Mobile Camera Research Articles

Non-invasive blood glucose estimation method based on the phase delay between oxy- and deoxyhemoglobin using visible and near-infrared spectroscopy.

Many researchers have attempted to estimate blood glucose levels (BGLs) noninvasively using near-infrared (NIR) spectroscopy. However, the optical absorption change induced by blood glucose is weak in the NIR region and often masked by interference from other components such as water and hemoglobin. Instead of using direct optical absorption by glucose, this study proposes an index calculated from oxy- and deoxyhemoglobin signals that shows a good correlation with BGLs while using conventional visible and NIR spectroscopy. The metabolic index, which is based on tissue oxygen consumption, was derived through analytical methods and further verified and reproduced in a series of glucose challenge experiments. Blood glucose estimation units were prototyped by utilizing commercially available smart devices. Our experimental results showed that the phase delay between the oxy- and deoxyhemoglobin signals in near-infrared spectroscopy correlates with BGL measured by a conventional continuous glucose monitor. The proposed method was also confirmed to work well with visible spectroscopy systems based on smartphone cameras. The proposed method also demonstrated excellent repeatability in results from a total of 19 oral challenge tests. This study demonstrated the feasibility of non-invasive glucose monitoring using existing photoplethysmography sensors for pulse oximeters and smartwatches. Evaluating the proposed method in diabetic or unhealthy individuals may serve to further increase its practicality.

Feasibility of images acquired using smartphone camera for marginal and internal fit of fixed dental prosthesis: comparison and correlation study

This study aimed to measure marginal and internal fit using images captured with both an optical microscope and a smartphone camera, comparing the fit measurement performance of these devices and analyzing their correlation. Working casts (with 10 posterior and 10 anterior teeth) created to fabricate fixed dental prostheses were used. These working casts were scanned using a desktop scanner (E1) to design an interim crown, and the designed interim crown was fabricated using a three-dimensional (3D) printer. Utilizing the silicone replica technique, the fabricated interim crown replicated the fit, which was then captured using both an optical microscope and a smartphone camera. The captured images were used to measure the marginal and internal fit according to the imaging device. Intraclass correlation coefficients (ICC) were used for reliability analysis according to the imaging device. Furthermore, the Wilcoxon signed-rank test was adopted for the comparative evaluation of the marginal and internal fit between the imaging devices (α = 0.05). The measurement results of the marginal and internal fit according to the optical microscope and smartphone camera did exhibit a significant difference (P < 0.05). The ICC between the two devices showed an “excellent” agreement of over 0.9 at all measurement points (P < 0.001). A smartphone camera could be used to obtain images for evaluating the marginal and internal fit.

94 Smartphone integration in anemia detection for small ruminants with Convolutional Neural Networks

Abstract Anemia, often caused by internal parasites like Haemonchus contortus, presents significant health and productivity challenges for small ruminants. The primary goal of this study was to accurately distinguish between healthy and anemic goats using an image classification system focused on eye conjunctiva images. Over 5,000 eye conjunctiva images from 75 goats were collected at Fort Valley State University farms over a 3-mo period using smartphone cameras. These images were randomly divided into training (70%) and testing (30%) datasets, with each group containing five subfolders corresponding to FAMACHA scores of 1, 2, 3, 4, and 5. A Convolutional Neural Network (CNN) algorithm was utilized for image analysis, incorporating data augmentation techniques such as Resize, RandomHorizontalFlip, RandomVerticalFlip, and RandomRotation. The CNN model was built on the Google Colaboratory platform using CUDA 11.2 and the PyTorch machine learning framework, incorporating three ConvNet layers. The model training used the Adam Optimizer with a slower learning rate of 0.001 and a weight decay of 0.0001 to prevent exploding gradient issues, alongside ReLU and the cross-entropy loss function over 1000 epochs. Results demonstrate that the CNN model was highly effective in classifying eye conjunctiva images of goats to detect anemia based on FAMACHA scores.

Microsurgical Practice with Use of Smartphone Camera as the Microscopic Field.

Accessibility of microsurgical equipment is a major barrier to proper training of surgeons before live patient free flap surgery. A technique is presented that uses a smartphone camera as the microscopic field, eliminating the need for an expensive operative microscope for surgical practice. A convenient and cost-effective simulation protocol could reduce the time frame of the microsurgery learning curve. Furthermore, the use of the smartphone video function may allow improved feedback by mentors, improving access and communication between microsurgical teachers and learners. The PocketSuture smartphone stand is a commercially available device that allows the smartphone camera to be used as magnification. The proposed education protocol included suture practice, vessel dissection, and free tissue transfer in nonliving animal models, with vessel anastomosis and patency confirmation performed with a smartphone camera for field magnification. Video of the suturing technique allowed feedback from the mentor. A progressive suturing protocol leading to the ability to perform microsurgical anastomosis on nonliving animal models was developed. The basic costs for the stand, instrument set and suture were less than $500. The PocketSuture smartphone stand can be used for microsurgical training with real-time video for plastic surgery learners with limited access to microscopes and local mentors.

How to Optimize Epidermal Approximation During Wound Suturing Using a Smartphone Camera

How to Optimize Epidermal Approximation During Wound Suturing Using a Smartphone Camera

Facial recognition based on enhanced neural network

Accurate automatic face recognition (FR) has only become a practical goal of biometrics research in recent years. Detection and recognition are the primary steps for identifying faces in this research, and The Viola-Jones algorithm implements to discover faces in images. This paper presents a neural network solution called modify bidirectional associative memory (MBAM). The basic idea is to recognize the image of a human's face, extract the face image, enter it into the MBAM, and identify it. The output ID for the face image from the network should be similar to the ID for the image entered previously in the training phase. The tests have conducted using the suggested model using 100 images. Results show that FR accuracy is 100% for all images used, and the accuracy after adding noise is the proportions that differ between the images used according to the noise ratio. Recognition results for the mobile camera images were more satisfactory than those for the Face94 dataset.

Validity of resting heart rate derived from contact-based smartphone photoplethysmography compared with electrocardiography: a scoping review and checklist for optimal acquisition and reporting.

With the rise of smartphone ownership and increasing evidence to support the suitability of smartphone usage in healthcare, the light source and smartphone camera could be utilized to perform photoplethysmography (PPG) for the assessment of vital signs, such as heart rate (HR). However, until rigorous validity assessment has been conducted, PPG will have limited use in clinical settings. We aimed to conduct a scoping review assessing the validity of resting heart rate (RHR) acquisition from PPG utilizing contact-based smartphone devices. Our four specific objectives of this scoping review were to (1) conduct a systematic search of the published literature concerning contact-based smartphone device-derived PPG, (2) map study characteristics and methodologies, (3) identify if methodological and technological advancements have been made, and (4) provide recommendations for the advancement of the investigative area. ScienceDirect, PubMed and SPORTDiscus were searched for relevant studies between January 1st, 2007, and November 6th, 2022. Filters were applied to ensure only literature written in English were included. Reference lists of included studies were manually searched for additional eligible studies. In total 10 articles were included. Articles varied in terms of methodology including study characteristics, index measurement characteristics, criterion measurement characteristics, and experimental procedure. Additionally, there were variations in reporting details including primary outcome measure and measure of validity. However, all studies reached the same conclusion, with agreement ranging between good to very strong and correlations ranging from r = .98 to 1. Smartphone applications measuring RHR derived from contact-based smartphone PPG appear to agree with gold standard electrocardiography (ECG) in healthy subjects. However, agreement was established under highly controlled conditions. Future research could investigate their validity and consider effective approaches that transfer these methods from laboratory conditions into the "real-world", in both healthy and clinical populations.

Keyboard and Mouse Free Music Controller

Abstract: Human-machine interface (HMI) is a crucial area of research as gestures have the potential to efficiently control and interact with computers. Many applications for hand detection have been created as a result of the pervasive use of built-in cameras in computers, smartphones, and tablets. For the majority of users, however, many of these are not useful. A straightforward concept for a keyboard- and mouse-free music controller is presented in this research. Using MATLAB code that integrates skin detection, area labelling, erosion, dilation, and motion differentiation, a music player controller is developed using the real-time frame tracking feature of a camera. Three hand detection algorithms are created and assessed for maximum performance and accuracy. Real-time hand detection for operating the music player is provided by the algorithm, which is created with efficiency and speed in mind.

Mobile-Based convolutional neural network model for the early identification of banana diseases

This study aimed to deploy a deep learning model in a mobile application for the early identification of Fusarium Wilt and Black Sigatoka in bananas. In this paper, a Convolutional Neural Network (CNN) model for the classification of Black Sigatoka banana disease and Fusarium Wilt disease is assessed. A dataset of 27,360 images of diseased and healthy banana leaves and stalks that were collected from the farms using a mobile phone camera served as the training data for this model. An extra class of 407 images that are not of the banana plant downloaded from the internet was used to help the model detect other images not of the banana plant. The CNN model achieved an accuracy of 91.17 % and was deployed in a mobile application for the classification of the diseases. This study shows that deep learning can be implemented and assist in the early identification of banana diseases. The application could detect images of healthy and diseased banana leaves and stalks and images not of the banana plant with a confidence score of more than 90 % in less than five seconds per image and provide research-based mitigation recommendations.

Towards ultra-low-cost smartphone microscopy.

The outbreak of COVID-19 exposed the inadequacy of our technical tools for home health surveillance, and recent studies have shown the potential of smartphones as a universal optical microscopic imaging platform for such applications. However, most of them use laboratory-grade optomechanical components and transmitted illuminations to ensure focus tuning capability and imaging quality, which keeps the cost of the equipment high. Here, we propose an ultra-low-cost solution for smartphone microscopy. To realize focus tunability, we designed a seesaw-like structure capable of converting large displacements on one side into small displacements on the other (reduced to ∼9.1%), which leverages the intrinsic flexibility of 3D printing materials. We achieved a focus-tuning accuracy of ∼5 𝜇m, which is 40 times higher than the machining accuracy of the 3D-printed lens holder itself. For microscopic imaging, we used an off-the-shelf smartphone camera lens as the objective and the built-in flashlight as the illumination. To compensate for the resulting image quality degradation, we developed a learning-based image enhancement method. We used the CycleGAN architecture to establish the mapping from smartphone microscope images to benchtop microscope images without pairing. We verified the imaging performance on different biomedical samples. Except for the smartphone, we kept the full costs of the device under 4 USD. We think these efforts to lower the costs of smartphone microscopes will benefit their applications in various scenarios, such as point-of-care testing, on-site diagnosis, and home health surveillance. RESEARCH HIGHLIGHTS: We propose a solution for ultra-low-cost smartphone microscopy. Utilizing the flexibility of 3D-printed material, we can achieve focusing accuracy of ∼5 𝜇m. Such a low-cost device will benefit point-of-care diagnosis and home health surveillance.

Enforcement of Asn Discipline Laws Based on The Online Personnel System Application And Its Impact on Performance In The Office of The Lubuk Sikarah District, Solok City

This research examines disciplinary law enforcement through the Online Civil Service System (KEPO), its obstacles and impact on ASN discipline. This type of research is normative juridical supported by empirical juridical. The data used is secondary and primary data. Secondary data was obtained from document studies and primary data was obtained by interviews. The data obtained was analyzed qualitatively, presented in a qualitative descriptive manner. First research results: In general, enforcement of disciplinary law through the KEPO system for ASN at the Kantor Camat Lubuk Sikarah is less effective. Even though discipline has been enforced by reducing or cutting additional employee income in accordance with Solok Mayor Regulation Number 17 of 2022 concerning Providing Additional Income to Regional ASN Employees. Second, the obstacles in enforcing disciplinary law through the KEPO system are: a) the legal factor is still weak: regulating the presence of a 50 meter radius, b) the law enforcement factor is still reluctant, c) the server facility factor is inadequate, d) the community understanding factor ASN in Kantor Camat Lubuk Sikarah regarding KEPO, e) Cultural Factors in the work environment in Lubuk Sikarah District, ASN's culture is reticent. Another finding from the research: there are ASNs who do not yet have an Android cellphone (camera) and a stable internet network. Meanwhile, the impact of implementing or enforcing ASN disciplinary laws on ASN performance in general has relatively increased, although it is still not effective.

An integrated liposome-based microfluidic strategy for rapid colorimetric analysis: A case study of microRNA-21 detection

In this study, a novel integrated liposome-based microfluidic platform combined with a smartphone was designed for the rapid colorimetric detection of microRNA-21 (miRNA-21) in real samples. The flowing surface-functionalized liposomes were first captured by nucleic acid-functionalized Au nanoparticles in the microfluidic chip. In the presence of miRNA-21, the DNA strand modified on the surface of Au nanoparticles hybridized with the target to form double-stranded products and was cleaved by duplex-specific nuclease (DSN) enzyme, causing the liposomes to be re-released. Then, as the liposomes in the colorimetric module were lysed and the “cellular” contents were released, a step-by-step “glucose-glucose oxidase-3,3′,5,5′-tetramethylbenzidine (TMB)” colorimetric reaction process catalyzed by the G-quadruplex/hemin was triggered. The grayscale values were recorded and recognized by the smartphone camera for miRNA-21 analysis. The advantages of the present strategy included the portability of smartphone-based colorimetric assay, the encapsulation and transport of reactants by liposomes and the low solvent usage of microfluidic chip. Under optimal conditions, this assay exhibited a wide linear range from 1 pM to 1 nM (r2 = 0.9981), and the limit of detection of miRNA-21 was as low as 0.27 pM. Moreover, the high specificity of this strategy allowed its successful application to the rapid analysis of miRNA-21 in real blood serum samples of people with type 2 diabetes.

Model for Determining the Psycho-Emotional State of a Person Based on Multimodal Data Analysis

The paper aims to develop an information system for human emotion recognition in streaming data obtained from a PC or smartphone camera, using different methods of modality merging (image, sound and text). The objects of research are the facial expressions, the emotional color of the tone of a conversation and the text transmitted by a person. The paper proposes different neural network structures for emotion recognition based on unimodal flows and models for the margin of the multimodal data. The analysis determined that the best classification accuracy is obtained for systems with data fusion after processing each channel separately and obtaining individual characteristics. The final analysis of the model based on data from a camera and microphone or recording or broadcast of the screen, which were received in the “live” mode, gave a clear understanding that the quality of the obtained results is highly dependent on the quality of the data preparation and labeling. This is directly related to the fact that the data on which the neural network is trained is highly qualified. The neural network with combined data on the penultimate layer allows a psycho-emotional state recognition accuracy of 0.90 to be obtained. The spatial distribution of emotion analysis was also analyzed for each data modality. The model with late fusion of multimodal data demonstrated the best recognition accuracy.

The Mobilizing Power of Visual Media Across Stages of Social-Mediated Protests

ABSTRACT The popularity of camera phones, the availability of photo-editing apps, and the rise of visually oriented social media platforms have made it convenient for citizens to produce and circulate visual content in contentious politics. While scholars have increasingly recognized the role of visuals in mobilizing social-mediated protests, how different types of visuals affect message engagement across different stages of protests remains underexplored. For this study, we analyzed approximately ten million tweets from Twitter for three social-mediated protests (Black Lives Matter, Stop Asian Hate, and Women’s March). We found that posts with images and videos generally attracted more audience engagement than their textual counterparts. Unpacking the role of visual media across different modalities and stages of social-mediated protests, we found that the superior effects of visuals were generally more pronounced during the ignition phase of the protest than the periods before and after. By applying unsupervised image clustering on millions of protest visuals, we systematically established four common visual content categories: crowd-based protest photos, non-crowd-protest human photos, non-human photos, and non-photograph visuals. We revealed heterogeneous effects on audience engagement across content categories and protests, and explored these categories through qualitative analysis of most-engaged visuals. These findings enrich our understanding of the mobilizing power of visual media in social movements and shed light on effective communication strategies regarding social inequalities.

Dual-FRET aptasensor for rapid screening of Ochratoxin A in food samples

Simple, low cost, and sensitive OTA screening method is important for food safety control. The discovery of the OTA aptamer enabled the development of various aptasensors, but the structural change caused by target binding is not appreciable enough, thus limiting the sensitivity. The introduction of various nanomateirals could amplify the structure change, but such aptasensors are difficult to be standardized, thus is not suitable for screening analysis. Herein, we proposed a full DNA-based dual-FRET aptasensor, in which the aptamer was rigidified to amplify the OTA-induced structure change. Meanwhile, a dual FRET strategy was employed to increase the OTA detection sensitivity. Upon both experimental and theoretical selection, aptamer OBA33 was selected, leading to a detection limit of 2.1 nM that is lower than the limit set by Chinese National Food Safety Standard (12 nM), thus is appealing for screening of potential OTA-positive cases in a large number of rice samples. To facilitate rapid screening analysis, a mini 3D-printed sensor device was constructed by integrating LED excitation, cellphone camera image taking, and RGB extraction. Due to the technological maturity of DNA production, the proposed aptasensor could be easily standardized for fast OTA screening analysis (15 min).

Evaluation of smartphone-assisted infrared thermal imaging efficiency in carpal tunnel syndrome

BackgroundThermography is an industrial method for surface temperature measurements, and although it is medically safe and non-invasive, its place in daily practice has been limited. With the development of technology, thermal cameras have become more accessible and practical via adaptation to mobile phones. Among patients evaluated with bilateral nerve conduction studies (NCS) for suspected carpal tunnel syndrome (CTS), those with electrophysiological findings consistent with mild-to-moderate unilateral CTS were accepted for this prospective study. The hands with positive NCS findings were the study group, and the unaffected hands were the control group. The images were evaluated with the thermal analysis software (FLIR Tools ver. 6.4, Windows 10) and compared with NCS for statistical significance. In addition, thermal images were examined by three orthopaedic surgeons, and interobserver correlation was analyzed. Our study aims to evaluate the mobile phone-assisted thermal camera (FLIR One Pro, FLIR Systems, Wilsonville, OR, USA) as a suitable tool to diagnose CTS.Results48 patients, 35 women and 13 men were included in the study. Bilaterally, a total of 96 hands were evaluated. 18 patients had mild, and 30 patients had moderate NCS stages unilaterally. The mean temperature difference at the region of interest in the palm, first and third fingers were statistically significant between the study and control groups (p < 0.05). NCS values and stages were correlated with the temperature difference in the third finger (p = 0.002). The inter-observer reliability was high (ICC = 0.858) while detecting temperature differences.ConclusionsSince smartphone-assisted thermal cameras are easy and convenient to use, we think they are helpful in the daily practice of diagnosing mild-to-moderate carpal tunnel syndrome.

Development of an Autonomous Component Testing System with Reliability Improvement Using Computer Vision and Machine Learning

This study evaluated computer vision-based models, including Histogram Analysis, Logistic Regression, Sift-SVM, and Deep learning models, in an autonomous testing system developed for smartphone camera modules. System performance was assessed in a practical factory setting with workers operating the system, and metrics such as processing time, sensitivity, specificity, accuracy, and defect rate were evaluated. Based on the results, the Sift-SVM model demonstrated the greatest potential for enhancing the reliability of the system with a processing time of 0.01578 seconds, a sensitivity of 99.811%, and a reduction in the failure rate to 1888 PPM. The study findings suggest that Sift-SVM has the potential to be practically applied in the industry, thus improving the speed and accuracy of automatic defect detection in manufacturing and reducing the defect rate.

Penerapan Aplikasi Klasifikasi Hukum Tajwid Menggunakan Image Processing

Tajwid is an important science that regulates the way of reading the verses of the Al-Qur’an properly. Learning Tajwid means knowing the meaning that corresponds to the correct recitation. Learning to read the Al-Qur’an tends to be done traditionally in a place of learning or by calling a teacher to the house. Learning in this way has some drawbacks, such as the limited availability of trained and competent teachers because not all areas have sufficient access to these teachers. Dependence on schedules and locations can be a constraint for students with limited mobility or busy schedules. The role of the teacher is still important in learning tajwid, especially in providing effective explanations, guidance, and feedback. However, to overcome these shortcomings, integration with independent and technology-based learning methods can help improve the accessibility, flexibility, and quality of tajwid learning. The classification of tajwid laws using image processing allows users to see the results of inputting images of verses of the Al-Qur’an into the type of detected nun sukun tajwid and how to recite it. The initial stage of this system in detecting tajwid laws from uploaded images is the input of images by users, which can be done in two ways, namely by directly taking pictures using a smartphone camera or uploading images from the gallery. This is followed by the OCR process to detect the Arabic text contained in the image and provide diacritics for that Arabic text. Finally, letter classification is carried out after nun sukun and classification of tajwid laws contained in accordance with the detected letters after nun sukun. This system has an accuracy rate of 92.18% from the classification results that have been carried out.

APPLICATION OF AUGMENTED REALITY IN MOBILE BASED HIJAIYAH LETTER RECOGNITION APPLICATIONS USING MULTIMEDIA DEVELOPMENT METHODS LIFE CYCLE

Islamic religious education is an important aspect of Muslim life, and learning Arabic and Hijaiyah letters is an integral part of Islamic education. The use of augmented reality (AR) technology in educational setting is rapidly increasing. This study aims to apply the AR concepts to the creation of mobile Hijaiyah letters using Multimedia Development Life Cycle (MDLC) methodology. The application of AR in this application allows users to interactively learn each hijaiyah letter. Through a mobile device camera, users can see hijaiyah letters that appear virtually on the device screen. The development of this application is based on the needs of users in understanding hijaiyah letters in a more attractive and interactive way. Thus, this application is expected to facilitate users, especially children, to learn hijaiyah letters more effectively.

Analisis Faktor-faktor yang Mempengaruhi Minat Masyarakat Menabung di Bank Syariah Indonesia

Islamic banking is an Islamic economic phenomenon that has become an alternative and increasingly popular with people around the world, including millennials. Not only to accommodate the interests of Muslims, but also to integrate personal interests and community benefits in a balanced form. Islamic banking in Indonesia is currently experiencing rapid development. As a country with the largest Muslim population in the world, Indonesia certainly has a very important role in building the Islamic economy. This can be used as a great opportunity by Bank Syariah Indonesia in optimizing its role to support the country's plan to develop Islamic finance in Indonesia. It can be seen that the number of customers at Bank Syariah Indonesia is still far from expectations when viewed from the large Muslim population in Indonesia. This indicates that there are still many Muslim residents in Indonesia who still do not use the services of Sharia Banks in Indonesia. This indicates that there are still many Muslim residents in Indonesia who still do not use the services of Sharia Banks in Indonesia. This can arise by several factors that are the reason why the Indonesian Muslim population is still reluctant to use the services of Bank Syariah Indonesia. The large gap between the Muslim population in Indonesia and the number of customers of Bank Syariah Indonesia is one of the attractions for researchers to conduct an analysis of the Factors Influencing Public Interest in Saving at Bank Syariah Indonesia (BSI). Interest can be interpreted as an interest or tendency to choose what is desired. One example is the interest and tendency towards interest in saving, for example a religiously inclined person will choose Islamic banks over conventional banks because based on Islamic religious law which prohibits his people from transacting ribawi (interest). According to (crow &amp; crow 1998) states that there are six that affect interest, namely: income factors, job stability factors, financial goal factors, risk avoidance factors, knowledge factors, social and cultural environmental factors. While the author in conducting this study used descriptive qualitative methods with data collection techniques used was to conduct structured interviews to the community with the help of research instruments in the form of mobile phone cameras as photo documentation and recordings during interviews with the people of Bandung City which at this time became a source of research information. The results obtained from research conducted by the author to the people of Bandung city resulted in income, risk avoidance / security factors and social and cultural environmental factors are factors for people interested in saving their money in Indonesian Islamic banks (BSI).&#x0D; Keywords: Bank Syariah Indonesia, community, Interest.