Smartphone Models Research Articles

Oscillometric blood pressure measurements on smartphones using vibrometric force estimation

This paper proposes a smartphone-based method for measuring Blood Pressure (BP) using the oscillometric method. For oscillometry, it is necessary to measure (1) the pressure applied to the artery and (2) the local blood volume change. This is accomplished by performing an oscillometric measurement at the finger’s digital artery, whereby a user presses down on the phone’s camera with steadily increasing force. The camera is used to capture the blood volume change using photoplethysmography. We devised a novel method for measuring the force applied of the finger without the use of specialized smartphone hardware with a technique called Vibrometric Force Estimation (VFE). The fundamental concept of VFE relies on a phenomenon where a vibrating object is dampened when an external force is applied on to it. This phenomenon can be recreated using the phone’s own vibration motor and measured using the phone’s Inertial Measurement Unit (IMU). A cross device reliability study with three smartphones of different manufacturers, shape, and prices results in similar force estimation performance across all smartphone models. In an N = 24 proof of concept study of the BP measurement, the smartphone technique achieves a mean absolute error of 9.21 mmHg and 7.77 mmHg of systolic and diastolic BP, respectively, compared to an FDA approved BP cuff. The vision for this technology is not necessarily to replace existing BP monitoring solutions, but rather to introduce a downloadable smartphone software application that could serve as a low-barrier hypertension screening measurement fit for widespread adoption.

Smartphone Aided Funduscopy in Albino Rabbits

Aims: The methodology, utility and clinical feasibility of using a smartphone camera to obtain images of ocular fundus of albino rabbits with ocular hypertension and normotension were studied. A comparative analysis of fundus images obtained from two popularly used smartphone models, android and iOS device was performed on 36 adult albino rabbits. Study Design: A cross-sectional study involving two cohorts of ocular hypertensive and normotensive albino rabbits. Place and Duration of Study: Department of Veterinary Surgery and Radiology, College of Veterinary and Animal Sciences, Mannuthy for an eight-week period (2023). Methodology: A total of 36 adult albino rabbits (18 males and 18 females; age range 6- 8 months of age) with ocular normotensive eyes and unilateral ocular hypertension-induced models were used. The conscious animals were gently restrained in an acrylic restrainer. Instillation of topical mydriatic solution (tropicamide 0.8% and phenylephrine 5%) facilitated visual examination and enhanced field of view along with auxiliary condensing lenses of 20 and 40 Diopters. A comparison of fundus images obtained from two popularly used smartphone models, android and iOS devices was performed on 36 adult albino rabbits. Results: Fundus images comprising the optic nerve head and peripapillary retina were obtained with adequate spatial resolution and detail. The variation between optic disc of ocular hypertensive eye revealed mild edema and cupping of optic disc. Normal merangiotic retina and optic disc were clearly obtained in normotensive rabbit eyes. The iOS device used in this case yielded better images in resolution and field of view compared to the android version used in this case. Conclusion: Smart phone funduscopy offered good detail in contrast and resolution even in amelanotic eyes of albino rabbits using inbuilt technology without the need for an external adapter or sophisticated additional software. It is an animal-friendly futuristic solution for digital documentation, telemedicine, teaching and clinical prognosis formulation.

Analysis and Comparison of Social Media Applications Using Forensic Software on Mobile Devices

With the integration of mobile systems into daily life, social media applications used especially on Android and iOS platforms contain a significant amount of sensitive information. Social media applications on mobile systems have huge personal and sensitive content. Therefore, it is important to design effective techniques for forensic analysis of social media applications and to detect personal data. In this research, three different paid mobile forensic software and 4 different brands and models of smartphones with different operating systems were used and analyzed. The study shows that private messages, e-mails, time information, shared data, location and time information, and other personal data can be obtained by a forensic expert who performs an examination, and it is seen that one software can access the deleted data, but another software cannot access it. In proportion to the technology used in today’s world, mobile forensics incidents are increasing day by day, and a competitive environment is created among the software used to illuminate these incidents. With this competition, software companies dealing with forensic informatics are trying to obtain different data to illuminate forensic events that may occur due to the active use of social media accounts with the developing technology, and software that does not meet the needs in the face of this situation remains in the background. The criminal elements in the investigation areas of mobile forensics differ daily, and the scope of crimes in the virtual environment is expanding with the developing technology. Therefore, mobile forensic analysis applications should be successful in social media applications other than standard data.

Mozart: A Mobile ToF System for Sensing in the Dark Through Phase Manipulation

Sensing in low-light and dark environments has a wide range of applications. However, existing sensing technologies suffer several major challenges, such as excessive noise and low resolution. In this work, we propose Mozart - a new mobile sensing system that leverages off-the-shelf Time-of-Flight (ToF) depth cameras to generate high-resolution and rich-in-texture maps for applications in dark scenarios. The design of Mozart is based on our key observation that the phase components of ToF measurements can be manipulated to expose texture information. Through in-depth analysis of the physical reflection model, we show that the textures can be exposed and enhanced using highly compute-efficient phase manipulation functions. Moreover, by exploiting the physics texture models, we propose an autoencoderbased unsupervised learning approach that can automatically learn efficient representations from phase components to generate high-resolution maps. We implemented Mozart on several Android smartphone models and an edge testbed with standalone ToF camera platforms for various applications in the dark. The results show that Mozart can work in real time and delivers significant improvement over existing sensing technologies. The demo video at https:// www.youtube.com/watch?v=L_sxyTZxIdU shows that Mozart offers a low-cost, highperformance sensing technology for nextgeneration applications in the dark.

Characterizing smartphone capabilities for seismic and structural monitoring

As seismic events continue to pose significant threats to urban infrastructure, leveraging smartphones equipped with accelerometers for real-time monitoring has gained prominence. To ascertain the reliability and sensitivity of smartphone-based measurements, an in-depth characterization of their response is essential. This article presents a thorough characterization of the performance of typical accelerometers installed on three distinct smartphone models. For this, a novel experimental apparatus has been developed to conduct a comparative study involving three different smartphones against a reference accelerometer. We determine each accelerometer’s transfer functions for Fourier frequencies 0.1–40 Hz, evidencing main differences and demonstrating a higher sensitivity than expected. Possible implementation in future distributed networks of heterogeneous and synchronized sensors, capable of independently generating and validating timely alerts in particular seismic events, are also discussed.

Examining the relationship between smartphone characteristics and the prevalence of hand discomfort among university students

BackgroundStudents are among the groups that use smartphones for long periods throughout the day and night. Therefore, this study aimed to examine the relationship between smartphone characteristics and the prevalence of hand discomfort among university students.MethodsThis study included 204 university students, selected based on their willingness to participate and inclusion criteria. Participants reported hand pain and discomfort by completing the Cornell Hand Discomfort Questionnaire (CHDQ). Personal information was collected through a demographic questionnaire. Smartphone characteristics were obtained from the Internet based on the smartphone model self-reported by students.ResultsAccording to the Cornell questionnaire, 59.3% of students reported experiencing discomfort in their right hand, while 38.2% reported discomfort in their left hand due to smartphone use. Furthermore, 36.3% of students reported experiencing pain in two or more regions on their right hand, while 20.1% reported pain in two or more areas on their left hand. More than half of the students in the right hand (53.5%) and more than one-third (33.3%) in the left hand obtained pain scores of more than 1.5. The chi-square test indicated a statistically significant relationship between the weight of the smartphone and the prevalence of discomfort in the right hand (χ2 = 4.80, p = 0.03). Furthermore, a statistically significant relationship was found between the discomfort or pain scores experienced in both hands and the number of painful areas in those hands (right hand: χ2 = 219.04, p = 0.00; left hand: χ2 = 213.13, p = 0.00).ConclusionsSmartphone use can cause discomfort and pain in the hands of university students. The physical characteristics of the smartphone, such as its weight, play a significant role in contributing to right-hand-related pain among students. It is important to consider ergonomic factors in smartphone design and usage to reduce musculoskeletal problems among users, especially students.

An Artificial Neural Network approach to assess road roughness using smartphone-based crowdsourcing data

Monitoring road surface conditions is a crucial task for road authorities to develop effective infrastructure maintenance programs. Despite smartphones have been introduced as cost-effective and real-time solution for this purpose, several challenges must be addressed before their real-world application. This study investigates the utilization of smartphone-based crowdsourcing data and Artificial Neural Networks (ANN) to enhance the precision of road surface condition estimation. Initially, data are collected from four different smartphone models mounted in various vehicles, including vertical acceleration, geographic location, and speed. The root mean square of the vertical acceleration data, along with vehicle speed, is then employed as input features for the ANN, while the true International Roughness Index (IRI) values serve as the corresponding output features. Comparative analysis between ANN and regression models based on statistical metrics such as Mean Squared Error (MSE) and Pearson correlation revealed that ANN outperforms regression models. The obtained MSE and Pearson correlation values for ANN (0.56 and 0.91) surpass those of regression models (0.72 and 0.88). Moreover, results indicated that utilizing crowdsourcing smartphone data yielded superior outcomes compared to using a single smartphone for this purpose.

Evaluation of proudP: A sound-based approach to uroflowmetry.

We sought to assess the performance of the proudP AI algorithm, integrated into a mobile application, in estimating uroflow curves and parameters using recorded urination sounds. A direct comparison was made between the peak flow rate (Qmax), voided volume, and uroflow curves predicted by the proudP algorithm and those obtained through established validation methods. A hardware uroflow simulator replicated uroflow profiles by precisely controlling water flow rates and extracting corresponding sound data. Ten uroflow profiles, representing typical patterns observed in male subjects, were selected. Simulation experiments with proudP were conducted using a standard toilet setup. The uroflow simulator was calibrated to reproduce uroflow profiles, and validation was performed against a Flowmaster uroflowmetry device. Statistical analysis included descriptive summaries, Bland-Altman analysis, and Concordance Correlation Coefficient (CCC) analysis. The proudP accurately captured various uroflow patterns generated by the simulator, with low standard deviations in Qmax predictions and biases near zero. The SDs of voided volume were slightly larger, primarily due to uroflow patterns with extended voiding times. The study validated the accuracy of proudP against in-office uroflowmetry, demonstrating robustness across different smartphone models. proudP proved to be as accurate as in-office uroflowmetry in estimating uroflow rate across various patterns. Its convenience in home monitoring offers patients a means to observe their urination patterns accurately, while enabling healthcare professionals to gain detailed insights remotely. proudP emerges as an essential solution for clinical practice and urological research.

PsySuite: An android application designed to perform multimodal psychophysical testing.

In behavioral sciences, there is growing concern about the inflation of false-positive rates due to the amount of under-powered studies that have been shared in the past years. While problematic, having the possibility to recruit (lots of) participants (for a lot of time) is realistically not achievable for many research facilities. Factors that hinder the reaching of optimal sample sizes are, to name but a few, research costs, participants' availability and commitment, and logistics. We challenge these issues by introducing PsySuite, an Android app designed to foster a remote approach to multimodal behavioral testing. To validate PsySuite, we first evaluated its ability to generate stimuli appropriate to rigorous psychophysical testing, measuring both the app's accuracy (i.e., stimuli's onset, offset, and multimodal simultaneity) and precision (i.e., the stability of a given pattern across trials), using two different smartphone models. We then evaluated PsySuite's ability to replicate perceptual performances obtained using a classic psychophysical paradigm, comparing sample data collected with the app against those measured via a PC-based setup. Our results showed that PsySuite could accurately reproduce stimuli with a minimum duration of 7ms, 17ms, and 30ms for the auditory, visual, and tactile modalities, respectively, and that perceptual performances obtained with PsySuite were consistent with the perceptual behavior observed using the classical setup. Combined with the high accessibility inherently supported by PsySuite, here we ought to share the app to further boost psychophysical research, aiming at setting it to a cheap, user-friendly, and portable level.

Development of 3D-printed universal adapter in enhancing retinal imaging accessibility

BackgroundThe revolutionary technology of smartphone-based retinal imaging has been consistently improving over the years. Smartphone-based retinal image acquisition devices are designed to be portable, easy to use, and cost-efficient, which enables eye care to be more widely accessible especially in geographically remote areas. This enables early disease detection for those who are in low- and middle- income population or just in general has very limited access to eye care. This study investigates the limitation of smartphone compatibility of existing smartphone-based retinal image acquisition devices. Additionally, this study aims to propose a universal adapter design that is usable with an existing smartphone-based retinal image acquisition device known as the PanOptic ophthalmoscope. This study also aims to simulate the reliability, validity, and performance overall of the developed prototype.MethodsA literature review has been conducted that identifies the limitation of smartphone compatibility among existing smartphone-based retinal image acquisition devices. Designing and modeling of proposed adapter were performed using the software AutoCAD 3D. For the proposed performance evaluation, finite element analysis (FEA) in the software Autodesk Inventor and 5-point scale method were demonstrated.ResultsPublished studies demonstrate that most of the existing smartphone-based retinal imaging devices have compatibility limited to specific older smartphone models. This highlights the benefit of a universal adapter in broadening the usability of existing smartphone-based retinal image acquisition devices. A functional universal adapter design has been developed that demonstrates its compatibility with a variety of smartphones regardless of the smartphone dimension or the position of the smartphone’s camera lens. The proposed performance evaluation method generates an efficient stress analysis of the proposed adapter design. The end-user survey results show a positive overall performance of the developed universal adapter. However, a significant difference between the expert's views on the developed adapter and the quality of images is observed.ConclusionThe compatibility of existing smartphone-based retinal imaging devices is still mostly limited to specific smartphone models. Besides this, the concept of a universal and suitable adapter for retinal imaging using the PanOptic ophthalmoscope was presented and validated in this paper. This work provides a platform for future development of smartphone-based ophthalmoscope that is universal.

Possibility of Accessing Information Via Smartphone Applications for People with Visual Impairment: A Field Study

The study aims to determine the most popular smartphone applications that Benghazi residents with visual impairments use to obtain information, as well as the most popular smartphone models that meet their practical and scientific needs. To meet the research objectives, a questionnaire was given to some individuals with visual impairments via websites on the Internet and social media, and the Google Forms program was utilized to build the data that identifies the smartphone applications used to access information from the target group in the study. After the questionnaire was distributed, data was gathered and examined. The study produced several findings, including that the iPhone is the most popular smartphone model that most visually impaired people in Benghazi wish to buy because of its applications' reputation for being the easiest to use regarding information access. The most popular applications used to help people with visual impairments access information on smartphones are screen readers, with a percentage of 84.2%) and (3.5%) using screen magnification applications, while 12.3% use both. However, the high price has contributed to people with visual disabilities using another type, which is Samsung. The necessity of offering ongoing courses to enable individuals with visual impairments to stay up to date with the technological advancements that facilitate information access is one of the recommendations and proposals that will improve the accessibility of information for those with visual impairments through smartphone applications—promoting the use of technology in information centers and educational settings where some individuals with vision impairments are present.

Employing Gamified Crowdsourced Close-Range Sensing in the Pursuit of a Digital Twin of the Earth

Advances in mobile consumer technology, especially in smartphones, have given rise to numerous new crowdsourcing opportunities. With the miniaturization of sensors and the growing number of smartphone models containing a wide range of them, we are now capable of creating digital imitations of real-world objects using everyday mobile devices. In this study, we created four augmented reality (AR) applications utilizing LiDAR sensors that were designed for crowdsourcing the creation of such digital imitations, i.e., digital twins. A user study was undertaken where video recordings, audio recordings, point cloud, and mesh data were collected in forest settings. Each approach was evaluated in terms of the characteristics and quality of the crowdsourced data acquired, as well as the participants’ behavior and experience. Our findings demonstrate that through gamification approaches, we can influence not only the user experience but also the type and quality of the crowdsourced data. Our findings offer guidance on which gamification dynamics and interactions are best suited for several types of crowdsourcing tasks. We also show that the collection of meaningful data is not limited to specific interactions or experiences and suggest that there are multiple routes in gamification design for reaching the desired outcomes.

Internal validation of Automated Visual Evaluation (AVE) on smartphone images for cervical cancer screening in a prospective study in Zambia.

Visual inspection with acetic acid (VIA) is a low-cost approach for cervical cancer screening used in most low- and middle-income countries (LMICs) but, similar to other visual tests, is subjective and requires sustained training and quality assurance. We developed, trained, and validated an artificial-intelligence-based "Automated Visual Evaluation" (AVE) tool that can be adapted to run on smartphones to assess smartphone-captured images of the cervix and identify precancerous lesions, helping augment VIA performance. Prospective study. Eight public health facilities in Zambia. A total of 8204 women aged 25-55. Cervical images captured on commonly used low-cost smartphone models were matched with key clinical information including human immunodeficiency virus (HIV) and human papillomavirus (HPV) status, plus histopathology analysis (where applicable), to develop and train an AVE algorithm and evaluate its performance for use as a primary screen and triage test for women who are HPV positive. Area under the receiver operating curve (AUC); sensitivity; specificity. As a general population screening tool for cervical precancerous lesions, AVE identified cases of cervical precancerous and cancerous (CIN2+) lesions with high performance (AUC = 0.91, 95% confidence interval [CI] = 0.89-0.93), which translates to a sensitivity of 85% (95% CI = 81%-90%) and specificity of 86% (95% CI = 84%-88%) based on maximizing the Youden's index. This represents a considerable improvement over naked eye VIA, which as per a meta-analysis by the World Health Organization (WHO) has a sensitivity of 66% and specificity of 87%. For women living with HIV, the AUC of AVE was 0.91 (95% CI = 0.88-0.93), and among those testing positive for high-risk HPV types, the AUC was 0.87 (95% CI = 0.83-0.91). These results demonstrate the feasibility of utilizing AVE on images captured using a commonly available smartphone by nurses in a screening program, and support our ongoing efforts for moving to more broadly evaluate AVE for its clinical sensitivity, specificity, feasibility, and acceptability across a wider range of settings. Limitations of this study include potential inflation of performance estimates due to verification bias (as biopsies were only obtained from participants with visible aceto-white cervical lesions) and due to this being an internal validation (the test data, while independent from that used to develop the algorithm was drawn from the same study).

A Preliminary Evaluation of the Diagnostic Performance of a Smartphone-Based Machine Learning-Assisted System for Evaluation of Clinical Activity Score in Digital Images of Thyroid-Associated Orbitopathy.

Background: We previously developed a machine learning (ML)-assisted system for predicting the clinical activity score (CAS) in thyroid-associated orbitopathy (TAO) using digital facial images taken by a digital single-lens reflex camera in a studio setting. In this study, we aimed to apply this system to smartphones and detect active TAO (CAS ≥3) using facial images captured by smartphone cameras. We evaluated the performance of our system on various smartphone models and compared it with the performance of ophthalmologists with varying clinical experience. Methods: We applied the preexisting ML architecture to classify photos taken with smartphones (Galaxy S21 Ultra, iPhone 12 pro, iPhone 11, iPhone SE 2020, Galaxy M20, and Galaxy A21S). The performance was evaluated with smartphone-captured images from 100 patients with TAO. Three ophthalmology residents, three general ophthalmologists with <5 years of clinical experience, and three oculoplastic specialists independently interpreted the same set of images taken under a studio environment and compared their results with those generated by the smartphone-based ML-assisted system. Reference CAS was determined by a consensus of three oculoplastic specialists. Results: Active TAO (CAS ≥3) was identified in 28 patients. Smartphone model used in capturing facial images influenced active TAO detection performance (F1 score 0.59-0.72). The smartphone-based system showed 74.5% sensitivity, 84.8% specificity, and F1 score 0.70 on top three smartphones. On images from all six smartphones, average sensitivity, specificity, and F1 score were 71.4%, 81.6%, and 0.66, respectively. Ophthalmology residents' values were 69.1%, 55.1%, and 0.46. General ophthalmologists' values were 61.9%, 79.6%, and 0.55. Oculoplastic specialists' values were 73.8%, 90.7%, and 0.75. This smartphone-based ML-assisted system predicted CAS within 1 point of reference CAS in 90.7% using facial images from smartphones. Conclusions: Our smartphone-based ML-assisted system shows reasonable accuracy in detecting active TAO, comparable with oculoplastic specialists and outperforming residents and general ophthalmologists. It may enable reliable self-monitoring for disease activity, but confirmatory research is needed for clinical application.

The influence of the COVID-19 pandemic on physical activity in Stockholm County - Evidence from time series models of smartphone measured daily steps data spanning over 3 years

BackgroundIt has been reported that physical activity levels decreased during the COVID-19 pandemic. Previous studies often relied on self-reported physical activity, which has low accuracy. Studies based on objectively measured physical activity have had short data collection periods, thereby not allowing the consideration of pre-pandemic levels of physical activity or the influence over the different waves of the pandemic. MethodsIn this study, we utilize smartphone-measured step data from a nonprobability sample in Stockholm County, Sweden, where measures to limit the spread of COVID-19 differed from those in many other countries. The results are based on 522 individuals and 532,739 person-days with step data spanning from 2019 to 2021. Generalized additive models were fitted for each individual, and meta-regression was used to combine the results from individual models. ResultsDaily steps decreased during the first wave but increased during the third wave compared to individual pre-pandemic levels. The decrease in daily steps occurred primarily in young individuals and those with occupations allowing remote work. Individuals of retirement age on the contrary increased their daily steps during the same period. ConclusionsThis study reveal that the influence of the COVID-19 pandemic was temporary and that younger age and the possibility of working from home were associated with a decreasing trend in physical activity.

Context-assisted personalized pedestrian dead reckoning localization with a smartphone

ABSTRACT Pedestrian Dead Reckoning (PDR) plays an important role in multi-sensor fusion of indoor positioning due to its autonomy and continuity advantages. The robustness of PDR significantly impacts indoor positioning accuracy, but various pedestrians and mobility contexts pose challenges for reliable step detection and accurate step length estimation. This paper proposes a context-assisted personalized PDR localization solution to address these challenges. Firstly, by exploiting temporal and frequency domain features, an enhanced step detection method is developed to mitigate false step detection, especially during unfavorable actions of pedestrians. Subsequently, a personalized step length model is proposed, and its parameters are dynamically updated online using other high-precision sensors available within a multi-sensor fusion positioning solution. Moreover, the personalized step length model is further refined using mobility context knowledge. Finally, a novel context-assisted pedestrian velocity model is established for PDR localization to enhance positioning accuracy, particularly when there are changes in mobility contexts. The results demonstrate that the robustness of step detection is improved, and the false detection rate is reduced from 13% to 2%. For various smartphone users, the proposed context-assisted personalized step length model exhibits a relative error of 2.01%, in contrast to 7.06% observed with the traditional flat model. Consequently, the accuracy of walking distance is enhanced from 92.2% to 98.9%, and the PDR localization error is reduced from 2.49 m to 1.63 m. Importantly, the proposed solution exhibits more robust and consistent performance across different pedestrians, smartphone models, and challenging mobility contexts.

Detecting Forged Audio Files Using "Mixed Paste" Command: A Deep Learning Approach Based on Korean Phonemic Features.

The ubiquity of smartphones today enables the widespread utilization of voice recording for diverse purposes. Consequently, the submission of voice recordings as digital evidence in legal proceedings has notably increased, alongside a rise in allegations of recording file forgery. This trend highlights the growing significance of audio file authentication. This study aims to develop a deep learning methodology capable of identifying forged files, particularly those altered using "Mixed Paste" commands, a technique not previously addressed. The proposed deep learning framework is a composite model, integrating a convolutional neural network and a long short-term memory model. It is designed based on the extraction of features from spectrograms and sequences of Korean consonant types. The training of this model utilizes an authentic dataset of forged audio recordings created on an iPhone, modified via "Mixed Paste", and encoded. This hybrid model demonstrates a high accuracy rate of 97.5%. To validate the model's efficacy, tests were conducted using various manipulated audio files. The findings reveal that the model's effectiveness is not contingent on the smartphone model or the audio editing software employed. We anticipate that this research will advance the field of audio forensics through a novel hybrid model approach.

On-site wind speed recovery from smartphone audio: Time domain deep learning approach, laboratory validation and outdoor field test

On-site wind speed information is paramount in various engineering domains. Conventional methods, reliant on expensive equipment and skilled operators, present challenges for practical uses. This study presents a novel time domain approach to recover wind speed from smartphone audio time series using deep learning. Leveraging the ubiquity of smartphones, the proposed approach offers a cost-effective and accessible alternative for wind speed measurement based on audio signals, allowing personnels to obtain on-site wind speed using smartphones. The model is built upon muti-layer architecture of one-dimensional convolutional neural network, and is trained and fine-tuned with high-fidelity wind tunnel laboratory data. The model’s performance is tested across different smartphone models and outdoor environments to assess its generalizability. The presented approach is generally effective, providing an overall accuracy with mean squared error of 0.2 to 0.9 (m/s)2. Although the model’s accuracy diminishes slightly in outdoor settings, it still shows favorable agreement with specialized wind gauges.

New-generation electronic appliances and cardiac implantable electronic devices: a systematic literature review of mechanisms and in vivo studies.

Cardiac implantable electronic device (CIED) functions are susceptible to electromagnetic interference (EMI) from electromagnetic fields (EMF). Data on EMI risks from new-generation electronic appliances (EA) are limited. We performed a systematic literature review on the mechanisms of EMI, current evidence, and recently published trials evaluating the effect of EMF on CIEDs from electric vehicles (EV), smartphone, and smartwatch technology and summarize its safety data. Electronic databases, including PubMed and EMBASE, were searched for in vivo studies evaluating EMF strength and incidence between CIEDs and commercial EVs, new-generation smartphones, and new-generation smartwatches. A total of ten studies (three on EVs, five on smartphones, one on smartphones, one on smartphones and smartwatches) were included in our systematic review. There was no report of EMI incidence associated with EVs or smartwatches. Magnet-containing smartphones (iPhone 12) can cause EMI when placed directly over CIEDs - thereby triggering the magnet mode; otherwise, no report of EMI was observed with other positions or smartphone models. Current evidence suggests CIED recipients are safe from general interaction with EVs/HEVs, smartphones, and smartwatches. Strictly, results may only be applied to commercial brands or models tested in the published studies. There is limited data on EMI risk from EVs wireless charging and smartphones with MagSafe technology.

Accuracy of newly developed color determination application for masticatory performance: Evaluating color-changeable chewing gum.

Color-changeable chewing gums are used to evaluate masticatory performance, as measured by a colorimeter or visually based on a color scale. Although anyone can use a color scale, the evaluation accuracy depends on the evaluator. We develop an application that can determine the degree of color change in chewing gum using smartphone images, making color evaluation accurate and easy to measure. For the application, 60 chewed gum samples were prepared. Two shots were captured using two smartphone models. To create the application algorithm, a formula was developed to approximately map the color value from the smartphone images to the true value using a colorimeter. A basic validation was performed on 60 new samples covering a range of colors, followed by a field validation on 100 healthy dentate participants aged 20-39 years. The intraclass correlation coefficient for two repeated shots had a high value ≥ 0.97 in the basic and field validations, confirming reliability. No significant differences were observed in the paired t-test and Wilcoxon signed-rank test, and a significant and strong correlation (correlation coefficient ≥ 0.92) was observed between the evaluation values using the colorimeter and the basic and field validations. Bland-Altman plots further confirmed the validity of the application. A software application was developed to enable easy, quick, and accurate determination of the masticatory performance of a chewing gum from images taken using a smartphone with highly reliable and validated results.