Modern Smartphones Research Articles

Clinical validation of a novel smartphone application for measuring visual acuity

The rise of telemedicine in the wake of the COVID-19 pandemic has posed challenges for ophthalmologists and optometrists, as there are hardly any smartphone applications that meet high accuracy requirements for obtaining visual acuity (VA) in clinical use. The application vision.app is a novel smartphone app that adjusts the angular size of the optotype based on the face-device distance in real time. The high spatial resolution of modern smartphone screens facilitates measurements at a handheld distance, which makes it particularly valuable for use in pediatric populations which struggle to focus on distant stimuli.

Refresh Rate and Graphical Benchmarks for Mobile VR Application Development

Virtual reality (VR) technology is quickly becoming more accessible to the general public due to the availability and capabilities of modern smartphone devices. However, such mobile devices are not as powerful as high-end desktop systems where VR is mostly established. Running demanding VR apps leads to performance issues such as lag, excessive heat, and fast battery drainage. To avoid these problems, software factors must be optimised. The user evaluation (N=51)(N=51) involved presenting multiple VR scenes (with varying frame rates), requiring participants to judge which scenes felt smooth; the results indicate that anything below 50 FPS was tolerable at best and nauseating at worst. To also measure the performance impact of various software settings, benchmarks were conducted on different smartphones. The results highlight the effects when varying the number of displayed on-screen objects, as well as outlining which settings should be avoid when specifically targeting mobile VR platforms. Heat and battery life were found to be non-issues at recommended performance levels. The proposed work established valuable guidelines which can be helpful for real time applications in development time reduction and complexity simplification from graphical and refresh rate optimization perspectives.

A practical approach based on Bluetooth Low Energy and Neural Networks for indoor localization and targeted devices’ identification by smartphones

With the constant development of innovative technologies and the resulting growth of new services available on the market, applications that aim to control devices employing smartphones include more extensive selection menus increasingly. This condition can lead to non-intuitive use of the system. As a result, the user may reap an adverse experience while practicing these applications. An indoor tracking system must create a control method with a dynamic user interface and estimate the device pointed to by a smartphone. The use of Bluetooth Low Energy (BLE) and the fingerprinting technique, based on Received Signal Strength Indication (RSSI) values, can be a feasible solution for indoor localization. Furthermore, to obtain ever more precise indoor localization systems, there is continuous research on artificial neural networks’ employment as they adapt more quickly to changes in the RSSI values. This paper proposes a new approach to control devices through smartphones based on the joint application of BLE, fingerprinting, and neural networks. The system offers a dynamic user interface that changes according to the target device through sensors commonly located in modern smartphones.

Adaptive Algorithm for Gait Segmentation Using a Single IMU in the Thigh Pocket

Gait segmentation may help monitoring the evolution of patients during rehabilitation treatment through the analysis of properly defined metrics. Many algorithms detect gait events using Inertial Measurement Units (IMUs). However, most of them require the IMUs to be attached to the body, or to assistive devices. Often individuals must go to a gait laboratory which owns specialized equipment so that gait analysis can be performed. IMUs are present in modern smartphones. An IMU<inline-formula> <tex-math notation="LaTeX">$/$ </tex-math></inline-formula>smartphone carried in the pocket during daily activities could allow the analysis of much more data in a more comfortable setting. We address the detection of four gait events: Heel-Strike (HS), Flat-Foot (FF), Heel-Off (HO) and Toe-Off (TO) using a single noisy IMU attached to a smartphone placed inside the thigh pocket. Gait was modelled as a four-state left-right Hidden Markov Model (HMM) whose observations follow multivariate Gaussian distributions. The decoding in post-processing was performed with a modified Viterbi decoder that accounts for a rule-based (RB) detection of TO. To validate the approach, experiments were performed in nine subjects with no gait abnormalities. Our algorithm obtained median F1-Scores <inline-formula> <tex-math notation="LaTeX">$\geq0.955$ </tex-math></inline-formula> for all events in intra-subject evaluation and median F1-Scores <inline-formula> <tex-math notation="LaTeX">$\geq0.757$ </tex-math></inline-formula> in inter-subject evaluations (with fast training and with a populational model). It demonstrated high generalization capability in our dataset and competitive performances when compared to other algorithms using high-quality IMUs attached to the body. This work is a step towards accurate and refined gait segmentation using a smartphone carried in the pocket.

Colocated MIMO antenna pair based on modal orthogonality for 5G terminal applications

AbstractIn this letter, a novel colocated 2 × 2 multiple‐input multiple‐output (MIMO) antenna pair has been proposed. The pair consists of a monopole antenna (J‐source) and a slot antenna (M‐source) integrated at the same location on the terminal ground plane. The inherent orthogonality between the electric source (J) and magnetic source (M) has been exploited to isolate the antenna elements from each other without deploying any additional decoupling component. The proposed 2 × 2 MIMO antenna pair is extended to an 8 × 8 MIMO antenna design for terminal applications. The measured peak isolation between the antenna elements is 25 dB and the impedance bandwidth of the antennas is above 200 MHz with a 3 dBi peak gain, and the envelope correlation coefficient is less than 0.1. The design is proposed for 5G applications operating at the 3.5 GHz band and is a strong candidate for modern smartphone applications.

Data mining of IoT based sentiments to classify political opinions

ABSTRACT In recent years, an exponential increase in the usage of social network services has been observed. These community services are typically used through different applications including personal computers, multiple applications of modern smartphones and wearable technologies. Proper identification and separation of different languages text, topic-based classification of text and classification of active users based on their published comments and posts are major challenges. In this research, our primary focus is to deal with English text collected through different IoT applications to analyse posts/comments to categorise people’s opinion in politics. We have developed an IoT framework model for collecting data from social media especially Facebook, preprocessed and clean data to be used for analysis, and separation of data based on different languages. Sentiment analysis techniques are used to detect polarisation of the individual user. The proposed system clustered IoT individuals based on their comments and posts and successfully detected political polarisation. The proposed approach obtained encouraging results with a precision of 66.7%, a recall of 71.4%, and an F-measure of 69.0% in the case of annotated data of 50 users and a precision of 75.0%, a recall of 87.1%, and F-measure of 80.6% in the case of annotated data of 100 users.

Using Smartphones to Prevent Cross-Functional Team Knowledge Hiding: The Impact of Openness &amp; Neuroticism

The study examines the behaviour of two characteristic traits on knowledge retention, mediated using modern smartphone usage in the corporate environment of cross-functional teams. For the investigation, a self-made model was applied that associates the personality, focusing on the traits of neuroticism and openness towards new concepts and statistically evaluates the effect of increased technological usage. A dataset of 101 individuals working in cross-functional teams was used to test the model. A total of four main hypotheses were developed and statistically tested. Exalted presence of openness towards new experiences was evaluated as a critical factor leading to decreasing knowledge retention. Furthermore, although it was deducted, that neuroticism could not be verified as a knowledge limiting factor in the applied model, smartphone usage could drastically reduce its potentially dangerous factors by using efficient and short communication channels, reaffirmation and commendation by higher-ups and colleagues alike to bolster the missing self-esteem and self-consciousness of neurotic individuals. In contrast, openness was not connected significantly to deliberate knowledge hiding, as individuals incorporating this trait presumably tend to use technological tools of all kinds inherently due to their eagerness to experiment.

Color as a Trigger: the Effect of Chromatic Characteristics of Touch Buttons on the Motivation

The object of the study is modern smartphone users, the subject is the influence of chromatic characteristics of touch buttons on the motivation. The purpose of the study was to experimentally test the hypothesis that various chromatic characteristics of buttons that a person sees on a touch screen (their brightness, tone, saturation) can act as visual triggers and have a noticeable effect on the motivation to touch them. The experiment involved 48 people with normal color vision (24 men and 24 women) aged 19 to 21 years. The color stimuli of the experiment were developed on the basis of the PCCS color system (Practical Color Coordinate System) and included 52 shades. The analysis evaluated the frequency of occurrence of (1) individual colors; (2) groups of colors; (3) colors of a certain tone; (4) the probability of choosing a certain color out of two. The frequency of occurrence was assessed using procedures and methods of visual statistics. To estimate the probability of choosing a certain shade from two, the method of associative rules was used. The analysis showed that the most motivating are the shades with high saturation of four tones – bright blue and yellow, green and red colors. Women are more motivated by red and red-purple shades, men – by yellow, orange, green and green-blue. At the same time, the strategy of choosing one shade out of two in a pair is more predictable for women than for men. The obtained data have a wide application potential. They can be used in the design of websites, web applications, interactive educational materials, as well as training programs for users with different socio-demographic characteristics.

Cross-sectional study to analyse the pattern of smartphone usage among adolescents in urban slum of a metropolitan city

Background: Adolescents are defined as young people between the ages of 10 to 19 years. Smartphone is a term used to distinguish mobile phone with advanced features from basic feature phones. Modern smartphones currently include all the features of a laptop, including web browsing, Wi-Fi and 3rd-party apps etc. Indian teens are currently driving the smartphone market in India.Methods: A cross-sectional study was done in an urban slum of a metropolitan city.100 adolescents aged between 10 to 19 years were assessed using pre-structured questionnaire and prior informed parental consent and assent were taken. The questionnaire included questions about socio-demographic profile and questions pertaining to the use of smartphones in relation to time and frequency and regarding use of internet and games on the phone by the adolescents.Results: Among the 100 participants, mean age was 14.29 years. 93% of the participants were using smartphones, 48.4% were using smartphones since 1 to 3 years and 46.2% used phones between 1 to 3 hours daily. 96.7% used internet on the phone, 72.04% participants have social media accounts and 64.5% played games on phones. The most relevant smartphone function was social networking.Conclusions: Results show increasing dependency of adolescents on smartphones. Although smartphones offer several conveniences in our life, we need to be aware of the negative effects of smartphone use, the most concerning aspect being smartphone addiction.

A reliable and unobtrusive approach to display area detection for imperceptible display camera communication

Object framework detection has been extensively studied in computer vision for applications such as document digitization and whiteboard scanning. Similarly, it is essential for display-camera communication systems, particularly when imperceptible data modulation is employed to enable simultaneous video playback and data transmission. Reliable and accurate localization of the encoded display area is critical for data demodulation and decoding. However, existing systems typically adapt established methods developed for other applications that do not meet the system requirements for high-rate data transmission.In this article, we propose a novel method for display area detection in the camera images by embedding a new localization marker into the display corners. While the localization marker is less obtrusive than conventional fiducial markers, our detection algorithm demonstrated excellent reliability regardless of the display content and background, according to simulation and experimental results. In addition, the detector achieved subpixel accuracy and real-time performance with modern smartphones.

Comparison of braking properties of selected vehicle with different methods

The aim of this article was to perform a practical measurement of the braking properties of a selected Škoda Yeti vehicle on a wet and dry asphalt road and then evaluate and compare the measured data using a decelerograph and a mobile application mSTK. It is a new application for measuring driving dynamics, which was developed for the needs of technical inspection stations in Slovakia. The subject was the intensive braking of a Škoda Yeti passenger car with a fully compressed service brake from different velocities and on different surfaces. A total of 18 measurements were performed, of which 9 on the dry and 9 on the wet surface. This is a new alternative method for recording vehicle driving dynamics. The developed application in conjunction with modern smartphones can thus compete with the current decelographer, both in terms of technical parameters, measurement accuracy (total average deviation 2.11%) and ease of use. The results and data processed in this way are presented in the final part of the paper.

Testing a Drop of Liquid Using Smartphone LiDAR

We present the first system to determine fluid properties using the LiDAR sensors present on modern smartphones. Traditional methods of measuring properties like viscosity require expensive laboratory equipment or a relatively large amount of fluid. In contrast, our smartphone-based method is accessible, contactless and works with just a single drop of liquid. Our design works by targeting a coherent LiDAR beam from the phone onto the liquid. Using the phone's camera, we capture the characteristic laser speckle pattern that is formed by the interference of light reflecting from light-scattering particles. By correlating the fluctuations in speckle intensity over time, we can characterize the Brownian motion within the liquid which is correlated with its viscosity. The speckle pattern can be captured on a range of phone cameras and does not require external magnifiers. Our results show that we can distinguish between different fat contents as well as identify adulterated milk. Further, algorithms can classify between ten different liquids using the smartphone LiDAR speckle patterns. Finally, we conducted a clinical study with whole blood samples across 30 patients showing that our approach can distinguish between coagulated and uncoagulated blood using a single drop of blood.

EmoTouch Web – a web-based system for continuous real time studies with smartphones, tablets, and desktop computers

emoTouch Web is a new web-based system for designing, conducting and evaluating continuous real-time studies in all disciplines dealing with time-bound phenomena, such as music, theatre, dance, film, commercials, lectures, speeches or sport events. The system uses modern web and network technologies to easily turn every modern smartphone, tablet, or computer into a flexible and reliable research tool for continuous response studies in laboratory, online and live settings. Even the participant’s own devices can be used easily (“Bring-Your-Own-Device”, BYOD). The system contains coordinated tools for the graphical and numerical review, evaluation and analysis of the collected real time data in longitudinal and cross-section. emoTouch Web may be used free of charge for scientific purposes.

Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron

Applied sensing technology has made it possible for human beings to experience a revolutionary aspect of the science and technology world. Along with many other fields in which this technology is working wonders, human locomotion activity recognition, which finds applications in healthcare, smart homes, life-logging, and many other fields, is also proving to be a landmark. The purpose of this study is to develop a novel model that can robustly handle divergent data that are acquired remotely from various sensors and make an accurate classification of human locomotion activities. The biggest support for remotely sensed human locomotion activity recognition (RS-HLAR) is provided by modern smartphones. In this paper, we propose a robust model for an RS-HLAR that is trained and tested on remotely extracted data from smartphone-embedded sensors. Initially, the system denoises the input data and then performs windowing and segmentation. Then, this preprocessed data goes to the feature extraction module where Parseval’s energy, skewness, kurtosis, Shannon entropy, and statistical features from the time domain and the frequency domain are extracted from it. Advancing further, by using Luca-measure fuzzy entropy (LFE) and Lukasiewicz similarity measure (LS)–based feature selection, the system drops the least-informative features and shrinks the feature set by 25%. In the next step, the Yeo–Johnson power transform is applied, which is a maximum-likelihood-based feature optimization algorithm. The optimized feature set is then forwarded to the multilayer perceptron (MLP) classifier that performs the classification. MLP uses the cross-validation technique for training and testing to generate reliable results. We designed our system while experimenting on three benchmark datasets namely, MobiAct_v2.0, Real-World HAR, and Real-Life HAR. The proposed model outperforms the existing state-of-the-art models by scoring a mean accuracy of 84.49% on MobiAct_v2.0, 94.16% on Real-World HAR, and 95.89% on Real-Life HAR. Although our system can accurately differentiate among similar activities, excessive noise in data and complex activities have shown an inverse effect on its performance.

Development of a Smartphone Application Serving Pavement Management Engineers

Determining the presence and location of pavement defects is vital to the timely maintenance of road infrastructure. Many applications exist for the purpose of tracking user-reported pavement distresses and roadway hazards, but few leverage the capabilities of modern smartphones. This pilot study aims to establish proof of concept that a custom-built smartphone application possesses the ability to utilize onboard smartphone sensors to collect global positioning system (GPS) and vibrational data with accuracy and sensitivity suitable for detecting pavement defects. An application was developed for Android and iOS that utilizes smartphone GPS, accelerometer, and gyroscope sensors to sample and log location, vibrational, and rotational data. The datasets obtained by the application are used to quantitatively establish the application’s ability to detect abnormalities in sampled data when in the presence of pavement defects or abnormal road conditions versus a baseline noise margin under no pavement defects. Additionally, the true sampling frequencies of both platforms are established to compare against user-specified sampling frequencies to determine true sampling performance. Furthermore, the datasets obtained from the Android and iOS platforms are compared to establish that there are differences in sensor hardware used that affect the sensor responses. Results from analysis of the datasets support the conclusion that the application successfully samples smartphone sensors, which can be used to detect defects in pavement when mounted to a vehicle windshield, as well as having the ability to provide relatively accurate and precise location data, given commercial smartphone GPS accuracy limitations.

Car accidents, smartphone adoption and 3G coverage

This paper examines the relationship between smartphone use by drivers and traffic accidents in California between 2001 and 2013. In order to estimate smartphone use, we first show that widespread adoption of modern smartphones began in 2009 after the release of the iPhone 3G and T-Mobile G1. This information is combined with annual 3G coverage maps that are constructed from cellular tower information in a machine learning framework. In a difference-in-differences framework, we estimate the combined effect of smartphone adoption and 3G coverage along quarter-mile road segments. Controlling for census tract population density, road and year fixed effects, Poisson regression results show that there is a statistically significant increase in the traffic accident rate along a road segment when smartphone use becomes possible. Our preferred specification suggests smartphones caused accident rates to increase by 2.9 percent, resulting in 3500 additional accidents per year in California. Event study results rule out the possibility that our smartphone treatment is capturing a trend in the accident rate. The results are robust to a variety of specifications and consistent with individual-level studies showing that cell phone use leads to lower driving quality. The findings also provide guidance for policies aimed at reducing cell phone related accidents and distracted driving.

Evaluation of Static Autonomous GNSS Positioning Accuracy Using Single-, Dual-, and Tri-Frequency Smartphones in Forest Canopy Environments.

Determining the current position in a forest is essential for many applications and is often carried out using smartphones. Modern smartphones now support various GNSS constellations and multi-frequency analyses, which are expected to provide more accurate positioning. This study compares the static autonomous GNSS positioning accuracy under forest conditions of four multi-frequency multi-constellation smartphones as well as six single-frequency smartphones and a geodetic receiver. Measurements were carried out at 15 different study sites under forest canopies, with 24 measurements lasting approximately 10 min each taken for the 11 GNSS receivers. The results indicate that, on average, multi-frequency smartphones can achieve a higher positioning accuracy. However, the accuracy varies greatly between smartphones, even between identical or quasi-identical tested smartphones. Therefore, no accuracy should be generalised depending on the number of usable frequencies or constellations, but each smartphone should be considered separately. The dual-frequency Xiaomi Mi 10 clearly stands out compared with the other smartphone with a DRMS of 4.56 m and has a 34% lower absolute error than the best single-frequency phone.

A New Forensic Video Database for Source Smartphone Identification: Description and Analysis

In recent years, the field of digital imaging has made significant progress, so that today every smartphone has a built-in video camera that allows you to record high-quality video for free and without restrictions. On the other hand, rapidly growing internet technology has contributed significantly to the widespread use of digital video via web-based multimedia systems and mobile smartphone applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital videos have become affordable nowadays, security issues have become threatening and spread worldwide. One of the security issues is identifying source cameras on videos. There are some new challenges that should be addressed in this area. One of the new challenges is individual source camera identification (ISCI), which focuses on identifying each device regardless of its model. The first step towards solving the problems is a popular video database recorded by modern smartphone devices, which can also be used for deep learning methods that are growing rapidly in the field of source camera identification. In this paper, a smartphone video database named Qatar University Forensic Video Database (QUFVD) is introduced. The QUFVD includes 6000 videos from 20 modern smartphone representing five brands, each brand has two models, and each model has two identical smartphone devices. This database is suitable for evaluating different techniques such as deep learning methods for video source smartphone identification and verification. To evaluate the QUFVD, a series of experiments to identify source cameras using a deep learning technique are conducted. The results show that improvements are essential for the ISCI scenario on video.

DIGITAL MEASURING COMPLEX BASED ON ARDUINO NANO 33 BLE SENSE

The laboratory and educational research works are an important component of modern school education and STEM education. The use of digital measuring systems can significantly save time on measurement and mathematical processing of their results. A modern smartphone can become a kind of digital measuring system after installing the Arduino Science Journal or Phyphox (Physical Phone Experiments) mobile application on it. These applications allow you to read, write and analyze data from sensors available on a particular smartphone model, and from external sensors that are part of a digital measuring system built using Arduino. The newest compact platform of the Arduino family is the Arduino Nano 33 BLE Sense, which contains a built-in group of sensors. The compactness and low power consumption on the one hand, and the capabilities of mobile applications Arduino Science Journal and Phyphox – on the other hand, allow you to create an autonomous portable digital measuring system with wireless data transmission, which creates additional opportunities and facilities for laboratory and educational research. The possibilities of the proposed digital measuring complex are investigated on the example of laboratory work “Determination of capacitance and energy of a charged capacitor”. As a result of the reproduction of this laboratory work, a fairly close coincidence of the calculated and experimental parameters was obtained. Further development of research character for the considered laboratory work is offered.

Approach for road surface quality monitoring based on intellectual analysis of smartphone’s sensors data

Road safety directly depends on the quality of roads. And despite the constant increase in the length of roads, monitoring of their quality is still carried out by the efforts of employees of road maintenance organizations. Such an approach, as well as the high cost of evaluation work, lead to untimely resolution of the identified problems. Moreover, the intensity of traffic on the roads, as well as the speed of vehicles and their total mass, are steadily increasing. That is why solutions to automate this process are being actively researched and implemented in practice. This paper presents an original approach to monitoring the quality of road surface, the key difference of which is the construction of a monitoring system based on a modern smartphone that has a number of significant sensors, the ability to take high-resolution photos and videos, impressive computing capabilities, and the presence of high-speed data transfer interfaces.