Development Of Smartphone Application Research Articles

Rice Leaf Diseases Classification Using Deep Learning Algorithms for Smartphone Application Development: An Empirical Study

Rice is the most widely consumed grain across the world. The rice plants often suffer from diseases. Early detection of such diseases and adopting remedial measures can help the farmers to avoid major losses and can produce best-quality crops in large quantities. However, the conventional rice leaf disease detection techniques are often not accurate, time-consuming and sometimes require laboratory testing. In this context, automatic rice leaf disease detection techniques are presented based on the various deep learning classifiers (namely MobileNetV2, ResNet50, VGG16 and Le-Net5) and an Android application is also developed in order to instantly determine the possible rice diseases from the uploaded rice leaf images captured by the smartphone. The developed models are tested using the publicly available benchmark rice leaf dataset containing three types of rice leaf diseases, namely bacterial leaf blight, leaf smut and brown spot. Experimental results show that MobileNetV2 model performed better compared to other models in terms of classification accuracy, recall and [Formula: see text]-score. The results of statistical significance test also confirmed the superiority of the MobileNetV2 model over other compared deep learning models.

Recommendations for the Development of Psychological Smartphone Applications in the Context of Bariatric Surgery: Focus Groups with Professionals and Patients.

To explore the experiences and preferences of patients and healthcare professionals regarding the development of an app to provide psychological intervention to improve emotion regulation in the context of bariatric surgery (BS). Sixteen people (6 patients who underwent BS and 10 professionals) participated in two separate focus group sessions. We performed a content analysis of transcribed focus group discussions to extract and organize categories, subcategories and areas. Both sets of stakeholders provided information about how to develop and implement an app. According to participants' comment, content should include information (i.e., nutrition, exercise) and emotional regulation skills. Patients and professionals mentioned that the app should include visual information, continuous emotional assessments and peer contact. It was also mentioned that the app should be used before and after BS and its contents should be developed by a multidisciplinary team (i.e., collaboration of endocrinologist, nutritionists and psychologists). Participants in both focus groups considered technology to be useful in the context of BS, especially as part of blended interventions (combining face-to-face and online sessions). Patients and professionals seem to be receptive towards the use of technology in a BS context. Specific recommendations are identified for designing and implementing app solutions for BS. More efforts should be made in the future to develop and implement evidence-based apps according to patients and professionals' needs.

2C1-1 Development of smartphone application "Aller-Pre" for infant allergy research

2C1-1 Development of smartphone application "Aller-Pre" for infant allergy research

Smartphone Camera for Angiographic Computer Vision in Vascular Medicine

Aim: This study aimed to develop a TensorFlow Lite algorithm for angiography classification and to deploy it on a basic mobile smartphone device, thereby verifying the proof of concept for creating a comprehensive end-to-end mobile computer vision application for vascular medicine. Materials and Methods: After ethical approval by the local ethics committee, we collected institutional and open source peripheral angiograms of lower limbs. The angiograms were labeled by a researcher with more than 10 years of experience in vascular surgery. The labeling included dividing the angiograms according to their anatomical pattern into the Global Limb Anatomic Staging System (GLASS). The model was developed using the open-source TensorFlow framework for general image classification and deployed as an Android application. Results: The model utilized 700 angiograms, distributed as follows within the femoropoliteal GLASS disease (fp) categories: fp0 – 187 images, fp1 – 136 images, fp2 – 128 images, fp3 – 97 images, fp4 – 152 images. The reference dataset included 372 non-angiographic images (not_angio). Consequently, the entire model included 1,072 images. After training and deployment, the model demonstrated the following performance: a mean accuracy of 0.72. The best self-reported accuracy per class was for fp0 0.72, fp4 0.83 and not_angio 1.0 classes. Conclusion: We discovered that a smartphone camera could be utilized for angiographic computer vision through end-to-end applications accessible to every healthcare professional. However, the predictive abilities of the model are limited and require improvement. The development of a robust angiographic computer vision smartphone application should incorporate an upload function, undergo validation through head-to-head human-machine comparisons, potentially include segmentation, and feature a prospective design with explicit consent for using collected data in the development of AI models.

'The way to obtain freedom and equality': Experiences and needs of Thai adolescent mothers in terms of the use smartphone applications for breastfeeding support.

Breastfeeding brings about a positive impact on both child and maternal health in the short and long terms. However, adolescent mothers have a lower breastfeeding initiation rate and a shorter breastfeeding duration than adult mothers. Although mobile applications have been found to be the most widely used platform for breastfeeding support, there is still a lack of design specific for adolescents. To explore the experiences, perspectives and needs of adolescent mothers and professional nurses using smartphone applications for breastfeeding support and the influence of this technology on healthcare in Thailand. This formative qualitative research of the 'Development of smartphone application for promoting breastfeeding and learning of infant responsiveness for Thai teenage pregnant women' study intends to design a smartphone application and develop an implementation plan. A purposive sample was used to elicit experiences from adolescent mothers and nurses. Data were collected from 48 adolescent mothers through in-depth interviews with six focus group discussions and 12 nurses, following the data saturation principle. Thematic analysis was conducted, and potential factors and needs were mapped to the capability, opportunity, and motivation model of behaviour change (COM-B). Most adolescent mothers perceived the usefulness of smartphone apps as breastfeeding support resources. The qualitative findings of adolescent mothers were grouped into the following three themes: a friendly breastfeeding tool; allows them to manage their breastfeeding activities; and enhances the accessibility and equality of breastfeeding support. The professional nurses perceived the benefits of using smartphone applications in their work, which included the following three themes: reducing workload and making their work easier; preparation is always better; and increasing the standards of breastfeeding support. Adolescent mothers and professional nurses showed favourable attitudes toward smartphone apps for breastfeeding support. These smartphone apps should be tailored to these groups to achieve optimal BF outcomes.

Development of Smartphone Application for Calculating the Low Back Pain Risk

Development of Smartphone Application for Calculating the Low Back Pain Risk

Technology-based interventions for tobacco smoking prevention and treatment: a 20-year bibliometric analysis (2003-2022).

Substance abuse, particularly tobacco smoking, is a significant global public health concern. Efforts have been made to reduce smoking prevalence and promote cessation, but challenges, such as nicotine addiction, marketing tactics by tobacco industry, and cultural acceptability hinder progress. Technology has emerged as a potential tool to address these challenges by providing innovative scalable interventions. The objective of the study was to analyze and map scientific literature on technology-based intervention for tobacco prevention and treatment. A bibliometric methodology was conducted. Scopus database was used to retrieve relevant research articles published between 2003 and 2022. The analysis included publication trends, key contributors, research hotspots, research themes, the most impactful articles, and emerging research topics. A total of 639 articles were found, with a slow and fluctuating growth pattern observed after 2011. The Journal of Medical Internet Research was the most prominent journal in the field. The United States was the leading country in the field, followed up by the United Kingdom, and the Netherlands. Research hotspots included smoking cessation, randomized controlled trials, and technology-based methods such as internet, mHealth, smartphone apps, text messages, and social media. Four primary research themes were identified: development of smartphone applications, efficacy of text messaging interventions, acceptance and effectiveness of smartphone applications, and interventions targeting young adults and students using mobile phone and social media platforms. The top 10 cited articles demonstrated effectiveness of digital interventions in promoting smoking cessation rates and reducing relapse rates. Emerging research topics included the use of virtual reality interventions, interventions for specific populations through personalized tools, and technology-based interventions in non-Western countries. The findings of the current study highlight the potential of technology to address the challenges associated with tobacco smoking. Further future research in this area is warranted to continue advancing the field and developing effective and evidence-based interventions to combat tobacco smoking.

Development and Pilot Testing of the Tremor Retrainer Smartphone Application for the Treatment of Functional Tremor.

Functional tremor is a common and disabling condition with limited treatment options. A prior proof-of-concept pilot study sought to translate entrainment, a key diagnostic feature of functional tremor, into a treatment strategy. The Tremor Retrainer smartphone application was developed though a collaboration between neurologists and a software engineer. It analyzes data from smartphone accelerometers to measure baseline tremor frequency, then provides auditory cues at a lower frequency for the patient to match with flexion-extension movements at the wrist. The application provides continuous biofeedback on performance via a visual gauge. Patients with functional tremor underwent a one-week treatment protocol with the Tremor Retrainer application and provided feedback on usability and acceptability to guide software programming. Three pediatric patients completed the one-week protocol and their feedback was used to modify the software. All patients felt that the application was easy to use and could be effective in treating functional tremor. The Tremor Retrainer smartphone application uses auditory cues and a visual gauge to provide a personalized and widely accessible entrainment-based intervention. Pilot testing in pediatric patients provided key feedback for application design. The Tremor Retrainer smartphone application modulates functional tremor frequency by providing pulsed auditory cues for a patient to match with wrist flexion-extension movements while receiving continuous biofeedback via a visual gauge. This adaption of the diagnostic sign of entrainment has potential as an accessible treatment for patients with functional tremor.

Abstract 235: Smartphone Dispatch of Trained and Untrained Volunteer Responders for Out-of-Hospital Cardiac Arrest: A Big Data Study Based on Five Years of Experience With the SAUV Life App

Introduction: The use of smartphones to dispatch volunteer responders for out-of-hospital cardiac arrest (OHCA) has emerged as a promising strategy for improving response times. The SAUV Life app was progressively implemented in France since 2018. In the event of a suspected OHCA, volunteer responders were dispatched by emergency medical services and received remote guidance through the app to the location of the alert, enabling them to initiate or assist in CPR. Hypothesis: Trained volunteers were more likely to arrive at the scene before medical teams and to initiate CPR compared to untrained volunteers. Methods: We conducted a retrospective observational study to analyse user profiles and interventions from January 1, 2018, to November 30, 2022. Data were automatically collected during interventions and stored in a confidential and anonymized file. Statistical analyses were performed using Python libraries. Volunteers were classified as trained if they possessed a first aid training certificate or an equivalent qualification. Results: During the study period, a total of 631,221 volunteers subscribed to the app. The mean age of volunteers was 40.5 ± 14.5 years. Among them, 461,892/631,221 (73.2%) were categorized as trained volunteers, while 169,329/631,221 (26.8%) as untrained volunteers. There were 1,102,317 alerts recorded, resulting in a total of 21,986 interventions. Among these interventions, 3,687/21,986 (16.8%) involved a volunteer arriving before medical teams, with 4602/4850 (94.9%) being trained volunteers and 248/4850 (5.1%) being untrained volunteers. Trained volunteers were more likely to arrive at the scene before medical teams compared to untrained volunteers, 4602/798102 (0.6%) vs 248/304215 (0.1%) respectively (p < 0.001). Trained volunteers were more likely to initiate CPR compared to untrained volunteers: 3137/4602 (68.2%) vs 121/248 (48.2%) respectively (p < 0.001). During the study period, 522 patients for whom an alert was triggered achieved return of spontaneous circulation. Conclusion: The development of smartphone applications for emergency dispatch following OHCA must continue to be promoted for both trained and untrained populations, but it is essential to prioritize first aid training.

Impact of personal innovativeness, perceived smartphone ease of use and mobile self-efficacy on smartphone-based personal information management practices

PurposeAlthough, smartphones have facilitated users to keep their personal information, nonetheless, less has been investigated about factors affecting personal information management (PIM) practices. Therefore, this study aims to investigate how personal innovativeness, perceived ease of use (PEOU) and mobile self-efficacy affect PIM (e.g. finding/re-finding, keeping, organizing and maintaining) practices.Design/methodology/approachQuantitative research design was used in this study. The authors collected data from 222 students of information management from public sector universities using a questionnaire. PLS modeling technique was used to analyze data.FindingsThe authors noted that personal innovativeness significantly impacts finding/refinding information, whereas it insignificantly affects keeping, organizing and maintaining information practices. In addition, smartphone PEOU significantly influences information finding and refinding, information keeping and organizing information, whereas insignificantly influences information maintaining. Moreover, mobile self-efficacy was noted to be significantly associated with finding and refinding information, information keeping, information organizing and information maintaining.Originality/valueThis research is an important contribution to the body of existing literature, as it proposed an integrated model based on constructs extracted from Technology Acceptance Model (TAM), Social Cognitive Theory, personal innovativeness and PIM. This study also has practical significance because the findings of this study would be helpful for smartphone application developers and LIS school directors to design programs for information literacy.

How is mhealth and web-based telemedicine implemented in patient with hypertension? : a narrative scoping review

In this digital era, telemedicine has become one of the cutting-edge approaches that is feasible for hypertension management. Despite the proliferation of trial research, reviews of how telemedicine is practiced are limited. The aim of this study is to review how telehealth is applied in patients with hypertension. A scoping review according to the Joanna Briggs Institute Reviewer Manual's instructions was used with a narrative approach to the type of RCT intervention articles. The search was conducted through electronic databases Scopus, PubMed, and Medline-OVID with the keywords "Telemedicine" and "hypertension". Of 1945 articles, 13 studies met the criteria and were reviewed. Telemedicine interventions including mhealth-based and web-based provide various ways to manage and provide telemonitoring for BP. The utilization of m-health can be through the development of smartphone applications, text messaging, or the use of current social media. Whereas web-based utilization mostly uses email consultations and not many uses paid websites but are integrated with mobile phones. Telemedicine whether mhealth or web-based makes it easier for healthcare professionals to manage and monitor patients' BP, offering a sustainable positive impact in the future.

Current Progress of Ratiometric Fluorescence Sensors Based on Carbon Dots in Foodborne Contaminant Detection

Carbon dots (CDs) are widely used in the detection of foodborne contaminants because of their biocompatibility, photoluminescence stability, and ease of chemical modification. In order to solve the interference problem of complexity in food matrices, the development of ratiometric fluorescence sensors shows great prospects. In this review, the progress of ratiometric fluorescence sensors based on CDs in foodborne contaminant detection in recent years will be summarized, focusing on the functionalized modification of CDs, the fluorescence sensing mechanism, the types of ratiometric fluorescence sensors, and the application of portable devices. In addition, the outlook on the development of the field will be presented, with the development of smartphone applications and related software helping to better enable the on-site detection of foodborne contaminants to ensure food safety and human health.

An artificial intelligence-enabled smartphone app for real-time pressure injury assessment.

The management of chronic wounds in the elderly such as pressure injury (also known as bedsore or pressure ulcer) is increasingly important in an ageing population. Accurate classification of the stage of pressure injury is important for wound care planning. Nonetheless, the expertise required for staging is often not available in a residential care home setting. Artificial-intelligence (AI)-based computer vision techniques have opened up opportunities to harness the inbuilt camera in modern smartphones to support pressure injury staging by nursing home carers. In this paper, we summarise the recent development of smartphone or tablet-based applications for wound assessment. Furthermore, we present a new smartphone application (app) to perform real-time detection and staging classification of pressure injury wounds using a deep learning-based object detection system, YOLOv4. Based on our validation set of 144 photos, our app obtained an overall prediction accuracy of 63.2%. The per-class prediction specificity is generally high (85.1%–100%), but have variable sensitivity: 73.3% (stage 1 vs. others), 37% (stage 2 vs. others), 76.7 (stage 3 vs. others), 70% (stage 4 vs. others), and 55.6% (unstageable vs. others). Using another independent test set, 8 out of 10 images were predicted correctly by the YOLOv4 model. When deployed in a real-life setting with two different ambient brightness levels with three different Android phone models, the prediction accuracy of the 10 test images ranges from 80 to 90%, which highlight the importance of evaluation of mobile health (mHealth) application in a simulated real-life setting. This study details the development and evaluation process and demonstrates the feasibility of applying such a real-time staging app in wound care management.

Development and real-life use assessment of a self-management smartphone application for patients with inflammatory arthritis. A user-centred step-by-step approach.

Mobile health applications (apps) are increasing in interest to enhance patient self-management. Few apps are actually used by patients and have been developed for patients with inflammatory arthritis (IA) treated with disease-modifying anti-rheumatic drugs which use entails risk of adverse effects such as infections. To develop Hiboot, a self-management mobile app for patients with IA, by using a user-centred step-by-step approach and assess its real-life use. The app development included first a qualitative study with semi-guided audiotaped interviews of 21 patients to identify the impact of IA on daily life and patient treatments practices and an online cross-sectional survey of 344 patients to assess their health apps use in general and potential user needs. A multidisciplinary team developed the first version of the app via five face-to-face meetings. After app launch, a second qualitative study of 21 patients and a users' test of 13 patients and 3 rheumatologists led to the app's current version. The number of app installations, current users and comments were collected from the Google Play store and the Apple store. The qualitative study revealed needs for counselling, patient-health professional partnership, and skills to cope with risk situations; 86.8% participants would be ready to use an app primarily on their rheumatologist's recommendation. Six functionalities were implemented: a safety checklist before treatment administration, aids in daily life situations based on the French academic recommendations, treatment reminders, global well-being self-assessment, periodic counselling messages, and a diary. The Hiboot app was installed 20,500 times from September 2017 to October 2020, with 4300 regular current users. Scores were 4.4/5 stars at Android and iOS stores. Hiboot is a free self-management app for patients with IA developed by a step-by-step process including patients and health professionals. Further evaluation of the Hiboot benefit is needed.

Improved Global Navigation Satellite System filtering methods for the mobile six-minute walk test

Abstract Heart failure (HF) is a chronic disease with high hospitalizations rates and increased mortality. The six-minute walk test (6MWT) is a standardized method to assess the functional health of HF patients by measuring the distance walked in six minutes [1]. The improved 6MWT with new filter algorithms, based on the development of smartphone application [9], can be used everywhere with a good global navigation satellite system (GNSS) signal. This paper tests multiple filtering approaches to reduce the measured distance error in the application.We were able to decrease the average relative error of the application from 4.5% to 2.0%. Additionally, the number of experiments with relative error over our chosen clinically significant difference of 5% is reduced by 90%. Keeping these results in mind, the system can be considered clinically acceptable.

Designing, Developing, and Efficiency Evaluation of a Smartphone Application for Blood Donation

This study aimed to design, develop, and evaluate efficiency of a human-based “U-Blood App” prototype for blood unavailability in Thailand. This study adopted a mixed-method design. The results of the study revealed these key findings. First, the needs analysis of 32 key informants (50% males and a mean age of 40.6 years) indicated that the features of the User Experience (UE) and the User Interface (UI) should contain blood donor’s qualification, general information record of the blood donor’s health, blood donation appointment and notification schedule, the application download and installation, a simple guide for the application user, the hospital logo, and necessary information. Second, the evaluation by experts revealed that the quality of the prototype is high (X¯=4.79) and the quality of UI ( X¯=4.79) was higher than that of UE (X¯=4.70). Lastly, the end users of 65 samples (50.76\% females and a mean age of 48 years) are highly satisfied with the prototype (X¯=4.68). The findings advanced the understanding of the impacts of human factors on the development of smart phone application for blood donation. The overall results cannot be generalized in the long term. Future inquiry should work on this limitation.

Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model.

To quantitatively assess pathological gait, we developed a novel smartphone application for full-body human motion tracking in real time from markerless video-based images using a smartphone monocular camera and deep learning. As training data for deep learning, the original three-dimensional (3D) dataset comprising more than 1 million captured images from the 3D motion of 90 humanoid characters and the two-dimensional dataset of COCO 2017 were prepared. The 3D heatmap offset data consisting of 28 × 28 × 28 blocks with three red–green–blue colors at the 24 key points of the entire body motion were learned using the convolutional neural network, modified ResNet34. At each key point, the hottest spot deviating from the center of the cell was learned using the tanh function. Our new iOS application could detect the relative tri-axial coordinates of the 24 whole-body key points centered on the navel in real time without any markers for motion capture. By using the relative coordinates, the 3D angles of the neck, lumbar, bilateral hip, knee, and ankle joints were estimated. Any human motion could be quantitatively and easily assessed using a new smartphone application named Three-Dimensional Pose Tracker for Gait Test (TDPT-GT) without any body markers or multipoint cameras.

Smartphone Distance Measurement Application Using Image Processing Methods

This study introduces a new method to measure the distance of an object from an observer location using image processing methods by utilizing smartphone camera technology. This study involved the development of an application for distance measurement where users can measure the distance of an object using their smartphone. For this study, the basic image processing method was used to determine the proportional width of the object marker which then be used to derive the distance to the object. The application has been successfully developed where the distance from the smartphone to the object marker can be obtained with an accuracy of +/-0.05 meters for distances of less than 3 meters. However, further research needs to be conducted to enhance the application modules for better distance measurement accuracy. This study indicated that the development of smartphone applications using image processing methods has great potential to be applied in various distance measurement applications in the near future, especially in data collection activities for the production of small-scale topographic maps or for land surveying applications.

Development of smartphone applications for bystander cardiopulmonary resuscitation in the prehospital setting in Thailand

Objective The survival of out-of-hospital cardiac arrest (OHCA) patients depends on early bystander chest compression and rapid public access defibrillation. This study aimed to design and develop a smartphone application for bystander cardiopulmonary resuscitation (BCPR) in a prehospital setting in Thailand. Methods The four steps of the study were planning, development, evaluation, and production of applications through online meetings and submission of a linked questionnaire to 19 experts for consensus. Results Among the total of 19 experts, 93% strongly agreed with the development of the smartphone application. They agreed with the operating modes as follows: 100% with emergency calls by number and video calls, 100% with dispatchers and bystanders to locate OHCA and automated external de­fibrillator (AED), 94% with teaching of BCPR by video, 88% with pediatric cardiopulmonary resuscitation (CPR), 100% with tracking of real-time feedback to BCPR by voice, 88% with contacting bystanders by phone number, 83% with applications that can transmit signals from OHCA to nearby bystanders within 400 m, and 89% with first aid for daily life. Consequently, the application’s main operating modes are emergency calls, identification of OHCA and AED locations, responder call, and first aid. Conclusion We designed and developed a new smartphone application for BCPR in Thailand. The following study will test a smartphone application for training and outcome measures in Bangkok, Thailand. Keywords: Out-of-hospital cardiac arrest, Cardiopulmonary resuscitation, Smartphone

Development of Smartphone Application-Based Learning Devices to Improve Learning Outcomes and Independent Learning of Students at Nurul Amaliyah High School Tanjung Morawa

This study aims to describe: 1) Validity, Practicality, and Effectiveness of Learning Devices based smartphone application in Mathematics Learning on Matrix Subject; 2) Improving student learning outcomes on the Matrix Subject after using the developed learning devices 3) Increasing the student learning independence in Matrix subjects after using learning devices developed. This study is development research with 4D Model in XI class Senior High School Of Nurul Amaliyah Tanjung Morawa. From the first trial and second trial obtained: 1) Learning Devices of Smartphone Application to improve Learning Outcomes and Independent Learning is Valid, Practice, and Effective. 2) In the first and second trials, there was an increase in learning outcomes from an average score of 60 increasing to an average score of 78.7. 3) In the first and second trials there was an increase in learning independence from 3,36 in the first trial and increasing to 3,9 in the second trial. Thus, the development of smartphone application-based learning devices can improve student learning outcomes and independent learning in XI Class IPA SMA S Nurul Amaliyah Tanjung Morawa. Keywords: Development of Learning Devices, Development of Smartphone Application , 4D Model, Learning Outcomes, Learning Independence. DOI: 10.7176/JEP/13-7-07 Publication date: March 31 st 2022