Capabilities Of Smartphones Research Articles

In-situ synthesis of 2D nanozymes-coated cellulose nanofibers on paper-based chips for portable detection of biothiols

BackgroundSimple, fast and low-cost paper-based analytical devices (PADs) have a good application prospect for point-of-care detection of GSH. However, effective immobilization of functional nanomaterials onto cellulose, as a critical factor in the construction of PADs, presents numerous difficulties and challenges. ResultsIn this study, we have developed an exceptionally straightforward and environmentally friendly synthetic approach by using ovalbumin (OVA) as a bio-mineralization template for the preparation of MnO2 nanosheets. The MnO2 nanosheets produced in the solution phase exhibited excellent intrinsic nano-enzyme activity and biodegradability. The OVA-MnO2 nanosheets can effectively oxidize Amplex red in the absence of H2O2, enabling sensitive detection of GSH with a linear range of 5 nM–10 μM and a detection limit as low as 2.8 nM. Furthermore, we utilized this method to facilitate in situ synthesis of OVA-MnO2 nanosheets directly on paper substrates. This approach eliminates the need for conventional stirring and centrifugation steps, greatly simplifying the fabrication process while reducing material usage and time expenditure. Characterization of the chemical composition and morphology confirmed the intimate growth of the 2D nano-enzymes on the cellulose fibers. Utilizing smartphone capabilities, the OVA-MnO2 nanosheet-modified PAD enabled instrument-free detection of GSH, demonstrating high sensitivity (0.74 μM) and a wide linear response range (1–1000 μM). SignificanceThe synthesis of MnO2 nanosheets directly on cellulose substrates substantially streamlines the modification workflow of PADs and reduces detection costs, offering new avenues for clinical diagnostics of relevant diseases.

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.

LEDY: An Android Application-Based Learning Media Development to Improve Students’ Writing Skill

Smartphones can play a pivotal role in enhancing the learning process. Additionally, the multimedia capabilities of smartphones, such as interactive videos and educational games, make the learning process more engaging and enjoyable. The problem by students encompassed a shortage of interactive learning materials, challenges in understanding English content, restricted chances for practical application, and a sense of monotony in the learning process. Therefore, this research aims to design an Android application-basedlearning media, especially on writing skills. This research is a Research and Development (R&D) with the ADDIE model. The subject of this research is tenth-grade students at SMK Wikrama 1 Jepara, with data collected through questionnaires and a pretest-posttest procedure. The effectiveness of the Android application was assessed by using the N-Gain method in analyzing pretest and post-test data. The test outcomes demonstrated an 83.8%success rate, falling within the appropriate category. The pre-test and post-test scores exhibited improvement, with the N-Gain analysis revealing a value of 0.60, signifying enhancementin writing skills. It means that using LEDY an Android application-based learning media, contributes to an improved comprehension among 10th-grade students at SMK Wikrama 1 Jepara, particularly in the area of procedure text material.

A pyrene-based hydrogen-bonded framework with excimer induced ratiometric emission for discriminative luminescence detection of metal ions

Discriminatory fluorescence detection of various metal ions is of significant importance in environmental and health-related applications. A luminescent hydrogen-bonded organic framework (HOF) material, PFC-1 was synthesized using 1,3,6,8-tetra(4-carboxylbenzene) pyrene (H4TBAPy) as the organic building block. The fluorescence behavior of PFC-1 is influenced by the concentration of the suspension, demonstrating different emissions characteristic of monomer and excimer fluorescence, which are highly sensitive to the surrounding environment. This allows for the potential differentiation and sensing of different target analytes. PFC-1 showed discriminatory fluorescence sensing performance towards metal ions such as Al3+, Sc3+, Cr3+, and Cu2+, with changes in fluorescence intensity, emission peak shifts, and intensity ratio changes between monomer and excimer emissions. Furthermore, a smartphone-based detection strategy was proposed, leveraging color recognition capabilities of smartphones for onsite and real-time sensing. The work demonstrate that PFC-1 is a promising material for the development of portable, cost-effective fluorescent sensors for onsite and real-time detection of metal ions. The integration of PFC-1 with smartphone technology paves the way for practical applications in environmental monitoring, industrial processes, and healthcare diagnostics.

Enhanced peroxidase-like activity based on electron transfer between platinum nanoparticles and Ti3C2TX MXene nanoribbons coupled smartphone-assisted hydrogel platform for detecting mercury ions

BackgroundHeavy metal pollution poses a serious threat to the ecological environment. Mercury ion (Hg2+) is a class of highly toxic heavy metal ions, which is bioaccumulative, difficult to breakdown, and has a significant affinity with sulfur and thiol-containing proteins, which seriously affects environmental safety and human health. Nanozyme-based sensing methods are expected to be used to detect toxic heavy metal ions. However, the application of precious metal nanozymes to develop portable sensors with simplicity, high stability, and high sensitivity has not been explored to a large extent. ResultsIn this paper, based on MXene's unique adsorption capacity for certain precious metal ions, PtNPs/Ti3C2TXNR composites were successfully prepared by in-situ growth of Pt nanoparticles (PtNPs) on the surface of Ti3C2TX MXene nanoribbons (Ti3C2TXNR) using the hydrothermal technique. Experimental data revealed PtNPs/Ti3C2TXNR exhibited superior peroxidase-like activity, attributed to the synergistic effect of well-dispersed ultrasmall PtNPs and electron transfer effect. Hg2+ can significantly inhibit enzyme-like activity of PtNPs/Ti3C2TXNR due to specific capture and partial in-situ reduction of PtNPs, so a colorimetric sensor was constructed for ultra-trace detection of Hg2+ with a linear range of 0.2 nM and 400 nM. Furthermore, using the portable detecting capabilities of smartphones and hydrogel, a smartphone-assisted hydrogel sensing platform of Hg2+ was constructed. Notably, the two-mode sensing platforms exhibited outstanding detection performance with LOD values as low as 15 pM (colorimetric) and 26 pM (hydrogel), respectively, superior to recently reported nanozyme-based Hg2+ sensors. SignificanceCompared with other methods, the PtNPs/Ti3C2TXNR-based dual-mode sensor designed in this paper has superior sensitivity, high selectivity, simple operation and portability. In particular, the dual-output sensing strategy enables self-confirmation of detection results, greatly improving the reliability of the sensor, and is expected to be used for the on-site determination of trace mercury ions.

Decimeter-Level Accuracy for Smartphone Real-Time Kinematic Positioning Implementing a Robust Kalman Filter Approach and Inertial Navigation System Infusion in Complex Urban Environments.

New smartphones provide real-time access to GNSS pseudorange, Doppler, or carrier-phase measurement data at 1 Hz. Simultaneously, they can receive corrections broadcast by GNSS reference stations to perform real-time kinematic (RTK) positioning. This study aims at the real-time positioning capabilities of smartphones using raw GNSS measurements as a conventional method and proposes an improvement to the positioning through the integration of Inertial Navigation System (INS) measurements. A U-Blox GNSS receiver, model ZED-F9R, was used as a benchmark for comparison. We propose an enhanced ambiguity resolution algorithm that integrates the traditional LAMBDA method with an adaptive thresholding mechanism based on real-time quality metrics. The RTK/INS fusion method integrates RTK and INS measurements using an extended Kalman filter (EKF), where the state vector x includes the position, velocity, orientation, and their respective biases. The innovation here is the inclusion of a real-time weighting scheme that adjusts the contribution of the RTK and INS measurements based on their current estimated accuracy. Also, we use the tightly coupled (TC) RTK/INS fusion framework. By leveraging INS data, the system can maintain accurate positioning even when the GNSS data are unreliable, allowing for the detection and exclusion of abnormal GNSS measurements. However, in complex urban areas such as Qazvin City in Iran, the fusion method achieved positioning accuracies of approximately 0.380 m and 0.415 m for the Xiaomi Mi 8 and Samsung Galaxy S21 Ultra smartphones, respectively. The subsequent detailed analysis across different urban streets emphasized the significance of choosing the right positioning method based on the environmental conditions. In most cases, RTK positioning outperformed Single-Point Positioning (SPP), offering decimeter-level precision, while the fusion method bridged the gap between the two, showcasing improved stability accuracy. The comparative performance between the Samsung Galaxy S21 Ultra and Xiaomi Mi 8 revealed minor differences, likely attributed to variations in the hardware design and software algorithms. The fusion method emerged as a valuable alternative when the RTK signals were unavailable or impractical. This demonstrates the potential of integrating RTK and INS measurements for enhanced real-time smartphone positioning, particularly in challenging urban environments.

A novel bearing health sensing technique using smartphone

Bearing health sensing has drawn increasing attention as bearing plays a vulnerable yet essential role in modern industry. Nevertheless, conventional health sensing technologies involve dedicated sensor installation and high costs, which may not meet the monitoring and sensing demands. Given this, we present a health sensing technique to capture the fault information of bearing by leveraging an off-the-shelf smartphone. Specifically, we first exploit only a readily available smartphone to capture the audio signal of bearing. Given the captured audio signal, an adaptive time–frequency masking is proposed to separate the noise, and the fault impulse is then sharpened by the singular value decomposition (SVD). The extensive experimental results illustrate that our technique is able to enhance the bearing health sensing capability of smartphones under various real-world scenarios. Even in noisy mining field conditions, the framework still achieves a notable performance improvement rate of 169 %.

Evaluating smartphone-based optical readouts for immunoassays in human and veterinary healthcare: A comparative study

Recent advances have significantly enhanced the use of smartphone devices for medical diagnostics. This study uses high-resolution cameras in mobile devices to capture and process bioassay images, enabling the quantification of diverse biomarkers across a range of diagnostic tests conducted on 96-well microplates. The study evaluates the effectiveness of this technology through protein quantification techniques and immunoassays that generate colorimetric responses at specific wavelengths. It includes the assessment of bicinchoninic acid and Bradford protein quantification methods, alongside a conventional immunoassay for detecting mare antibodies in colostrum to monitor foal immunodeficiencies. Further application involves the readout of magneto-actuated immunoassays aimed at quantifying bacteria. The results obtained from benchtop spectrophotometry at 595, 562, and 450 nm are compared with those acquired using a smartphone, which identified color intensities in shades of blue, purple, and yellow. This comparison yields promising correlations for the samples tested, suggesting a high degree of accuracy in the smartphone capability to analyze bioassay outcomes. The analysis via smartphone is facilitated by a specific app, which processes the images captured by the phone camera to quantify color intensities corresponding to different biomarker concentrations. Detection limits of 12.3 and 22.8 μg mL−1 for the bicinchoninic acid assay and 36.7 and 45.4 μg mL−1 for the Bradford are obtained for protein quantification using the spectrophotometer and the smartphone app, respectively. For mare's antibodies in colostrum, the values are 1.14 and 1.72 ng mL−1, while the detection of E. coli is performed at 2.0 x 104 and 2.9 × 104 CFU mL−1, respectively. This approach offers further advantages, including wide availability, cost-effectiveness, portability, compared to traditional and expensive benchtop instruments.

A study of strontium aluminates for all optical contactless sensing applications using smartphone interrogation

In the present paper we study the spectral and time responses of Eu and Dy doped strontium aluminates and compare the results obtained by means of a standard spectrometer and a smartphone. The advantages of each measurement tool are outlined. It is shown that smartphones equipped with simple transmission diffraction grating can efficiently be used in contactless sensing applications and as measurements tools for the study of the spectral and time dependent responses of phosphors. We also show that smartphones are up to an order of magnitude faster in measuring rise and decay time responses and allow a more detailed analysis of luminescence dynamics compared to spectrometers. A method of duty cycle scanning of the time responses is proposed to study the spectrally and time dependent luminescence structure dynamics which is related to the individual distribution of traps in the phosphors. These smartphone capabilities justify their use as affordable interrogation instruments for time-decay encoded phosphorescent sensors.

Smartphone-based serious games for mental health: a scoping review

AbstractThe use of smartphone-based Serious Games in mental health care is an emerging and promising research field. Combining the intrinsic characteristics of games (e.g., interactiveness, immersiveness, playfulness, user-tailoring and engaging nature) with the capabilities of smartphones (e.g., versatility, ubiquitous connectivity, built-in sensors and anywhere–anytime nature) yields great potential to deliver innovative psychological treatments, which are engaging, effective, fun and always available. This article presents a scoping review, based on the PRISMA (scoping review extension) guidelines, of the field of smartphone-based serious games for mental health care. The review combines an analysis of the technical characteristics, including game design, smartphone and game-specific features, with psychological dimensions, including type and purpose of use, underlying psychological frameworks and strategies. It also explores the integration of psychological features into Serious Games and summarizes the findings of evaluations performed. A systematic search identified 40 smartphone-based Serious Games for mental health care. The majority consist of standalone and self-administrable interventions, applying a myriad of psychological strategies to address a wide range of psychological symptoms and disorders. The findings explore the potential of Serious Games as treatments and for enhancing patient engagement; we conclude by proposing several avenues for future research in order to identify best practices and success factors.

A smartphone-based diagnostic analyzer for point-of-care milk somatic cell counting

BackgroundMastitis, a pervasive and detrimental disease in dairy farming, poses a significant challenge to the global dairy industry. Monitoring the milk somatic cell count (SCC) is vital for assessing the incidence of mastitis and the quality of raw cow's milk. However, existing SCC detection methods typically require large-scale instruments and specialized operators, limiting their application in resource-constrained settings such as dairy farms and small-scale labs. To address these limitations, this study introduces a novel, smartphone-based, on-site SCC testing method that leverages smartphone capabilities for milk somatic cell identification and enumeration, offering a portable and user-friendly testing platform. ResultsThe central findings of our study demonstrate the effectiveness of the proposed method for counting milk somatic cells. Its on-site applicability, facilitated by the microfluidic chip, optical system, and smartphone integration, heralds a paradigm shift in point-of-care testing (POCT) for dairy farms and smaller laboratories. This approach bypasses complex processing and presents a user-friendly solution for real-time SCC monitoring in resource-limited settings. This device boasts several unique features: small size, low cost (<$1,000 total manufacturing cost and <$1 per test), and high accuracy. Remarkably, it delivers test results within just 2 min. Actual-sample testing confirmed its consistency with results from the commercial Bentley FTS/FCM cytometer, affirming the reliability of the proposed method. Overall, these results underscore the potential for transformative change in dairy farm management and laboratory testing practices. SignificanceIn summary, this study concludes that the proposed smartphone-based method significantly contributes to the accessibility and ease of SCC testing in resource-limited environments. By fostering the use of POCT technology in food safety control, particularly in the dairy industry, this innovative approach has the potential to revolutionize the monitoring and management of mastitis, ultimately benefiting the global dairy sector.

StresSense: Real-Time detection of stress-displaying behaviors

BackgroundWrist-worn gadgets like smartphones are ideal for unobtrusively gathering user data, in various fields such as health and fitness monitoring, communication, and productivity enhancement. They seamlessly integrate into users' daily lives, providing valuable insights and features without the need for constant attention or disruption. In sensitive domains like mental health, these devices provide user-friendly, privacy-protected means of diagnosis and treatment, offering a secure and cost-effective avenue for seeking help. ObjectivesThis study addresses the limitations of traditional mental health assessment techniques, such as intrusive sensing and subjective self-reporting, by harnessing the unobtrusive data collection capabilities of smartphones. Equipped with accelerometers and other sensors, these devices offer a novel approach to mental health research. Our objective was to develop methods for real-time detection of stress and boredom behavior markers using smart devices and machine learning algorithms. MethodologyBy leveraging data from accelerometers (A), gyroscopes (G), and magnetometers (M), we compiled a dataset indicative of stress-related behaviors and trained various machine-learning models for predictive accuracy. The methodology involved collecting data from motion sensors (A, G, and M) on the dominant arm's wrist-worn smartphone, followed by data preprocessing, transformation from time series format, and training a Deep Neural Network (DNN) model for activity recognition. FindingsRemarkably, the DNN achieved an accuracy of 93.50% on test data, outperforming traditional and ensemble machine learning methods across different window sizes, and demonstrated real-time accuracy of 77.78%, validating its practical application. ConclusionIn conclusion, this research presents a novel dataset for detecting stress and boredom behaviors using smartphones, reducing reliance on costly devices and offering a more objective assessment. It also proposes a DNN-based method for wrist-worn devices to accurately identify complex activities associated with stress and boredom, with benefits in terms of privacy and user convenience. This advancement represents a significant contribution to the field of mental health research, providing a less intrusive and more user-friendly approach to monitoring mental well-being.

A Wearable Inertial Sensor Approach for Locomotion and Localization Recognition on Physical Activity.

Advancements in sensing technology have expanded the capabilities of both wearable devices and smartphones, which are now commonly equipped with inertial sensors such as accelerometers and gyroscopes. Initially, these sensors were used for device feature advancement, but now, they can be used for a variety of applications. Human activity recognition (HAR) is an interesting research area that can be used for many applications like health monitoring, sports, fitness, medical purposes, etc. In this research, we designed an advanced system that recognizes different human locomotion and localization activities. The data were collected from raw sensors that contain noise. In the first step, we detail our noise removal process, which employs a Chebyshev type 1 filter to clean the raw sensor data, and then the signal is segmented by utilizing Hamming windows. After that, features were extracted for different sensors. To select the best feature for the system, the recursive feature elimination method was used. We then used SMOTE data augmentation techniques to solve the imbalanced nature of the Extrasensory dataset. Finally, the augmented and balanced data were sent to a long short-term memory (LSTM) deep learning classifier for classification. The datasets used in this research were Real-World Har, Real-Life Har, and Extrasensory. The presented system achieved 89% for Real-Life Har, 85% for Real-World Har, and 95% for the Extrasensory dataset. The proposed system outperforms the available state-of-the-art methods.

Smart Health Monitoring System of Agricultural Machines: Deep Learning-based Optimization with IoT and AI

Implementing intelligent monitoring systems for Agricultural Machinery (AM) is hindered by the intricate and costly nature of the Internet of Things (IoT) sensor technologies. The heavy reliance on cloud and fog computing, the availability of network infrastructure, and the need for expert knowledge pose challenges in rural areas that lack network connectivity. Using edge devices, such as smartphones, which possess significant computational capabilities, is a potential solution that has not yet been fully realized in the commercial sphere. Furthermore, the increasing demand from users for economically viable and user-friendly technology serves as a driving force for transitioning away from expensive and intricate sensors towards more cost-effective alternatives. In the IoT era, there is anticipated to be a widespread network connection between a vast array of AM and service centers. Using smartphone applications has increased the potential for edge computation on smartphones to significantly aid in network traffic control. The development of an Artificial Intelligence (AI) - -based data analytic method poses a significant challenge due to the need to optimize for the limited computational capabilities of smartphones. However, the users’ demand for affordable technology renders it resistant to easy penetration. This paper uses IoT and AI to propose a Smart Health Monitoring System for Agricultural Machines with Deep Learning-based Optimization (SHMAM-DLO). This paper aims to propose a Fusion Genetic Algorithm (FGA) methodology and Artificial Neural Network (ANN) for optimization during monitoring the health of AM. The proposed approach enables cost-effective utilization of smartphone end devices by leveraging their built-in microphones instead of relying on expensive IoT sensors.

FAKTOR INFLUENCING CIMB NIAGA'S MOBILE PHONE ACCOUNT CONTINUATION USAGE: A STUDY OF TECHNOLOGY ACCEPTANCE MODEL (TAM)

As smartphone capabilities, technological complexity, and other developments increase, banks must adapt to meet demand. CIMB Niaga, a private-owned bank, has some digital banking products, one of which is a mobile phone savings account. The objective of this study is to prove and analyze whether the TAM variables affect the continued use of CIMB Niaga’s mobile phone account. These variables include perceived usefulness, perceived ease of use, perceived risk, perceived trust, and perceived satisfaction. Using 70 respondents who hold CIMB Niaga’s mobile phone account, the regression analysis reveals that, unless perceived risk, all variables in the TAM variables positively influence the continued use of mobile phone accounts. Perceived risk negatively impacts the usage and continuation of mobile phone accounts. This study provides evidence to the management of the CIMB bank regarding factors that may encourage the frequency of using a digital banking product.

Faktor Influencing CIMB Niaga's Mobile Phone Account Continuation Usage: A Study of Technology Acceptance Model (TAM)

As smartphone capabilities, technological complexity, and other developments increase, banks must adapt to meet demand. CIMB Niaga, a private-owned bank, has some digital banking products, one of which is a mobile phone savings account. The objective of this study is to prove and analyze whether the TAM variables affect the continued use of CIMB Niaga’s mobile phone account. These variables include perceived usefulness, perceived ease of use, perceived risk, perceived trust, and perceived satisfaction. Using 70 respondents who hold CIMB Niaga’s mobile phone account, the regression analysis reveals that, unless perceived risk, all variables in the TAM variables positively influence the continued use of mobile phone accounts. Perceived risk negatively impacts the usage and continuation of mobile phone accounts. This study provides evidence to the management of the CIMB bank regarding factors that may encourage the frequency of using a digital banking product.

Use of Smartphone-Based Video Directly Observed Therapy to Increase Tuberculosis Medication Adherence: An Interventional Study.

Tuberculosis (TB) treatment through Directly Observed Therapy (DOT) has an alternative form of video surveillance therapy (VOT) that utilizes the technological capabilities of smartphones to provide patients with low-cost access to doctors without impacting their work and personal life. We aimed to assess TB patients' drug compliance, perceptions, and feasibility towards smartphone-based video direct observed therapy (VDOT) in Jeddah, KSA. We conducted a prospective non-randomized interventional study. We delivered smartphone-based VDOT among previously unstudied patients to monitor adherence to the treatment regimen. The expected total number of VDOT sessions was1200. We conducted post-intervention interviews to assess acceptability and satisfaction. In this study, we included 20 participants, 16 of whom were males, with a mean age of 34.3 (±12.5) years. No side effects to the treatments were identified in all participants. The adherence rate for the total period was 93% and 99.5%, measured by the first and second methods, respectively. Most participants were satisfied with the VDOT experience, the time spent on sessions, and the approach's privacy. This study provides promising results for the feasibility and effectiveness of smartphone-based VDOT for TB treatment to increase adherence which was indicated by a high compliance rate, acceptability, and high satisfaction level.

Diabetes type i self-monitoring using mobile devices: architecture blueprint using cloud and machine learning

The modern capabilities of smartphones, cloud technologies and machine learning techniques have created possibilities of creation of innovative approaches to monitor chronic deseases. This study represents an architecture blueprint aimed at improving the efficiency of type I diabetes self-monitoring with help of mobile devices. The approach is based on a machine learning algorithm trained on diverse data sets, which offers users insights and personalized health recommendations. The platform helps improving the accuracy of diabetes tracking, provides people with immediate feedback based on history analytical data. The study highlights the merge of medical and technology fields and set a baseground for future improvements.

The Role of Telemedicine Technology in Stroke Patient Care

Technological advances in the health sector are one of the factors in meeting optimal health, especially in places that provide health services. The use of technology in helping health services is one of the uses of telemedicine technology in treating stroke patients. This writing aims to discover the role of telemedicine technology in the care of stroke patients. The research method used in this study is descriptive-analytical. The study was conducted to analyze selected literature from various sources, which will later become a new idea related to the role of telemedicine technology in the care of stroke patients. The results of this study show that Telemedicine is considered very important for handling and providing therapy to patients, which is undoubtedly supported by the correct diagnosis. Telemedicine with health interventions through the capabilities of information technology, either smartphones or other wireless networks, extensively provides benefits and opportunities for the world of health to have a good influence in improving the quality of life of patients with stroke.

Circular economy meets building automation

This paper demonstrates the concept of reusing discarded smartphones to connect the end-of-life of e-wastes with the start-of-life of smart buildings. Two control-related and one communication-related case studies have been conducted experimentally to evaluate the applicability. Diverse controlled systems, control tasks, and algorithms have been considered. In addition, the sufficiency of communication with external agents has been quantified. The proof-of-concept experiments indicate the technical feasibility and applicability to typical tasks with satisfactory performance. As the capabilities of smartphones improve over time, higher computing performance and lower communication latency can be expected, which enhances the prospect of the proposed reuse concept.