Data-Sharing Method for Multi-Smart Devices at Close Range

Chung, MyoungBeom; Ko, Ilju

doi:10.1155/2015/931765

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…By performing a quick analysis of the results and with reference to relevant literature, a glaring gap was identified in the turnaround time and availability of point-of-care resources for interpreting the genetically modified HIV strains for appropriate antiretroviral drug choices. It was also understood that with the current mobile telephony systems, different forms of data can now be shared easily among various devices [ 27 ]. To create the initial prototype of the app, the output identified during this stage was translated into modules for better understanding and ownership of the whole app [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

An Android-Based Mobile App (ARVPredictor) for the Detection of HIV Drug-Resistance Mutations and Treatment at the Point of Care: Development Study

et al. 2022

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Background HIV/AIDS remains one of the major global human health challenges, especially in resource-limited environments. By 2017, over 77.3 million people were infected with the disease, and approximately 35.4 million individuals had already died from AIDS-related illnesses. Approximately 21.7 million people were accessing ART with significant clinical outcomes. However, numerous challenges are experienced in the delivery and accurate interpretation of data on patients with HIV data by various health care providers at different care levels. Mobile health (mHealth) technology is progressively making inroads into the health sector as well as medical research. Different mobile devices have become common in health care settings, leading to rapid growth in the development of downloadable software specifically designed to fulfill particular health-related purposes. Objective We developed a mobile-based app called ARVPredictor and demonstrated that it can accurately define HIV-1 drug-resistance mutations in the HIV pol gene for use at the point of care. Methods ARVPredictor was designed using Android Studio with Java as the programming language and is compatible with both Android and iOS. The app system is hosted on Nginx Server, and network calls are built on PHP’s Laravel framework handled by the Retrofit Library. The DigitalOcean offers a high-performance and stable cloud computing platform for ARVPredictor. This mobile app is enlisted in the Google Play Store as an “ARVPredictor” and the source code is available under MIT permissive license at a GitHub repository. To test for agreement between the ARVPredictor and Stanford HIV Database in detecting HIV subtype and NNRT and NRTI mutations, a total of 100 known HIV sequences were evaluated. Results The mobile-based app (ARVPredictor) takes in a set of sequences or known mutations (protease, reverse transcriptase and integrase). It then returns inferred levels of resistance to selected nucleoside, nonnucleoside protease, and integrase inhibitors for accurate HIV/AIDS management at the point of care. The ARVPredictor identified similar HIV subtypes in 98/100 sequences compared with the Stanford HIV Database (κ=0.98, indicating near perfect agreement). There were 89/100 major NNRTI and NRTI mutations identified by ARVPredictor, similar to the Stanford HIV Database (κ=0.89, indicating near perfect agreement). Eight mutations classified as major by the Stanford HIV Database were classified as others by ARVPredictor. Conclusions The ARVPredictor largely agrees with the Stanford HIV Database in identifying both major and minor proteases, reverse transcriptase, and integrase mutations. The app can be conveniently used robustly at the point of care by HIV/AIDS care providers to improve the management of HIV infection.

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Section: Methodsmentioning

confidence: 99%

An Android-Based Mobile App (ARVPredictor) for the Detection of HIV Drug-Resistance Mutations and Treatment at the Point of Care: Development Study

et al. 2022

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“…Many SbSs do not need to be online for data transfer; 'Bluetooth' (32/886) and 'near field communication (NFC)' (10/886) technologies do not require internet connections for data transfer. Still, both approaches only have a limited range (Bluetooth ¼ 10e15 m, NFC ¼ 0.1 m) requiring proximity [193,194]. Unlike batterydraining Bluetooth-based devices, NFC-based sensors are batteryfree and affordable.…”

Section: Offline Wireless Data Transfermentioning

confidence: 99%

Best practices and current implementation of emerging smartphone-based (bio)sensors – Part 1: Data handling and ethics

Ross

Zhao

Bosman

et al. 2023

TrAC Trends in Analytical Chemistry

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“…He proposed a smart guide system that sends the trigger signal to a smart device to provide information [13]. Chung proposed a data-sharing method between smart devices using Wi-Fi and high-frequency signals that are composed of two base signals and one variable signal to transmit signals more strongly [14]. He then proposed an application that could deliver high-frequency signals to TV viewers to provide advertisements for certain products [15].…”

Section: Previous Workmentioning

confidence: 99%

A Disembarking Notification Application for Public Transportation Using Inaudible Frequency

Chung

2022

Mobile Information Systems

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Currently, many people enjoy videos and music content through their smart devices while using public transportation. However, because passengers focus so much on content on their smart devices, they sometimes forget to disembark and miss their destination stations. Therefore, in this paper, we propose an application that can notify users via smart devices when they approach the drop-off point in public transportation using an inaudible high frequency. Inaudible frequency signals are generated with announcements from speakers installed on subways and city buses. Smart devices receive and analyze the signals through their built-in microphones and notify users when they reach the drop-off point. We tested destination notifications with the proposed system and 10 smart devices to evaluate its performance. According to the test results, the proposed system showed 99.4% accuracy on subways and 99.2% accuracy on city buses. Moreover, we compared these results to those using only subway app in subways, and our proposed system achieved far better outcomes. Thus, the proposed system could be a useful technology for notifying smart device users when to get off public transport, and it will become an innovative technology for global public transportation by informing users of their desired stations using speakers.

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Data-Sharing Method for Multi-Smart Devices at Close Range

Cited by 7 publications

References 15 publications

An Android-Based Mobile App (ARVPredictor) for the Detection of HIV Drug-Resistance Mutations and Treatment at the Point of Care: Development Study

An Android-Based Mobile App (ARVPredictor) for the Detection of HIV Drug-Resistance Mutations and Treatment at the Point of Care: Development Study

Best practices and current implementation of emerging smartphone-based (bio)sensors – Part 1: Data handling and ethics

A Disembarking Notification Application for Public Transportation Using Inaudible Frequency

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