karenptang

Estrellita: empowering caregivers of preterm infants

Preterm birth is often associated with long-term health impairments. Because of these health concerns, caring for preterm infants can often be an stressful experience for parents. To address this challenge, we've created Estrellita, a system that: (1) helps parent record, communicate, and understand data collected about their infants on a frequent basis and (2) improves parent and clinician feelings of efficacy and quality of care.

Estrellita consists of two parts: a mobile application for caregivers and a web portal for clinicians. Using the mobile application, parents can record observations of daily living (ODLs) for their infant and for themselves, share their data with clinical providers, and visualize a history of their ODL data. Through the website, healthcare providers can interact with the parent and keep abreast with the infant's ODLs through a series of simple visualizations and data summaries.

Tang, K.P., Cheng, K.G., Hirano, S., Nagel, M., Baker, D., and Hayes, G.R. (2011). Addressing the Design Challenges for a Clinically-Informed Data Capture Tool Targeted for Caregivers of Premature Infants. Workshop on Interactive Systems in Healthcare (WISH '11).
[local pdf] [talk]

Sometimes Less is More: usable privacy for social location sharing

One often-cited adoption barrier to location-sharing applications is that they do not adequately address end-user privacy concerns for sharing location data. In my dissertation, I explore how relatively simple privacy techniques (namely using location disclosure abstractions) can influence end-user privacy concerns at four different stages: how users reason about location sharing, how users configure their privacy preferences, how users interpret visual representations of their location information, and what kinds of outcomes can be expected from users that share location abstractions.

Tang, K.P., Hong, J.I., and Siewiorek, D.P. (2012). The Implications of Offering More Disclosure Choices for Social Location Sharing. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '12). Austin, Texas (May 5-10, 2012). New York: ACM Press. To appear.
[official pdf] [local pdf] Tang, K.P., Hong, J.I., and Siewiorek, D.P. (2012). Privacy and Sharing Outcomes of Using Location Abstractions. Workshop on Reconciling Privacy with Social Media at ACM Conference on Computer Supported Cooperative Work (CSCW '12).
[local pdf] Tang, K.P., Hong, J.I., and Siewiorek, D.P. (2011). Understanding How Visual Representations of Location Feeds Affect End-User Privacy Concerns. Proceedings of the 13th International Conference on Ubiquitous Computing (Ubicomp '11).
[official pdf] [local pdf] Tang, K.P. (2010). "Sometimes Less is More": Multi-Perspective Exploration of Disclosure Abstractions in Location-Aware Social Mobile Applications. Ph.D. Dissertation. Carnegie Mellon University, Human-Computer Interaction Institute.
[official pdf] [local pdf] Tang, K.P., Lin, J.L., Hong, J. I., and Siewiorek, D.P. (2010). Rethinking Location Sharing: Exploring the Implications of Social-Driven vs. Purpose-Driven Location Sharing. Proceedings of the 12th International Conference on Ubiquitous Computing (Ubicomp '10).
[official pdf] [local pdf]

IMBuddy: privacy controls and feedback for context-aware instant messaging

One often-cited adoption barrier to location-sharing applications is that they do not adequately address end-user privacy concerns for sharing location data. In my dissertation, I explore how relatively simple privacy techniques (namely using location disclosure abstractions) can influence end-user privacy concerns at four different stages: how users reason about location sharing, how users configure their privacy preferences, how users interpret visual representations of their location information, and what kinds of outcomes can be expected from users that share location abstractions.

Hsieh, G., Tang, K.P., Low, W.Y., and Hong, J.I. (2007). Field Deployment of IMBuddy: A Study of Privacy Control and Feedback Mechanisms for Contextual Instant Messengers. Proceedings of the 9th International Conference on Ubiquitous Computing (Ubicomp '07).
[official pdf] [local pdf] Cornwell, J., Fette, I., Hsieh, G., Prabaker, M., Rao, J., Tang, K.P., Vaniea, K., Bauer, L., Cranor, L., Hong, J., McLaren, B., Reiter, M. and Sadeh, N. (2007). User-Controllable Security and Privacy for Pervasive Computing. Proceedings of the 8th IEEE Workshop on Mobile Computing Systems and Applications (HotMobile '07).
[official pdf] [local pdf] Tang, K.P. (2007). Privacy Mechanisms for Context-Aware, Group-based Mobile Social Software. Doctoral Symposium at the ACM Symposium on User Interface Software and Technology (UIST '07).
[official pdf] [local pdf]

Hitchhiking: anonymous participatory sensing of busyness

Hitchhiking is a privacy-sensitive manner way of building a certain class of location-based services. Bustle is an example of a Hitchhiking application that can answer questions like "How busy is it at the cafe?" and "How long are the lines at the airport?" Bustle works by counting the number of wireless devices in an area and using that count to estimate the number of people.

Tang, K.P., Fogarty, J., Keyani, P., and Hong, J.I. (2006). Putting People in their Place: An Anonymous and Privacy-Sensitive Approach to Collecting Sensed Data in Location-Based Applications. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '06).
[official pdf] [local pdf] Fogarty, J., Hong, J.I., Keyani, P., and Tang, K.P. (2006). Anonymous and Privacy-Sensitive Collection of Sensed Data in Location-Based Application. Workshop on Mobile Social Software at the ACM Conference on Human Factors in Computing Systems (CHI '06).
[local pdf] Tang, K.P., Fogarty, J., Keyani, P., and Hong, J.I. (2006). Bustle: Using Hitchhiking to Monitor Meaningful Locations. Proceedings of the 7th IEEE Workshop on Mobile Computing Systems and Applications (HotMobiles '06).
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GSM-based localization for mobile phones

This projects looks at triangulating a user's location and activity information based on GSM fingerprinting. Using this technique, we can detect the places that people visit without using GPS, and can achieve median localization accuracies of 5 and 75 meters for indoor and outdoor environments, respectively. We've also developed algorithms for detecting whether a GSM mobile phone is moving. These algorithms, in early experiments, show excellent promise and require nothing from the mobile phone other than radio signals that the phone must have to perform its normal function.

Varshavsky, A., Chen, M., de Lara, E., Froehlich, J., Haehnel, D., Hightower, J., LaMarca, A., Potter, F., Sohn, T., Tang, K.P., and Smith, I. (2006). Are GSM phones THE solution for localization? Proceedings of the 7th IEEE Workshop on Mobile Computing Systems and Applications (HotMobile '06).
[official pdf] [local pdf] Smith, I. , Chen, M., Varshavsky, A., Sohn, T., and Tang, K.P. (2005). Algorithms for Detecting Motion of a GSM Mobile Phone. Workshop on Location Awareness and Community at the European Conference on Computer-Supported Cooperative Work (ECSCW '05).
[local pdf]

eWatch: context-aware wearable computing

The eWatch prototype senses a user's activities based on its built-in accelerometer, temperature, light, and microphone sensors. The accelerometers and microphone provide input data for its interruptibility model. Depending on a message's importance and the user's interruptibility, different vibration and visual notification patterns are sent to the user. The eWatch is transparently integrated into the user's environment as a wearable computing device and can communicate via Bluetooth to other devices.

Smailagic, A., Siewiorek, D.P., Maurer, U., Rowe, A., and Tang, K.P. (2005). eWatch: Context Sensitive System Design Case Study. Proceedings of IEEE Computer Society Annual Symposium on VLSI (ISVLSI '05).
[official pdf] [local pdf] Smailagic, A., Siewiorek, D.P., Maurer, U., Rowe, A., and Tang, K.P. (2005). A Context-Specific Electronic Design and Prototyping Course. Proceedings of the 2005 IEEE International Conference on Microelectronic Systems Education (MSE '05).
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Predicting a programmer's interruptibility

The computer and communication systems that office workers currently use tend to interrupt at inappropriate times or unduly demand attention because they have no way to determine when an interruption is appropriate. Sensor-based statistical models of human interruptibility offer a potential solution to this problem. Prior work to examine such models has primarily reported results related to social engagement, but it seems that task engagement is also important. Using a sensor-based statistical approach to model human interruptibility, we examined task engagement by studying programmers working on a realistic programming task and empirically determining their interruptibility during various types of programming tasks.

Fogarty, J., Ko, A.J., Aung, H.H., Golden, E., Tang, K.P., and Hudson, S.E. (2005). Examining Task Engagement in Sensor-Based Statistical Models of Human Interruptibility. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '05).
[official pdf] [local pdf]