions and the subjective acceptance of users towards eye tracking on handheld mobile devices. In a four-phase user study (N=17), participants used a smartphone eye tracking app, were interviewed before and after viewing a video showing the amount of sensitive and personal data that could be derived from eye movements, and had their privacy concerns measured. Our findings 1) show factors that influence users' and bystanders' attitudes toward eye tracking on mobile devices such as the algorithms' transparency and the developers' credibility and 2) support designing mechanisms to allow for privacy-aware eye tracking solutions on mobile-devices.

Establishment of a Landscape Information Model (LIM) and AI Convergence Plan through the 3D Digital Transformation of Railway Surroundings

Digital transformation projects have been undertaken in the land transportation and railway industries, including the introduction of various smart construction technologies. With the expansion of policies to increase the share of railway transportation as an environmentally sustainable means of transportation that meets the needs of the carbon-neutral era, 3D digital information is required throughout the entire chain of railway construction, route selection, status analysis, design, construction, and maintenance. The need for scientific and rational decision making is increasing. In this study, based on point cloud data acquired by an unmanned aerial vehicle (UAV) and a handheld mobile device, the landscape infrastructure around a railway was digitally converted, and a railway Landscape Information Model (LIM) process that modeled various types of landscape information was derived. Additionally, through the voxelization of 3D data, information regarding a railway’s surrounding environment, analyzed as a 3D volume concept and a convergence plan with deep-learning-based artificial intelligence (AI) technology, was presented through object recognition using a clustering algorithm. A railway LIM dataset could be created from a total of seven major categories, and massive data processing through AI convergence will be a future possibility through optimization of the point cloud data clustering algorithm. The future of the railway industry requires the establishment of a railway LIM for the integrated management of a railway’s surrounding environment and building information modeling (BIM) of structures such as tunnels. The railway LIM process has potential for use in various fields, such as environmental management and safety improvement for disaster prevention.

An augmented reality maintenance assistant with real-time quality inspection on handheld mobile devices

Advances of industry 4.0 are enabling augmented reality (AR) devices to be deployed across the manufacturing sector to enhance worker engagement and performance. This evolution of AR in the workspace is driving changes in how workers on the factory floor interact with the enterprise. AR is evolving to provide work instructions for assembly and maintenance. Recent demonstrations show the capability to allow for quality inspection to be integrally evaluated as part of the AR workflow. Previous integrations of quality include the ability for manual assessment to be logged into the enterprise as part of completing work instructions; however, this is largely based on the perception of the user to determine if they accurately performed the AR instructions. This study is designed to demonstrate the ability of an enterprise-level AR maintenance experience to go beyond manual inspection by embedding real-time quality assessment into the work instructions. Essentially, the goal is to enable the enterprise to perform a self-assessment of the quality of the work performed by maintenance personnel. Furthermore, the AR instructions enable additional instruction to empower maintenance personnel to correct their work prior to moving on to other tasks. The novelty of this work is focused on deployment on a mobile device with integrated vision inspection using the same device that is being used to deploy the AR work instructions. This work enumerates several essential qualities of mobile quality inspection tools and outlines some of the challenges associated with the development of such a system. Finally, results from testing the system demonstrate that handheld mobile devices can be used to capture inspection images while simultaneously deploying AR work instructions. Analysis of the image correction, timing for vision process of the quality check, and overall performance of the integrated system are presented.

Multidirectional Differential RSS Technique for Indoor Vehicle Navigation

While indoor vehicle navigation is an essential component in large-scale parking garages of major cities, technical limitations and challenging propagation environments considerably affect the accuracy of existing localization techniques. We propose practical and robust proximity and direction detection schemes of vehicles for indoor navigation services using low-cost beacons. Specifically, the proposed solution allows a handheld mobile device within a moving vehicle to autonomously detect its approximate position and moving direction by only observing received signal strength (RSS) values of beacon signals. The proposed approach essentially exploits the differential RSS technique over perpendicularly oriented directional beams to reduce the impact of uncontrolled factors, including the vehicle and the mobile device. A low-cost multidirectional beacon prototype is developed using Bluetooth technology. The localization performance is evaluated using 96 beacons in an underground parking garage within an area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$394.8 \mathrm {m} \times 304.3 \mathrm {m}$ </tex-math></inline-formula> . Experimental results show that the 90th percentiles of the average proximity and direction detection errors are 0.8 and 1.64 m, respectively. Furthermore, our proposed scheme provides robust proximity and direction detection performance with various vehicles and mobile devices’ orientations.

Design and development of dual band CPW fed antenna for WLAN and WiFi applications

A dual band coplanar waveguide (CPW) fed antenna is constructed, analyzed, and designed for WLAN and WiFi applications in this paper. The antenna has a compact size of 25 ? 25 ? 1.5 mm3. The antenna structure comprises of a slotted radiating patch over FR4 substrate surrounded by staircase shaped ground plane. As part of the analysis of the characteristics parameters such as the return loss, VSWR, and radiation pattern, HFSS 11.1 is utilized. There is a resonance at two frequencies of 3.3 GHz and 5.82 GHz, with an upper frequency bandwidth of 1.89 GHz. In addition to the antenna design, measurement results are presented. Mobile handheld devices can use the proposed antenna for WLAN and WiFi applications that operate in ISM bands.

The Transgender Flipping Point: How Trans Instagrammers Flip the Script on Identity

Since Time magazine’s now iconic 2014 cover story featuring trans actress Laverne Cox proclaimed the contemporary moment ‘The Transgender Tipping Point’, there has been much debate about the recent proliferation of trans representations across all media sectors. Trans scholars, culture producers, artists, and activists have argued that this is not only a misconception, but it occludes the corpus of trans visual culture made by and for trans culture producers reflecting a wider diversity of trans experiences that do not readily reflect dominant cultural paradigms. Defined as a self-image made with a hand-held mobile device and shared via social media platforms, the selfie has facilitated self-imaging becoming a ubiquitous part of globally networked contemporary life. Beyond this, selfies have facilitated a diversity of image-making practices and enabled otherwise representationally marginalized constituencies to insert self-representations into visual culture. A close look at the prolific selfie practice of Black British, gender-non-conforming, trans-femme performance artist Travis Alabanza reveals their use of Instagram to be a critical intervention into contemporary culture. In self-imaging complex, expansive, and intersectional identity, Alabanza’s oeuvre produces new visual exemplars that defy stereotypes and erasures produced by dominant culture while simultaneously challenging our previously held conceptions of identity and self-portraiture. Alabanza’s work decolonizes the relationship between the subject and the portrait, encouraging viewers to consider the complex dialectical relationship between images, aesthetics, and communication about identity, performativity, gender, racialization, class, and subcultural affiliations.

JSAnalyzer: A Web Developer Tool for Simplifying Mobile Web Pages through Non-critical JavaScript Elimination

The amount of JavaScript used in web pages has substantially grown in the past decade, leading to large and complex pages that are computationally intensive for handheld mobile devices. Due to the increasing usage of these devices to access today’s web, and to accommodate the needs of a large number of mobile web users who solely rely on low-end devices, we propose “JSAnalyzer,” an easy-to-use tool that enables web developers to quickly optimize JavaScript usage in their pages and to generate simpler versions of these pages for mobile web users. JSAnalyzer is motivated by the widespread use of non-critical JavaScript elements, i.e., those that have negligible (if any) impact on the page’s visual content and interactive functionality. JSAnalyzer allows the developer to selectively enable or disable JavaScript elements in any given page while visually observing their impact on the page to (1) accurately identify any non-critical JavaScript elements and (2) create a simplified page with these elements removed. Our quantitative evaluation shows that, given a low-end mobile phone, JSAnalyzer achieves an increase of nearly 90% in Google’s lighthouse performance score while reducing the page load time by 30%. A qualitative study of 22 users shows that the lighter pages produced by JSAnalyzer maintain more than 90% visual similarity compared to the original pages. Moreover, JSAnalyzer was evaluated by 69 developers, showing that it scores nearly 90% in terms of usefulness and usability while retaining the page’s content and functionality. Finally, we show that JSAnalyzer outperforms state-of-the-art solutions in terms of timing speedups and resource savings.

A comparative study of user experienced mobile broadband performance

Even though the proliferation of handheld mobile communication devices has deepened Internet and broadband penetration in Nigeria, users of such services may not have detailed quantitative performance comparison between the services provided by different network operators. The study here reported is therefore an attempt to bridge the information gap. A measurement testbed based on the Raspberry Pi platform was developed to autonomously measure the mobile broadband performance of third generation (3G) and fourth generation (4G) broadband networks of the four major mobile network operators in the country. Under static access conditions, the system was set up to measure the upload throughput, download throughput and latency from a user-centric perspective. The measurements were taken by simultaneously sampling the networks of four mobile broadband service providers (9Mobile, Airtel, Globacom and MTN) over a period of three weeks in the city of Uyo, Nigeria. The results obtained revealed that Airtel achieved the highest peak download rate for 3G throughput, and this was only 1.41 Mbps higher than the least performing MNO. Also, it was found that MTN’s download rate peaked at 35.06 Mbps, and this was over 300% of the rate achieved by the fourth rated network in terms of the 4G download throughput metric. The measurement results also revealed that peak usage periods, defined as the hours between 7pm and 11pm, witnessed higher variability in throughput rates than other periods.

Investigating the Effects of Online English Film Dubbing Activities on the Intelligibility and Comprehensibility of Chinese Students’ English Pronunciation

Intelligibility and comprehensibility in English learners’ pronunciation plays a vital role in effective communication. Empirical studies show that Chinese English learners have difficulties achieving a high level of intelligibility and comprehensibility in their English pronunciation, one possible cause being the lack authentic context for pronunciation practice. Mobile-assisted Language Learning (MALL), i.e., language learning assisted or enhanced through the use of a handheld mobile device, is reported to provide a more authentic and therefore robust learning environment for EFL learners. This research explored the impact of online English film dubbing activities using mobile phones (MALL) on the intelligibility and comprehensibility of Chinese English learners’ English pronunciation. We recruited two classes of first-year college students, one as the experimental group and the other the control group. Recordings of the two classes were collected before and after the online English film dubbing activities, and a questionnaire was administered to the experimental group. The findings show that online English film dubbing activities positively influenced the performance of the EG’s English pronunciation in intelligibility and comprehensibility. The results from the questionnaires showed general positive attitudes towards the online English film dubbing activity.

BAMCloud: a cloud based Mobile biometric authentication framework

There has been an exponential increase in the number of users switching to mobile banking. Therefore, various countries are adopting biometric solutions as security measures. Biometric technologies provide the potential security framework to make banking more convenient and secure than it has ever been. These technologies are gaining much popularity because of the ease in capturing biometric data in real-time using one’s mobile phone. At the same time, the exponential growth of enrollment in the biometric system produces a massive amount of high-dimensional data. To overcome performance-related issues arising due to the resulting data deluge, this paper aims to propose a distributed mobile biometric system based on a high-performance cluster Cloud. In this paper, a Cloud-based mobile biometric authentication framework (BAMCloud) is proposed that uses dynamic signatures for authentication. The process flow of the BAMCloud system involves capturing data using any handheld mobile device, followed by its storage, preprocessing, and training of the system in a distributed manner over the Cloud. MapReduce has been implemented on the Hadoop platform to reduce the processing time. For model training, The Levenberg-Marquardt backpropagation neural network has been used. It achieves a speed of 8.5 times the original speed and performance of 96.23%. Furthermore, the cost-benefit analysis of the implemented system shows that the cost of implementation and execution of the system is less than the existing ones. The experiments demonstrate that better performance is achieved by implementing the proposed framework as compared to other methods used in recent literature.