We present a mobile instrumentation toolkit, AWARE, an open-source effort to develop an extensible and reusable platform for capturing, inferring, and generating context on mobile devices. Mobile phones are sensor-rich but resource-constrained, and therefore several considerations need to be addressed when creating a research tool that ensures problem-free context collection. We demonstrate how AWARE can mitigate researchers’ effort when building mobile data-logging tools and context-aware applications, with minimal battery impact. By encapsulating implementation details of sensor data retrieval and exposing the sensed context as higher-level abstractions, AWARE shifts the focus from software development to data analysis, both quantitative and qualitative. We have evaluated AWARE in several case studies and discuss its use, power consumption, and scalability.

/pdf/aware-mobile-context-instrumentation-framework-mtdmiz17ct.pdf

AWARE: Mobile Context Instrumentation Framework

We consider the detection of activities from non-cooperating individuals with features obtained on the radio frequency channel. Since environmental changes impact the transmission channel between devices, the detection of this alteration can be used to classify environmental situations. We identify relevant features to detect activities of non-actively transmitting subjects. In particular, we distinguish with high accuracy an empty environment or a walking, lying, crawling or standing person, in case-studies of an active, device-free activity recognition system with software defined radios. We distinguish between two cases in which the transmitter is either under the control of the system or ambient. For activity detection the application of one-stage and two-stage classifiers is considered. Apart from the discrimination of the above activities, we can show that a detected activity can also be localized simultaneously within an area of less than 1 meter radius.

RF-Sensing of Activities from Non-Cooperative Subjects in Device-Free Recognition Systems Using Ambient and Local Signals

An increasing number of pervasive healthcare systems are being designed, that allow people to monitor and get feedback on their health and wellness. To address the challenges of self-management of mental illnesses, we have developed the MONARCA system - a personal monitoring system for bipolar patients. We conducted a 14 week field trial in which 12 patients used the system, and we report findings focusing on their experiences. The results were positive; compared to using paper-based forms, the adherence to self-assessment improved; the system was considered very easy to use; and the perceived usefulness of the system was high. Based on this study, the paper discusses three HCI questions related to the design of personal health technologies; how to design for disease awareness and self-treatment, how to ensure adherence to personal health technologies, and the roles of different types of technology platforms.

http://www.itu.dk/people/bardram/pdf/bardram.chi2013.pdf

Designing mobile health technology for bipolar disorder: a field trial of the monarca system

The ability to automatically recognize a person’s behavioral context can contribute to health monitoring, aging care, and many other domains. Validating context recognition in the wild is crucial to promote practical applications that work in real-life settings. The authors collected more than 300,000 minutes of sensor data with context labels from 60 subjects. Unlike previous studies, these subjects used their own personal phone, in any way that was convenient to them, and engaged in their routine in their natural environments. Unscripted behavior and unconstrained phone usage resulted in situations that were harder to recognize. The authors demonstrate how fusion of multimodal sensors is important for resolving such cases. They present a baseline system and encourage researchers to use their public dataset to compare methods and improve context recognition in the wild.

Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches

Driving aggressively increases the risk of accidents Assessing a person's driving style is a useful way to guide aggressive drivers toward having safer driving behaviors A number of studies have investigated driving style, but they often rely on the use of self-reports or simulators, which are not suitable for the real-time, continuous, automated assessment and feedback on the road In order to understand and model aggressive driving style, we construct an in-vehicle sensing platform that uses a smartphone instead of using heavyweight, expensive systems Utilizing additional cheap sensors, our sensing platform can collect useful information about vehicle movement, maneuvering and steering wheel movement We use this data and apply machine learning to build a driver model that evaluates drivers' driving styles based on a number of driving-related features From a naturalistic data collection from 22 drivers for 3 weeks, we analyzed the characteristics of drivers who have an aggressive driving style Our model classified those drivers with an accuracy of 905% (violation-class) and 81% (questionnaire-class) We describe how, in future work, our model can be used to provide real-time feedback to drivers using only their current smartphone

/pdf/a-smartphone-based-sensing-platform-to-model-aggressive-3wn3yurh9z.pdf

A smartphone-based sensing platform to model aggressive driving behaviors

Much research in ubiquitous computing assumes that a user's phone will be always on and at-hand, for collecting user context and for communicating with a user. Previous work with the previous generation of mobile phones has shown that such an assumption is false. Here, we investigate whether this assumption about users' proximity to their mobile phones holds for a new generation of mobile phones, smart phones. We conduct a data collection field study of 28 smart phone owners over a period of 4 weeks. We show that in fact this assumption is still false, with the within arm's reach proximity being true close to 50% of the time, similar to the earlier work. However, we also show that smart phone proximity within the same room (arm+room) as the user is true almost 90% of the time. We discuss the reasons for these phone proximities and the implications of this on the development of mobile phone applications, particularly those that collect user and environmental context, and delivering notification to users. We also show that we can accurately predict the proximity at the arm level and arm+room level with 75 and 83% accuracy, respectively, with features simple to collect and model on a mobile phone. Further we show that for several individuals who are almost always within the arm+room level, we can predict this level with over 90% accuracy.

http://gtubicomp.pbworks.com/w/file/fetch/46471141/fp537-dey.pdf

Getting closer: an empirical investigation of the proximity of user to their smart phones

Autism education programs for children collect and use large amounts of behavioral data on each student. Staff use paper almost exclusively to collect these data, despite significant problems they face in tracking student data in situ, filling out data sheets and graphs on a daily basis, and using the sheets in collaborative decision making. We conducted fieldwork to understand data collection and use in the domain of autism education to explain why current technology had not met staff needs. We found that data needs are complex and unstandardized, immediate demands of the job interfere with staff ability to collect in situ data, and existing technology for data collection is inadequate. We also identified opportunities for technology to improve sharing and use of data. We found that data sheets are idiosyncratic and not useful without human mediation; improved communication with parents could benefit children's development; and staff are willing, and even eager, to incorporate technology. These factors explain the continued dependence on paper for data collection in this environment, and reveal opportunities for technology to support data collection and improve use of collected data.

Why do they still use paper?: understanding data collection and use in Autism education

This is a science fiction story that describes a world in the not-too-distant future in which context aware technologies, ubiquitous computing, and persuasive technologies are virtually everywhere. This world is navigated by a character of the utmost average credentials who, because he has lived in this technology-saturated world his whole life, does not really notice how much he relies on it. Throughout his day he and his colleagues rely on digital personal assistants who have over time learned more ore less every intricacy of their user's behavior. These digital assistants manage their user's work and social life to maximum efficiency and have become absolutely necessary in everything they do, which raises the question—what happens when they are absent?

Kevin Tassini

Papers

Getting closer: an empirical investigation of the proximity of user to their smart phones

Why do they still use paper?: understanding data collection and use in Autism education

The Magician's Assistant