Donnie H. Kim

SensLoc: sensing everyday places and paths using less energy

Continuously understanding a users location context in colloquial terms and the paths that connect the locations unlocks many opportunities for emerging applications. While extensive research effort has been made on efficiently tracking a users raw coordinates, few attempts have been made to efficiently provide everyday contextual information about these locations as places and paths. We introduce SensLoc, a practical location service to provide such contextual information, abstracting location as place visits and path travels from sensor signals. SensLoc comprises of a robust place detection algorithm, a sensitive movement detector, and an on-demand path tracker. Based on a users mobility, SensLoc proactively controls active cycle of a GPS receiver, a WiFi scanner, and an accelerometer. Pilot studies show that SensLoc can correctly detect 94% of the place visits, track 95% of the total travel distance, and still only consume 13% of energy than algorithms that periodically collect coordinates to provide the same information.

Discovering semantically meaningful places from pervasive RF-beacons

Detecting visits to semantically meaningful places is important for many emerging mobile applications. We present PlaceSense, a place discovery algorithm suitable for mobile devices that exploits pervasive RF-beacons. By relying on separate mechanisms to detect entrance to and departure from a place and buffering overlapping data for subsequent visits, it is more robust than the state-of-the-art, especially in detecting short visits, places where people are mobile, or where inconsistent beacons are prevalent due to interference. We experimentally evaluate PlaceSenses effectiveness in discovering semantically meaningful places, and compare with other approaches that use coordinates or RF-beacon fingerprints. Our results demonstrate that PlaceSense correctly discovers 92% (compared to between 28% and 65% for previous work) of the visited places and accurately detects their entrance and departure times from both real-life and scripted data sets.

AndWellness: an open mobile system for activity and experience sampling

Advances in mobile phone technology have allowed phones to become a convenient platform for real-time assessment of a participants health and behavior. AndWellness, a personal data collection system, uses mobile phones to collect and analyze data from both active, triggered user experience samples and passive logging of onboard environmental sensors. The system includes an application that runs on Android based mobile phones, server software that manages deployments and acts as a central repository for data, and a dashboard front end for both participants and researchers to visualize incoming data in real-time. Our system has gone through testing by researchers in preparation for deployment with participants, and we describe an initial qualitative study plus several planned future studies to demonstrate how our system can be used to better understand a users health related habits and observations.

Feasibility testing of an automated image-capture method to aid dietary recall.

Background/Objectives:The accuracy of dietary recalls might be enhanced by providing participants with photo images of foods they consumed during the test period.Subjects/Methods:We examined the feasibility of a system (Image-Diet Day) that is a user-initiated camera-equipped mobile phone that is programmed to automatically capture and transmit images to a secure website in conjunction with computer-assisted, multipass, 24-h dietary recalls in 14 participants during 2007. Participants used the device during eating periods on each of the three independent days. Image processing filters successfully eliminated underexposed, overexposed and blurry images. The captured images were accessed by the participants using the ImageViewer software while completing the 24-h dietary recall on the following day.Results:None of the participants reported difficulty using the ImageViewer. Images were deemed ‘helpful’ or ‘sort of helpful’ by 93% of participants. A majority (79%) of users reported having no technical problems, but 71% rated the burden of wearing the device as somewhat to very difficult, owing to issues such as limited battery life, self-consciousness about wearing the device in public and concerns about the field of view of the camera.Conclusion:Overall, these findings suggest that automated imaging is a promising technology to facilitate dietary recall. The challenge of managing the thousands of images generated can be met. Smaller devices with a broader field of view may aid in overcoming self-consciousness of the user with using or wearing the device.

Employing user feedback for semantic location services

Just as coordinate-oriented location-based applications have exploded recently with mapping services, new semantic location services will be critical for the next wave of killer applications. People are going to want everyday applications to have location-awareness that goes beyond simple numerical latitude and longitude. Loci is a new semantic location service layer that employs user feedback to bridge the gap between machine-learned and human-defined places. Advances in place learning techniques have provided us the tools to detect nearly 95% of the visits we make to places and the distances we travel. The difficulty of recovering the remaining 5% comes from designing parameters that work for every user in every place. Based on a user study with 29 participants over three weeks, we show that the level of user feedback required by the service is acceptable and most of the users are willing to provide help to improve their experiences with the service. Our results suggest that user feedback has the potential to significantly improve semantic location services, but requires well-timed prompting mechanisms to improve the quality of the feedback.

PDVLoc: A Personal Data Vault for Controlled Location Data Sharing

Location-Based Mobile Service (LBMS) is one of the most popular smartphone services. LBMS enables people to more easily connect with each other and analyze the aspects of their lives. However, sharing location data can leak peoples privacy. We present PDVLoc, a controlled location data-sharing framework based on selectively sharing data through a Personal Data Vault (PDV). A PDV is a privacy architecture in which individuals retain ownership of their data. Data are routinely filtered before being shared with content-service providers, and users or data custodian services can participate in making controlled data-sharing decisions. Introducing PDVLoc gives users flexible and granular access control over their location data. We have implemented a prototype of PDVLoc and evaluated it using real location-sharing social networking applications, Google Latitude and Foursquare. Our user study of 19 participants over 20 days shows that most users find that PDVLoc is useful to manage and control their location data, and are willing to continue using PDVLoc.