4,813 research outputs found
Context Data Categories and Privacy Model for Mobile Data Collection Apps
Context-aware applications stemming from diverse fields like mobile health,
recommender systems, and mobile commerce potentially benefit from knowing
aspects of the user's personality. As filling out personality questionnaires is
tedious, we propose the prediction of the user's personality from smartphone
sensor and usage data. In order to collect data for researching the
relationship between smartphone data and personality, we developed the Android
app TYDR (Track Your Daily Routine) which tracks smartphone data and utilizes
psychometric personality questionnaires. With TYDR, we track a larger variety
of smartphone data than similar existing apps, including metadata on
notifications, photos taken, and music played back by the user. For the
development of TYDR, we introduce a general context data model consisting of
four categories that focus on the user's different types of interactions with
the smartphone: physical conditions and activity, device status and usage, core
functions usage, and app usage. On top of this, we develop the privacy model
PM-MoDaC specifically for apps related to the collection of mobile data,
consisting of nine proposed privacy measures. We present the implementation of
all of those measures in TYDR. Although the utilization of the user's
personality based on the usage of his or her smartphone is a challenging
endeavor, it seems to be a promising approach for various types of
context-aware mobile applications.Comment: Accepted for publication at the 15th International Conference on
Mobile Systems and Pervasive Computing (MobiSPC 2018
Robust modeling of human contact networks across different scales and proximity-sensing techniques
The problem of mapping human close-range proximity networks has been tackled
using a variety of technical approaches. Wearable electronic devices, in
particular, have proven to be particularly successful in a variety of settings
relevant for research in social science, complex networks and infectious
diseases dynamics. Each device and technology used for proximity sensing (e.g.,
RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with
specific biases on the close-range relations it records. Hence it is important
to assess which statistical features of the empirical proximity networks are
robust across different measurement techniques, and which modeling frameworks
generalize well across empirical data. Here we compare time-resolved proximity
networks recorded in different experimental settings and show that some
important statistical features are robust across all settings considered. The
observed universality calls for a simplified modeling approach. We show that
one such simple model is indeed able to reproduce the main statistical
distributions characterizing the empirical temporal networks
Smartphone App Usage Analysis : Datasets, Methods, and Applications
As smartphones have become indispensable personal devices, the number of smartphone users has increased dramatically over the last decade. These personal devices, which are supported by a variety of smartphone apps, allow people to access Internet services in a convenient and ubiquitous manner. App developers and service providers can collect fine-grained app usage traces, revealing connections between users, apps, and smartphones. We present a comprehensive review of the most recent research on smartphone app usage analysis in this survey. Our survey summarizes advanced technologies and key patterns in smartphone app usage behaviors, all of which have significant implications for all relevant stakeholders, including academia and industry. We begin by describing four data collection methods: surveys, monitoring apps, network operators, and app stores, as well as nine publicly available app usage datasets. We then systematically summarize the related studies of app usage analysis in three domains: app domain, user domain, and smartphone domain. We make a detailed taxonomy of the problem studied, the datasets used, the methods used, and the significant results obtained in each domain. Finally, we discuss future directions in this exciting field by highlighting research challenges.Peer reviewe
Understanding the Social Context of Eating with Multimodal Smartphone Sensing: The Role of Country Diversity
Understanding the social context of eating is crucial for promoting healthy
eating behaviors by providing timely interventions. Multimodal smartphone
sensing data has the potential to provide valuable insights into eating
behavior, particularly in mobile food diaries and mobile health applications.
However, research on the social context of eating with smartphone sensor data
is limited, despite extensive study in nutrition and behavioral science.
Moreover, the impact of country differences on the social context of eating, as
measured by multimodal phone sensor data and self-reports, remains
under-explored. To address this research gap, we present a study using a
smartphone sensing dataset from eight countries (China, Denmark, India, Italy,
Mexico, Mongolia, Paraguay, and the UK). Our study focuses on a set of
approximately 24K self-reports on eating events provided by 678 college
students to investigate the country diversity that emerges from smartphone
sensors during eating events for different social contexts (alone or with
others). Our analysis revealed that while some smartphone usage features during
eating events were similar across countries, others exhibited unique behaviors
in each country. We further studied how user and country-specific factors
impact social context inference by developing machine learning models with
population-level (non-personalized) and hybrid (partially personalized)
experimental setups. We showed that models based on the hybrid approach achieve
AUC scores up to 0.75 with XGBoost models. These findings have implications for
future research on mobile food diaries and mobile health sensing systems,
emphasizing the importance of considering country differences in building and
deploying machine learning models to minimize biases and improve generalization
across different populations
Predicting Personality Traits Using Smartphone Sensor Data and App Usage Data
Human behavior is complex -- often defying explanation using traditional mathematical models. To simplify modeling, researchers often create intermediate psychological models to capture aspects of human behavior. These intermediate forms, such as those gleaned from personality inventories, are typically validated using standard survey instruments, and often correlate with behavior. Typically these constructs are used to predict stylized aspects of behavior. Novel sensing systems have made tracking behavior possible with unprecedented fidelity, posing the question as to whether the inverse process is possible: that is, inferring psychological constructs for individuals from behavioral data. Modern smartphones contain an array of sensors which can be filtered, combined, and analyzed to provide abstract measures of human behavior. Being able to extract a personal profile or personality type from data directly obtainable from a mobile phone without participant interaction could have applications for marketing or for initiating social or health interventions. In this work, we attempt to model a particularly salient and well-established personality inventory, the Big Five framework. Daily routines of participants were measured from parameters readily available from smartphones and supervised machine learning was used to create a model from that data. Cross validation-based evaluation demonstrated that the root mean squared error was sufficiently small to make actionable predictions about a person's personality from smartphone logs, but the model performed poorly for personality outliers
Overview of context-sensitive technologies for well-being
Today smart devices such as smartphones, smartwatches and activity trackers are widely available and accepted in most developed societies. These devices present a broad set of sensors capable of extracting detailed information about different situations of daily life, which, if used for good, have the potential to improve the quality of life not only for individuals but also for the society in general. One of the key areas where this type of information can help to improve the quality of life is in healthcare since it allows to monitor and infer the current level of well-being of the smart devices carriers. In this paper, some of the available literature about well-being sensing through context-aware data is reviewed. Also, the main types of mechanisms used in these studies are identified. These mechanisms are related to monitoring, generalization, inference, feedback, energy management and privacy. Furthermore, a description of the mechanisms used in each study is presented.info:eu-repo/semantics/acceptedVersio
Overview of context-sensitive technologies for well-being
Today smart devices such as smartphones,
smartwatches and activity trackers are widely available and
accepted in most developed societies. These devices present a
broad set of sensors capable of extracting detailed information
about different situations of daily life, which, if used for good,
have the potential to improve the quality of life not only for
individuals but also for the society in general. One of the key
areas where this type of information can help to improve the
quality of life is in healthcare since it allows to monitor and infer
the current level of well-being of the smart devices carriers. In
this paper, some of the available literature about well-being
sensing through context-aware data is reviewed. Also, the main
types of mechanisms used in these studies are identified. These
mechanisms are related to monitoring, generalization, inference,
feedback, energy management and privacy. Furthermore, a
description of the mechanisms used in each study is presented.info:eu-repo/semantics/publishedVersio
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