App Interface Research Articles

Intensive Longitudinal Data Collection Using Microinteraction Ecological Momentary Assessment: Pilot and Preliminary Results.

BackgroundEcological momentary assessment (EMA) uses mobile technology to enable in situ self-report data collection on behaviors and states. In a typical EMA study, participants are prompted several times a day to answer sets of multiple-choice questions. Although the repeated nature of EMA reduces recall bias, it may induce participation burden. There is a need to explore complementary approaches to collecting in situ self-report data that are less burdensome yet provide comprehensive information on an individual’s behaviors and states. A new approach, microinteraction EMA (μEMA), restricts EMA items to single, cognitively simple questions answered on a smartwatch with single-tap assessments using a quick, glanceable microinteraction. However, the viability of using μEMA to capture behaviors and states in a large-scale longitudinal study has not yet been demonstrated.ObjectiveThis paper describes the μEMA protocol currently used in the Temporal Influences on Movement & Exercise (TIME) Study conducted with young adults, the interface of the μEMA app used to gather self-report responses on a smartwatch, qualitative feedback from participants after a pilot study of the μEMA app, changes made to the main TIME Study μEMA protocol and app based on the pilot feedback, and preliminary μEMA results from a subset of active participants in the TIME Study.MethodsThe TIME Study involves data collection on behaviors and states from 246 individuals; measurements include passive sensing from a smartwatch and smartphone and intensive smartphone-based hourly EMA, with 4-day EMA bursts every 2 weeks. Every day, participants also answer a nightly EMA survey. On non–EMA burst days, participants answer μEMA questions on the smartwatch, assessing momentary states such as physical activity, sedentary behavior, and affect. At the end of the study, participants describe their experience with EMA and μEMA in a semistructured interview. A pilot study was used to test and refine the μEMA protocol before the main study.ResultsChanges made to the μEMA study protocol based on pilot feedback included adjusting the single-question selection method and smartwatch vibrotactile prompting. We also added sensor-triggered questions for physical activity and sedentary behavior. As of June 2021, a total of 81 participants had completed at least 6 months of data collection in the main study. For 662,397 μEMA questions delivered, the compliance rate was 67.6% (SD 24.4%) and the completion rate was 79% (SD 22.2%).ConclusionsThe TIME Study provides opportunities to explore a novel approach for collecting temporally dense intensive longitudinal self-report data in a sustainable manner. Data suggest that μEMA may be valuable for understanding behaviors and states at the individual level, thus possibly supporting future longitudinal interventions that require within-day, temporally dense self-report data as people go about their lives.

An mHealth App for Fibromyalgia-like Post-COVID-19 Syndrome: Protocol for the Analysis of User Experience and Clinical Data.

BackgroundPost–COVID-19 syndrome, also referred as “long covid,” describes persisting symptoms after SARS-CoV-2 infection, including myalgia, fatigue, respiratory, or neurological symptoms. Objective symptoms are often lacking, thus resembling a fibromyalgia-like syndrome. Digital therapeutics have shown efficiency in similar chronic disorders such as fibromyalgia, offering specific disease monitoring and interventions such as cognitive behavioral therapy or physical and respiratory exercise guidance.ObjectiveThis protocol aims to study the requirements and features of a new mobile health (mHealth) app among patients with fibromyalgia-like post–COVID-19 syndrome in a clinical trial.MethodsWe created a web application prototype for the post–COVID-19 syndrome called “POCOS,” as a web-based rehabilitation tool aiming to improve clinical outcomes. Patients without organ damage or ongoing inflammation will be included in the study. App use will be assessed through user experience questionnaires, focus groups, and clinical data analysis. Subsequently, we will analyze cross-sectional and longitudinal clinical data.ResultsThe developed mHealth app consists of a clinically adapted app interface with a simplified patient-reported outcome assessment, monitoring of medical interventions, and disease activity as well as web-based instructions for specific physical and respiratory exercises, stress reduction, and lifestyle instructions. The enrollment of participants is expected to be carried out in November 2021.ConclusionsUser experience plays an important role in digital therapeutics and needs to be clinically tested to allow further improvement. We here describe this process for a new app for the treatment of the fibromyalgia-like post–COVID-19 syndrome and discuss the relevance of the potential outcomes such as natural disease course and disease phenotypes.International Registered Report Identifier (IRRID)PRR1-10.2196/32193

Design and Realization of Contrast Medium Biphasic Hyperbaric Injector

A biphasic hyperbaric injector based on BLDC is designed for alternate and mixed injection of contrast medium and normal saline in the process of contrast medium injection in hospital. The driver hardware and algorithm are optimized especially for high-pressure and high-speed injection requirements. The interface APP is designed with parameter-input and real-time pressure-plotting of injector's ports as two main functions. The whole device can realize the preset function and has high stability after testing.

Using Mobile Phone Apps to Deliver Rural General Practitioner Services: Critical Review Using the Walkthrough Method.

BackgroundThe widespread use of mobile phones represents new frontiers for improving access to health care. This includes using mobile apps to deliver general practitioner (GP) services in rural areas. However, the wider adoption of apps for increasing access to rural GP services relies on understanding how they might intersect with the rural health system context.ObjectiveThis research aims to critically review mobile apps for delivering GP services in a rural health service context using the walkthrough method.MethodsThe sample comprised 3 GP service apps under the top 100 list in the medical category in the Apple App Store (also available via the Google Play Store) in Australia as of June 2020. The walkthrough method was applied to extract data and critique the explicit factors, such as the app interface elements, and implicit factors, such as the embedded cultural features related to use for people in rural settings. Data analysis was undertaken between 3 researchers over 6 months applying the walkthrough method and using critical reflection.ResultsThere were 3 main themes: improving rural access, addressing rural health care needs, and providing quality of care. App-based GP services may improve rural GP service availability. However, this may be at a relatively superficial level that does not encompass the scope and intensity of the services needed in rural areas (including relevant chronic and emergency care) at a cost that rural patients can afford. The apps showed signs of limited tailoring to the cultural dimensions of rural health care as a barrier to rural use. Patients generally self-selected to use GP service apps with limited support, potentially leading to inappropriate uptake especially by disadvantaged groups with lower health literacy. Although the apps claimed to avail most GP services (70%-80% in some cases), it emerged after enrollment that emergency, complex, and serious conditions might be excluded, potentially imposing more complex caseloads on in-person rural GPs. Apps provided limited information about continuity and coordination of care and sharing information with rural GPs, potentially leading to fragmented and low-quality care. There was commonly no assurance of rural skills and experience of physicians staffing apps despite the wider scope of skills needed to be effective in rural general practice.ConclusionsGP apps may increase the availability of GP services, but they may require clearer exclusions, appropriate use through decision-making tools, more rural-tailored interfaces, and capacity to align appointment times and costs with patients with complex needs to engage and be useful in a rural context. It is also important to consider how these app-based services could share information with local health care staff for safety and continuity of rural primary care. Finally, information about the physicians’ rural training and experience is critical for quality.

On the Performance of Cloud-Based mHealth Applications: A Methodology on Measuring Service Response Time and a Case Study

With the increasing use of smartphones, performance monitoring and the analysis of mobile applications (apps) are gaining momentum. Smartphones are resource-constrained devices. Thus, mobile apps typically rely on cloud services for the execution of resource-intensive functionalities, storage, and computation power. Measuring the user experience is crucial for the development and maintenance of mobile apps. Such characterization requires testing specific traits such as network connectivity, battery levels, server loads, and operating conditions. This paper presents a technique for the measurement-based performance assessment of cloud backend and mobile networks that support mobile app services. The feasibility of the technique is demonstrated through a representative case study of an app developed for medication adherence management among breast cancer patients undergoing endocrine hormone therapy (EHT). The app leverages cloud technologies to provide a portable, cost-effective, and convenient monitoring environment. Nonfunctional performance and load testing is performed by modeling third-party cloud backend services. The experimental results of the case study demonstrate the feasibility of the approach for monitoring and analyzing the backend service response times with different mobile device configurations, such as regular or power-saving battery modes and LTE or Wi-Fi mobile network connectivity, under server loading. The methodology is validated through statistical analyses of the experimental performance data involving confidence intervals, tail latencies, and analysis of variance. The results address the occurrence of server loading and its impact on the response times which relates to the quality of the user experience. We establish the effect of server loading on the responsiveness of the user interface (UI) of the mobile app considered in this case study. The proposed technique will allow developers to conduct similar measurement-based performance studies for various mobile apps leveraging cloud-based backend services.

IoT-Based Instrumentation Development for Reaction Time, Kick Impact Force, and Flexibility Index Measurement

This paper presents an Internet of Thing (IoT)-based instrumentation development using vibration sensor, force sensor, and Blynk app to obtain the reaction time, kick impact force, and flexibility index based on visual stimulants. Besides the Blynk app, vibration, and force sensors, other main components of the prototype are the Arduino NodeMCU microcontroller, and three LEDs (yellow, red, and green). The developed prototype was able to record the reaction time, kick impact force, and flexibility index. These outputs could be viewed not only on the Organic Light Emitting Diode (OLED) display but also on the smartphone via the Blynk app interface. To evaluate the performance of the developed prototype, four male Silat athletes weighing between 61kg to 80kg were recruited for this experiment. They were requested to undergo a Simple Reaction Time (SRT) task which requires them to perform three trials of the front kick. From the SRT findings, it can be deduced that 75% of the participants reacted faster towards green LED with the fastest reaction time recorded was 1485.2±126.7ms compared to yellow and red LEDs. In the future, the design of the hardware in terms of circuitry and hardware casing will be improved for a better prototype presentation. Secondly, a big sample size of subjects and different branches of combat sports will be recruited to assist in further analysis. Furthermore, the sound stimuli will be included in future studies. Lastly, further research should be conducted to assess the effect of colored stimuli on the reaction time of the athletes.

A web-based application for face detection in real-time images and videos

Face detection is widely used in the consumer industry such as advertising, user interfaces, video streaming apps and in many security applications. Every application has its own demands and constraints, and hence cannot be fulfilled by a single face detection algorithm. In this work, we developed an interactive web-based application for face detection in real-time images and videos. Pretrained face detection algorithms, namely Haar cascade classifier, HOG-based frontal face detector, Multi-task Cascaded Convolutional Neural Network (MTCNN) and Deep Neural Network (DNN), were used in the web-based application. A performance analysis of these face detection algorithms is done for various parameters such as different lighting conditions, face occlusion and frame rate. The web app interface can be used for an easy comparison of different face detection algorithms. This will help the user to decide on the algorithm that suits their purpose and requirements for various applications.

The Role of Creative App Interface and UX Design as a Critical Factor in Enhancing Technopreneurial Innovation and Improving Tech Start-ups’ Success Rates

The Role of Creative App Interface and UX Design as a Critical Factor in Enhancing Technopreneurial Innovation and Improving Tech Start-ups’ Success Rates

EMPAIA App Interface: An open and vendor-neutral interface for AI applications in pathology

Background and objectiveArtificial intelligence (AI) apps hold great potential to make pathological diagnoses more accurate and time efficient. Widespread use of AI in pathology is hampered by interface incompatibilities between pathology software. We studied the existing interfaces in order to develop the EMPAIA App Interface, an open standard for the integration of pathology AI apps. MethodsThe EMPAIA App Interface relies on widely-used web communication protocols and containerization. It consists of three parts: A standardized format to describe the semantics of an app, a mechanism to deploy and execute apps in computing environments, and a web API through which apps can exchange data with a host application. ResultsFive commercial AI app manufacturers successfully adapted their products to the EMPAIA App Interface and helped improve it with their feedback. Open source tools facilitate the adoption of the interface by providing reusable data access and scheduling functionality and enabling automatic validation of app compliance. ConclusionsExisting AI apps and pathology software can be adapted to the EMPAIA App Interface with little effort. It is a viable alternative to the proprietary interfaces of current software. If enough vendors join in, the EMPAIA App Interface can help to advance the use of AI in pathology.

The Usability of BenKids Mobile Learning App in Vocabulary Teaching for Preschool

At present, learning mobile applications (apps) have become highly popular and are widely used. However, they will only be accepted by users if their usability is of an acceptable level. Usability tests assist to measure the impact of a mobile learning app; researchers focus on the compatibility of the app interface with the cognitive skills of children and easy usage. The “BenKids” app has been designed especially for pre-school children (aged three to five). It was created for android devices to teach vocabulary to pre-school children by providing the user with an easy and simple interface. This study aimed to demonstrate a relationship between learning and the usability of the “BenKids” mobile app and explore its impact on children’s learning. The usability of the “BenKids” app was measured with 10 pre-school children. The results showed the effective usability of the app and strongly impacts learning.

The Life Goals Self-Management Mobile App for Bipolar Disorder: Consumer Feasibility, Usability, and Acceptability Study.

BackgroundLife Goals is an evidence-based self-management intervention that assists individuals with bipolar disorder (BD) by aligning BD symptom coping strategies with their personal goals. The intervention can be availed via in-person and telephonic sessions, and it has been recently developed as an individualized, customizable mobile app.ObjectiveWe examined the feasibility, usability, and acceptability of the Life Goals self-management app among individuals diagnosed with BD who used the app for up to 6 months.MethodsA total of 28 individuals with BD used the Life Goals app on their personal smartphone for 6 months. They completed key clinical outcome measurements of functioning, disability, and psychiatric symptoms at baseline, 3 months, and 6 months, in addition to a poststudy survey about usability and satisfaction.ResultsParticipants used the app for a median of 25 times (IQR 13-65.75), and for a longer time during the first 3 months of the study. The modules on depression and anxiety were the most frequently used, accounting for 35% and 22% of total usage, respectively. Overall, the study participants found the app useful (15/25, 60%) and easy to use (18/25, 72%), and they reported that the screen displayed the material adequately (22/25, 88%). However, less than half of the participants found the app helpful in managing their health (10/25, 40%) or in making progress on their wellness goals (9/25, 36%). Clinical outcomes showed a trend for improvements in mental and physical health and mania-related well-being.ConclusionsThe Life Goals app showed feasibility of use among individuals with BD. Higher user engagement was observed in the initial 3 months with users interested more frequently in the mood modules than other wellness modules. Participants reported acceptability with the ease of app use and satisfaction with the app user interface, but the app showed low success in encouraging self-management within this small sample. The Life Goals app is a mobile health technology that can provide individuals with serious mental illness with more flexible access to evidence-based treatments.

Perception and Initial Adoption of Mobile Health Services of Older Adults in London: Mixed Methods Investigation.

BackgroundAdvances in mobile technology and public needs have resulted in the emergence of mobile health (mHealth) services. Despite the potential benefits of mHealth apps, older adults face challenges and barriers in adopting them.ObjectiveThe aims of this study are to understand older adults’ perception of mHealth services and to discover the barriers that older adults face in the initial adoption of mHealth apps.MethodsThis paper systematically analyzed main determinants related to mHealth services and investigated them through questionnaires, interviews, and a workshop. Two studies were carried out in London. In study 1, the questionnaires with follow-up interviews were conducted based on the literature review to uncover older adults’ perception (including perceived usefulness, perceived ease of use, and perceived behavioral control) of mHealth services. Study 2 was a workshop helping older adults to trial selected mHealth apps. The workshop was conducted by the first author (JP) with assistance from 5 research students. The barriers that older adults faced in the initial adoption period were observed. The interviews and workshop were audiotaped and transcribed. Descriptive statistics and the thematic analysis technique were used for data analysis.ResultsIn total, 30 older adults in London completed the questionnaires and interviews in study 1. The results of study 1 show that the lack of obvious advantage, low reliability, scary information, and the risk of privacy leakage would decrease older adults’ perceived usefulness of mHealth services; the design of app interface would directly affect the perceived ease of use; and aging factors, especially the generation gap, would create barriers for older users. In total, 12 participants took part in the workshop of study 2, including 8 who took part in study 1. The results of study 2 identified that access to technology, the way of interaction, the risk of money loss, heavy workload of using an mHealth app, and different lifestyle are influential factors to older adults’ adoption of mHealth services.ConclusionsThe perceptions of mHealth services of older adults were investigated; the barriers that older adults may face in the initial adoption stage were identified. On the basis of the synthesis of these results, design suggestions were proposed, including technical improvement, free trial, information clarification, and participatory design. They will help inform the design of mHealth services to benefit older adults.

Kindfull-Digital Character Book Effectiveness: A User Satisfaction Approach

This study aims to examine the effect of user competence on system quality and user satisfaction with the use of the kindfull-digital character book apps. The quality of the system is measured by indicators of flexibility and accessibility. Then, user satisfaction affected the acceptance of a system. Survey method with quantitative measurements were used. Respondents where users of the application include teachers’ and parents. Data were collected using a questionnaire. The analysis technique used is Structural Equation Modeling - Partial Least Square. The results showed that user competence had an effect on system quality. Then, competent users improve system quality. Furthermore, the quality of the system can increase user satisfaction. Satisfied users will always use IS. Thus, resulted monitoring will also be more accurate, complete and timely. Based on these results, were suggested to increase the user's knowledge and skills. So, the quality of the system will improve. The perceived satisfaction will increase their use. Then, application usage training is needed to improve their competence. Thus, the quality of the system can be improved through flexibility and accessibility. The app interface design should be made more user-friendly by providing simple features, compliant content accompanied by interested display.

Ambulatory Cardiovascular Monitoring Via a Machine‐Learning‐Assisted Textile Triboelectric Sensor (Adv. Mater. 41/2021)

Personalized Healthcare In article number 2104178, Jun Chen and co-workers report a waterproof textile-based wearable cardiovascular monitoring system that is developed via integration of a triboelectric sensor, a signal-processing circuit, a Bluetooth module, and a customized user-friendly app interface. With the assistance of machine-learning algorithms, this system can perform continuous blood-pressure monitoring against body motion and perspiration.

Towards Automatic Detection of Nonfunctional Sensitive Transmissions in Mobile Applications

While mobile apps often need to transmit sensitive information out to support various functionalities, they may also abuse the privilege by leaking the data to unauthorized third parties. This makes us question: Is the given transmission required to fulfill the app functionality? In this paper, we make the first attempt to automatically identify suspicious transmissions from app visual interfaces, including app names, descriptions, and user interfaces. We design and implement a novel framework called FlowIntent to detect nonfunctional transmissions at both software and network levels. During the exercising of the given apps, FlowIntent automatically detects privacy-sharing transmissions and determines their purposes by utilizing the fact that mobile users rely on visible app interface to perceive the functionality of the app at certain context. The characterizations of nonfunctional network traffic are then summarized to provide network level protection. FlowIntent not only reduces the false alarms caused by traditional taint analysis, but also captures the sensitive transmissions missed by widely-used taint analysis system TaintDroid. Evaluation using 2125 sharing flows collected from more than a thousand running instances shows that our approach achieves about 94 percent accuracy in detecting nonfunctional transmissions.

Smart user experience medical app interface design based on mobile devices

AbstractWith the improvement of people's living standards, the characteristics of people's consumption needs changing from material to spiritual are very obvious, and the focus is now on the importance of health awareness. However, current medical resources are limited and scattered, and medical needs cannot be met. In order to solve these problems, the research of medical application (APP) for mobile devices is of great significance. In order to improve the usability of medical apps and reduce the difficulty of operation, this article mainly introduces the interface design of the smart user experience medical apps based on mobile devices. This article conducts APP page management through technical analysis of mobile devices and user experience‐oriented design. The framework design of the browser and the construction of the interface Model‐View‐Controller (MVC) design mode, and then through the questionnaire survey method to the user experience needs and expectations of the medical APP to design the main key of the page function. Finally, a smart user experience medical APP interface was designed. The practicability is as high as 90%, and the operation simplification is as high as 80%.

B-PO03-006 PATIENT EXPERIENCE OF AN IMPLANTED CARDIAC MONITOR FOR HYPERTROPHIC CARDIOMYOPATHY

Hypertrophic cardiomyopathy (HCM) is considered the most common inherited heart disease affecting 1 in 500 individuals. Those with HCM are at increased risk of non-sustained ventricular tachycardia (NSVT) and sudden cardiac death. Guidelines recommend HCM patients undergo arrhythmia monitoring every 1-2 years to determine candidacy for primary prevention implantable cardioverter defibrillators (ICD). A variety of implantable cardiac monitors (ICMs) are currently approved for use with varying technology and interface platforms. Unlike other ICMs, the Abbott Confirm Rx™ ICM has the capability of connecting directly to the individual’s smart phone via Bluetooth technology. This enables real-time transmission of cardiac events, patient-reported symptoms and the potential for two-way communication with care providers enabling direct feedback of alert severity to the patient. The purpose of this study was to explore individuals’ experiences and understand the psychosocial effects of patients with HCM who underwent implantation of the Confirm Rx™ ICM and participated in a prospective trial looking at a novel two-way communication alert system. Using an exploratory qualitative approach, semi-structured interviews were conducted by telephone with 10 participants who received a Confirm Rx™ ICM. Analysis revealed three key themes 1) psychological impact 2) educational needs and 3) technology expectations. Participants reported that receiving feedback from real-time transmissions resulted in improved understanding of symptoms and reduced patient anxiety. Over time, this led to fewer health system encounters. Some participants reported uncertainty regarding when to send manual transmissions, identifying a possible gap in educational needs. Lastly, participants reported the smart phone app interface did not meet expectations with regards to the amount of data displayed to patients, with patients expecting more data available to them. Overall, participants report psychological benefit utilizing a smart phone app to facilitate two-way communication of ICM transmissions. Future directions include addressing gaps in educational needs and technology expectations and deliverables.

P5 The Baltimore Urban Food Distribution (BUD) Pilot Study: A Food Systems Intervention Leveraging Technology and Community-Based Strategies

<h3>Background</h3> Under-resourced urban communities in the United States commonly lack access to healthy foods, largely due to a food system that favors the provision of calorie-dense processed foods to main food sources. The Baltimore Urban food Distribution (BUD) study is a multi-level, multi-component systems intervention that leverages technology to move fresh foods from local suppliers to corner stores located in food-scarce neighborhoods of Baltimore, Maryland. <h3>Objective</h3> To develop, pilo<i>t</i> test, and evaluate the BUD application (app) among 1 producer and 2-3 corner stores. <h3>Study Design, Setting, Participants</h3> Formative research to design the BUD app began in 2016 and is ongoing. We will recruit 2-3 corner stores to participate in a first-round pilot test. These stores will receive subsidized kale and mustard greens through in-app purchasing and delivery directly from the supplier between May 1-July 31, 2021. Point-of-purchase marketing strategies (shelf-talkers, recipe cards, display cases) will increase consumer awareness and demand. Adobe Photoshop, Illustrator, and XD will aid in the design of the app user interface. Google Firebase will ensure security of in-app purchasing and data tracking. <h3>Measurable Outcome/Analysis</h3> This first-round pilot test will be evaluated pre- and post-intervention at the supplier- and retailer-levels using structured impact questionnaires, and open-ended questions. Quantitative analyses will assess number of promoted items ordered, stocked, and sold biweekly, and daily app usage, captured in-app. <h3>Results</h3> Stakeholders (n = 27) at multiple levels of the food system found a prototype of the app to have high feasibility for use, demonstrating particular support for collective purchasing and delivery. This pilot test will further identify psychosocial changes, biweekly stocking and sales data, daily app usage, and key challenges and strengths of the app. <h3>Conclusion</h3> BUD uniquely intervenes at multiple levels of the local food system. This first-round pilot test will inform a randomized controlled trial set to begin in Fall 2021 among a sample of 38 corner stores.

Artificial Intelligence based Intelligent Tourism System-Go My Way

At the moment, we find that tourists usually spend more time planning their trip because they need to spend every minute. In this context, this application aims to identify the main computer needs to support the improvement of the tourist promotion point, using the mobile application proposal. Currently, for regular tourists and travelers they spend a lot of time planning and deciding on their trip to achieve maximum satisfaction. In this case, the app aims to identify the main computer needs to support the development of the tourist promotion point. This paper suggests a model for use in an intelligent visitor information system. It uses the concept of knowledge-base. The model will be based on a study of human behavior as a tourism guide. It builds a relationship between an information-based system and a guide, to provide a service to any visitor who meets their needs and the purpose of obtaining location information. There are different modules, different methods of acquisition methods and a shorter way to acquire the ingenuity of the artificial intelligence in this thesis. The proposed system should be designed in such a way that it works on most devices namely palmtop and mobiles. So it can be helpful when visiting new places. This application will find the route using user terms. The short-term method of finding an algorithm should work well and in the right way in most cases. The system must find a method that fulfills the user's terms, indicating the name of the item, images related to a brief description of the location. It should also be able to find the distance, time and travel costs to your destination and over time the user can also make bookings using the app interface only.

E-Waste Bin for Disinfection and Waste Management of Masks

The corona virus pandemic has shook the entire world and the major precautionary measure for this is to wear masks. The demand and supply of these masks has increased drastically, with majority of population using surgical three, two or one ply masks which is generally thrown after its use. The first thing that is in contact with the masks is nose and mouth of the person wearing it. These masks are becoming carrier of the virus due to improper disposal methods used. Until the masks are not disinfected they are not safe to be disposed in dumping yards. There are many rag pickers who enter the dumping yards to collect recyclable wastes or the person who collects the garbage from individual homes become people of high risk to the exposure of this virus. The proposed work presents a novel approach to eliminate this problem by employing a smart electronic bin which has a disinfection section which will disinfect the masks using UV-C lights and then it will be dumped into the bin installed just below the unit. This bin has a level sensor which will monitor the level of waste in the bin and whenever it gets full, it will notify the concerned authority on his mobile using Blynk app. The Blynk app interface is designed to present the status of level of waste in the bin.