Anthony LaMarca

Activity sensing in the wild: a field trial of ubifit garden

Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer peoples activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.

Location disclosure to social relations: why, when, & what people want to share

Advances in location-enhanced technology are making it easier for us to be located by others. These new technologies present a difficult privacy tradeoff, as disclosing ones location to another person or service could be risky, yet valuable. To explore whether and what users are willing to disclose about their location to social relations, we conducted a three-phased formative study. Our results show that the most important factors were who was requesting, why the requester wanted the participants location, and what level of detail would be most useful to the requester. After determining these, participants were typically willing to disclose either the most useful detail or nothing about their location. From our findings, we reflect on the decision process for location disclosure. With these results, we hope to influence the design of future location-enhanced applications and services.

The Mobile Sensing Platform: An Embedded Activity Recognition System

Activity-aware systems have inspired novel user interfaces and new applications in smart environments, surveillance, emergency response, and military missions. Systems that recognize human activities from body-worn sensors can further open the door to a world of healthcare applications, such as fitness monitoring, eldercare support, long-term preventive and chronic care, and cognitive assistance. Wearable systems have the advantage of being with the user continuously. So, for example, a fitness application could use real-time activity information to encourage users to perform opportunistic activities. Furthermore, the general public is more likely to accept such activity recognition systems because they are usually easy to turn off or remove.

Accuracy characterization for metropolitan-scale Wi-Fi localization

Location systems have long been identified as an important component of emerging mobile applications. Most research on location systems has focused on precise location in indoor environments. However, many location applications (for example, location-aware web search) become interesting only when the underlying location system is available ubiquitously and is not limited to a single office environment. Unfortunately, the installation and calibration overhead involved for most of the existing research systems is too prohibitive to imagine deploying them across, say, an entire city. In this work, we evaluate the feasibility of building a wide-area 802.11 Wi-Fi-based positioning system. We compare a suite of wireless-radio-based positioning algorithms to understand how they can be adapted for such ubiquitous deployment with minimal calibration. In particular, we study the impact of this limited calibration on the accuracy of the positioning algorithms. Our experiments show that we can estimate a users position with a median positioning error of 13-40 meters (depending upon the characteristics of the environment). Although this accuracy is lower than existing positioning systems, it requires substantially lower calibration overhead and provides easy deployment and coverage across large metropolitan areas.

Accurate GSM indoor localization

Accurate indoor localization has long been an objective of the ubiquitous computing research community, and numerous indoor localization solutions based on 802.11, Bluetooth, ultrasound and infrared technologies have been proposed. This paper presents the first accurate GSM indoor localization system that achieves median accuracy of 5 meters in large multi-floor buildings. The key idea that makes accurate GSM-based indoor localization possible is the use of wide signal-strength fingerprints. In addition to the 6-strongest cells traditionally used in the GSM standard, the wide fingerprint includes readings from additional cells that are strong enough to be detected, but too weak to be used for efficient communication. Experiments conducted on three multi-floor buildings show that our system achieves accuracy comparable to an 802.11-based implementation, and can accurately differentiate between floors in both wooden and steel-reinforced concrete structures.

Extending document management systems with user-specific active properties

Document properties are a compelling infrastructure on which to develop document management applications. A property-based approach avoids many of the problems of traditional heierarchical storage mechanisms, reflects document organizations meaningful to user tasks, provides a means to integrate the perspectives of multiple individuals and groups, and does this all within a uniform interaction framework. Document properties can reflect not only categorizations of documents and document use, but also expressions of desired system activity, such as sharing criteria, replication management, and versioning. Augmenting property-based document management systems with active properties that carry executable code enables the provision of document-based services on a property infrastructure. The combination of document properties as a uniform mechanism for document management, and active properties as a way of delivering document services, represents a new paradigm for document management infrastructures. The Placeless Documents system is an experimental prototype developed to explore this new paradigm. It is based on the seamless integration of user-specific, active properties. We present the fundamental design approach, explore the challenges and opportunities it presents, and show our architectures deals with them.

Flatland: new dimensions in office whiteboards

Flatland is an augmented whiteboard interface designed forinformal office work. Our research investigates approaches tobuilding an augmented whiteboard in the context of continuous, longterm office use. In particular, we pursued three avenues ofresearch based on input from user studies: techniques for themanagement of space on the board, the ability to flexibly applybehaviors to support varied application semantics, and mechanismsfor managing history on the board. Unlike some previously reportedwhiteboard systems, our design choices have been influenced by adesire to support long-term, informal use in an individual officesetting.

Learning and recognizing the places we go

Location-enhanced mobile devices are becoming common, but applications built for these devices find themselves suffering a mismatch between the latitude and longitude that location sensors provide and the colloquial place label that applications need. Conveying my location to my spouse, for example as (48.13641N, 11.57471E), is less informative than saying “at home.” We introduce an algorithm called BeaconPrint that uses WiFi and GSM radio fingerprints collected by someones personal mobile device to automatically learn the places they go and then detect when they return to those places. BeaconPrint does not automatically assign names or semantics to places. Rather, it provides the technological foundation to support this task. We compare BeaconPrint to three existing algorithms using month-long trace logs from each of three people. Algorithmic results are supplemented with a survey study about the places people go. BeaconPrint is over 90% accurate in learning and recognizing places. Additionally, it improves accuracy in recognizing places visited infrequently or for short durations—a category where previous approaches have fared poorly. BeaconPrint demonstrates 63% accuracy for places someone returns to only once or visits for less than 10 minutes, increasing to 80% accuracy for places visited twice.

Presto: an experimental architecture for fluid interactive document spaces

Traditional document systems use hierarchical filing structures as the basis for organizing, storing and retrieving documents. However, this structure is very limited in comparison with the rich and varied forms of document interaction and category management in everyday document use. Presto is a prototype document management system providing rich interaction with documents through meaningful, user-level document attributes, such as “Word file,” “published paper,” “shared with Jim,” “about Presto,” or “currently in progress” Document attributes capture the multiple different roles that a single document might play, and they allow users to rapidly reorganize their document space for the task at hand. They also provide a basis for novel document systems design and new approaches to document management and interaction. In this article, we outline the motivations behind this approach, describe the principal components of our implementation, discuss architectural consequences, and show how these support new forms of interactions with large personal document spaces.

Mobility detection using everyday GSM traces

Recognition of everyday physical activities is difficult due to the challenges of building informative, yet unobtrusive sensors. The most widely deployed and used mobile computing device today is the mobile phone, which presents an obvious candidate for recognizing activities. This paper explores how coarse-grained GSM data from mobile phones can be used to recognize high-level properties of user mobility, and daily step count. We demonstrate that even without knowledge of observed cell tower locations, we can recognize mobility modes that are useful for several application domains. Our mobility detection system was evaluated with GSM traces from the everyday lives of three data collectors over a period of one month, yielding an overall average accuracy of 85%, and a daily step count number that reasonably approximates the numbers determined by several commercial pedometers.