Mobile Camera Research Articles

Inclusive and Accurate Clinical Diagnostics Using Intelligent Computation and Smartphone Imaging.

Smartphone-based colorimetry has been widely applied in clinical analysis, although significant challenges remain in its practical implementation, including the need to consider biases introduced by the ambient imaging environment, which limit its potential within a clinical decision pathway. In addition, most commercial devices demonstrate variability introduced by manufacturer-to-manufacturer differences. Here, we undertake a systematic characterization of the potential imaging interferences that lead to this limited performance in conventional smartphones and, in doing so, provide a comprehensive new understanding of smartphone color imaging. Through derivation of a strongly correlated parameter for sample quantification, we enable real-time imaging, which for the first time, takes the first steps to turning the mobile phone camera into an analytical instrument - irrespective of model, software, and the operating systems used. We demonstrate clinical applicability through the imaging of patients' skin, enabling rapid and convenient diagnosis of cyanosis and measurement of local oxygen concentration to a level that unlocks clinical decision-making for monitoring cardiovascular disease and anemia. Importantly, we show that our solution also accounts for the differences in individuals' skin tones as measured across the Fitzpatrick scale, overcoming potential clinically significant errors in current optical oximetry.

Validation of Automated Countermovement Vertical Jump Analysis: Markerless Pose Estimation vs. 3D Marker-Based Motion Capture System

This study aimed to validate the automated temporal analysis of countermovement vertical jump (CMJ) using MMPose, a markerless pose estimation framework, by comparing it with the gold-standard 3D marker-based motion capture system. Twelve participants performed five CMJ trials, which were simultaneously recorded using the marker-based system and two smartphone cameras capturing both sides of the body. Key kinematic points, including center of mass (CoM) and toe trajectories, were analyzed to determine jump phases and temporal variables. The agreement between methods was assessed using Bland–Altman analysis, root mean square error (RMSE), and Pearson’s correlation coefficient (r), while consistency was evaluated via intraclass correlation coefficient (ICC 3,1) and two-way repeated-measures ANOVA. Cohen’s effect size (d) quantified the practical significance of differences. Results showed strong agreement (r > 0.98) with minimal bias and narrow limits of agreement for most variables. The markerless system slightly overestimated jump height and CoM vertical velocity, but ICC values (ICC > 0.91) confirmed strong reliability. Cohen’s d values were near zero, indicating trivial differences, and no variability due to recording side was observed. Overall, MMPose proved to be a reliable alternative for in-field CMJ analysis, supporting its broader application in sports and rehabilitation settings.

WOUND SURFACE MEASUREMENT USING DIGITAL PLANIMETRY IN DOGS

The study was carried out to evaluate the feasibility and efficacy of digital planimetry apps for wound measurement in dogs. Twenty four clinical cases of dogs with the history of wound presented for wound management were randomly divided into four groups consisting of six animals each and wound measurements were taken on 0, 2, 4 and 7th day. The wound was measured with length, breadth, area and circumference using Imito Measure mobile application through smart phone camera. Based on reduction in wound size healing rate was assessed. Digital planimetry applications are accurate and precise in wound measurement compare to standard ruler method and tracing method.

Pixel Binning Effects of Smartphone Camera on Three-Dimensional (3D) Model Reconstructed Crime Scene

Pixel Binning Effects of Smartphone Camera on Three-Dimensional (3D) Model Reconstructed Crime Scene

Optical Image Processing using Eulerian Amplification

Recent advancements in digital photography, particularly through mobile phone cameras, microscopes, and satellite imaging, have made high-resolution image capture increasingly accessible. However, these captured images often contain subtle changes in color resolution and density that are challenging to observe with the naked eye. This study explores the application of Eulerian amplification as a method to detect and enhance such small-amplitude variations, making them visible for improved analysis. Unlike traditional approaches, Eulerian methods are particularly effective for processing smooth structures and high-quality printed materials where minor amplifications in color can reveal significant grading details. Using a pixel-by-pixel Eulerian path analysis, this method allows for a detailed comparison of color intensity values on a fine 8-bit scale, which ranges from 0 to 255. Through this analysis, color distribution patterns are visualized across the spatial structure of the image, which is particularly beneficial for identifying specific variations in quality in printed materials, such as highly graded cards. To manage large image structures, spatial decomposition using a multi-level Gaussian pyramid technique is employed. By applying temporal filtering within select frequency bands (0.4–4 Hz) at a coarse pyramid level, the algorithm effectively pools spatial data, enabling the detection of color density shifts that may indicate image defects. The amplified image data is further processed using a classifier neural network, which provides high sensitivity and specificity in detecting edge defects, image centering issues, scuffs, and breakages. The system demonstrates promising results in identifying true positives while maintaining low false-negative rates, thus confirming the reliability of deep machine learning algorithms in high-sensitivity defect detection. This paper presents the significance of Eulerian amplification in optical image processing, offering a robust tool for precise, automated defect detection across a variety of high-resolution image types.

Performance Analysis of a Color-Code-Based Optical Camera Communication System

In this study, we present a visible light communication (VLC) system that analyzes the performance of an optical camera communication (OCC) system, utilizing a mobile phone camera as the receiver and a computer monitor as the transmitter. By creating color channels in the form of a 4 × 4 matrix within a frame, we determine the parameters that affect the successful transmission of data packets. Factors such as the brightness or darkness of the test room, the light color of the lamp in the illuminated environment, the effects of daylight when the monitor is positioned in front of a window, and issues related to dead pixels and light bleed originating from the monitor’s production process have been considered to ensure accurate data transmission. In this context, we utilized the PyCharm, Pydroid, Python, Tkinter, and OpenCV platforms for programming the transmitter and receiver units. Through the application of image processing techniques, we mitigated the effects of daylight on communication performance, thereby proposing a superior system compared to standard VLC systems that incorporate photodiodes. Additionally, considering objectives such as the maximum number of channels and the maximum distance, we regulated the sizes of the channels, the distances between the channels, and the number of channels. The NumPy library, compatible with Python–Tkinter, was employed to determine the color levels and dimensions of the channels. We investigate the effects of RGB and HSV color spaces on the data transmission rate and communication distance. Furthermore, the impact of the distance between color channels on color detection performance is discussed in detail.

Smartphone based TLC approach versus conventional densitometric measurement for the simultaneous determination of donepezil and memantine, content uniformity testing along with greenness and whiteness assessment

Thin layer chromatography (TLC) is a straightforward and economical technique that plays a major role in analytical field. Introducing the amenities of smartphones as accessible alternative detectors to TLC separation methods elaborates the cost-effectiveness of this technique and supports the appropriate assay of various aliphatic compounds after visualization by suitable reagents. Herein, two TLC coupled with smartphone's camera approaches are proposed for the assay of donepezil hydrochloride (DON) and memantine hydrochloride (MEM), a combined medication used in the management of Alzheimer's disease. Separation was achieved using a developing system of ethyl acetate, methanol, toluene, and ammonia (33%, w/v) in a ratio of 4:1:4:0.2, by volume. The dried plates were scanned at 190 nm for the densitometric assay, then visualized with modified Dragendorff's reagent and photographed by smartphone's camera. The data were integrated by displaying the photos in either ImageJ software or Color-Picker application. The findings produced from the three detection approaches were contrasted over concentration ranges of 1–18 & 4–28 μg/spot for the densitometric method, while the range was 1–20 & 1–30 μg/spot for the ImageJ method and 3–18 & 2–20 μg/spot for the Color-Picker method for DON and MEM, respectively. The methods were suitable for assaying both drugs in their pure form and combined pharmaceutical formulations. Densitometric and ImageJ-based methods were applied to evaluate the dosage forms content uniformity. The developed methods were assessed by three metric tools for greenness and sustainability, namely Green Analytical Procedure Index, Analytical GREEnness Metric Approach, and White Analytical Chemistry.

Smartphone three-dimensional imaging for body composition assessment using non-rigid avatar reconstruction.

Modern digital anthropometry applications utilize smartphone cameras to rapidly construct three-dimensional humanoid avatars, quantify relevant anthropometric variables, and estimate body composition. In the present study, 131 participants ([73 M, 58 F] age 33.7 ± 16.0 y; BMI 27.3 ± 5.9 kg/m2, body fat 29.9 ± 9.9%) had their body composition assessed using dual-energy X-ray absorptiometry (DXA) and a smartphone 3D scanning application using non-rigid avatar reconstruction. The performance of two new body fat % estimation equations was evaluated through reliability and validity statistics, Bland-Altman analysis, and equivalence testing. In the reliability analysis, the technical error of the measurement and intraclass correlation coefficient were 0.5-0.7% and 0.996-0.997, respectively. Both estimation equations demonstrated statistical equivalence with DXA based on ±2% equivalence regions and strong linear relationships (Pearson's r 0.90; concordance correlation coefficient 0.89-0.90). Across equations, mean absolute error and standard error of the estimate values were ~ 3.5% and ~ 4.2%, respectively. No proportional bias was observed. While continual advances are likely, smartphone-based 3D scanning may now be suitable for implementation for rapid and accessible body measurement in a variety of applications.

Predicting Counterfeits from Smartphone Multi-Images Using Deep Learning

Counterfeiting is a pervasive issue in the collectible card market, posing significant risks to collectors, investors, and the industry at large. The trade of counterfeit cards not only undermines the value of genuine collectibles but also deceives consumers who invest their resources into fraudulent items. Despite advancements in counterfeit detection technologies, many counterfeit cards remain undiagnosed and continue to circulate in the marketplace, primarily due to the reliance on traditional methods of verification, which are often time-consuming and require expert evaluation. This study aims to leverage the increasing capabilities of widely available consumer technologies, specifically smartphones, to develop an innovative approach for the early detection of counterfeit collectible cards. By utilizing the multi-image signals acquired from smartphone cameras, we hypothesize that significant differences in color, type font, and material reflectiveness associated with counterfeit cards can be identified through machine learning techniques, particularly convolutional neural networks (CNNs). To evaluate this hypothesis, we analyzed a comprehensive dataset of 22,298 individual trading cards collected through the Digital Grading Company smartphone app. The dataset consisted of user-submitted images, which were systematically categorized into training, development, and test datasets to facilitate model training and validation. A robust 34-layer CNN architecture was employed to analyze these multi-image signals and predict the prevalence of counterfeit cards. The model's performance was measured using the area under the receiver operating characteristic curve (AUC), providing a quantitative assessment of its discriminatory ability. Our results revealed that of the total cards analyzed, 6.0% were identified as counterfeit, with the CNN model achieving an AUC of 0.772 (95% CI 0.747 - 0.797) in the test dataset. This indicates a reasonable level of discrimination in detecting counterfeit cards based solely on the multi-image data. The findings suggest that deep learning technologies can significantly enhance counterfeit detection processes, providing collectors and industry stakeholders with a powerful tool to combat fraud. This study represents the first proof-of-concept demonstration of utilizing smartphone-based imaging for the detection of counterfeits in collectible trading cards. By validating the effectiveness of deep learning in this context, we pave the way for future research that can explore more sophisticated algorithms and techniques to further improve detection accuracy. Ultimately, our approach could lead to the development of accessible applications for collectors and consumers, empowering them to make informed decisions and protect their investments in the collectible market.

Superwettable microchip for visual detection of date-rape drug in beverages

Gamma-hydroxybutyric acid (GHB), which is used in drug-facilitated sexual assault, is typically delivered in the form of gamma-butyrolactone (GBL) in beverage carriers. In response to the growing need of methods for rapid and on-site sensing of the drugs, we carried out an investigation aimed at developing a superwettable microchip for visual detection of the date-rape drug GBL in adulterated beverages. The superwettable microchip is comprised of Fe3O4 nanoparticles which aggregate to form a reactive spot on a superhydrophobic fabric. The spot captures and confines microdroplets containing GBL on the superhydrophobic substrate. Fe(III), generated by HCl promoted reaction of the Fe3O4 spot, then participates in a colorimetric reaction with GBL to form a naked eye observable colored complex. Quantitative determination of GBL in beverages is achieved by analyzing the red proportion of a smartphone camera derived image. The detection range of this assay protocol is ca. 0.1–10 mg mL−1, which meets the requirements to detect potentially dangerous concentration of GBL in drinks. The detection stability of the superwettable microchip for GBL in water and common beverages is maintained over at least 30 cycles.

Development of CadR-based cadmium whole cell biosensor for visual detection of environmental Cd2+

BackgroundAs a threat to human health and public health, cadmium (Cd) pollution has received widespread social concern. Our previously constructed CadR-based bacterial whole cell biosensor (WCB) epCadR5 showed high sensitivity and specificity in cadmium detection. However, the application of the sensor is still hindered by the need for laboratory equipment to read the fluorescence signal output. In this study, we aimed to optimizing the sensor to make it available for visual detection of environmental cadmium and simplify the detection process to advance practical application of the sensor. ResultsBy replacing the constitutive promoter with J110, the fluorescence signal output of the sensor was significantly increased and the fluorescence leakage was decreased. In addition, the fluorescence signal output of green fluorescence protein (GFP) was enhanced by the addition of a 5′ untranslated region (5′-UTR) mlcR10. The fluorescence signal output of the WCB is sufficiently robust to be visible and distinguishable to the naked eye, which is of paramount importance for visual detection. The sensor readout can be conveniently recorded by mobile phone camera and quantified. For ease of on-site application, the WCB's visual detection procedures and conditions were further optimized and simplified. The WCB demonstrated good linearity and detection limit (1.81 μg/L) for visual detection of Cd2+ without the assistance of bulky laboratory equipment. For the detection of real environmental samples, the WCB visual detection results were close to those of WCB-flow cytometry (FACS) and graphite furnace atomic absorption spectroscopy (GFAAS). SignificanceIn this work, we developed an easy-to-use, on-site and visual detection biosensor for monitoring environmental Cd2+. It will advance the utilization of cadmium WCBs in practical settings. The optimization and simplification strategy in the study also provide new insights into the visualization of other bacterial biosensors, and will advance the practical application of WCBs.

The development of rapid immunoassays for the urinary analysis of 1-hydroxypyrene glucuronide facilitate both laboratory and on-site polycyclic aromatic hydrocarbon biomonitoring

Polycylic Aromatic Hydrocarbons (PAHs) are produced during the incomplete burning of organic materials. PAH sources include vehicle exhaust, tobacco smoke and waste incineration. Environmental and occupational exposures to PAHs are known to occur. Cancer is a significant endpoint of PAH exposure and several occupations associated with high PAH exposure have been classified by IARC as carcinogenic to humans (Group 1).Pyrene is a common component of PAH mixtures and metabolism of pyrene leads to the excretion of 1-hydroxypyrene glucuronide (1-OHPyrG) in urine. Laboratory measurement of urinary 1-OHPyrG is employed in occupational and environmental biomonitoring programmes. The production of an anti-1-OHPyrG monoclonal antibody would allow the development of a PAH biomonitoring ELISA facilitating large scale laboratory screening and routine testing.The development of a lateral flow immunoassay and the production of a field test (point of use test) would greatly increase the value of biomonitoring. A novel Lateral Flow has been developed which employs an anti-1-OHPyrG sheep monoclonal antibody (Mab) to capture the PAH metabolite. The captured metabolite is visualised through a second Mab raised against the Mab-1-OHPyrG immune complex. This sandwich assay provides a positive correlation between the assay signal and biomarker concentration.A Smartphone camera allows signal measurement and a carefully considered ‘app’ provides result interpretation and data analysis. Results are provided in an exposed/not-exposed format. Performance of the lateral flow was confirmed through a comparative study and field trial. The development of a lateral flow test provides "real-time" analysis to occupational health professionals. On-site screening allows the immediate confirmation of safe working practice, provides immediate reassurance to those involved in potentially hazardous activities and greatly increases the efficacy of biomonitoring.

Thermal Threat Monitoring Using Thermal Image Analysis and Convolutional Neural Networks

Monitoring of the vital signs or environment of disabled people is currently very popular because it increases their safety, improves their quality of life and facilitates remote care. The article proposes a system for automatic protection against burns based on the detection of thermal threats intended for blind or visually impaired people. Deep learning methods and CNNs were used to analyze images recorded by mobile thermal cameras. The proposed algorithm analyses thermal images covering the field of view of a user for the presence of objects with high or very high temperatures. If the user’s hand appears in such an area, the procedure warning about the possibility of burns is activated and the algorithm generates an alarm. To achieve this effect, the thermal images were analyzed using the 15-layered convolutional neural network proposed in the article. The proposed solution provided the efficiency of detecting threat situations of over 99% for a set of more than 21,000 images. Tests were carried out for various network configurations, architecture and both the accuracy and precision of hand detection was 99.5%, whereas sensitivity reached 99.7%. The effectiveness of burn risk detection was 99.7%—a hot object—and the hand appeared simultaneously in the image. The presented method allows for quick, effective and automatic warning against thermal threats. The optimization of the model structure allows for its use with mobile devices such as smartphones and mobile thermal imaging cameras.

Visible diffuse reflectance smartphone spectrometer with high spectral accuracy

A smartphone-based spectrometer employing principle of diffuse reflection is reported for the surface analysis of solid samples. The instrument utilizes a thin-film grating to diffract incoming light, while a diffuse reflecting surface projects the image of this diffracted light onto the detector plane. The CMOS camera of smartphone camera directly captures the diffusely reflected photons within its limited field-of-view thus eliminating the need for collection, conditioning and converging optics. The optical setup of the instrument provides facility to calibrate the spectral response considering the nonlinear distribution of the wavelength across the diffraction direction. Additional correction in the detector response at different light intensity results a reduced spectral error with a maximum wavelength resolution of δλ=0.08 nm/pixel in the camera within the spectral range Δλ = (400 – 700) nm. As a proof of the concept, the instrument demonstrates successful detection of color pigments in food samples by absorption measurement of the samples at an average spectral error < 6 %. The distinct absorption peak associated with standard food colors are compared against the absorption profile of unknown food colors used in pastry cake. This field-functional smart analysis with internet connectivity opens opportunity of identifying food adulteration by using toxic chemical colors at the point-of-test and immediate reporting to others. The overall instrument is fabricated by utilizing low-cost and light weight plastic wood to make compact (110 mm × 105 mm × 125 mm), robust, inexpensive (∼$ 50) and suitable for field-portable (∼145 gm) hand-held operation.

Browser-Based Object Detection System for Isolating Plastic Bottles using the COCO-SSD Model

Plastic waste, especially in urban environments and water bodies, poses a significant environmental threat. This paper presents a browser-based object detection system for identifying and isolating plastic bottles using state-of-the-art machine learning models. The system leverages TensorFlow.js, ML5.js, and P5.js libraries along with the COCO-SSD model to detect plastic bottles in real time using a mobile camera interface. By employing a browser-based architecture, the system offers cross-platform functionality, eliminating the need for server-based computations or specialised hardware. Experimental evaluation showed high detection accuracy across various environments, underscoring the potential for real-world applications in waste management and recycling efforts.

Mantas Kvedaravičius’s Mariupolis (2016) and Šarūnas Bartas’s Frost (2017): Soft Advocacy for the Ukrainians in the Russo-Ukrainian War

This article examines the visual testimonies and evidence of the Russo-Ukrainian War in its initial phase, shortly after Russia annexed Crimea in 2014, in the films of two Lithuanian filmmakers, Šarūnas Bartas and Mantas Kvedaravičius, who took the stance of advocates and made their internationally acclaimed films, namely the mixed-genre feature Frost (2017, dir. Š. Bartas) and the documentary Mariupolis (2016, dir. M. Kvedaravičius), in war zones, when international media coverage of the ongoing military operations on Ukrainian territory was gradually decreasing, along with political and other support. In these films, viewers do not see the apparent warfare, the clashes between the Ukrainian army and Russian troops or separatist fighters. But they do see the testimonies of Ukrainians, captured on film and mobile phone cameras, and they see images of despair and anxiety, destroyed settlements and lives that prove that the war has been going on since 2014. The article explores what makes these films an instrument of soft advocacy for the Ukrainians in the war, drawing on the prevailing concepts of advocacy in film and media studies.

Using a Smartphone Camera at the Bedside to Detect and Quantify Residual Blood Clots in Single-Use Dialyzers with Paired-Image Classification

Using a Smartphone Camera at the Bedside to Detect and Quantify Residual Blood Clots in Single-Use Dialyzers with Paired-Image Classification

Nasal Light Reflex: A Useful Intraoperative Tool in Correction of Cleft Lip Nasal Deformity.

The correction of a unilateral cleft lip nasal deformity remains a challenge to cleft surgeons. It is difficult to obtain a routinely predictable outcome. This is in part due to there being no objective intraoperative method to assess the correction. We have come up with a simple objective intraoperative method to plan and assess the correction of the nasal deformity. This is done by assessing the nasal light reflex using a mobile phone camera to define the deformity. The points of the desired correction of the lower lateral cartilage are transposed onto the patient. Once the lip and nose repair is completed, another photograph is taken to assess the nasal light reflex and assess the extent of correction. If this is inadequate, further nasal correction is performed. We have used this procedure in 122 cleft lip patients with 93 complete and 29 incomplete. We have found this to be a useful objective intraoperative method to assist in obtaining improved nasal correction in cleft lip patients. As is well known, relapse of the nose is common in cleft lip repairs, but this method allows one to better gauge the correction at the primary surgery. Nasal light reflex should be added to the armamentarium of cleft surgeons to assist in superior correction of the nose, which is an item that continues to vex these surgeons.

A Study of Onychoscopic Findings in Various Dermatological Disorders

Context: About 10% of all the dermatological conditions are comprised nail disorders. It is challenging for the dermatologists to diagnose nail disorders quickly without the help of invasive procedures. Aims: The aim of the study was to evaluate onychoscopic patterns associated with various nail disorders. Settings and Designs: Hospital-based cross-sectional observational study was conducted at a tertiary care teaching institute in Maharashtra, India from November 2019 to May 2021. Materials and Methods: One hundred and fifty patients, with a history and clinical presentation suggestive of nail disorders, were recruited in the study after confirming the clinical diagnosis. Patients having secondarily modified lesions were excluded. Dry as well as wet onychoscopy using polarized and nonpolarized modes was performed with the help of DermLite 4 dermoscope with an attached OnePlus 8T mobile phone camera to capture and save the dermoscopic images. Data were compiled and statistically analyzed using SPSS version 21.0. Statistical Analysis Used: Categorical variables were expressed in frequency and percentages. Data were entered in Microsoft Excel and analyzed using SPSS version 20. P value (using Z-test for proportions) was calculated wherever needed. Results: The majority of the patients were in the age group of 21–50 years with slight male preponderance. Overall most common onychoscopic findings studied in all the disorders were the nail plate alterations followed by chromonychia. The most common nail changes associated with connective tissue disorders were changes in the proximal nail fold capillaries along with changes in the curvature. The most common change observed in onychomycosis was chromonychia. Aurora borealis, spikes, and jagged edges of onycholytic plate with ruin pattern of hyperkeratosis were specific to onychomycosis. Inflammatory disorders of the nail unit (e.g., lichen planus and nail psoriasis) showed nail plate alterations with increased nail plate fragility. Conclusions: Some onychoscopic patterns are observed consistently with certain diseases, and these can be used for their diagnosis, hence, avoiding unnecessary biopsies, thus improving the patient outcome.

Eye Based Interactive Communication System

Eye-based communication systems, like the Blink-To-Speak language, are essential tools for individuals with motor neuron disorders, enabling them to articulate their needs and emotions. These systems leverage eye movements as a means of interaction, providing a vital communication channel for those with severe speech impairments. By interpreting specific eye gestures, users can effectively convey their thoughts, fostering a sense of independence and improving their quality of life. Many of the existing eye-tracking technologies tend to be complex and costly, limiting their accessibility in low-income regions. Using a mobile phone camera, the system captures real-time video frames to detect and track the user’s eyes, leveraging advanced facial landmark detection. Four primary eye movements—Left, Right, Up, and Blink—form the basis of the Blink-To-Live communication model, allowing users to communicate a range of more than 60 daily commands. By translating sequences of three eye gestures, the system generates readable sentences that are displayed on a screen and can be vocalized through synthesized speech. Unlike other sensor-dependent systems, Blink-To-Live is designed for affordability, flexibility, and ease of use, making it accessible without the need for specialized software or hardware. A prototype has been tested on a range of participants, showing positive results in terms of ease of use, flexibility, and cost-effectiveness