Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Appropriation of mobile cultural resources for learning

Copyright © 2010 IGI Global. This article proposes appropriation as the key for the recognition of mobile devices - as well as the artefacts accessed through, and produced with them - as cultural resources across different cultural practices of use, in everyday life and formal education. The article analyses the interrelationship of users of mobile devices with the structures, agency and practices of, and in relation to what the authors call the "mobile complex". Two examples are presented and some curricular options for the assimilation of mobile devices into settings of formal learning are discussed. Also, a typology of appropriation is presented that serves as an explanatory, analytical frame and starting point for a discussion about attendant issues

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US

Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

Technology use in everyday life: Implications for designing for older users

This study examines the experience and attitudes of older adults towards technology and how they compare with younger age groups. Two hundred and thirty seven participants completed an extensive questionnaire exploring their daily lifestyle, use of technology, attitudes towards technology, and perceived difficulty of household devices. The main findings from the study were; (1) there was a strong motivation to learn or to continue learning to use computers by the older group; (2) social connectedness influenced how the older group used or would like to use technology and also why some preferred not to use it; and finally (3) there was an age-related increase in perceived difficulty for many household devices, however some devices maintained intergenerational usability. These finding can be used to inform the design of future intergenerational household technologies

Comparing Techniques for Mobile Interaction with Objects from the Real World.

Mobile interaction with objects from the real world is gaining in popularity and importance as different mobile technologies increasingly provide the basis for the extraction and usage of information from physical objects. So far, Physical Mobile Interaction is used in rather simple ways. This paper presents a comparison and evaluation of more complex and sophisticated techniques for Physical Mobile Interaction. The results indicate the importance of usability guidelines that pay attention to these new interaction techniques

Living and Learning With New Media: Summary of Findings From the Digital Youth Project

Summarizes findings from a three-year study of how new media have been integrated into youth behaviors and have changed the dynamics of media literacy, learning, and authoritative knowledge. Outlines implications for educators, parents, and policy makers

Changing my life one step at a time – using the Twelve Step program as design inspiration for long term lifestyle change

To explore how people manage and maintain life style change, we conducted interviews with eight members of different Twelve Step Fellowships with 2-23 years of recovery about how they maintain and develop their recovery in everyday life. They reported how identification, sharing, and routines are keys to recovery. Our lessons for design concerns how these concepts support recovery in a long term perspective: Sharing to contribute in a broader sense to the fellowship and to serve as an example for fellow members created motivation even after 20 years of recovery; reflecting over routines in recovery was essential since life is constantly changing and routines need to fit into everyday life; concrete gestures were helpful for some of the abstract parts of the recovery work, such as letting go of troubling issues. Design aimed to support maintenance of lifestyle change needs to open up for ways of sharing that allow users to contribute their experiences in ways that create motivation, and support users in reflecting over their routines rather than prompting them on what to do