Current Generation Of Smartphones Research Articles

Management of Odonto-onycho-dermal dysplasia: the role of botulinum toxin

Various methods exist for measuring wound dimensions, including photography, comparison, ruler, or graph techniques1. With the advancement of current-generation smartphones equipped with high-quality built-in cameras, accessibility to such technology has become more widespread and affordable. Leveraging specific applications developed for these smartphones has provided numerous advantages in the medical field. One such software, Imitomeasure, has been designed to measure wound dimensions without direct contact with the wound site. Unlike traditional clinical measurements involving rulers or tape, Imitomeasure offers a non-contact approach, reducing the risk of infection transmission to the patient. The efficacy of Imitomeasure has been evaluated, revealing its effectiveness in wound dimension assessment. In this investigation, the dimensions of bedsores located over bilateral greater trochanteric regions and presacral region of a patient who was bed-ridden due to Parkinson disease were assessed using the Imitomeasure application on a smartphone.

Assessment of wound dimensions using a smartphone-based application - Imitomeasure in wound assessment

Dimensions of wounds can be measured using several methods such as a photograph, a comparison, a ruler, or a graph. Current generation smartphones have high quality in-built cameras, and they are easily accessible than before and have become affordable. Making use of certain applications built for these smartphones have helped achieve various benefits in medical field. A software called Imitomeasure has been developed for the purpose of measuring wound dimensions without touching the wound. This app has the advantage over clinical measurement using rule or tape, in that, it is a non-contact method, hence less chances of spreading infection to that patient. The effectiveness of Imitomeasure has been assessed, and it has been found to be a very effective application in measuring wound dimensions. In this study, the size of wounds over gluteal region has been measured using Imitomeasure application in a smartphone.

Evaluation of Wound Measurements Utilizing a Smartphone App - Imitomeasure for Wound Analysis

Various methods exist for measuring wound dimensions, including photography, comparison, ruler, or graph techniques1. With the advancement of current-generation smartphones equipped with high-quality built-in cameras, accessibility to such technology has become more widespread and affordable. Leveraging specific applications developed for these smartphones has provided numerous advantages in the medical field. One such software, Imitomeasure, has been designed to measure wound dimensions without direct contact with the wound site. Unlike traditional clinical measurements involving rulers or tape, Imitomeasure offers a non-contact approach, reducing the risk of infection transmission to the patient. The efficacy of Imitomeasure has been evaluated, revealing its effectiveness in wound dimension assessment. In this investigation, the dimensions of bedsores located over bilateral greater trochanteric regions and presacral region of a patient who was bed-ridden due to Parkinson disease were assessed using the Imitomeasure application on a smartphone.

(304) Effects of Wireless Radiation Exposure on Genetic Variants in Human Sperm: A Preliminary Prospective Study

Abstract Introduction In the present era, the extensive use of modern cell phones has raised the health risks associated with the radiofrequency-electromagnetic radiation (RF-EMR) they emit, which the human body can absorb. RF-EMR exposure can potentially harm the process of spermatogenesis by increasing oxidative stress, elevating mitochondrial reactive oxygen species (ROS), and triggering the formation of free radicals that leads to sperm DNA damage. Our previous research revealed decreased sperm motility and viability in semen samples exposed to RF-EMR through WiFi calling. Objective To investigate the effects of WiFi radiation on genetic variants in human sperm using current-generation smartphones. Methods Six healthy, normozoospermic men aged 25-35 provided semen samples for this study. A current generation smartphone was used in WiFi calling mode, specifically through Whatsapp voice call, to serve as the source of RF-EMR. The call duration was six hours. Semen samples were analyzed based on the 2010 WHO guidelines before and after the radiation exposure. The genomics facility at our university extracted DNA from semen samples of each participant before and after RF-EMR exposure to perform whole exome sequencing with alignment to the reference genome. After sequencing, we compared genetic variants for each participant before and after RF-EMR exposure. Results After six hours of exposure, a significant decrease in sperm progressive motility was observed (p<0.05). Following exposure, the median number of exonic, nonsynonymous variants absent before exposure increased to 4 (IQR 3). Across the four successfully sequenced samples, we found 20 new, exonic, nonsynonymous variants after RF-EMR exposure. Significantly, male reproductive tissues expressed 12 variants, five associated with ciliary function and two related to mitochondrial function (see Table 1). Gene Chromosome Variant Location Function SPDYE11 Chr7 A > C Spermatids • Acetylcholine receptor activity • Cell cycle regulation SPDYE17 Chr7 A > C Spermatids • Protein kinase activity • Cell cycle regulation PRB2 Chr12 T > G salivary gland • Cell cycle regulation GGT2 Chr22 G > A Spermatids • Cell cycle regulation RGPD3 Chr2 C > T Spermatids • Ciliary function EPPK1 Chr8 G > A Spermatids, spermatocytes • Epithelial structure GOLGA6L4 Chr15 G > A Spermatids, spermatocytes • Ciliary function TCP11X2 ChrX T > C Spermatids • Acrosome reaction • Cilia function • Spermatid development HRNR Chr1 A > G Mitochondria • Metal ion binding POUF1 Chr13 > GCC Mitochondria • Structure & function of spermatozoa NPIPB13 Chr16 T > A Spermatids • Ciliary function • Sperm motility NBPF11 Chr1 A > C Spermatids, Sertoli cells, Leydig cells • Cell cycle regulation • Microtubule development ANKRD20A1 Chr9 T > A Spermatocytes • Ciliary function Table 1. Candidate genes with variants after six hours of phone radiation exposure Conclusions Based on our pilot study, we observed a decrease in sperm progressive motility and an increase in potentially related genetic variants associated with sperm motility following six hours of exposure to RF-EMR. These preliminary results highlight the need for further research to investigate the effects of RF-EMR exposure on semen parameters and to determine the clinical significance of these genetic variants. Disclosure No.

Telemedical Support Using Smartphones for Spine Surgery in Low- and Middle-Income Countries.

Objective: Low- and middle-income countries (LMICs) face many challenges compared to industrialized nations, most notably in regard to the health care system. Patients often have to travel long distances to receive medical care with few reliable transportation mechanisms. In time-critical emergencies, this is a significant disadvantage. One specialty that is particularly affected by this is spine surgery. Within this field, traumatic injuries and acutely compressive pathologies are often time-critical. Increasing global networking capabilities through internet access offers the possibility for telemedical support in remote regions. Recently, high-performance cameras and processors became available in commercially available smartphones. Due to their wide availability and ease of use, this could provide a unique opportunity to offer telemedical support in LMICs. Methods: We conducted a feasibility study with a neurosurgical institution in east Africa. To ensure telemedical support, a commercially available smartphone was selected as the experimental hardware. Preoperatively, resolution, contrast, brightness, and color reproduction were assessed under theoretical conditions using a test chart. Intraoperatively, the image quality was assessed under different conditions. In the first step, the instrumentation table was displayed, and the mentor surgeon marked an instrument that the mentee surgeon should recognize correctly. In the next evaluation step, the surgical field was shown on film and the mentor surgeon marked an anatomical structure, and in the last evaluation step, the screen of the X-ray machine was captured, and the mentor surgeon again marked an anatomical structure. Subjective image quality was rated by two independent reviewers using the similar modified Likert scale as before on a scale of 1-5, with 1 indicating inadequate quality and 5 indicating excellent quality. Results: The image quality during the video calls was rated as sufficient overall. When evaluating the test charts, a quality of 97% ± 5 on average was found for the chart with the white background and a quality of 84% ± 5 on average for the chart with the black background. The color reproduction, the contrast, and the reproduction of brightness were rated excellent. Intraoperatively, the visualization of the instrument table was also rated excellent. Visualization of the operative site was rated 1.5 ± 0.5 on average and it was not possible to recognize relevant anatomical structures with the required confidence for surgical procedures. Image quality of the X-ray screen was rated 1.5 ± 0.9 on average. Conclusion: Current generation smartphones have high imaging performance, high computing power, and excellent connectivity. However, relevant anatomical structures during spine surgery procedures and on the X-ray screen in the operating room could not be identified with reliability to provide adequate surgical support. Nevertheless, our study showed the potential in smartphones supporting surgical procedures in LMICs, which could be helpful in other surgical fields.

Accelerometers in Our Pocket: Does Smartphone Accelerometer Technology Provide Accurate Data?

This study evaluates accelerometer performance of three new state of the art smartphones and focuses on accuracy. The motivating research question was whether accelerator accuracy obtained with these off-the-shelf modern smartphone accelerometers was or was not statistically different from that of a gold-standard reference system. We predicted that the accuracy of the three modern smartphone accelerometers in human movement data acquisition do not differ from that of the Vicon MX motion capture system. To test this prediction, we investigated the comparative performance of three different commercially available current generation smartphone accelerometers among themselves and to a gold-standard Vicon MX motion capture system. A single subject design was implemented for this study. Pearson's correlation coefficients® were calculated to verify the validity of the smartphones' accelerometer data against that of the Vicon MX motion capture system. The Intraclass Correlation Coefficient (ICC) was used to assess the smartphones' accelerometer performance reliability compared to that of the Vicon MX motion capture system. Results demonstrated that (a) the tested smartphone accelerometers are valid and reliable devices for estimating accelerations and (b) there were not significant differences among the three current generation smartphones and the Vicon MX motion capture system's mean acceleration data. This evidence indicates how well recent generation smartphone accelerometer sensors are capable of measuring human body motion. This study, which bridges a significant information gap between the accuracy of accelerometers measured close to production and their accuracy in actual smartphone research, should be interpreted within the confines of its scope, limitations and strengths. Further research is warranted to validate our arguments, suggestions, and results, since this is the first study on this topic.

Effect of Radiofrequency Electromagnetic Radiation Emitted by Modern Cellphones on Sperm Motility and Viability: An In Vitro Study

BackgroundCellphones emit radiofrequency electromagnetic radiation (RF-EMR) for transmission of data for social media communication, web browsing, and music/podcast streaming. Use of Bluetooth ear buds has probably prolonged the time during which cellphones reside in the trouser pockets of men. It has been postulated that RF-EMR increases oxidative stress and induces free radical formation. ObjectiveTo investigate the effect of wireless-spectrum (4G, 5G, and WiFi) RF-EMR emitted by modern smartphones on sperm motility and viability and explore whether these effects can be mitigated using a physical barrier or distance. Design, setting, and participantsSemen samples were obtained from fertile normozoospermic men aged 25–35 yr. A current-generation smartphone in talk mode was used as the RF-EMR source. A WhatsApp voice call was made using either 4G, 5G, or WiFi wireless connectivity. We determined if exposure effects were mitigated by either a cellphone case or greater distance from the semen sample. Outcome measurements and statistical analysisThe semen samples were analyzed according to 2010 World Health Organization laboratory guidelines. Statistical analysis was performed using SPSS v.28. Results and limitationsWe observed decreases in sperm motility and viability with WiFi exposure but not with exposure to 4G or 5G RF-EMR. With large variability among smartphones, continued research on exposure effects is needed. ConclusionsOur exploratory study revealed that sperm motility and viability are negatively impacted by smartphones that use the WiFi spectrum for data transmission. Patient summaryWe looked at the effect of cellphone use on sperm motility and viability. We found that cellphones using WiFi connectivity for data usage have harmful effects on semen quality in men.

Klasifikasi Physical Activity Berbasis Sensor Accelorometer, Gyroscope, dan Gravity menggunakan Algoritma Multi-class Ensemble GradientBoost

The current generation of smartphones is increasingly sophisticated, equipped with several sensors such as accelerometer, gravity sensor, and gyroscope that can be used to recognize human activities such as going up stairs, going down stairs, running and walking. To get information, the data will be grouped using statistical methods. The performance of statistical methods has shortcomings in classifying data because of the procedures that must be met. To cover this shortcoming, the ensemble technique is used. In this paper, we propose to apply the Multi-Class Ensemble Gradientboost algorithm to improve the performance of the logistic regression method in classifying such as climbing stairs, descending stairs, running and walking. The process of taking data using a smartphone by designing an Android-based .apk system. Then, the entire dataset was separated into training data and test data with a comparison percentage of 70:30. The results obtained show that the Multi-Class Ensemble Gradientboost algorithm succeeded in increasing the logistic regression performance by 27.93%

Estimation of forest canopy structure and understory light using spherical panorama images from smartphone photography

Abstract Accurate estimates of forest canopy structure are central for a wide range of ecological studies. Hemispherical photography (HP) is a popular tool to estimate canopy attributes. However, traditional HP methods require expensive equipment, are sensitive to exposure settings, and produce limited resolution which dramatically affects the accuracy of gap fraction estimates. As an alternative, hemispherical images can be extracted from spherical panoramas produced by many smartphone camera applications. I compared hemispherical photos captured with a digital single lens reflex camera and 180° lens to those extracted from smartphone spherical panoramas (SSP). The SSP HP method leverages built-in features of current generation smartphones to produce sharper images of higher resolution, resulting in more definition of fine canopy structure. Canopy openness and global site factor from SSP HP are highly correlated with traditional methods (R2 > 0.9), while leaf area index estimates are lower, especially in more closed canopies where traditional methods fail to capture fine gaps.

Magnetic Interference on Cardiac Implantable Electronic Devices From Apple iPhone MagSafe Technology.

BackgroundMagnet wireless charging is being utilized increasingly in current generation smartphones. Apple's MagSafe is a proprietary wireless charging technology with an array of magnets that has the capacity to generate magnet fieldstrength >50 gauss (G). We hypothesize that there is clinically significant magnet interference caused by Apple's MagSafe technology on cardiac implantable electronic devices (CIED).Methods and ResultsThis study has an in vivo and an ex vivo component. The in vivo component consists of consecutive patients who presented to the electrophysiology laboratory with previously implanted CIEDs. The iPhone 12 Pro Max was directly placed on the skin over the pocket of these patients and the effect was studied by device interrogation. For the ex vivo component of the study, CIEDs from major device companies were tested for magnetic interference caused by iPhone 12 Pro Max through unopened packages. We found that iPhone 12 Pro Max resulted in clinically identifiable magnet interference in 3/3 (100%) participants in vivo and in 8/11 (72.7%) devices ex vivo.ConclusionsApple's iPhone 12 Pro Max MagSafe technology can cause magnet interference on CIEDs and has the potential to inhibit lifesaving therapy.

An Indoor Positioning-Based Mobile Payment System Using Bluetooth Low Energy Technology.

The development of information technology has paved the way for faster and more convenient payment process flows and new methodology for the design and implementation of next generation payment systems. The growth of smartphone usage nowadays has fostered a new and popular mobile payment environment. Most of the current generation smartphones support Bluetooth Low Energy (BLE) technology to communicate with nearby BLE-enabled devices. It is plausible to construct an Over-the-Air BLE-based mobile payment system as one of the payment methods for people living in modern societies. In this paper, a secure indoor positioning-based mobile payment authentication protocol with BLE technology and the corresponding mobile payment system design are proposed. The proposed protocol consists of three phases: initialization phase, session key construction phase, and authentication phase. When a customer moves toward the POS counter area, the proposed mobile payment system will automatically detect the position of the customer to confirm whether the customer is ready for the checkout process. Once the system has identified the customer is standing within the payment-enabled area, the payment system will invoke authentication process between POS and the customer’s smartphone through BLE communication channel to generate a secure session key and establish an authenticated communication session to perform the payment transaction accordingly. A prototype is implemented to assess the performance of the proposed design for mobile payment system. In addition, security analysis is conducted to evaluate the security strength of the proposed protocol.

Wide Disagreement Between Alternative Assessments of Premorbid Physical Activity: Subjective Patient and Surrogate Reports and Objective Smartphone Data.

Surrogate-decision maker and patient self-reported estimates of the distances walked prior to acute illness are subjective and may be imprecise. It may be possible to extract objective data from a patient's smartphone, specifically, step and global position system data, to quantify physical activity. The objectives were to 1) assess the agreement between surrogate-decision maker and patient self-reported estimates of distance and time walked prior to resting and daily step-count and 2) determine the feasibility of extracting premorbid physical activity (step and global position system) data from critically ill patients. Prospective cohort study. Quaternary ICU. Fifty consecutively admitted adult patients who owned a smartphone, who were ambulatory at baseline, and who remained in ICU for more than 48 hours participated. There was no agreement between patients and surrogates for all premorbid walking metrics (mean bias 108% [99% lower to 8,700% higher], 83% [97% to 2,100%], and 71% [96% to 1,080%], for distance, time, and steps, respectively). Step and/or global position system data were successfully extracted from 24 of 50 phones (48%; 95% CI, 35-62%). Surrogate-decision makers, but not patient self-reported, estimates of steps taken per day correlated with smartphone data (surrogates: n = 13, ρ = 0.56, p < 0.05; patients: n = 13, ρ = 0.30, p = 0.317). There was a lack of agreement between surrogate-decision maker and patient self-reported subjective estimates of distance walked. Obtaining premorbid physical activity data from the current-generation smartphones was feasible in approximately 50% of patients.

Improving the Accuracy of Direct Geo-referencing of Smartphone-Based Mobile Mapping Systems Using Relative Orientation and Scene Geometric Constraints.

This paper introduces a new method which facilitate the use of smartphones as a handheld low-cost mobile mapping system (MMS). Smartphones are becoming more sophisticated and smarter and are quickly closing the gap between computers and portable tablet devices. The current generation of smartphones are equipped with low-cost GPS receivers, high-resolution digital cameras, and micro-electro mechanical systems (MEMS)-based navigation sensors (e.g., accelerometers, gyroscopes, magnetic compasses, and barometers). These sensors are in fact the essential components for a MMS. However, smartphone navigation sensors suffer from the poor accuracy of global navigation satellite System (GNSS), accumulated drift, and high signal noise. These issues affect the accuracy of the initial Exterior Orientation Parameters (EOPs) that are inputted into the bundle adjustment algorithm, which then produces inaccurate 3D mapping solutions. This paper proposes new methodologies for increasing the accuracy of direct geo-referencing of smartphones using relative orientation and smartphone motion sensor measurements as well as integrating geometric scene constraints into free network bundle adjustment. The new methodologies incorporate fusing the relative orientations of the captured images and their corresponding motion sensor measurements to improve the initial EOPs. Then, the geometric features (e.g., horizontal and vertical linear lines) visible in each image are extracted and used as constraints in the bundle adjustment procedure which correct the relative position and orientation of the 3D mapping solution.

Virtual reality for resuscitation using an iPhone

Purpose: Simulation has been used in training of medical personnel for some time. However CPR training for laypersons generally utilizes only CPR simulators and conventional video. It rarely gives the actual experience of CPR administration in the field. The current generation of smart phones is capable of displaying virtual reality (VR) environments that can be visually striking and render the appearance of actually being present. Thus, the purpose of this work is to make use of virtual reality for enhancing the training of laypersons in CPR.

Understanding Application-Battery Interactions on Smartphones: A Large-Scale Empirical Study

Current generation of smartphones is running more and more complex applications that reduce the battery life to as short as several hours. Thus, it becomes very important to understand the diversities of applications installed on smartphones and how batteries are consumed across different applications. This paper presents a large-scale battery study on smartphones focusing on diversities in applications and users. Based on application and battery traces collected on over 80 000 Android smartphones for a four-week period, we analyze the battery discharging patterns, the types of mobile applications, and the usage and energy consumption patterns for these applications. During the analysis, we introduce a novel method to calculate the energy consumption rate for each application based on coarse-grained battery data collected with a lightweight monitoring tool. Based on the results, we present some observations and discuss possible improvements on smartphone designs and mobile application development. We also compare our results to some previous studies wherever it is possible.

Mobile Jump Assessment (mJump): A Descriptive and Inferential Study.

BackgroundVertical jump tests are used in athletics and rehabilitation to measure physical performance in people of different age ranges and fitness. Jumping ability can be analyzed through different variables, and the most commonly used are fly time and jump height. They can be obtained by a variety of measuring devices, but most are limited to laboratory use only. The current generation of smartphones contains inertial sensors that are able to record kinematic variables for human motion analysis, since they are tools for easy access and portability for clinical use.ObjectiveThe aim of this study was to describe and analyze the kinematics characteristics using the inertial sensor incorporated in the iPhone 4S, the lower limbs strength through a manual dynamometer, and the jump variables obtained with a contact mat in the squat jump and countermovement jump tests (fly time and jump height) from a cohort of healthy people.MethodsA cross sectional study was conducted on a population of healthy young adults. Twenty-seven participants performed three trials (n=81 jumps) of squat jump and countermovement jump tests. Acceleration variables were measured through a smartphone’s inertial sensor. Additionally, jump variables from a contact mat and lower limbs dynamometry were collected.ResultsIn the present study, the kinematic variables derived from acceleration through the inertial sensor of a smartphone iPhone 4S, dynamometry of lower limbs with a handheld dynamometer, and the height and flight time with a contact mat have been described in vertical jump tests from a cohort of young healthy subjects. The development of the execution has been described, examined and identified in a squat jump test and countermovement jump test under acceleration variables that were obtained with the smartphone.ConclusionsThe built-in iPhone 4S inertial sensor is able to measure acceleration variables while performing vertical jump tests for the squat jump and countermovement jump in healthy young adults. The acceleration kinematics variables derived from the smartphone’s inertial sensor are higher in the countermovement jump test than the squat jump test.

A natural walking monitor for pulmonary patients using mobile phones.

Mobile devices have the potential to continuously monitor health by collecting movement data including walking speed during natural walking. Natural walking is walking without artificial speed constraints present in both treadmill and nurse-assisted walking. Fitness trackers have become popular which record steps taken and distance, typically using a fixed stride length. While useful for everyday purposes, medical monitoring requires precise accuracy and testing on real patients with a scientifically valid measure. Walking speed is closely linked to morbidity in patients and widely used for medical assessment via measured walking. The 6-min walk test (6MWT) is a standard assessment for chronic obstructive pulmonary disease and congestive heart failure. Current generation smartphone hardware contains similar sensor chips as in medical devices and popular fitness devices. We developed a middleware software, MoveSense, which runs on standalone smartphones while providing comparable readings to medical accelerometers. We evaluate six machine learning methods to obtain gait speed during natural walking training models to predict natural walking speed and distance during a 6MWT with 28 pulmonary patients and ten subjects without pulmonary condition. We also compare our model's accuracy to popular fitness devices. Our universally trained support vector machine models produce 6MWT distance with 3.23% error during a controlled 6MWT and 11.2% during natural free walking. Furthermore, our model attains 7.9% error when tested on five subjects for distance estimation compared to the 50-400% error seen in fitness devices during natural walking.

Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency

Mobile devices are resource-limited systems that provide a large number of services and features. Smartphones, for example, implement advanced functionalities and services for the final user, in addition to conventional communication capabilities. Machine Learning algorithms can help in providing such advanced functionalities, but mobile systems suffer from issues related to their resource-limited nature such as, for example, limited battery capacity and processing power and, therefore, even simple pattern recognition activities can become too demanding, in this respect. We propose here a method to design a Human Activity Recognition algorithm, which takes into account the fact that only limited resources are available for its execution. In particular, we restrict the hypothesis space of possible recognition models by applying some advanced concepts from Statistical Learning Theory, so as to force the selection of models with good generalization ability but low computational complexity. Then, the learned model can be effectively implemented on a mobile and resource-limited device: the experiments, carried out on a current-generation smartphone, show the benefits of the proposed approach in terms of both model accuracy and battery duration.

SUPAR: Smartphone as a ubiquitous physical activity recognizer for u-healthcare services.

Current generation smartphone can be seen as one of the most ubiquitous device for physical activity recognition. In this paper we proposed a physical activity recognizer to provide u-healthcare services in a cost effective manner by utilizing cloud computing infrastructure. Our model is comprised on embedded triaxial accelerometer of the smartphone to sense the body movements and a cloud server to store and process the sensory data for numerous kind of services. We compute the time and frequency domain features over the raw signals and evaluate different machine learning algorithms to identify an accurate activity recognition model for four kinds of physical activities (i.e., walking, running, cycling and hopping). During our experiments we found Support Vector Machine (SVM) algorithm outperforms for the aforementioned physical activities as compared to its counterparts. Furthermore, we also explain how smartphone application and cloud server communicate with each other.

An Object-Oriented Smartphone Application for Structural Finite Element Analysis

Smartphones are becoming increasingly ubiquitous both in general society and the workplace. Recent increases in mobile processing power have shown the current generation of smartphones has equivalent processing power to a supercomputer from the early 1990s. Many industries have abandoned desktop computing and are now entirely reliant on mobile devices. Given these facts it is logical that smartphones are considered as the next platform for finite element analysis (FEA). This paper presents an architecture for a smartphone FEA application using object-oriented programming. A MVC design pattern is adopted and a demonstration FEA application for the Android smartphone platform is presented.