Mobile Camera Research Articles

Grading Acanthosis Nigricans Using a Smartphone and Color Analysis: A Novel Noninvasive Method to Screen for Impaired Glucose Tolerance and Type 2 Diabetes.

The objective of this study was to develop ANcam, a novel method for identifying acanthosis nigricans (AN) using a smartphone camera and computer-aided color analysis for noninvasive screening of people with impaired glucose tolerance (IGT). Adult and juvenile participants with or without diagnosed type 2 diabetes were recruited in Trinidad and Tobago. After obtaining informed consent, participants' history, demographics, anthropometrics, and A1C were collected and recorded. Three subject matter experts independently graded pictures of the posterior neck and upper back using the ANcam smartphone application and Burke methods. A correlation matrix investigated 25 color channels for association with hyperpigmentation, and the diagnostic thresholds were determined with a receiver operating characteristic curve analysis. For the 227 participants with captured images and A1C values, the cyan/magenta/yellow/black (CMYK) model color channel CMYK_K was best correlated with IGT at an A1C cutoff of 5.7% (39 mmol/mol) (R = 0.45, P <0.001). With high predictive accuracy (area under the curve = 0.854), the cutoff of 7.67 CMYK_K units was chosen, with a sensitivity of 81.1% and a specificity of 70.3%. ANcam had low interrater variance (F = 1.99, P = 0.137) compared with Burke grading (F = 105.71, P <0.001). ANcam detected hyperpigmentation on the neck at double the self-reported frequency. Elevated BMI was 2.9 (95% CI 1.9-4.3) times more likely, elevated blood pressure was 1.7 (95% CI 1.2-2.4) times more likely, and greater waist-to-hip ratio was 2.3 (95% CI 1.4-3.6) times more likely with AN present. ANcam offers a sensitive, reproducible, and user-friendly IGT screening tool to any smartphone user that performs well with most skin tones and lighting conditions.

Automatic Detection of Faults in Simulated Race Walking from a Fixed Smartphone Camera

Abstract Automatic fault detection is a major challenge in many sports. In race walking, judges visually detect faults according to the rules. Hence, automatic fault detection systems will help a training of race walking without experts’ visual judgement. Some studies have attempted to use sensors and machine learning to automatically detect faults. However, there are problems associated with sensor attachments and equipment such as a high-speed camera, which conflict with the visual judgement of judges, and the interpretability of the fault detection models. In this study, we proposed an automatic fault detection system for non-contact measurement. We used pose estimation and machine learning models trained based on the judgements of multiple qualified judges to realize fair fault judgement. We verified them using smartphone videos of normal race walking and walking with intentional faults in several athletes including the medalist of the Tokyo Olympics. The results show that the proposed system detected faults with an average accuracy of over 90%. We also revealed that the machine learning model detects faults according to the rules. In addition, the intentional faulty walking movement of the medalist was different from that of other walkers. This finding informs realization of a more general fault detection model.

Empowering product photography skills among teenagers in Gunung Village, Boyolali Regency

Smartphones are devices equipped with numerous fascinating features, leading people to become increasingly attached to and dependent on this technology. However, in Gunung Village, there are still relatively few individuals who can utilize this medium for profitable production. One example is the utilization of the smartphone camera feature for product photography. The aim of this community service is to enhance teenagers' understanding and skills in practicing the basics of product photography using a smartphone. This enhancement enables them to present their potential products more professionally. The method employed involves providing instruction on basic photography and engaging in practical product photography sessions. The focus of this service is on processed food products from the partners. The outcomes of this service were significant, as evidenced by an average increase of 34.28% in test results regarding product photography material. Additionally, participants successfully produced product photos aligned with the objectives and techniques of product photography.

Body composition estimates using a 2D image analysis system across different environmental conditions: An agreement study.

This study examined the agreement between %Fat measurements using a smartphone-based application (IMAGE) across different environmental conditions. A single reference image was obtained using an 8 MP smartphone camera under Ambient Light in front of a white background. Additional photos were obtained using a 0.7 MP, 5 MP, and 12 MP smartphone cameras; low-, moderate-, and bright-lighting conditions; and various color backgrounds including black, green, orange, and gray. %Fat measured using the 0.7 MP camera (27.8 ± 6.2 %Fat) was higher than the reference (26.8 ± 6.1 %Fat) (p < 0.001). The black (32.0 ± 12.0 %Fat), green (27.5 ± 6.3 %Fat), and gray (27.8 ± 6.3 %Fat) backgrounds yielded higher %Fat than the white (p = 0.03, 0.01, and 0.001). All camera, lighting, and background conditions were strongly correlated with the reference (all intraclass correlation coefficient [ICC] >0.98, all standard error of the estimate [SEE] <1.5 %Fat, all p < 0.001), except the black background which yielded poorer agreement with the white background (ICC = 0.69, SEE = 4.5%, p < 0.001). %Fat from IMAGE were strongly correlated across various environmental conditions.

Patient-led teledermatology for skin lesion triage: aservice evaluation of the DyplensTM dermoscope.

Despite the huge improvement in smartphone cameras, there has not been any real interest in the UK in pursuing patient-facing teledermatology within the sphere of skin lesion triage. High-specification dermoscopic images can be generated with smartphone attachments, but, to date, no formal clinical trial has been performed to establish the efficacy and feasibility of these consumer-level dermoscopes in skin lesion triage. The objectives of this study were to assess the ability of patients to capture dermoscopic images using a smartphone attachment, and to identify the safety and diagnostic accuracy of consumer-level dermoscopy in triaging out benign skin lesions from the 2-week-wait (2WW) cancer pathway. We recruited 78 patients already attending a face-to-face clinic at two locations. They were provided with instruction leaflets and asked to obtain dermoscopic and macroscopic images of their lesion(s) using their own smartphones. The images (and a brief history) were distributed to five experienced blinded assessors (consultants), who were asked to state their working diagnosis and outcome (reassurance, routine review or 2WW pathway), as they would in teledermatology. We compared their outcomes to the gold-standard in-person diagnosis and/or histological diagnosis, where available. The device achieved 100% sensitivity in diagnosing melanoma and squamous cell carcinoma (SCC). The specificity for the diagnoses of melanoma (89%) and SCC (83%) was high. The overall diagnostic accuracy was 77% for both benign and malignant lesions, The diagnostic accuracy was high for seborrhoeic keratosis (91%) and simple naevi (81%). Patient-captured dermoscopic images using bespoke smartphone attachments could be the future in safely triaging out benign lesions.

A digital image-based colorimetric method for measuring free acidity in edible vegetable oils

The standard method used to quantify free acidity (FA) in vegetable oil is neutralization titration, which requires many toxic chemicals and depends on an analyst's experience in detecting endpoints. Here, a digital image colorimetry (DIC) method using a smartphone camera was developed to measure FA in vegetable oils. A cupric acetate solution was used to produce the colorimetric reaction. The coloured solutions were imaged, and R values (from the RGB colour system) were calibrated against the respective FAs in the standards. The FA values of the samples were determined by standard addition calibration. These results were compared to measurements of FA obtained by the standard titrimetric method. An excellent correlation was obtained, with an R2 of 0.98 and a mean absolute error of 0.06%. The chemicals needed for analysis were reduced by approximately 90%. Thus, DIC is a less subjective and more economical method for determining FA in vegetable oils.

A Study on Lexicons Used in Metajen Tradition in Sukadana Village, Karangasem Regency

This research aims to know the types of lexicons used during the Metajen tradition in Sukadana Village, Karangasem Regency. This study is a qualitative study using 2 (two) experts in this field, namely the elderly (penua) and also the local village apparatus. The instruments in this study are the researchers themselves, the observation sheet, the interview guide, the recording tool and the phone camera. From this study, a number of lexicons and cultural meanings can be found in the study: a total of 64 (sixty-four), displayed in (24) lexicon about type or roosters, fourteen (14) Lexicons about betting code, eight (8) lexicons about instruments used, (7) lexicon for roles of people in the tradition, and eleven (11) lexicons of type of taji or sharp knives used and how it is being installed in the feet of the roosters. The cultural meaning of this activity was a component of a cultural ceremonies known as the mecaru ceremony or Tabuh Rah, which is identical to the Hindu ceremony specifically any Bhuta Yadnya upakara.

Research in the information age and the risks of researcher vicarious trauma

Exposure to traumatic material during research may traumatize scholars, contributing to negative health outcomes and curtailing research work. This article argues the social sciences have been slow to address the challenges engagement with potentially traumatizing material pose to researchers. The article reviews key literature on vicarious or secondary trauma and outlines how it might affect scholars. It considers examples from the professions, and scholarly fields including the technology industry, international development, countering violent extremism studies, and genocide studies which have already begun to grapple with these challenges. A case study focused on Myanmar-focused social sciences research outlines how recent improvements in mobile phone camera technology, Internet availability, and international linkages enabled by social media increase researchers’ exposure to potentially traumatizing material. A description of Researcher Vicarious Trauma is proposed and suggestions for how scholars, institutions, and professional associations might positively tackle the contemporary challenges of Researcher Vicarious Trauma are made.

Evaluating Color Transformation Quality and Accuracy of Prosumer and Mobile Phone Cameras for High Dynamic Range Cultural Heritage Documentation

Evaluating Color Transformation Quality and Accuracy of Prosumer and Mobile Phone Cameras for High Dynamic Range Cultural Heritage Documentation

Visualisasi Gedung Kampus 1 Universitas Muhammadiyah Sidoarjo MenggunakanAugmented Reality Sebagai Media Informasi

The making of this application is as a promotional medium to increase the number of new students and introduce the campus when PMB conducts campus tours. Visualization of augmented reality (AR) campus 1 buildings at Muhammadiyah University of Sidoarjo as an information medium that can combine real and virtual objects in 3D buildings in real time. This study uses the Marker Based Tracking Method to produce applications that can display building visuals in 3D and can scan markers by pointing the smartphone camera at brochures from various angles including: top, left and right sides and back.

Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our codes and dataset are available at https://github.com/lepotatoguy/aqi.

Use of smartphones for social networking-men vs women:case study of Bangladesh

This research looked on the gender gap in smartphone app usage and the impact of gender on smartphone-based social networks and social support in Bangladesh, a south Asian country. The sample of smartphone-owning 250 college/university students was surveyed online. Results showed that women were more likely to utilize a smartphone's camera than men were, but men were more inclined to make phone calls and use apps developed expressly for smartphones. Moreover, the findings revealed unique behaviors associated with the use of smartphones in the upkeep of interpersonal connections. Women were more likely to think bonding ties enhanced while using smartphones for text communications, whereas men were more inclined to believe more bridge links expanded. According to these findings, women tend to be more likely to use their cellphones to improve already-strong connections, whilst men appear to be more prone to use them to boost already-weak social relationships.

Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images

Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully automated image processing pipeline leveraging deep learning and a large wound segmentation dataset to perform wound detection and following prediction of the Photographic Wound Assessment Tool (PWAT), automatizing the clinical judgement of the adequate wound healing. Starting from images acquired by smartphone cameras, a series of textural and morphological features are extracted from the wound areas, aiming to mimic the typical clinical considerations for wound assessment. The resulting extracted features can be easily interpreted by the clinician and allow a quantitative estimation of the PWAT scores. The features extracted from the region-of-interests detected by our pre-trained neural network model correctly predict the PWAT scale values with a Spearman's correlation coefficient of 0.85 on a set of unseen images. The obtained results agree with the current state-of-the-art and provide a benchmark for future artificial intelligence applications in this research field.

Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell Phones

Integrating deep learning for crop monitoring presents opportunities and challenges, particularly in object detection under varying environmental conditions. This study investigates the efficacy of image preprocessing methods for olive identification using mobile cameras under natural light. The research is grounded in the broader context of enhancing object detection accuracy in variable lighting, which is crucial for practical applications in precision agriculture. The study primarily employs the YOLOv7 object detection model and compares various color correction techniques, including histogram equalization (HE), adaptive histogram equalization (AHE), and color correction using the ColorChecker. Additionally, the research examines the role of data augmentation methods, such as image and bounding box rotation, in conjunction with these preprocessing techniques. The findings reveal that while all preprocessing methods improve detection performance compared to non-processed images, AHE is particularly effective in dealing with natural lighting variability. The study also demonstrates that image rotation augmentation consistently enhances model accuracy across different preprocessing methods. These results contribute significantly to agricultural technology, highlighting the importance of tailored image preprocessing in object detection models. The conclusions drawn from this research offer valuable insights for optimizing deep learning applications in agriculture, particularly in scenarios with inconsistent environmental conditions.

Comparison of Shoulder Range of Motion Quantified with Mobile Phone Video-Based Skeletal Tracking and 3D Motion Capture-Preliminary Study.

The accuracy of human pose tracking using smartphone camera (2D-pose) to quantify shoulder range of motion (RoM) is not determined. Twenty healthy individuals were recruited and performed shoulder abduction, adduction, flexion, or extension, captured simultaneously using a smartphone-based human pose estimation algorithm (Apple's vision framework) and using a skin marker-based 3D motion capture system. Validity was assessed by comparing the 2D-pose outcomes against a well-established 3D motion capture protocol. In addition, the impact of iPhone positioning was investigated using three smartphones in multiple vertical and horizontal positions. The relationship and validity were analysed using linear mixed models and Bland-Altman analysis. We found that 2D-pose-based shoulder RoM was consistent with 3D motion capture (linear mixed model: R2 > 0.93) but was somewhat overestimated by the smartphone. Differences were dependent on shoulder movement type and RoM amplitude, with adduction the worst performer among all tested movements. All motion types were described using linear equations. Correction methods are provided to correct potential out-of-plane shoulder movements. Shoulder RoM estimated using a smartphone camera is consistent with 3D motion-capture-derived RoM; however, differences between the systems were observed and are likely explained by differences in thoracic frame definitions.

Field Evaluation of a Mobile App for Assisting Blind and Visually Impaired Travelers to Find Bus Stops.

GPS location-based navigation apps are insufficient to aid blind and visually impaired (BVI) travelers for micro-navigation tasks, such as finding the exact location of bus stops. The resulting large gaps could lead to BVI travelers missing their bus. We evaluated the ability of a signage detection mobile app, All_Aboard, to guide BVI travelers precisely to the bus stops compared to Google Maps alone. The All_Aboard app detected bus stop signs in real-time via smartphone camera using a deep neural network model, and provided distance coded audio feedback to help localize the detected sign. BVI individuals used the All_Aboard and Google Maps app to localize 10 bus stops each in downtown and suburban Boston, Massachusetts. For each bus stop, the subjects used both apps to navigate as close as possible to the physical bus stop sign, starting from 30 to 50meters away. The outcome measures were success rate and gap distance between the app-indicated location and the actual physical location of the bus stop. The study involved 24 legally blind participants (mean age [SD] = 51 [14] years; 11 [46%] women). The success rate of the All_Aboard app (91%) was significantly higher than the Google Maps (52%, P < 0.001). The gap distance when using the All_Aboard app was significantly lower (mean = 1.8, 95% confidence interval [CI] = 1.2-2.3meters) compared to the Google Maps alone (mean = 7, 95% CI = 6.5-7.5meters, P < 0.001). All_Aboard micro-navigation app guided BVI travelers to bus stops more accurately and reliably than a location-based macro-navigation app alone. The All_Aboard app together with a macro-navigation app can potentially help BVI individuals independently access public transportation.

The development and evaluation of a homemade laparoscopic endotrainer using the IDEAL framework and MISTELS scoring system: a pilot study.

A prospective IDEAL stage 2a pilot study was carried out at a tertiary care center to evaluate the reliability of a homemade laparoscopic endotrainer, following the MISTEL and IDEAL framework guidelines. The validated modified (MISTELS) score was used to test the reliability of a low-cost laparoscopic trainer with commonly found components built by surgeons. Basic household materials including a cellular phone (camera source), wifi (linking source) and freely available webcam apps were used for construction. Five basic laparoscopic skills were performed by general surgery resident volunteers with minimal (1-6 months') laparoscopic surgery experience and tested and retested based on efficiency and precision. To determine interrater reliability, two trained observers scored all subjects. The Cronbach alpha test was used to test for internal consistency between tasks. The Interclass correlation coefficient is used for test and retest reliability. 15 (80% male, mean age 28 ± 5 years) residents were included. The interrater and test-retest reliabilities for the total scores of the basic laparoscopic skills tests were 0.952 (95% CI, 0.895-0.981) and 0.64 (95% CI, 0.35-0.77), respectively. The Cronbach Alpha for the first assessment test and retest was 0.83. The MISTELS metrics had excellent reliability, exceeding the threshold level of 0.8. It is possible to assemble a low-cost, reliable trainer at home to enhance laparoscopic skills during residency.

Combination of headspace single-drop microextraction with smartphone-based on-drop colorimetric detection for determination of ammonia and total nitrogen in environmental and food samples

In the present study, a simple, environmentally friendly and inexpensive analytical method was proposed based on the combination of headspace single-drop microextraction with smartphone-based on-drop colorimetric detection for determination of ammonia (free ammonia and ammonium ion) in environmental and food samples. This method was also coupled with micro Kjeldahl digestion for determining total nitrogen content (or protein) in foods. Ammonia was extracted into an aqueous microdrop containing o-phthalaldehyde (OPA)-Na2SO3 reagent, and simultaneously converted to a colored derivative (1-sulfonatoisoindole). The colored microdrop was directly imaged by smartphone camera as a detector, and the captured image was processed with a color recognition app for ammonia quantification. The influence of experimental variables on the sensitivity of this method was studied. The linear range of the method was achieved from 0.25 to 10 mg L−1 for ammonium with the detection limit of 0.07 mg L−1. The intraday and interday relative standard deviations were between 2.5 and 5.5 % at the concentration levels of 0.5 and 5 mg L−1. This method was satisfactorily applied to determining ammonia and total nitrogen in environmental and food samples with ammonia and total nitrogen recoveries in the range of 90–109 %. In comparison with the Berthelot and classical Kjeldahl methods, there was no considerable difference in accuracy between these methods and the present method.

Traffic Counting using YOLO Version-8 (Case Study of Jakarta-Cikampek Toll Road)

You Only Look Once (YOLO) version 8 is the latest version of YOLO. YOLO is a common object detection model that offers faster and more accurate results. YOLO applications provide numerous benefits in the fields of health care, traffic control, vehicle safety, energy, agriculture, and industry. The purpose of this article is to use advancements in information technology to automate the process of manually recording traffic counts on the highway. The method utilized in this study is to record a video of traffic movements with a smartphone camera and save it in MP4 format. Calculations are performed at the office after receiving recorded video and utilizing a program written by the author that makes use of Python, Ppencv, Pytorch, and YOLO version 8 software. When passing through a counter box, the traffic volume is counted and saved in Excel format (.xls). The video records footage near the Halim area of the Jakarta-Cikampek toll road. With a measurement accuracy of 99.63% for cars, 96.66% for buses, and 98.55% for trucks, the accuracy attained using YOLO version 8 is fairly satisfactory for detecting vehicle volume and categorization.

A bilateral feature fusion network for defect detection on mobile cameras

Defect detection in mobile phone cameras constitutes a critical aspect of the manufacturing process. Nonetheless, this task remains challenging due to the complexities introduced by intricate backgrounds and low-contrast defects, such as minor scratches and subtle dust particles. To address these issues, a Bilateral Feature Fusion Network (BFFN) has been proposed. This network incorporates a bilateral feature fusion module, engineered to enrich feature representation by fusing feature maps from multiple scales. Such fusion allows the capture of both fine and coarse-grained details inherent in the images. Additionally, a Self-Attention Mechanism is deployed to garner more comprehensive contextual information, thereby enhancing feature discriminability. The proposed Bilateral Feature Fusion Network has been rigorously evaluated on a dataset of 12,018 mobile camera images. Our network surpasses existing state-of-the-art methods, such as U-Net and Deeplab V3+, particularly in mitigating false positive detection caused by complex backgrounds and false negative detection caused by slight defects. It achieves an F1-score of 97.59%, which is 1.16% better than Deeplab V3+ and 0.99% better than U-Net. This high level of accuracy is evidenced by an outstanding precision of 96.93% and recall of 98.26%. Furthermore, our approach realizes a detection speed of 63.8 frames per second (FPS), notably faster than Deeplab V3+ at 57.1 FPS and U-Net at 50.3 FPS. This enhanced computational efficiency makes our network particularly well-suited for real-time defect detection applications within the realm of mobile camera manufacturing.