Common Mobile Devices Research Articles

Clinical Significance of Pose Estimation Methods Compared with Radiographic Parameters in Adolescent Patients with Idiopathic Scoliosis.

Human pose estimation, a computer vision technique that identifies body parts and constructs human body representations from images and videos, has recently demonstrated high performance through deep learning. However, its potential application in clinical photography remains underexplored. This study aimed to establish photographic parameters for patients with adolescent idiopathic scoliosis (AIS) using pose estimation and to determine correlations between these photographic parameters and corresponding radiographic measures. We conducted a study involving 42 patients with AIS who had undergone spinal correction surgery and conservative treatment. Preoperative photographs were captured using an iPhone 13 Pro mounted on a tripod positioned at the head of an X-ray tube. From the outputs of pose estimation, we derived five photographic parameters and subsequently conducted a statistical analysis to assess their correlations with relevant conventional radiographic parameters. In the sagittal plane, we identified significant correlations between photographic and radiographic parameters measuring trunk tilt angles. In the coronal plane, significant correlations were found between photographic parameters measuring shoulder height and trunk tilt and corresponding radiographic measurements. The results suggest that pose estimation, achievable with common mobile devices, offers potential for AIS screening, early detection, and continuous posture monitoring, effectively mitigating the need for X-ray radiation exposure. Level of Evidence: 3.

Introducing wave-particle duality with low-cost quantitative experiments on light diffraction

We present a quantitative optical low-cost experiment aimed at introducing students to wave-particle duality from a phenomenological point of view. The experiment, focused on light diffraction, clearly shows the intermediate crossover regime which characterize the transition from geometric optics (in which light behaves like a stream of particles) to the Fraunhofer diffraction regime (where the wave aspects are most evident). It can also be considered for introducing students to the uncertainty relations through diffraction of light waves. The experiments do not require expensive materials or specific laboratory instruments and employ the sensors and camera of common mobile devices. The experiments were tested with master students in mathematics and physics, who aim at becoming high school teachers.

Server-enabled mixed reality for flood risk communication: On-site visualization with digital twins and multi-client support

This study introduces a novel mobile mixed reality (MR) system to improve communication among stakeholders regarding climate-change-driven flood risk. This MR system enables stakeholders to visualize flood risks on common mobile devices, such as smartphones and tablets, offering perspectives from their respective environments. The system overcomes the computational limitations of previous mobile MR systems by rendering the MR images on a server. Furthermore, distributing users across multiple servers enables many users to simultaneously perform server-enabled MR, facilitating widespread engagement in flood risk communication. The system uses a viewport transformation to accurately display virtual floods and correct for real-time posture changes. A prototype tested in a high-risk flood area achieved real-time MR visualization at more than 16 frames per second, even on multiple devices. Future research will aim to promote stakeholder participation through more accurate visualization of virtual floods and improved interactive information sharing among users.

Performance Assessment of Kinematic GNSS Positioning with Smartphones Based on Post-Processing of Raw Observations

In recent years, there have been significant technological advances in the development of common mobile devices. This brought progress also in the area of positioning with these devices. Allowing access to raw GNSS observations recorded by mobile devices opened possibilities to apply advanced positioning techniques in order to achieve higher positioning accuracy. The paper describes the results of kinematic measurements of a single-frequency Samsung Galaxy S10+ smartphone and a dual-frequency Samsung Galaxy Note10+ smartphone. Observations were repeatedly collected at a 1.76 km long test route in an urban environment at a pedestrian speed. Real-time positioning by autonomous method as well as collection of raw observations into RINEX format and their subsequent post-processing by differential techniques and Precise Point Positioning technique were realized. The achieved results were compared against a reference line representing the real trajectory and also against results of a geodetic grade GNSS receiver. Positioning accuracy of mobile devices ranged from the first decimetres to tens of metres, depending on the environment, tested smartphone and used post-processing technique. Dual-frequency smartphone Samsung Galaxy Note 10+ provided a better performance compared to the single-frequency device. Real-time positioning based on a simple autonomous technique and smoothing algorithm for route optimization reached lower positioning errors compared to all solutions based on collecting raw observations and their consequent post-processing with mentioned techniques.Keywords: GNSS; Positioning; Post-processing; Smartphone.

Frugal Imaging Technique of Capillary Flow through Three-Dimensional Polymeric Printing Powders.

The development of novel imaging techniques of molecular and colloidal transport, including nanoparticles, is an area of active investigation in microfluidic and millifluidic studies. With the advent of three-dimensional (3D) printing, a new domain of materials has emerged, thereby increasing the demand for novel polymers. Specifically, polymeric powders, with average particle sizes on the order of a micron, are experiencing a growing interest from academic and industrial communities. Controlling material tunability at the mesoscopic to microscopic length scales creates opportunities to develop innovative materials, such as gradient materials. Recently, a need for micron-sized polymeric powders has been growing, as clear applications for the material are developing. Three-dimensional printing provides a high-throughput process with a direct link to new applications, driving investigations into the physio-chemical and transport interactions on a mesoscale. The protocol that is discussed in this article provides a non-invasive technique to image fluid flow in packed powder beds, providing high temporal and spatial resolution while leveraging mobile technology that is readily available from mobile devices, such as smartphones. By utilizing a common mobile device, the imaging costs that would normally be associated with an optical microscope are eliminated, resulting in a frugal-science approach. The proposed protocol has successfully characterized a variety of combinations of fluids and powders, creating a diagnostic platform for quickly imaging and identifying an optimal combination of fluid and powder.

End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices.

PurposeNocturnal sounds contain numerous information and are easily obtainable by a non-contact manner. Sleep staging using nocturnal sounds recorded from common mobile devices may allow daily at-home sleep tracking. The objective of this study is to introduce an end-to-end (sound-to-sleep stages) deep learning model for sound-based sleep staging designed to work with audio from microphone chips, which are essential in mobile devices such as modern smartphones.Patients and MethodsTwo different audio datasets were used: audio data routinely recorded by a solitary microphone chip during polysomnography (PSG dataset, N=1154) and audio data recorded by a smartphone (smartphone dataset, N=327). The audio was converted into Mel spectrogram to detect latent temporal frequency patterns of breathing and body movement from ambient noise. The proposed neural network model learns to first extract features from each 30-second epoch and then analyze inter-epoch relationships of extracted features to finally classify the epochs into sleep stages.ResultsOur model achieved 70% epoch-by-epoch agreement for 4-class (wake, light, deep, REM) sleep stage classification and robust performance across various signal-to-noise conditions. The model performance was not considerably affected by sleep apnea or periodic limb movement. External validation with smartphone dataset also showed 68% epoch-by-epoch agreement.ConclusionThe proposed end-to-end deep learning model shows potential of low-quality sounds recorded from microphone chips to be utilized for sleep staging. Future study using nocturnal sounds recorded from mobile devices at home environment may further confirm the use of mobile device recording as an at-home sleep tracker.

Evaluating the robustness of a contact-less mHealth solution for personal and remote monitoring of blood oxygen saturation.

MHealth technologies play a fundamental role in epidemiological situations such as the ongoing outbreak of COVID-19 because they allow people to self-monitor their health status (e.g. vital parameters) at any time and place, without necessarily having to physically go to a medical clinic. Among vital parameters, special care should be given to monitor blood oxygen saturation (SpO2), whose abnormal values are a warning sign for potential COVID-19 infection. SpO2 is commonly measured through the pulse oximeter that requires skin contact and hence could be a potential way of spreading contagious infections. To overcome this problem, we have recently developed a contact-less mHealth solution that can measure blood oxygen saturation without any contact device but simply processing short facial videos acquired by any common mobile device equipped with a camera. Facial video frames are processed in real-time to extract the remote photoplethysmographic signal useful to estimate the SpO2 value. Such a solution promises to be an easy-to-use tool for both personal and remote monitoring of SpO2. However, the use of mobile devices in daily situations holds some challenges in comparison to the controlled laboratory scenarios. One main issue is the frequent change of perspective viewpoint due to head movements, which makes it more difficult to identify the face and measure SpO2. The focus of this work is to assess the robustness of our mHealth solution to head movements. To this aim, we carry out a pilot study on the benchmark PURE dataset that takes into account different head movements during the measurement. Experimental results show that the SpO2 values obtained by our solution are not only reliable, since they are comparable with those obtained with a pulse oximeter, but are also insensitive to head motion, thus allowing a natural interaction with the mobile acquisition device.

Phase Difference Based Precise Indoor Tracking of Common Mobile Devices Using an Iterative Holographic Extended Kalman Filter

The 3D indoor localization of low-cost standard mobile devices represents an important research topic. Since the implementation of ultra-wideband localization systems requires elaborated hardware, a localization concept based on phase-difference-of-arrival (PDOA) evaluation of narrow band communication signals at spatially distributed antennas is favorable in many applications. Typically, PDOA measurements are used to estimate the angle-of-arrival (AOA) at several receivers, which are then combined via multiangulation. However, AOA estimation requires far field conditions, thereby limiting measurement sensitivity, and distorts measurements in a non linear fashion. To overcome these limitations, this paper proposes the iterative holographic extended Kalman filter (IHEKF), which directly evaluates the phase differences between spatially distributed antenna pairs. The IHEKF requires neither a specific waveform nor emitter–receiver synchronization and, therefore, represents a good candidate for localization within communication systems such as 5G/6G. Since the evaluation of phase differences is affected by phase ambiguity, the IHEKF is designed so that closely spaced antenna pairs are evaluated first and then more distant antennas are included successively to improve accuracy. The IHEKF’s capabilities are demonstrated via a 24 GHz narrow band measurement setup with strong multipath propagation, providing outstanding localization accuracy in the millimeter range without consuming any notable RF signal bandwidth.

Device Invariant Deep Neural Networks for Pulmonary Audio Event Detection Across Mobile and Wearable Devices.

Mobile and wearable devices are being increasingly used for developing audio based machine learning models to infer pulmonary health, exacerbation and activity. A major challenge to widespread usage and deployment of such pulmonary health monitoring audio models is to maintain accuracy and robustness across a variety of commodity devices, due to the effect of device heterogeneity. Because of this phenomenon, pulmonary audio models developed with data from one type of device perform poorly when deployed on another type of device. In this work, we propose a framework incorporating feature normalization across individual frequency bins and combining task specific deep neural networks for model invariance across devices for pulmonary event detection. Our empirical and extensive experiments with data from 131 real pulmonary patients and healthy controls show that our framework can recover up to 163.6% of the accuracy lost due to device heterogeneity for four different pulmonary classification tasks across two broad classification scenarios with two common mobile and wearable devices: smartphone and smartwatch.Clinical relevance- The methods presented in this paper will enable efficient and easy portability of clinician recommended pulmonary audio event detection and analytic models across various mobile and wearable devices used by a patient.

Ecological Momentary Assessment in Patients With an Acquired Brain Injury: A Pilot Study on Compliance and Fluctuations.

Ecological Momentary Assessment (EMA) promises to be a suitable method for capturing the dynamics in self-assessments through repeated measurements in naturalistic environments using common mobile devices. Therefore, EMA could increase the power of neuropsychological assessment by obtaining a more fine-grained picture of symptoms, limitations, and strengths in patients with an acquired brain injury (ABI) in real-life situations. The present study examined 15 patients with an ABI with cognitive and motor impairments. Following a semirandomized high-frequency sampling plan to assess EMA's feasibility and applicability, data were collected across 7 days. At eight prompts per day, patients were asked about their current activities, the social context they were in, their current mood, performance judgments of their own functional status, and the frequency of self-reflections. The average compliance rate was 71.6%. The fluctuations in patients' responses were measured in terms of variance distributions within simple (intercept only) three-level models and root mean square of successive difference values. They were sufficient, as shown, for example, by the mean within-person variability of 44.9% across all of the items studied. There were no significant correlations between patients' age, severity of depressive symptoms, or their level of functioning and their compliance with study participation or the variability of their responses. The results support the feasibility and applicability of EMA as an assessment technique in patients with an ABI. There are, however, limitations that should be considered when planning an assessment of brain-injured patients using EMA.

Walking in a smart city: Investigating the gait stabilization effect for biometric recognition via wearable sensors

Technology is expected to enhance life in a Smart City: everything is intelligent, digital, interconnected, and inclusive. In addition, all everyday activities are facilitated. This paper presents a biometric authentication strategy based on gait dynamics. The produced signals are acquired by the common mobile device accelerometers (especially those embedded in smartphones). The user has nothing to do but normally approach a controlled entry: authentication is automatically triggered by ambient elements (beacons). This transparent protocol entails user awareness of the authentication since the user has to install a suitable app, therefore it does not cause any covert privacy violation. In addition, it allows avoiding any explicit, possibly cumbersome authentication procedure. Last but not least, the use of a sensor directly embedded in everyday users’ equipment supports an efficient approach without the need for further hardware.

Assisting Individuals with Autism and Cognitive Disorders: An Augmented Reality-Based Framework

Individuals with autism require systematic assistance while dealing with the surrounding environment and its objects. The project aim is to develop a framework that could be of substantial help to people with autism and cognitive disorders. The framework is based on common mobile devices and freely available Augmented Reality (AR) applications. The Augmented Reality used in our approach is marker-based AR that employs a camera and a visual marker to trigger and present media content on the mobile device’s screen. The developed framework allows parents and teachers to easily create educational augmented environments for children with autism and cognitive disorders by populating a real-world space with visual markers of favorite cartoonish helpers that can evoke helpful AR content and embed it in the real-world environment. The paper analyzes and discusses the use of the proposed framework from conceptual and technological points of view.

\u201cFrom the moment I wake up I will use it\u2026every day, very hour\u201d: a qualitative study on the patterns of adolescents\u2019 mobile touch screen device use from adolescent and parent perspectives

BackgroundThe use of mobile touch screen devices, e.g. smartphones and tablet computers, has become increasingly prevalent among adolescents. However, little is known about how adolescents use these devices and potential influences on their use. Hence, this qualitative study explored adolescents’ perceptions on their patterns of use and factors influencing use, and perceptions and concerns from parents.MethodsSemi-structured interviews were conducted with adolescents (n = 36; 11 to 18 years) and their parents/caregivers (n = 28) in Singapore recruited to represent males and females across a range of ages from different socioeconomic groups. Prompts covered weekday and weekend use patterns, types of activities, perspectives on amount of use, parental control measures and concerns. Interviews were recorded and transcribed. Transcripts were coded and thematic analysis was carried out.ResultsSmartphone was the most common mobile device owned and used by many of the adolescents, while only some used a tablet. Many adolescents and their parents felt that adolescents’ MTSD use was high, frequent and ubiquitous, with frequent checking of device and multitasking during use. Reported influences of use included functional, personal and external influences. Some of the influences were irresistibility of mobile devices, lack of self-control, entertainment or relaxation value, and high use by peers, family and for schoolwork that contributed to high use, or school/parental control measures and lack of internet availability that limited use. Most adolescents were generally unconcerned about their use and perceived their usage as appropriate, while most parents expressed several concerns about their adolescents’ use and perceived their usage as excessive.ConclusionsThis study has provided rich insights into the patterns and influences of contemporary mobile device use by adolescents. Mobile device use has become an integral part of adolescents’ daily routines, and was affected by several functional, personal and external influences which either facilitated or limited their use. There also seemed to be a strong inclination for adolescents to frequently check and use their mobile devices. There is an urgent need to understand the implications of these common adolescent behaviours to inform advice for wise mobile device use by adolescents.

HDR Image Processing from Embedded Cell Phone Camera

In photo-realistic image synthesis the incoming light from the environment is particularly important. In this work we focus on capturing the incoming light in the form of the radiance map using a common mobile device. This involves the reconstruction of the spherical panorama sky-dome in high dynamic range (HDR) quality and save it to a usable data format. Our setup requires a common smartphone devices with an additional fish-eye lens attached to the camera. In this paper we discuss the calibration of our setup and the implementation of selected tone mapping operator (TMO) allowing satisfactory display of HDR images on the mobile device screen. Built-in cameras on mobile devices do not generally capture the HDR image. In this work, we describe an algorithm for capturing an HDR image on the Android platform. Using an optimization method we are able to acquire the camera response curve needed in the reconstruction of the HDR image from multiple snapshots. Subsequent HDR panoramas represent radiance sphere-maps of the incoming light from an environment. These radiance sphere-maps are useful in realistic image synthesis to illuminate the objects in the 3D scenes.

Kurtosis and skewness at pixel level as input for SOM networks to iris recognition on mobile devices

The increasing popularity of smartphones amongst the population laid the basis for a wide range of applications aimed at security and privacy protection. Very modern mobile devices have recently demonstrated the feasibility of using a camera sensor to access the system without typing any alphanumerical password. In this work, we present a method that implements iris recognition in the visible spectrum through unsupervised learning by means of Self Organizing Maps (SOM). The proposed method uses a SOM network to cluster iris features at pixel level. The discriminative feature map is obtained by using RGB data of the iris combined with the statistical descriptors of kurtosis and skewness. An experimental analysis on MICHE-I and UBIRISv1 datasets demonstrates the strengths and weaknesses of the algorithm, which has been specifically designed to require low processing power in compliance with the limited capability of common mobile devices.

Validation of a Mobile Device for Acoustic Coordinated Reset Neuromodulation Tinnitus Therapy.

Sound-based tinnitus intervention stimuli include broad-band noise signals with subjectively adjusted bandwidths used as maskers delivered by commercial devices or hearing aids, environmental sounds broadly described and delivered by both consumer devices and hearing aids, music recordings specifically modified and delivered in a variety of different ways, and other stimuli. Acoustic coordinated reset neuromodulation therapy for tinnitus reduction has unique and more stringent requirements compared to all other sound-based tinnitus interventions. These include precise characterization of tinnitus pitch and loudness, and effective provision of patient-controlled daily therapy signals at defined frequencies, levels, and durations outside of the clinic. The purpose of this study was to evaluate an approach to accommodate these requirements including evaluation of a mobile device, validation of an automated tinnitus pitch-matching algorithm and assessment of a patient's ability to control stimuli and collect repeated outcome measures. The experimental design involved direct laboratory measurements of the sound delivery capabilities of a mobile device, comparison of an automated, adaptive pitch-matching method to a traditional manual method and measures of a patient's ability to understand and manipulate a mobile device graphic user interface to both deliver the therapy signals and collect the outcome measures. This study consisted of 5 samples of a common mobile device for the laboratory measures and a total of 30 adult participants: 15 randomly selected normal-hearing participants with simulated tinnitus for validation of a tinnitus pitch-matching algorithm and 15 sequentially selected patients already undergoing tinnitus therapy for evaluation of patient usability. No tinnitus intervention(s) were specifically studied as a component of this study. Data collection involved laboratory measures of mobile devices, comparison of manual and automated adaptive tinnitus pitch-matching psychoacoustic procedures in the same participant analyzed for absolute differences (t test), variance differences (f test), and range comparisons, and assessment of patient usability including questionnaire measures and logs of patient observations. Mobile devices are able to reliably and accurately deliver the acoustic therapy signals. There was no difference in mean pitch matches (t test, p > 0.05) between an automated adaptive method compared to a traditional manual pitch-matching method. However, the variability of the automated pitch-matching method was much less (f test, p < 0.05) with twice as many matches within the predefined error range (±5%) compared to the manual pitch-matching method (80% versus 40%). After a short initial training, all participants were able to use the mobile device effectively and to perform the required tasks without further professional assistance.

Applying Augmented Reality to a Mobile-Assisted Learning System for Martial Arts using Kinect Motion Capture

In this study, to learn the routine of Tantui, a branch of martial arts was taken as an object of research. Fitts' stages of motor learning and augmented reality (AR) were applied to a 3D mobile-assisted learning system for martial arts, which was characterized by free viewing angles. With the new system, learners could rotate the viewing angle of an image freely in a three-dimensional space, zoom in or zoom out the image, and adjust its playback speed. The experimental participants were divided into two groups, i.e., the experimental group (EG) and the control group (CG). The same coach instructed the EG through the 3D mobile-assisted learning system with AR capability while the CG was taught through a 2D video-based learning system on common mobile devices. Finally, a one-way ANOVA was performed to investigate the differences in learning performance between the two groups. Also, questionnaire surveys and interviews were conducted to understand whether the 3D free viewing angle of AR was helpful to cognitive learning of motor skills, learning motivation, learning satisfaction, and conditions of technology acceptance. It is expected that this findings herein will contribute to mobile-assisted learning and martial arts teaching in the future.

A Secure Service Oriented Architecture Based Mobile SDI Model for Mineral Resources Management in India

Economic growth rate of any country largely depends on the development of mineral sector. Then, the level of technology employed for meeting the extraction conditions must meet environmental norms. Exploration of mineral resources in India and mining is a ready application for technologies for ensuring productivity and efficiency. Use of Geographical Information System (GIS) in mineral resources management is of great help, where each stakeholder can access, use and exchange spatial and nonspatial data for social, economic and environmental activities. With development of communication and information technology, integration of heterogeneous repository of data from the different data sources can be achieved by using Spatial Data Infrastructure (SDI). At the present scenario, the dynamic updating of data, data analysis, data visualisation and data uploading are lacking in most of the information system in GIS environment. For real time integrated mineral resources information monitoring, it is essential to integrate information system with geospatial database. Visualisation of integrated mineral resources information is possible on the desktop environment with the help of GIS. But it is not sufficient for updating the data in dynamic environment. It may be possible to perform dynamic tasks like update, analysis and visualisation of mineral resources information through the mobile devices with the modern high end mobile communication infrastructure. It makes it possible and very useful to take the decision at anytime from anywhere in the world. The data repositories can also be timely updated with the help of common mobile devices from any location, of course with security measure in place. In the present work, a three tier secure framework has been proposed for the mobile based SDI Model for the better management of mineral resources information infrastructure with the use of mobile devices by the integration of geospatial database. The interaction between the various services in the proposed Model has been modeled by using Unified Modeling Language (UML) use case and activity diagrams.

Mobile work: Ergonomics in a rapidly changing work environment.

Places of work have been completely transformed by innovations in mobile work tools and ever-present access to internet data. This article characterizes use patterns and provides preliminary considerations for productive and comfortable use of common mobile devices. Two surveys described trends in mobile work. In the first, ergonomics professionals who oversee programs reported common mobile devices, their users and what data is accessed. The second, an end user survey, explored common activities performed on mobile devices, duration of use and locations where mobile work is common. The survey results provide a baseline data point for the status of mobile work in early 2014. Research indicates that additional risks have been introduced to the neck, thumbs and hands when using mobile devices. Possible trends regarding device use and work locations emerge. Intervention studies provide some direction for the practitioner. Practical strategies are outlined to reduce exposure intensity and duration. Contemporary mobile work presents tremendous change and opportunity for ergonomists and researchers to keep pace with fitting the changing models of work to the person. Continued research is needed on current mobile device use patterns to better understand ergonomic risk exposure in this rapidly changing realm.

A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices.

In recent years, the incidence of skin cancer cases has risen, worldwide, mainly due to the prolonged exposure to harmful ultraviolet radiation. Concurrently, the computer-assisted medical diagnosis of skin cancer has undergone major advances, through an improvement in the instrument and detection technology, and the development of algorithms to process the information. Moreover, because there has been an increased need to store medical data, for monitoring, comparative and assisted-learning purposes, algorithms for data processing and storage have also become more efficient in handling the increase of data. In addition, the potential use of common mobile devices to register high-resolution images of skin lesions has also fueled the need to create real-time processing algorithms that may provide a likelihood for the development of malignancy. This last possibility allows even non-specialists to monitor and follow-up suspected skin cancer cases. In this review, we present the major steps in the pre-processing, processing and post-processing of skin lesion images, with a particular emphasis on the quantification and classification of pigmented skin lesions. We further review and outline the future challenges for the creation of minimum-feature, automated and real-time algorithms for the detection of skin cancer from images acquired via common mobile devices.