We present a system for connecting partners in long-distance relationships in bedrooms and at bedtime, a space and time that most couples share. Unlike communications explicitly initiated by users, our system is always-on, staying in the background and enabling remote presence without constant use. We present findings from a field study in which the system was deployed into participants’ bedrooms. The system includes an automated photo-stream (rather than video), which was found to provide a balance between the feeling of presence and privacy, and to remove the pressure to communicate. The system also includes a real-time shared inking canvas with disappearing ink, which was found to provide a rich versatile medium allowing for new patterns of communication, live interventions, and collaborative drawing. Learnings from how our system balances privacy and remote connectedness may also have relevance for other domains such as remote healthcare and education.

We present ThermalSense, a method for dynamically detecting and predicting thermal comfort by using thermographic imaging to look for the physiological markers of vasodilation or vasoconstriction. We describe how ThermalSense can be used to infer how to control heating and cooling systems and reduce energy use while maintaining comfort.

Platypus is the first system to localize and identify people by remotely and passively sensing changes in their body electric potential which occur naturally during walking. While it uses three or more electric potential sensors with a maximum range of 2 m, as a tag-free system it does not require the user to carry any special hardware. We describe the physical principles behind body electric potential changes, and a predictive mathematical model of how this affects a passive electric field sensor. By inverting this model and combining data from sensors, we infer a method for localizing people and experimentally demonstrate a median localization error of 0.16 m. We also use the model to remotely infer the change in body electric potential with a mean error of 8.8 % compared to direct contact-based measurements. We show how the reconstructed body electric potential differs from person to person and thereby how to perform identification. Based on short walking sequences of 5 s, we identify four users with an accuracy of 94 %, and 30 users with an accuracy of 75 %. We demonstrate that identification features are valid over multiple days, though change with footwear.

We explore the design space of energy-neutral situated displays, which give physical presence to digital information. We investigate three central dimensions: energy sources, display technologies, and wireless communications. Based on the power implications from our analysis, we present a thin, wireless, photovoltaic-powered display that is quick and easy to deploy and capable of indefinite operation in indoor lighting conditions. The display uses a low-resolution e-paper architecture, which is 35 times more energy-efficient than smaller-sized high-resolution displays. We present a detailed analysis on power consumption, photovoltaic energy harvesting performance, and a detailed comparison to other display-driving architectures. Depending on the ambient lighting, the display can trigger an update every 1 -- 25 minutes and communicate to a PC or smartphone via Bluetooth Low-Energy.

We present Picco, a tiny situated display for drawings and simple animations, which are created on a dedicated tablet app. Picco was designed to support playful messaging in the workplace through a glanceable desktop device that would place minimal demands on users. Two studies of the device at work demonstrated how crafting was an expression of intimacy when the device was used to connect the workplace to the home, and a way of demonstrating skill and humor to a broad audience when messages were sent amongst co-workers. However, the level of skill needed to produce these messages became a barrier to entry for some co-workers. Our findings suggest that visible ownership of a situated device, which can be personalized in other ways, can underpin a secondary level of participation that is crucial in supporting a sense of involvement when the level of crafting required can stifle more direct participation.

The layouts of the buildings we live in shape our everyday lives. In office environments, building spaces affect employees' communication, which is crucial for productivity and innovation. However, accurate measurement of how spatial layouts affect interactions is a major challenge and traditional techniques may not give an objective view.

We consider the problem of evaluating the predictive log likelihood of a previously un- seen document under a topic model. This task arises when cross-validating for a model hyperparameter, when testing a model on a hold-out set, and when comparing the performance of different fitting strategies. Yet it is known to be very challenging, as it is equivalent to estimating a marginal likelihood in Bayesian model selection. We propose a fast algorithm for approximating this likelihood, one whose computational cost is linear both in document length and in the number of topics. The method is a first-order approximation to the algorithm of Carvalho et al. (2010), and can also be interpreted as a one-particle, Rao-Blackwellized version of the "left-to-right" method of Wallach et al. (2009). On our test examples, the proposed method gives similar answers to these other methods, but at lower computational cost.

In this paper we present a room-to-room thermal model used to accurately predict temperatures in residential buildings. We evaluate the accuracy of this model with ground truth data from four occupied family homes (two in the UK and two in the US). The homes have differing construction and a range of heating infrastructure (wall-mounted radiators, underfloor heating, and furnace-driven forced-air). Data was gathered using a network of simple and sparse (one per room) temperature sensors, a gas meter sensor, and an outdoor temperature sensor. We show that our model can predict future indoor temperature trends with a 90th percentile aggregate error between 0.61--1.50°, when given boiler or furnace actuation times and outdoor temperature forecasts. Two existing models were also implemented and then evaluated on our dataset alongside Matchstick. As a proof of concept, we used data from a previous control study to show that when Matchstick is used to predict temperatures (rather than assuming a preset linear heating rate) the possible gas savings increase by up to 3%.

We describe the SAWUI architecture by which smartphones can easily show user interfaces for nearby appliances, with no modification or pre-installation of software on the phone, no reliance on cloud services or networking infrastructure, and modest additional hardware in the appliance. In contrast to appliances? physical user interfaces, which are often as simple as buttons, icons and LEDs, SAWUIs leverage smartphones? powerful UI hardware to provide personalized, self-explanatory, adaptive, and localized UIs.

In this paper we present the features of a new physical device prototyping platform called Microsoft .NET Gadgeteer along with our initial experiences using it to teach computer science in high schools. Gadgeteer makes it easy for newcomers to electronics and computing to plug together modules with varied functionality and to program the resulting system's behavior. We believe the platform is particularly suited to teaching modern programming concepts such as object-oriented, event-based programming and it could be a timely addition to established teaching tools given the current interest in improving high school computer science education in some regions. We have run a number of pilot studies in the US and in the UK with students of varying age and ability. Our results indicate that the tangible and expressive nature of Gadgeteer helps to engage and motivate a diverse set of students. We were also pleasantly surprised by the level of polish and sophistication of the devices which were built. We hope to further explore the potential of Gadgeteer for teaching in future work and we encourage others to build on our experiences.

In this paper we present a number of different physical construction techniques for prototyping functional electronic devices. Some of these approaches are already well established whilst others are more novel; our aim is to briefly summarize some of the main categories and to illustrate them with real examples. Whilst a number of different tools exist for building working device prototypes, for consistency the examples we present here are all built using the Microsoft .NET Gadgeteer platform. Although this naturally constrains the scope of this study, it also facilitates a basic comparison of the different techniques. Our ultimate aim is to enable others in the field to learn from our experiences and the techniques we present.

Researchers who develop new home technologies using connected devices (e.g. sensors) often want to conduct large-scale field studies in homes to evaluate their technology, but conducting such studies today is quite challenging, if not impossible. Considerable custom engineering is required to ensure hardware and software prototypes work robustly, and recruiting and managing more than a handful of households can be difficult and cost-prohibitive. To lower the barrier to developing and evaluating new technologies for the home environment, we call for the development of a shared infrastructure, called HomeLab. HomeLab consists of a large number of geographically distributed households, each running a common, flexible framework (e.g., HomeOS [4]) in which experiments are implemented. The use of a common framework enables engineering effort, along with experience and expertise, to be shared among many research groups. Recruitment of households to HomeLab can be organic: as a research group recruits (a few) households to participate in its field study, these households can be invited to join HomeLab and participate in future studies conducted by other groups. As the pool of households participating in HomeLab grows, we hope that researchers will find it easier to recruit a large number of households to participate in field studies.

Home heating is a major factor in worldwide energy use. Our system, PreHeat, aims to more efficiently heat homes by using occupancy sensing and occupancy prediction to automatically control home heating. We deployed PreHeat in five homes, three in the US and two in the UK. In UK homes, we controlled heating on a per-room basis to enable further energy savings. We compared PreHeat's prediction algorithm with a static program over an average 61 days per house, alternating days between these conditions, and measuring actual gas consumption and occupancy. In UK homes PreHeat both saved gas and reduced MissTime (the time that the house was occupied but not warm). In US homes, PreHeat decreased MissTime by a factor of 6-12, while consuming a similar amount of gas. In summary, PreHeat enables more efficient heating while removing the need for users to program thermostat schedules.

Microsoft .NET Gadgeteer is a new prototyping platform that makes it easier to construct, program and shape new kinds of computing objects. It is comprised of modular hardware, software libraries and 3D CAD support. Together, these elements support the key activities involved both in the rapid prototyping and the small-scale production of custom embedded, interactive and connected devices. We propose to organize and run a studio at TEI 2010 where participants are introduced to the platform and its capabilities. Participants will work in groups, assembling electronic modules, writing software and designing a case or enclosure for their device. The end-result will be that each group develops a fully functional device, which can be exhibited at the TEI demo session.

Long-term personal GPS data is useful for many UbiComp services such as traffic monitoring and environmental impact assessment. However, inference attacks on such traces can reveal private information including home addresses and schedules. We asked 32 participants from 12 households to collect 2 months of GPS data, and showed it to them in visualizations. We explored if they understood how their individual privacy concerns mapped onto 5 location obfuscation schemes (which they largely did), which obfuscation schemes they were most comfortable with (Mixing, Deleting data near home, and Randomizing), how they monetarily valued their location data, and if they consented to share their data publicly. 21/32 gave consent to publish their data, though most households' members shared at different levels, which indicates a lack of awareness of privacy interrelationships. Grounded in real decisions about real data, our findings highlight the potential for end-user involvement in obfuscation of their own location data.

We introduce activity-based navigation, which uses human activities derived from sensor data to help people navigate, in particular to retrace a "trail" previously taken by that person or another person. Such trails may include step counts, walking up/down stairs or taking elevators, compass directions, and photos taken along a user's path, in addition to absolute positioning (GPS and maps) when available. To explore the user experience of activity-based navigation, we built Greenfield, a mobile device interface for finding a car. We conducted a ten participant user study comparing users' ability to find cars across three different presentations of activity-based information as well as verbal instructions. Our results show that activity-based navigation can be used for car finding and suggest its promise more generally for supporting navigation tasks. We present lessons for future activity-based navigation interfaces, and motivate further work in this space, particularly in the area of robust activity inference.

RearType is a text input system for mobile devices such as Tablet PCs, using normal keyboard keys but on the reverse side of the device. The standard QWERTY layout is split and rotated so that hands gripping the device from either side have the usual keys under the fingers. This frees up the front of the device, maximizing the use of the display for visual output, eliminating the need for an onscreen keyboard and the resulting hand occlusion, and providing tactile and multi-finger text entry - with potential for knowledge transfer from QWERTY. Using a prototype implementation which includes software visualization of the keys to assist with learning, we conducted a study to explore the initial learning curve for RearType. With one hour's training, RearType typing speed was an average 15 WPM, and was not statistically different to a touchscreen keyboard.

Technology makes it possible to share many different types of information with coworkers. We conducted a large-scale survey (N=549) to better understand current sharing among coworkers, how people stay aware of collocated and remote coworkers, and whether their willingness to share different types of awareness information changes based on the location in which the information is displayed. Contrary to our expectations, the display location did not greatly affect what respondents were willing to share. Our results also suggest considerations for researchers building situated displays, as respondents had concerns about unintended viewers and encouraging people to visit their personal space when they were not present.

In this paper, we present a study of responses to the idea of being recorded by a ubicomp recording technology called SenseCam. This study focused on real-life situations in two North American and two European locations. We present the findings of this study and their implications, specifically how those who might be recorded perceive and react to SenseCam. We describe what system parameters, social processes, and policies are required to meet the needs of both the primary users and these secondary stakeholders and how being situated within a particular locale can influence responses. Our results indicate that people would tolerate potential incursions from SenseCam for particular purposes. Furthermore, they would typically prefer to be informed about and to consent to recording as well as to grant permission before any data is shared. These preferences, however, are unlikely to instigate a request for deletion or other action on their part. These results inform future design of recording technologies like SenseCam and provide a broader understanding of how ubicomp technologies might be taken up across different cultural and political regions.

In this paper, we propose a system for context- aware battery management that warns the user when it detects that the phone battery can run out before the next charging opportunity is encountered. At the heart of this system, are algorithms that predict: (1) when the next charging opportunity will be available, (2) how much call-time will be required by the user in the interim, and (3) how long the battery will last if the current set of applications continue to execute. We propose algorithms that process user's location traces and call-logs for making some of these predictions. We also propose a technique to predict battery consumption of applications. We present the design of the system and demonstrate its feasibility by experimentally showing that each of the prediction algorithms can perform with fairly high accuracy.

We study data transfer opportunities between wireless devices carried by humans. We observe that the distribution of the intercontact time (the time gap separating two contacts between the same pair of devices) may be well approximated by a power law over the range [10 minutes; 1 day]. This observation is confirmed using eight distinct experimental data sets. It is at odds with the exponential decay implied by the most commonly used mobility models. In this paper, we study how this newly uncovered characteristic of human mobility impacts one class of forwarding algorithms previously proposed. We use a simplified model based on the renewal theory to study how the parameters of the distribution impact the performance in terms of the delivery delay of these algorithms. We make recommendations for the design of well-founded opportunistic forwarding algorithms in the context of human-carried devices

Location-based computing (LBC) is becoming increasing important in both industry and academia. A key challenge is the pervasive deployment of LBC technologies; to be effective they must run on a wide variety of client platforms, including laptops, PDAs, and mobile phones, so that location data can be acquired anywhere and accessed by any application. Moreover, as a nascent area, LBC is experiencing rapid innovation in sensing technologies, the positioning algorithms themselves, and the applications they support. Lastly, as a newcomer, LBC must integrate with existing communications and application technologies, including web browsers and location data interchange standard.This paper describes our experience in developing the Place Lab architecture, a widely used first-generation open source toolkit for client-side location sensing. Using a layered, pattern-based architecture, it supports modular development in any dimension of LBC, enabling the field to move forward more rapidly as these innovations are shared with the community as pluggable components. Our experience shows the benefits of domain-specific abstractions, and how we overcame high-level language constraints to support a wide array of platforms in this emerging space. We also describe our experience in re-engineering parts of the architecture based on the needs of the user community, including insights on software licensing issues.

Abstract — Studying transfer opportunities between wireless devices carried by humans, we observe that the distribution of the inter-contact time, that is the time gap separating two contacts of the same pair of devices, exhibits a heavy tail such as one of a power law, over a large range of value. This observation is confirmed on six distinct experimental data sets. It is at odds with the exponential decay implied by most mobility models. In this paper, we study how this new characteristic of human mobility impacts a class of previously proposed forwarding algorithms. We use a simplified model based on the renewal theory to study how the parameters of the distribution impact the delay performance of these algorithms. We make recommendation for the design of well founded opportunistic forwarding algorithms, in the context of human carried devices. I.

Networked surfaces are surfaces such as desks which provide network connectivity to specially augmented devices, for example handheld computers. When the devices are physically placed on top of the surface, they can connect to different kinds of services-mainly, but not exclusively to send and receive data. This paper discusses challenges in implementing networked surfaces, paying particular attention to data flow issues, focusing on how the various software and hardware entities comprising the surface interact to transport data to and from objects.