Published papers

We present QueueVadis, a system that addresses the problem of estimating, in real-time, the properties of queues at commonplace urban locations, such as coffee shops, taxi stands and movie theaters. Abjuring the use of any queuing-specific infrastructure sensors, QueueVadis uses participatory mobile sensing to detect both (i) the individual-level queuing episodes for any arbitrarily-shaped queue (by a characteristic locomotive signature of short bursts of "shuffling forward" between periods of "standing") and (ii) the aggregate-level queue properties (such as expected wait or service times) via appropriate statistical aggregation of multi-person data. Moreover, for venues where multiple queues are too close to be separated via location estimates, QueueVadis also uses a novel disambiguation technique to separate users into multiple distinct queues. User studies, performed with 138 cumulative total users observed at 23 different real-world queues across Singapore and Japan, show that QueueVadis is able to (a) identify all individual queuing episodes, (b) predict service and wait times fairly accurately (with median estimation errors in the 10%-20% range), independent of the queue's shape, (c) separate users in multiple proximate queues with close to 80% accuracy and (d) provide reasonable estimates when the participation rate (the fraction of QueueVadis-equipped people in the queue) is modest.

Copyright © 2015 ACM. In today's ubiquitous computing environment where users carry, manipulate, and interact with an increasing number of networked devices, applications and web services, human attention is the new bottleneck in computing. It is therefore important to minimize a user's mental effort due to notifications, especially in situations where users are mobile and using multiple wearable and mobile devices. To this end, we propose Attelia II, a novel middleware that identifies breakpoints in users' lives while using those devices, and delivers notifications at these moments. Attelia II works in real-Time and uses only the mobile and wearable devices that users naturally use and wear, without any modifications to applications, and without any dedicated psycho-physiological sensors. Our in-The-wild evaluation in users' multi-device environment (smart phones and smart watches) with 41 participants for 1 month validated the effectiveness of Attelia. Our new physical activity-based breakpoint detection, in addition to the UI Event-based breakpoint detection, resulted in a 71.8% greater reduction of users' perception of workload, compared with our previous system that used UI events only. Adding this functionality to a smart watch reduced workload perception by 19.4% compared to random timing of notification deliveries. Our multi-device breakpoint detection across smart phones and watches resulted in about 3 times greater reduction in workload perception than our previous system.

We present Senbay, a novel smartphone-based platform for capturing and sharing synchronously-recorded video/sensor data stream using animated two-dimensional barcode. In the ubicomp environment, mobile devices have enabled us to capture video and various types of sensor data easily. However, currently we do not have a framework to instantly integrate video and those sensor data on smartphones and share them easily with other users on the net. In this paper, we present Senbay, a platform to integrate video and sensor data stream into single video on smartphones. With Senbay, sensor data stream will be embedded into a video as animated QR code in real-time, thus users can share the \sensor-federated" video easily on the web, through lots of popular video sharing services. This paper introduces the architecture and evaluation of Senbay and discusses future vision of the sensor-federated video sharing platform along with several applications.

Today, many users of mobile devices are continuously confronted with a huge variety of information: notifications from Facebook, new application updates, won badges, or reminders. This leads to an information overload, which makes it hard to stay focused. This workshop will investigate approaches towards smart attention management systems. We will discuss the fundamental challenges of smart notifications and the design of proactive notification mechanisms. We invite submissions that focus on the understanding of users and their current, mobile information handling. We further appreciate contributions that propose design concepts for the interaction with smart attention management systems. The expected workshop outcome is a summary of emerging challenges in the design and development of smart attention management systems as well as approaches to address them.

© 2003 by The USENIX Association. Remote execution can transform the puniest mobile device into a computing giant able to run resource-intensive applications such as natural language translation, speech recognition, face recognition, and augmented reality. However, easily partitioning these applications for remote execution while retaining application-specific information has proven to be a difficult challenge. In this paper, we show that automated dynamic repartitioning of mobile applications can be reconciled with the need to exploit application-specific knowledge. We show that the useful knowledge about an application relevant to remote execution can be captured in a compact declarative form called tactics. Tactics capture the full range of meaningful partitions of an application and are very small relative to code size. We present the design of a tactics-based remote execution system, Chroma, that performs comparably to a runtime system that makes perfect partitioning decisions. Furthermore, we show that Chroma can automatically use extra resources in an overprovisioned environment to improve application performance.

Have you ever talked to fish? If you can incorporate the feelings of your fish into breeding systems, life with your fish can become more comfortable, fun, and a greater experience overall. In breeding pets, owners often cannot properly understand their pet's internal state, which can lead to impaired health-or worse, loss of life-for the pet. In case of fish, such situations are more likely to occur than with dogs or cats, since owner-pet communication is more difficult with fish. Although fish are the most bred pets [1], information and communications technology (ICT)-based solutions for accelerating owner-pet interaction have not yet been sufficiently explored. We present "TalkingNemo," an aquarium fish breeding system that enables interaction between owners and their fish. TalkingNemo detects the condition of fish and their aquarium via a camera and sensors attached to the aquarium, and notifies users with speech balloons as from the perspective of the fish (Figure 1). TalkingNemo empowers us to breed fish in good condition over a longer period. This paper introduces the architecture and evaluates the results of TalkingNemo.

© 2015 Elsevier B.V. All rights reserved. As the amount of information to users increases with the trends of an increasing numbers of devices, applications, and web services, the new bottleneck in computing is human attention. To minimize users attentional overload, we propose a novel middleware "Attelia" that detects breakpoints of user's mobile interactions to deliver notifications adaptively. Attelia detects such timings in real-time, using only users phones, without any external sensors, and without any modifications to applications. Our extensive evaluation proved Attelias effectiveness. In-the-wild user study with 30 participants for 16 days showed that, specifically for the users with greater sensitivity for interruptive notification timings, notification scheduling in Attelia's breakpoint timing reduced users frustration by 28% in users' real smart phone environments.

© 2014 IEEE. Recent technological trends on mobile/wearable devices and sensors have been enabling increasing number of people to collect and store their 'life-logs' easily in their daily lives. Beyond exercise behavior change of individual user, our research focus is on the behavior change of teams, based on life-logging technologies and information sharing. In this paper, we propose and evaluate six different types of information sharing model among team members for their exercise promotion, leveraging concepts of 'competition' and 'collaboration'. According to our experimental mobile web application for exercise promotion and extensive user study among 64 total users for three weeks, the model with 'external competition' technique resulted the most effective performance for competitive teams such as sport teams.