App Name Research Articles

DO CHATBOTS PROVIDE RELIABLE INFORMATION ABOUT MOBILE APPS IN AUDIOLOGY?

IntroductionIn light of the growing interest in utilizing AI for information retrieval, assessing the accuracy and reliability of tools such as chatbots is essential. This study aims to evaluate the efficacy of chatbots in providing accurate information about mobile applications (apps) in the field of audiology.Material and methodsThe responses of the Gemini and ChatGPT chatbots to eight open-ended questions posed in Polish and English were compared. Each answer was assessed for correctness.ResultsGemini_ENG achieved the highest correctness with a score of 5 points (62.5%), while ChatGPT_PL scored 2 points (25%), and both Gemini_PL and ChatGPT_ENG scored 1 point (12.5%). Chatbots were most effective in recommending apps for older adults, with three of the four chatbots providing accurate recommendations. However, they struggled when asked to recommend apps for non-English speakers, to describe apps, or to provide direct links, with none of them scoring points in these areas.ConclusionsChatbots are currently unreliable sources of information about audiology apps. Depending on the language, there is significant variability in response accuracy. A good example is that Gemini_ENG performed far better than Gemini_PL. A major issue for all of them was the frequent fabrication of data, including the creation of nonexistent app names and incorrect links.

The Use of COVID-19 Mobile Apps in Connecting Patients with Primary Healthcare in 30 Countries: Eurodata Study.

The COVID-19 pandemic has necessitated changes in European healthcare systems, with a significant proportion of COVID-19 cases being managed on an outpatient basis in primary healthcare (PHC). To alleviate the burden on healthcare facilities, many European countries developed contact-tracing apps and symptom checkers to identify potential cases. As the pandemic evolved, the European Union introduced the Digital COVID-19 Certificate for travel, which relies on vaccination, recent recovery, or negative test results. However, the integration between these apps and PHC has not been thoroughly explored in Europe. To describe if governmental COVID-19 apps allowed COVID-19 patients to connect with PHC through their apps in Europe and to examine how the Digital COVID-19 Certificate was obtained. Design and setting: Retrospective descriptive study in PHC in 30 European countries. An ad hoc, semi-structured questionnaire was developed to collect country-specific data on primary healthcare activity during the COVID-19 pandemic and the use of information technology tools to support medical care from 15 March 2020 to 31 August 2021. Key informants belong to the WONCA Europe network (World Organization of Family Doctors). The data were collected from relevant and reliable official sources, such as governmental websites and guidelines. Patient's first contact with health system, governmental COVID-19 app (name and function), Digital COVID-19 Certification, COVID-19 app connection with PHC. Primary care was the first point of care for suspected COVID-19 patients in 28 countries, and 24 countries developed apps to complement classical medical care. The most frequently developed app was for tracing COVID-19 cases (24 countries), followed by the Digital COVID-19 Certificate app (17 countries). Bulgaria, Italy, Serbia, North Macedonia, and Romania had interoperability between PHC and COVID-19 apps, and Poland and Romania's apps considered social needs. COVID-19 apps were widely created during the first pandemic year. Contact tracing was the most frequent function found in the registered apps. Connection with PHC was scarcely developed. In future pandemics, connections between health system levels should be guaranteed to develop and implement effective strategies for managing diseases.

Evaluation of overweight control applications with cognitive-behavioral therapy approach: A systematic review.

Overweight and obesity lead to the development of physical diseases. Cognitive factors play a vital role in controlling one's weight. Currently, cognitive-behavioral therapy (CBT) interventions are recognized as a subcategory of lifestyle modification programs that can be implemented to control weight and modify eating patterns as well as physical activity. Nowadays, smartphone-based applications are utilized to implement behavioral interventions. The main purpose of this study is to evaluate the quality of CBT-based smartphone applications available on Google Play and the App Store in the field of overweight control. Smartphone-based utility applications available on Google Play and App Store were identified in March 2021. Weight control smartphone applications were obtained based on inclusion and exclusion criteria. The app name, platform, version, number of downloads, password protection, affiliations, and features of retrieved apps were tabulated. The Mobile Application Rating Scalewas utilized to evaluate the quality of the identified apps. Seventeen CBT-based weight control smartphone apps were retrieved. The average engagement, functionality, aesthetics, and information quality scores were 3.65, 3.92, 3.80, and 3.91, respectively. Also, the average score in an aspect containing the usefulness of the app, frequency of using the application, cost, and user satisfaction was 3.5. Future applications related to this field can be improved by providing a personalization program according to the needs of users and the possibility of online chatting with the therapist. Further improvements can be achieved by improving the areas of engagement, aesthetics, and subjective quality as well as having appropriate privacy policies.

Deep Learning Models and Social Governance Guided by Fair Policies

With the rapid advancement of information technology, artificial intelligence and machine learning have become the central technology tools for information sharing. To speed up the efficiency of information resource transmission of national government departments and improve the informatization level of government social management and public service systems, the persona system is designed using an artificial neural network, and a social service and management resource pool system is developed. The behavior data randomly generated by users in daily life is collected and cleaned, and training samples are extracted for training an artificial neural network. Next, the demographic attribute tags and interest tags are modelled, and the social service and management resource pool system is built and tested. Results show that for the population attribute label construction, the index value using the app name is mapped to 0 or 1, and the sample sampling ratio is set to 1.0. The proposed model achieved the overall accuracies of 85.2%, 74.5%, and 99.0% for the prediction of constructed age, academic qualifications, and interest label, respectively. The constructed system greatly deepens the visualization of the characteristics of social governance elements. The system can enhance the level of resource sharing by government departments and provide the foundation for spatial decision-making in smart social governance.

The Characteristics and Functionalities of Mobile Apps Aimed at Patients Diagnosed With Immune-Mediated Inflammatory Diseases: Systematic App Search.

BackgroundImmune-mediated inflammatory diseases (IMIDs) are systemic conditions associated with a high social and health impact. New treatments have changed the prognosis of IMIDs and have increased patient autonomy in disease management. Mobile apps have enormous potential to improve health outcomes in patients with IMIDs. Although a large number of IMID apps are available, the app market is not regulated, and functionality and reliability remain uncertain.ObjectiveOur aims are to review available apps for patients with IMIDs or caregivers and to describe the main characteristics and functionalities of these apps.MethodsWe performed an observational, cross-sectional, descriptive study of all apps for patients with IMIDs. Between April 5 and 14, 2021, we conducted a search of the App Store (iOS) and Play Store (Android) platforms. We used the names of the different IMIDs as search terms. The inclusion criteria were as follows: content related to IMIDs, English or Spanish language, and user population consisting of patients and health care consumers, including family and caregivers. The variables analyzed were as follows: app name, type of IMID, platform (Android or iOS), country of origin, language, category of the app, cost, date of the last update, size, downloads, author affiliation, and functionalities.ResultsWe identified 713 apps in the initial search, and 243 apps met the criteria and were analyzed. Of these, 37% (n=90) were on Android, 27.2% (n=66) on iOS, and 35.8% (n=87) on both platforms. The most frequent categories were health and well-being/fitness apps (n=188, 48.5%) and medicine (n=82, 37.9%). A total of 211 (82.3%) apps were free. The mean time between the date of the analysis and the date of the most recent update was 18.5 (SD 19.3) months. Health care professionals were involved in the development of 100 (41.1%) apps. We found differences between Android and iOS in the mean time since the last update (16.2, SD 14.7 months vs 30.3, SD 25.7 months) and free apps (85.6% vs 75.8%; respectively). The functionalities were as follows: general information about lifestyles, nutrition, or exercises (n=135, 55.6%); specific information about the disease or treatment (n=102, 42%); recording of symptoms or adverse events (n=51, 21%); agenda/calendar (n=44, 18.1%); reminder medication (n=41, 16.9%); and recording of patient-reported outcomes (n=41, 16.9%). A total of 147 (60.5%) apps had more than one functionality.ConclusionsIMID-related apps are heterogeneous in terms of functionality and reliability. Apps may be a useful complement to IMID care, especially inpatient education (their most frequent functionality). However, more than half of the IMID apps had not been developed by health care professionals or updated in the last year.

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.

Beyond the virus: a first look at coronavirus-themed Android malware.

As the COVID-19 pandemic emerged in early 2020, a number of malicious actors have started capitalizing the topic. Although a few media reports mentioned the existence of coronavirus-themed mobile malware, the research community lacks the understanding of the landscape of the coronavirus-themed mobile malware. In this paper, we present the first systematic study of coronavirus-themed Android malware. We first make efforts to create a daily growing COVID-19 themed mobile app dataset, which contains 4,322 COVID-19 themed apk samples (2,500 unique apps) and 611 potential malware samples (370 unique malicious apps) by the time of mid-November, 2020. We then present an analysis of them from multiple perspectives including trends and statistics, installation methods, malicious behaviors and malicious actors behind them. We observe that the COVID-19 themed apps as well as malicious ones began to flourish almost as soon as the pandemic broke out worldwide. Most malicious apps are camouflaged as benign apps using the same app identifiers (e.g., app name, package name and app icon). Their main purposes are either stealing users’ private information or making profit by using tricks like phishing and extortion. Furthermore, only a quarter of the COVID-19 malware creators are habitual developers who have been active for a long time, while 75% of them are newcomers in this pandemic. The malicious developers are mainly located in the US, mostly targeting countries including English-speaking countries, China, Arabic countries and Europe. To facilitate future research, we have publicly released all the well-labelled COVID-19 themed apps (and malware) to the research community. Till now, over 30 research institutes around the world have requested our dataset for COVID-19 themed research.

The effect of app store strategy on app rating: The moderating role of hedonic and utilitarian mobile apps

Concerns about the performance of mobile apps have spurred a stream of research on factors contributing to the success of mobile apps in the major app stores. However, identified success factors have not been synthesized into a unified framework. Similarly, reported findings typically address main effects. The present study introduces the concept of app store strategy to synthesize the mobile app success factors that developers implement in the major app stores. The study synthesizes four such factors consisting of app size, app versions (major and minor), revenue model and mobile app names. Further, drawing on IS literature, the study distinguishes between hedonic and utilitarian mobile apps. Using a dataset of mobile apps from Play Store (N = 8325), the study investigates to what extent app store strategy interacts with the hedonic and utilitarian value of mobile apps in driving app rating. The study employed a combination of traditional regression and machine learning approaches to test proposed hypotheses. The findings make a two-fold contribution to the literature first by introducing the concept of app store strategy to synthesize mobile app success factors and second by examining hedonic and utilitarian mobile apps as a boundary condition in the relationship between app store strategy and app rating.

Application of Developers’ and Users’ Dependent Factors in App Store Optimization

This paper presents an application of developers' and users' dependent factors in the app store optimization. The application is based on two main fields: developers’ dependent factors and users’ dependent factors. Developers’ dependent factors are identified as: developer name, app name, subtitle, genre, short description, long description, content rating, system requirements, page url, last update, what’s new and price. Users’ dependent factors are identified as: download volume, average rating, rating volume and reviews. The proposed application in its final form is modelled after mining sample data from two leading app stores: Google Play and Apple App Store. Results from analyzing collected data show that developer dependent elements can be better optimized. Names and descriptions of mobile apps are not fully utilized. In Google Play there is one significant correlation between download volume and number of reviews, whereas in App Store there is no significant correlation between factors.

Elements of Social Convoy Theory in Mobile Health for Palliative Care: Scoping Review.

BackgroundMobile health (mHealth) provides a unique modality for improving access to and awareness of palliative care among patients, families, and caregivers from diverse backgrounds. Some mHealth palliative care apps exist, both commercially available and established by academic researchers. However, the elements of family support and family caregiving tools offered by these early apps is unknown.ObjectiveThe objective of this scoping review was to use social convoy theory to describe the inclusion and functionality of family, social relationships, and caregivers in palliative care mobile apps.MethodsUsing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review guidelines, a systematic search of palliative care mHealth included (1) research-based mobile apps identified from academic searches published between January 1, 2010, and March 31, 2019 and (2) commercially available apps for app stores in April 2019. Two reviewers independently assessed abstracts, app titles, and descriptions against the inclusion and exclusion criteria. ed data covered app name, research team or developer, palliative care element, target audience, and features for family support and caregiving functionality as defined by social convoy theory.ResultsOverall, 10 articles describing 9 individual research-based apps and 22 commercially available apps were identified. Commercially available apps were most commonly designed for both patients and social convoys, whereas the majority of research apps were designed for patient use only.ConclusionsResults suggest there is an emerging presence of apps for patients and social convoys receiving palliative care; however, there are many needs for developers and researchers to address in the future. Although palliative care mHealth is a growing field, additional research is needed for apps that embrace a team approach to information sharing, target family- and caregiver-specific issues, promote access to palliative care, and are comprehensive of palliative needs.

Identifying the influence of various factor of apps on google play apps ratings

Google Play Store contains millions of Apps. These apps are downloaded and been used by millions and billions of users. Whenever a user browse or search for apps on Play store, a list of apps are shown to the user in which each app contains the app name along with its rating. Usually the user prefers to download highly rated apps because highly rated apps reflect users’ satisfaction. In order to gain high ratings, app developer uses different techniques and tweaks other than the app quality its self. Developers use attractive app titles, demanding icons, and other things to gain better ratings for their apps. However, there is no scientific approach to find the real impact of using attractive titles or any other such thing in order to gain higher ratings. Therefore, in this paper, we examine a number of factors of google play store apps and identify the influence of these factors using variable importance. For this purpose, real-world Google Play store apps dataset is used in this paper to identify the importance of these factors. For identification of important variables, Random Forest, Linear Regression Model and Support Vector Regression are used. The performance of the model is evaluated using standard performance evaluation techniques. The results show that some factors have higher significance and influence the app ratings. Moreover, keyword analysis has taken place to find the important words used in app title that results in higher and lower ratings.

The Characteristics and Quality of Mobile Phone Apps Targeted at Men Who Have Sex With Men in China: A Window of Opportunity for Health Information Dissemination?

BackgroundA number of mobile phone apps has been developed specifically for men who have sex with men (MSM). We will call these mobile phone apps MSM apps for simplicity. At present, the characteristics and quality and purpose of these MSM apps are unclear.ObjectiveThe aim of this study was to objectively and comprehensively evaluate the characteristics and quality of the MSM apps to assess whether they disseminated health information among the MSM in China.MethodsWe searched 2 dominant mobile phone app stores (Apple Store and Android Market) to obtain MSM apps using the keywords MSM, gay, lesbian, gay, bisexual, and transgender (LGBT), 男同，同志，男男性接触者，同性恋，基友, and 双性恋. Apps were excluded if they did not have a Chinese language interface or if their target population was not MSM. Basic information about the eligible apps for this study, such as app name, app store category, and date of last update was gathered from the specified app stores. The quality of apps was rated by 2 independent raters using Mobile App Rating Scale (MARS). The intraclass correlation coefficient (ICC) between raters was computed as a measure for interrater reliability of the MARS. All analyses were conducted using SPSS version 20.0 (SPSS Inc).ResultsA total of 575 apps were reviewed between September 15, 2018 and September 30, 2018, out of which, 532 apps were excluded. Finally, 43 apps were included. Of the 43 apps, 16 were from the Apple Store, 10 were from Android Market, and 17 were available in both app stores. In addition, 39 out of 43 apps were for social and sexual networking, whereas 10 contained sexual health information, for example, HIV/sexually transmitted diseases knowledge, HIV test, and condom use. The average rating was 4 stars. The number of downloads for 21 apps exceeded 10,000. A total of 31 apps had acceptable quality (as defined by a MARS score of >3), with functionality as the highest scoring domain, followed by information quality, esthetics, and engagement. Interrater reliability was excellent for the overall mean app quality scores (ICC=.946; 95% CI 0.904-0.970) and the subjective quality scores (ICC=.910; 95% CI 0.841-0.950).ConclusionsBy reviewing the available apps, we found that MSM apps are popular. The majority of MSM apps are for dating, whereas few of them contain HIV prevention and health information. The overall quality of the apps is acceptable. The utilization of mobile phone technologies is a promising way for delivering HIV prevention messages to MSM. We recommend that researchers and app developers should work together to disseminate health information for MSM via mobile technologies.

Health and Fitness Apps for Hands-Free Voice-Activated Assistants: Content Analysis.

BackgroundHands-free voice-activated assistants and their associated devices have recently gained popularity with the release of commercial products, including Amazon Alexa and Google Assistant. Voice-activated assistants have many potential use cases in healthcare including education, health tracking and monitoring, and assistance with locating health providers. However, little is known about the types of health and fitness apps available for voice-activated assistants as it is an emerging market.ObjectiveThis review aimed to examine the characteristics of health and fitness apps for commercially available, hands-free voice-activated assistants, including Amazon Alexa and Google Assistant.MethodsAmazon Alexa Skills Store and Google Assistant app were searched to find voice-activated assistant apps designated by vendors as health and fitness apps. Information was extracted for each app including name, description, vendor, vendor rating, user reviews and ratings, cost, developer and security policies, and the ability to pair with a smartphone app and website and device. Using a codebook, two reviewers independently coded each app using the vendor’s descriptions and the app name into one or more health and fitness, intended age group, and target audience categories. A third reviewer adjudicated coding disagreements until consensus was reached. Descriptive statistics were used to summarize app characteristics.ResultsOverall, 309 apps were reviewed; health education apps (87) were the most commonly occurring, followed by fitness and training (72), nutrition (33), brain training and games (31), and health monitoring (25). Diet and calorie tracking apps were infrequent. Apps were mostly targeted towards adults and general audiences with few specifically geared towards patients, caregivers, or medical professionals. Most apps were free to enable or use and 18.1% (56/309) could be paired with a smartphone app and website and device; 30.7% (95/309) of vendors provided privacy policies; and 22.3% (69/309) provided terms of use. The majority (36/42, 85.7%) of Amazon Alexa apps were rated by the vendor as mature or guidance suggested, which were geared towards adults only. When there was a user rating available, apps had a wide range of ratings from 1 to 5 stars with a mean of 2.97. Google Assistant apps did not have user reviews available, whereas most of Amazon Alexa apps had at least 1-9 reviews available.ConclusionsThe emerging market of health and fitness apps for voice-activated assistants is still nascent and mainly focused on health education and fitness. Voice-activated assistant apps had a wide range of content areas but many published in the health and fitness categories did not actually have a clear health or fitness focus. This may, in part, be due to Amazon and Google policies, which place restrictions on the delivery of care or direct recording of health data. As in the mobile app market, the content and functionalities may evolve to meet growing demands for self-monitoring and disease management.

Next-App Prediction by Fusing Semantic Information With Sequential Behavior

Next-app prediction is the task of predicting the next app that a user will choose to use on the smartphone. It helps to establish a variety of intelligent personalized services, such as fast-launch UI app, intelligent user-phone interactions, and so on. Since app names only provide limited semantic information, the intrinsic relation among apps cannot be fully exploited. Meanwhile, next-app to be used is largely determined by a sequence of apps that a user used recently. To address these challenging problems, this paper first enriches the semantic information of apps by extracting descriptive text of each app from the app store and thus proposes a topic model to transform apps as well as user preferences into latent vectors. Then, a set of nearest neighbors can be constructed based on the similarity of latent vectors and it is employed for training the prediction model. Furthermore, our prediction scheme is built on the temporal sequential data and is modeled by using the chain-augmented Naive Bayes model. Experimental results with a real smartphone application log data have demonstrated that our method achieves higher recall and DCG values compared with several baseline next-app prediction methods.

Influence of Privacy Priming and Security Framing on Android App Selection

Many users unknowingly grant mobile applications (apps) permission to access their personal information (Felt et al., 2012). This access is in part a result of the large number of permissions apps request and users’ difficulty in understanding the nature of these potentially harmful requests (Kelley et al., 2011; Liccardi et al., 2014). Laboratory studies have suggested that different approaches can be taken to curtail the likelihood of mobile users installing malicious apps. A large body of work investigating mobile app security agrees that it is indeed possible to get users to make safer and more knowledgeable decisions during the app selection process. Gates, Chen, Li, and Proctor (2014), for example, found that users made less risky decisions when they were presented with a summary risk or safety ranking that conveys the overall risk of an app. Most recently, the effects of priming manipulations have indicated that introducing an intervention before the app selection process begins may be just as effective (Rajivan & Camp, 2016). In the current study we combined the work that has been conducted on providing overall risk/safety information (Chen et al., 2014; Choe et al., 2013) with that focused on priming users with self-relevant privacy questions (Rajivan & Camp, 2016). We sought to determine whether the subjective privacy priming effect reported by Rajivan and Camp could be replicated and whether an objective priming condition (in which facts about what information apps can access were presented) would have a similar or possibly even stronger effect. Another concern was how these priming conditions would interact with positively- vs, negatively-framed safety rankings (safety and risk scores, respectively). Participants were recruited through Amazon Mechanical Turk (MTurk). Participants were presented with subjective or objective safety items before the app-selection task and with apps and their respective safety rankings during the selection process. Subjective priming was induced with the eight subjective items used by Rajivan and Camp (2016), whereas objective priming was by way of app-permission examples modified from Harbach, Hettig, Weber, and Smith (2014). In the control condition, participants completed the task without any priming material, similar to the study by Chen et al. (2015). Also, summary safety and risk rankings were varied for the app-selection task, as in Chen et al.’s (2015) study. The procedure closely followed that of Chen et al. (2015). Participants were provided with a demonstration of the elements of the apps they would be selecting. Each app was displayed with several pieces of information including the app icon, app name, developer, user rating (out of five stars), user rating count, permission safety or risk ranking (out of five circles), and a brief description of the app. The distributions of user ratings and permission safety/risk rankings were identical to those used by Chen et al. (2015). For the safety framing condition, a higher safety ranking indicated a safer app, whereas for the negative framing condition, a higher risk ranking indicated a riskier app. Participants were instructed to pick two out of the six apps for six groups of apps. We were able to replicate the findings of Chen et al. (2015), showing a greater impact from summary scores framed as safety rather than risk. The data suggest that participants were more sensitive to the safety rankings when higher rankings indicated low vs. high safety than low vs. high risk. We also replicated the findings of Rajivan and Camp (2016) in which subjective priming of security enhances participants’ consideration of the safety rankings. Moreover, we obtained evidence that objective priming with specific app permissions yields a similar benefit. Since priming with either kind of safety-relevant items resulted in safer app selections than a control condition without priming, we conclude that the benefit of priming is primarily due to the general activation of security and privacy as part of the task set. In sum, this study supports the notion that a multi-pronged approach where safety-related information is presented before the app selection process and a summary safety index is displayed may be the most effective way to improve users’ safety behaviors.

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.

GESMO- MOtion GESture based Mobile Application

has been number of android mobile apps available for clients to solve their daily problems. This paper presents a Motion Gesture based mobile (GESMO) app in Android which allows user to draw gestures in the air resulting opening of a desired app assigned to corresponding gesture. This gesture may be the first letter of an app name which is to be launched. In order to start the motion, user must press and hold any point on the screen until the motion is completed. Once motion completes, it is released. For sensing the motion, we used two built-in sensors from mobile devices: accelerometer and gyroscope. The paper proposes a two-stage approach for spotting and recognition of generated stroke gesture. The spotting stage uses a Support Vector Machine (SVM) to identify data fragments containing one stroke gesture. The recognition stage uses Hidden Markov Models (HMM) to generate the text representation from the motion sensor data. With this technique, you can successfully receive 70-80% accuracy in detecting an air gesture for mobile devices.