Evaluating Sensor Data in the Context of Mobile Crowdsensing

With the recent rise of the Internet of Things the prevalence of mobile sensors in our daily life experienced a huge surge. Mobile crowdsensing (MCS) is a new emerging paradigm that realizes the utility and ubiquity of smartphones and more precisely their incorporated smart sensors. By using the mobile phones and data of ordinary citizens, many problems have to be solved when designing an MCS-application. What data is needed in order to obtain the wanted results? Should the calculations be executed locally or on a server? How can the quality of data be improved? How can the data best be evaluated? These problems are addressed by the design of a streamlined approach of how to create an MCS-application while having all these problems in mind. In order to design this approach, an exhaustive literature research on existing MCS-applications was done and to validate this approach a new application was designed with its help. The procedure of designing and implementing this application went smoothly and thus shows the applicability of the approach

Seul avec son smartphone ? Les médiations culturelles et leurs traces dans l'usage

National audienceL'enquête exploratoire PRAtiques Culturelles et usages de l'Informatique Connectée (PRACTIC) s'appuie sur une analyse des traces d'usages de smartphones, associée à des données déclaratives (questionnaire et entretiens), pour comprendre comment ce terminal informatique est à la fois source de transformations dans les formes de consommation culturelle, de médiation (centralité de l'application, formes de prescription), et dans l'étude même de ces phénomènes. Trois approches des médiations culturelles sont abordées ici : socio-démographie des usages du smartphone, analyse de routines de consommation culturelle et formes de prescription d'applications

Seul avec son smartphone ? Les médiations culturelles et leurs traces dans l'usage

National audienceL'enquête exploratoire PRAtiques Culturelles et usages de l'Informatique Connectée (PRACTIC) s'appuie sur une analyse des traces d'usages de smartphones, associée à des données déclaratives (questionnaire et entretiens), pour comprendre comment ce terminal informatique est à la fois source de transformations dans les formes de consommation culturelle, de médiation (centralité de l'application, formes de prescription), et dans l'étude même de ces phénomènes. Trois approches des médiations culturelles sont abordées ici : socio-démographie des usages du smartphone, analyse de routines de consommation culturelle et formes de prescription d'applications

Seamless Interactions Between Humans and Mobility Systems

As mobility systems, including vehicles and roadside infrastructure, enter a period of rapid and profound change, it is important to enhance interactions between people and mobility systems. Seamless human—mobility system interactions can promote widespread deployment of engaging applications, which are crucial for driving safety and efficiency. The ever-increasing penetration rate of ubiquitous computing devices, such as smartphones and wearable devices, can facilitate realization of this goal. Although researchers and developers have attempted to adapt ubiquitous sensors for mobility applications (e.g., navigation apps), these solutions often suffer from limited usability and can be risk-prone. The root causes of these limitations include the low sensing modality and limited computational power available in ubiquitous computing devices. We address these challenges by developing and demonstrating that novel sensing techniques and machine learning can be applied to extract essential, safety-critical information from drivers natural driving behavior, even actions as subtle as steering maneuvers (e.g., left-/righthand turns and lane changes). We first show how ubiquitous sensors can be used to detect steering maneuvers regardless of disturbances to sensing devices. Next, by focusing on turning maneuvers, we characterize drivers driving patterns using a quantifiable metric. Then, we demonstrate how microscopic analyses of crowdsourced ubiquitous sensory data can be used to infer critical macroscopic contextual information, such as risks present at road intersections. Finally, we use ubiquitous sensors to profile a driver’s behavioral patterns on a large scale; such sensors are found to be essential to the analysis and improvement of drivers driving behavior.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163127/1/chendy_1.pd

Enabling Sophisticated Lifecycle Support for Mobile Healthcare Data Collection Applications

The widespread dissemination of smart mobile devices enables new ways of collecting longitudinal data sets in a multitude of healthcare scenarios. On the one hand, mobile data collection can be accomplished more effectively and quicker compared with validated paper-based instruments. On the other hand, it can increase the data quality significantly and enable data collection in scenarios not covered by existing approaches so far. Previous attempts to utilize smart mobile devices for collecting data in these scenarios, however, often struggle with high costs for developing and maintaining mobile applications, which need to run on a multitude of mobile operating systems. Therefore, in the QuestionSys project, we are developing a generic (i.e., platform-independent) framework for enabling mobile data collection and sensor data integration in healthcare scenarios. The latter, in turn, is addressed by a model-driven approach, which is shown this paper along with the core components of the QuestionSys framework. In particular, it is shown how healthcare experts are empowered to create mobile data collection and sensing applications on their own and with reasonable efforts

Exploring Smartphone Application Usage Logs with Declared Sociological Information

International audienceIn this paper we present an exploratory smartphone usage study with logs collected from users in the wild, combined with the sociodemographic, technological and cultural information provided by them. We observe a high diversity among users' most used applications, but by classifying applications into services we find significant correlations between service usage and socio-demographic profile. We discuss that sociological information has rich potential in characterizing smartphone usage and can be applied to interesting incentive strategies and use cases based on users' sociological context

Convergence of Gamification and Machine Learning: A Systematic Literature Review

Recent developments in human–computer interaction technologies raised the attention towards gamification techniques, that can be defined as using game elements in a non-gaming context. Furthermore, advancement in machine learning (ML) methods and its potential to enhance other technologies, resulted in the inception of a new era where ML and gamification are combined. This new direction thrilled us to conduct a systematic literature review in order to investigate the current literature in the field, to explore the convergence of these two technologies, highlighting their influence on one another, and the reported benefits and challenges. The results of the study reflect the various usage of this confluence, mainly in, learning and educational activities, personalizing gamification to the users, behavioral change efforts, adapting the gamification context and optimizing the gamification tasks. Adding to that, data collection for machine learning by gamification technology and teaching machine learning with the help of gamification were identified. Finally, we point out their benefits and challenges towards streamlining future research endeavors.publishedVersio

A Model-Driven Framework for Enabling Flexible and Robust Mobile Data Collection Applications

In the light of the ubiquitous digital transformation, smart mobile technology has become a salient factor for enabling large-scale data collection scenarios. Structured instruments (e.g., questionnaires) are frequently used to collect data in various application domains, like healthcare, psychology, and social sciences. In current practice, instruments are usually distributed and filled out in a paper-based fashion (e.g., paper-and-pencil questionnaires). The widespread use of smart mobile devices, like smartphones or tablets, offers promising perspectives for the controlled collection of accurate data in high quality. The design, implementation and deployment of mobile data collection applications, however, is a challenging endeavor. First, various mobile operating systems need to be properly supported, taking their short release cycles into account. Second, domain-specific peculiarities need to be flexibly aligned with mobile application development. Third, domain-specific usability guidelines need to be obeyed. Altogether, these challenges turn both programming and maintaining of mobile data collection applications into a costly, time-consuming, and error-prone endeavor. The Ph.D. thesis at hand presents an advanced framework that shall enable domain experts to transform paper-based instruments to mobile data collection applications. The latter, in turn, can then be deployed to and executed on heterogeneous smart mobile devices. In particular, the framework shall empower domain experts (i.e., end-users) to flexibly design and create robust mobile data collection applications on their own; i.e., without need to involve IT experts or mobile application developers. As major benefit, the framework enables the development of sophisticated mobile data collection applications by orders of magnitude faster compared to current approaches, and relieves domain experts from manual tasks like, for example, digitizing and analyzing the collected data

GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization

Recent research has demonstrated the capability of behavior signals captured by smartphones and wearables for longitudinal behavior modeling. However, there is a lack of a comprehensive public dataset that serves as an open testbed for fair comparison among algorithms. Moreover, prior studies mainly evaluate algorithms using data from a single population within a short period, without measuring the cross-dataset generalizability of these algorithms. We present the first multi-year passive sensing datasets, containing over 700 user-years and 497 unique users' data collected from mobile and wearable sensors, together with a wide range of well-being metrics. Our datasets can support multiple cross-dataset evaluations of behavior modeling algorithms' generalizability across different users and years. As a starting point, we provide the benchmark results of 18 algorithms on the task of depression detection. Our results indicate that both prior depression detection algorithms and domain generalization techniques show potential but need further research to achieve adequate cross-dataset generalizability. We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.Comment: Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Trac