Google Android Platform Research Articles

A Comprehensive Evaluation of Tinnitus Apps.

Purpose The use of mobile phones is on the rise worldwide. Many people who experience tinnitus often turn to the Internet and applications (apps) on their smartphones to acquire information on tinnitus and solutions to "cure" tinnitus. The aim of this study was to evaluate and summarize features offered by tinnitus-related apps on different mobile platforms. Method This study was conducted on 3 mobile platforms-Apple iOS, Google Android, and Windows. The key word tinnitus was searched, and all free and paid apps were downloaded. Relevant apps were reviewed in-depth and then classified into 4 categories based on their functionality-"tinnitus education, awareness, and prevention," "tinnitus assessment and measurement," "tinnitus management," and "misinformation." Results All mobile platforms yielded both free and paid tinnitus apps, allowing individuals to learn more about tinnitus, assess and measure their own tinnitus, gain access to different sounds to help manage their tinnitus, and find strategies to better cope with their tinnitus. The Google Android platform revealed the most number of tinnitus apps. The "tinnitus management" category consistently outperformed the other categories-containing the most number of apps and the most number of features. A small proportion of misinformation apps were detected across platforms. There were no significant differences in the total number of features between free and paid apps (except on the Windows platform). A cost-feature analysis revealed that more expensive apps did not necessarily offer more number of features. Conclusions This study reveals that a variety of tinnitus-related apps-both free and paid-are available on different mobile platforms. Clinicians should be aware that patients with tinnitus often rely on apps for additional help and should be prepared to direct them to valid apps. Implications for end users are discussed as well. Supplemental Material https://doi.org/10.23641/asha.8956775.

Moore's law and price trends of digital products: the case of smartphones

ABSTRACTSince the introduction of the iPhone by Apple in 2007 and Google's Android platform in 2009, the two systems have accounted for a total of 90% of the U.S. smartphone market. Apple, however, reaps most of the profit in the industry. In the second quarter of 2016, for example, Apple's iPhone gets 104% of the sector's profit. This suggests that the smartphone market resembles a Stackelberg leadership model. Despite Apple's strong market power, we investigate if the market leader is under pressure to be price competitive. We calculate a quality-adjusted price index for smartphones from 2007 to 2016. Our results show that the average price declines at an average rate of over 27% per year. The price trend is similar to other digit products such as computers, cameras, and portable music players. We observe that the large price decline reflects the effect of Moore's law, which predicts that the capacity of integrated circuits undergoes an exponential growth. The effect of Moore's law is incorporated into the Stackelberg model. We also observe that price trends of other digital products also follow a similar pattern. This suggest that the long-run price trends of digital consumer goods are somewhat independent of the market structures.

Evaluation Framework for Detecting Manipulated Smartphone Data

Ever improving technology allows smartphones to become an integral part of people's lives. The reliance on and ubiquitous use of smartphones render these devices rich sources of data. This data becomes increasingly important when smartphones are linked to criminal or corporate investigations. To erase data and mislead digital forensic investigations, endusers can manipulate the data and change recorded events. This paper investigates the effects of manipulating smartphone data on both the Google Android and Apple iOS platforms. The deployed steps leads to the formulation of a generic process for smartphone data manipulation. To assist digital forensic professionals with the detection of such manipulated smartphone data, this paper introduces an evaluation framework for detecting manipulated smartphone data. The framework uses key traces left behind as a result of the manipulation of smartphone data to construct techniques to detect the changed data. Assessment of the evaluation framework involves three distinct theoretical scenarios that involve the deletion and modification of existing data, as well as a failed attempt to insert fabricated data. The results produced by the evaluation framework suggest the framework can assist with the detection of manipulated smartphone data. The purpose of this research study was to demonstrate the manipulation of smartphone data and present an evaluation framework to detect such manipulated data.

Dynamic Deployment and Cost-Sensitive Provisioning for Elastic Mobile Cloud Services

As mobile customers gradually occupying the largest share of cloud service users, the effective and cost-sensitive provisioning of mobile cloud services quickly becomes a main theme in cloud computing. The key issues involved are much more than just enabling mobile users to access remote cloud resources through wireless networks. The resource limited and intermittent disconnection problems of mobile environments have intrinsic conflict with the continuous connection assumption of the cloud service usage patterns. We advocate that seamless service provisioning in mobile cloud can only be achieved with full exploitation of all available resources around mobile users. An elastic framework is proposed to automatically and dynamically deploy cloud services on data center, base stations, client units, even peer devices. The best deployment location is dynamically determined based on a context-aware and cost-sensitive evaluation model. To facilitate easy adoption of the proposed framework, a service development model and associated semi-automatic tools are provided such that cloud service developers can easily convert a service for execution on different platforms without porting. Prototype implementation and evaluation on the Google Cloud and Android platforms demonstrate that our mechanism can successfully maintain seamless services with very low overhead.

MotionGen: Interactive Design and Editing of Planar Four-Bar Motions for Generating Pose and Geometric Constraints

In this paper, we have presented a unified framework for generating planar four-bar motions for a combination of poses and practical geometric constraints and its implementation in MotionGen app for Apple's iOS and Google's Android platforms. The framework is based on a unified type- and dimensional-synthesis algorithm for planar four-bar linkages for the motion-generation problem. Simplicity, high-utility, and wide-spread adoption of planar four-bar linkages have made them one of the most studied topics in kinematics leading to development of algorithms and theories that deal with path, function, and motion generation problems. Yet to date, there have been no attempts to develop efficient computational algorithms amenable to real-time computation of both type and dimensions of planar four-bar mechanisms for a given motion. MotionGen solves this problem in an intuitive fashion while providing high-level, rich options to enforce practical constraints. It is done effectively by extracting the geometric constraints of a given motion to provide the best dyad types as well as dimensions of a total of up to six four-bar linkages. The unified framework also admits a plurality of practical geometric constraints, such as imposition of fixed and moving pivot and line locations along with mixed exact and approximate synthesis scenarios.

Evaluation of smartphone sound measurement applications using external microphones—A follow-up study

The National Institute for Occupational Safety and Health (NIOSH) conducted a follow-up study to examine the accuracy of smartphone sound measurement applications (apps) using external, calibrated, microphones. In the initial study, we examined 192 apps on the Apple (iOS) and Google (Android) platforms. Overall, 10 iOS apps met our selection criteria for occupational noise exposure measurements, and of those, only 4 iOS apps (SoundMeter, SPLnFFT, SPL Pro, and NoiSee) were within ±2 dB(A) of a reference microphone. For this study, we selected the same 4 iOS apps and examined their accuracy and performance using the MicW i436 and the Dayton Audio iMM-6 external microphones. The MicW i436 microphone is marketed as compliant with IEC-61672 class 2 sound level meter standard. Testing was conducted in a reverberant chamber using pink noise over seven nominal sound levels from 65 to 95 dB. The results showed an even closer agreement than the earlier study, with mean differences within ±1 dB(A) of the reference microphone. This study suggests that the use of external microphones, and the ability to calibrate the microphones and apps, improves the overall accuracy and precision of measurements, and removes many of the constraints and limitations associated with the built-in smartphones’ microphones.

Clinical Presentation of Tinnitus and a Review of, and Evidence Base for, Tinnitus Applications

A lack of evidence base presently exists, to validate the efficacy of mobile technology applications (apps) for tinnitus—highlighting a need for research. We reviewed tinnitus apps available via two popular smartphone operating systems: the Apple iOS and Google Android platforms. A March (2016) search using platforms available in the Austral-Asia region yielded over 260 tinnitus-related apps. Search parameters required apps to employ an English language format and feature the keyword “tinnitus” in the app name or description. Those apps retained for consideration (257) enabled 4 primary themes to emerge, featuring apps offering: (1) sound therapy approaches, (2) information, tips or assessment, (3) health promotion or alternative approaches, and (4) miscellaneous, non-therapeutic apps. Themes enabled further organization of related app characteristics into sub-groups. Numerous apps (44.3%) presented sound menus, ordered into a virtual library of presumably beneficial listening options. To place tinnitus apps in the context of current therapeutic considerations, an overview of the clinical presentation of tinnitus, and approaches aimed at mediating the perception of tinnitus and affective responses, precedes the review. We suggest future research addresses: the relative benefit(s) of one app over another, efficacy of tinnitus apps as a therapeutic option, long-term outcomes, and generalizability across populations.

A cross-sectional content analysis of Android applications for asthma.

Providing patients opportunities for self-management and education about their disease, asthma applications designed for use on an Android operating system can have positive health outcomes across the range of demographics who use mHealth applications. This study provides a content analysis of freely available Google Android Platform Mobile Applications for Asthma. A list of applications was collected on 26 October 2014, using the search feature of the Google Play Android platform and using the words and phrases "Asthma," "Lung Function" and "Peak Flow." Each application was coded for its approach to asthma self-management, based on categories adapted by Huckvale et al., which are based on the Global Initiative for Asthma and the National Asthma Education and Prevention Program. The characteristics of the 15 asthma applications are described. Most of the asthma applications' primary function focused on patient self-monitoring and self-assessment. Using the HON Code, we found low health information quality across all asthma applications. Android asthma applications can have positive outcomes in helping patients as they provide opportunities for self-management and education about their disease. Future research should continue to monitor and evaluate the development and use of mHealth Asthma Applications. Based on these findings, and their indication of a gap in existing research, subsequent studies can continue to evaluate the development and use of mHealth Asthma Applications with increasing methodological consistency to improve the quality of in-app health information.

Human Surveillance with Android basedS.M.A.R.T Emergency Application

Human Surveillance With Android based S.M.A.R.T(Smart Mobile Accessibility at Right Time) emergency application enables you to connect your android phone across the internet from a device powered by Google android platform. Once click,this application will send messages and call with the location you are in.As there are number of android application developed everyday, we realize the importance of an safety application.In this paper, we are proposing to use the smart phones for retrieving the location of the victim with continuous update of its location.Including one of the most interesting feature of continuous call which can be helpful in case of emergency( if a person is not responding to message at a particular instant), this call would be predefined one, making helper alert that an message from the victims side is been received. This application would be useful when someone is in need of you, by providing victim location, as it is crucial for helper to know victim location.This application is been developed in Eclipse IDE platform using an android smart phone.The application flow starts with a side selecting screen.

Evaluating the Model-Based Testing Approach in the Context of Mobile Applications

The popularity of portable devices has grown rapidly in recent years. Due to the high number and diversity of users, new testing approaches are necessary to reduce the occurrence of faults and ensure better quality in mobile applications. The major objective of this paper is to evaluate the use of Model-Based Testing (MBT) in the construction and implementation of automated tests to verify and validate mobility solutions developed in the Google Android platform. The research proposal is guided by three questions: (Q1) – “Can the concepts of MBT be used in its current state to verify and validate functional requirements in mobile applications?”; (Q2) – “What are the results and challenges identified from adoption of MBT in mobile applications?”; and (Q3) – “How effective were the models and test cases generated, implemented and executed in the mobile application evaluated?”. The results obtained from an experimental evaluation are discussed and related to questions of this research.

Use of smartphone sound measurement apps for occupational noise assessments

NIOSH conducted two studies to examine the accuracy of smartphone sound measurement applications (apps). The first study examined 192 sound measurement apps on the Apple (iOS) and Google (Android) platforms. Only 10 iOS apps met our selection criteria for functionality, measurement metrics, and calibration capability. The studies compared the performance of the apps with a reference microphone and with a professional type 1 sound level meter and a type 2 noise dosimeter. The results showed 4 iOS apps with means of differences within ± 2 dB(A) of the reference microphone. The Android-based apps lacked the features and functionalities found in iOS apps and showed a wide variance between the same app measurements on different devices. A follow-up study of the 4 iOS apps using calibrated external microphones (MicW i436 and Dayton Audio iMM-6), showed an even closer agreement with professional meters. Overall, the studies suggest that certain apps may be used for some occupational noise assessments but only if properly calibrated and used within the hardware limits of the mobile devices. NIOSH and EA LAB are collaborating to develop an occupational sound measurement app for iOS devices in an effort to improve awareness of the noise hazards in the workplace.

Comparing Potential and Actual Innovators: An Empirical Study of Mobile Data Services Innovation

Firms are increasingly opening up their innovation efforts to allow users to tap into the benefits they can offer, such as mobile data service (MDS) innovation on iOS and Google Android platforms. For this purpose, platforms typically provide toolkits to facilitate user participation, aiming to create an ecosystem for sustainable innovation. However, with the barriers to user innovation and attrition of existing innovators, it could be challenging for firms to attract and sustain users' MDS innovation. With the possible benefits from user innovation, and considering the challenges faced, firms need to understand how to influence potential user innovators to take part and to encourage extant user innovators to innovate again. However, there is a lack of comprehensive research and understanding of what drives users' intentions to innovate services and the differences in the antecedents of such intention between potential and actual user innovators. Further, although prior studies have suggested that toolkits can support user innovation, little research has theorized and empirically tested their influence. Motivated thus, this study proposes a model based on (1) user innovation theory to explain the antecedents (including toolkit support) of user MDS innovation intention and (2) construal level theory to explain the differential effects of the antecedents for actual and potential user innovators. We tested the model through survey data from potential and actual MDS user innovators on Google Android and iOS platforms. We find that trend leadership and anticipated extrinsic reward influence both potential and actual user innovators' intentions to innovate. However, anticipated recognition and toolkit support affect only actual user innovators, while anticipated enjoyment affects only potential user innovators. Interestingly, toolkit support strengthens the influence of anticipated enjoyment for actual user innovators but weakens its influence for potential user innovators. Further, potential user innovators value anticipated extrinsic rewards less than actual innovators do. The implications for research and practice are discussed.

Surgical Smartphone Applications Across Different Platforms

There are a vast array of smartphone applications that could benefit both surgeons and their patients. To review and identify all relevant surgical smartphone applications available for the Apple iPhone iOS and Google Android platform based on their user group and subspecialty for which they were designed. Both the literature using PubMed and Google Scholar were searched using the following terms: application$, smartphone$, app$, app*, surgery, surgical, surg*, general surgery, general surg*, bariatric$, urology and plastic surgery, ortho*, orthop(a)edic, cardiac surgery, cardiothoracic, neurosurgery, and ophthalmology. The search yielded 38 articles of which 23 were eligible. Each of the key specialties was searched in the Apple iTunes App Store for iPhone iOS and the Google Play Android application store. In total, there were 621 surgical applications for Apple iPhone iOS and 97 identified on Android's Google Play. There has been a 9-fold increase in the number of surgical applications available for the Apple iPhone iOS from 2009 to 2012. Of these applications there were 126 dedicated to plastic surgery, 79 to orthopedics, 41 to neurosurgical, 180 to general surgery, 36 to cardiac surgery, 121 to ophthalmology, and 44 to urology. There was a wide range of applications ranging from simple flashcards to be used for revision to virtual surgery applications that provided surgical exposure and familiarization with common operative procedures. Despite the plethora of surgical applications available for smartphones, there is no taxonomy for medical applications. Only 12% were affiliated with an academic institution or association, which highlights the need for greater regulation of surgical applications.

Estimating Demand for Mobile Applications in the New Economy

In 2013, the global mobile app market was estimated at over US$50 billion and is expected to grow to $150 billion in the next two years. In this paper, we build a structural econometric model to quantify the vibrant platform competition between mobile (smartphone and tablet) apps on the Apple iOS and Google Android platforms and estimate consumer preferences toward different mobile app characteristics. We find that app demand increases with the in-app purchase option wherein a user can complete transactions within the app. On the contrary, app demand decreases with the in-app advertisement option where consumers are shown ads while they are engaging with the app. The direct effects on app revenue from the inclusion of an in-app purchase option and an in-app advertisement option are equivalent to offering a 28% price discount and increasing the price by 8%, respectively. We also find that a price discount strategy results in a greater increase of app demand in Google Play compared with Apple App Store, and app developers can maximize their revenue by providing a 50% discount on their paid apps. Using the estimated demand function, we find that mobile apps have enhanced consumer surplus by approximately $33.6 billion annually in the United States, and we discuss various implications for mobile marketing analytics, app pricing, and app design strategies.This paper was accepted by Alok Gupta, special issue on business analytics.

Linked Observer 패턴을 적용한 안드로이드 게임 프레임워크 설계 및 구현

The changes in the game platform is appeared since the advent of smart-phones. Apple's iOS or Google's Android platform is gradually expanding their territories in the mobile game area. Android's platform fragmentation and the absence of game development framework act as barriers for game developments. In the development of game applications on the smart-phone, the standardized development procedures under game framework can achieve the productivity improvement. In this paper, we propose a new design pattern suitable for the design of a framework for new areas of application through the expansion of existing framework. The design and implementation of the Android game development framework is presented by taking advantage of existing design patterns and a proposed pattern. Also we propose the standardized development procedure of game applications under the proposed framework.

PMSE: A Personalized Mobile Search Engine

We propose a personalized mobile search engine (PMSE) that captures the users' preferences in the form of concepts by mining their clickthrough data. Due to the importance of location information in mobile search, PMSE classifies these concepts into content concepts and location concepts. In addition, users' locations (positioned by GPS) are used to supplement the location concepts in PMSE. The user preferences are organized in an ontology-based, multifacet user profile, which are used to adapt a personalized ranking function for rank adaptation of future search results. To characterize the diversity of the concepts associated with a query and their relevances to the user's need, four entropies are introduced to balance the weights between the content and location facets. Based on the client-server model, we also present a detailed architecture and design for implementation of PMSE. In our design, the client collects and stores locally the clickthrough data to protect privacy, whereas heavy tasks such as concept extraction, training, and reranking are performed at the PMSE server. Moreover, we address the privacy issue by restricting the information in the user profile exposed to the PMSE server with two privacy parameters. We prototype PMSE on the Google Android platform. Experimental results show that PMSE significantly improves the precision comparing to the baseline.

Interactive self-adaptive clutter-aware visualisation for mobile data mining

There is an emerging focus on real-time data stream analysis on mobile devices. A wide range of data stream processing applications are targeted to run on mobile handheld devices with limited computational capabilities such as patient monitoring, driver monitoring, providing real-time analysis and visualisation for emergency and disaster management, real-time optimisation for courier pick-up and delivery etc. There are many challenges in visualisation of the analysis/data stream mining results on a mobile device. These include coping with the small screen real-estate and effective presentation of highly dynamic and real-time analysis. This paper proposes a generic theory for visualisation on small screens that we term Adaptive Clutter Reduction ACR. Based on ACR, we have developed and experimentally validated a novel data stream clustering result visualisation technique that we term Clutter-Aware Clustering Visualiser CACV and its enhancement of enabling user interactivity that we term iCACV. Experimental results on both synthetic and real datasets using the Google Android platform are presented proving the effectiveness of the proposed techniques.

Paranoid Android: just how insecure is the most popular mobile platform?

Google's Android platform has spawned an ecosystem inhabited by enthusiasts, geeks, hackers (in the good sense), consumers and business users all keen for the latest toys and entranced not only by the utility and attractiveness of the system but also by its openness and community atmosphere. However, there's a dark element and its name is malware. In this first of three articles, to be run over successive months, Steve Mansfield-Devine looks at why this situation has come about, why Android has become a viable platform for cyber-criminals, and the economics driving the creation of malware. Sometimes it feels like we're reliving the 1990s. Google's Android platform has spawned an ecosystem inhabited by enthusiasts, geeks, hackers (in the good sense), consumers and business users all keen for the latest toys and entranced not only by the utility and attractiveness of the system but also by its openness and community atmosphere. However, just as with earlier versions of Windows, there's a dark element and its name is malware. In this first of three articles, to be run over successive months, we look at why this situation has come about and why Android has become a viable platform for cyber-criminals.

Google Android mobile phone applications for water quality information management

This paper introduces some experiences with developing mobile phone demonstrator applications for water quality information management using the Google Android platform. The work presented is part of an EU research project named LENVIS (Localised ENVironmental and health Information Services for all). The applications are focused on delivery of water quality information related to outdoor bathing waters in two case study areas in the Netherlands. Both monitored and modelled water quality information is delivered via mobile phone applications that are integrated with web applications. The applications also have functionalities for collecting user feedback on bathing water quality from the field and integrating it with the information provided by the water management authorities. Initial test results of the applications with targeted user groups are also presented, which demonstrate the promising potential of this technology for water quality information management applications and they indicate potential use in other application areas.

Drawing Graphs on a Smartphone

We present a system for the visualization of relational information on the smartphones. It is implemented on the iPhone and on the Google Android platforms and is based on a new visualization paradigm that poses interesting algorithmic challenges. We also show customizations of the system to explore and visualize popular social networks.