Embodiment in mixed reality describes the sensation of experiencing a virtual representation as an extension of one’s own body. While research has extensively examined embodiment in virtual reality (VR) and head-mounted augmented reality (AR), its impact on smartphones remains underexplored. This study examines how smartphone-based AR embodiment affects user engagement and cognitive performance in a comprehension task. A study involving 24 participants explored whether using a smartphone AR face-filter to embody a virtual audience member influenced the recall of a historical speech. Findings show that participants in the AR condition scored higher on a factual quiz than those in the control group. At the same time, stronger perceived embodiment, especially self-location, was negatively associated with quiz performance, consistent with Cognitive Load Theory. These results should be interpreted cautiously: our comparison contrasted a static image (no AR) with AR that included facial embodiment, so we did not include an “AR without embodiment” condition to fully separate AR novelty from embodiment. Stimuli were also restricted to a single speech and a single historical scene presented as a static image, limiting generalizability to other content and to dynamic or interactive AR. Finally, the sample was modest (N=24), so estimates are preliminary and warrant replication. We discuss implications for designing smartphone AR that balances engagement with cognitive efficiency.

Machine Learning (ML) is increasingly accessible to users with limited knowledge of its theoretical foundations. However, misapplying it can lead to negative consequences. This paper reports on a qualitative study designed to reveal challenges that novices encounter when learning about basic ML concepts and building their first models. Twenty participants were introduced to fundamental ML concepts for classification through an interactive tutorial involving an off-the-shelf GUI application, built their own ML model for a shape gesture dataset, and participated in a semi-structured interview. A thematic analysis revealed insights into these challenges, particularly around problem selection and multi-dimensionality, but also around what constitutes ML, algorithm selection , cross-validation, and interpreting visualizations. Despite these and other misconceptions, participants reflected on good model building practices, discussing that algorithm selection might require knowledge and context and that input features may introduce bias. We discuss the findings’ implications for the design of ML tools for novices.

Interactive Machine Learning (IML) enables users, including non-experts in ML, to iteratively train and improve ML models. However, limited research has been reported on how non-experts interact with these systems. Focusing on thematic analysis as a practical application, we report on a user study where 20 participants interacted with TACA, a functioning IML tool. Thematic analysis involves individual interpretation of ambiguous data, hence it is suited for and can benefit from the iterative customization of models supported by IML. Through a combination of interaction logs and semi-structured interviews, our findings revealed that, by using TACA, participants critically reflected on their analysis, gained new thematic insights, and adapted their interpretative stance. We also document misconceptions of ML concepts, positivist views, and personal blame for poor model performance. We then discuss how applications could be designed to improve the understanding of IML concepts and foster reflexive work practices.

Large Language Models (LLMs) are widely used in conversational agents (CAs) due to their ability to generate coherent and human-like text. However, their deployment raises significant privacy concerns, as users often share sensitive data in prompts. This data can be used to train the underlying LLM, introducing memorisation risks and challenging users' right to be forgotten. While these issues have been explored from a technical standpoint, little is known about how users perceive and navigate privacy issues in their day-to-day use of LLM-based CAs. To address this research gap, we conducted a survey of UK-based CA users (n=211) that focused on their privacy behaviours, self-disclosure boundaries, concerns, and awareness of LLM-specific privacy issues. We found that engagement with protective behaviours was low overall, and that many participants held inaccurate beliefs about the effects of deleting data and opting out of model training. Although participants were generally reluctant to share sensitive information during interactions, we identified several challenges to limiting self-disclosure in practice, such as balancing privacy with app utility. Lastly, we observed a nuanced relationship between privacy awareness and concern, and identified significant demographic effects. We propose design avenues for privacy-supportive tools, and discuss the implications of our work for regulation and governance.

The increasing adoption of machine learning (ML) raises ethical concerns, particularly regarding bias. This study explores how ML practitioners with limited experience in bias understand and apply bias definitions, detection measures, and mitigation methods. Through a take-home task, exercises, and interviews with 22 participants, we identified five key themes: sources of bias, selecting bias metrics, detecting bias, mitigating bias, and ethical considerations. Participants faced unresolved conflicts, such as applying fairness definitions in practice, selecting context-dependent bias metrics, addressing real-world biases, balancing model performance with bias mitigation, and relying on personal perspectives over data-driven metrics. While bias mitigation techniques helped identify biases in two datasets, participants could not fully eliminate bias, citing the oversimplification of complex processes into models with limited variables. We propose designing bias detection tools that encourage practitioners to focus on the underlying assumptions and integrating bias concepts into ML practices, such as using a harmonic mean-based approach, akin to the F1 score, to balance bias and accuracy.

The aim of the study reported here was to reflect and report on lessons learned from adapting HCI methods to fit into an agile development process delivering a digital intervention for people managing Long Covid. During the project, we maintained a record of all activities and interim design products. Subsequently, selected records were analysed qualitatively. Challenges included engaging patients managing this complex, debilitating health condition and fitting the digital intervention into different and evolving patient pathways provided by clinics. The key contributions of this paper are a description of the clinically supported self-management app, Living With Covid Recovery, and of adapted methods for user-centred design and testing; narratives on engaging diverse clinics integrating a digital health intervention in their care pathways; reflections on designing for diverse users; implications for design of future technologies for supported self-management; and insights into multidisciplinary working that are rarely discussed within HCI.

Energy communities are a key focus for governments around the world in support of more sustainable energy practices. However, interactive systems for supporting energy communities to coordinate around renewable energy resources are still lacking. We present SolarClub, a demand-shifting visualization system that supported households in coordinating their energy usage by booking energy-hungry activities when solar energy was available. We deployed SolarClub with four groups of neighbors (N=15) for a month. SolarClub successfully enabled neighbors to coordinate, even when some of those participating households were less flexible. While participants reported that SolarClub did not foster a feeling of community, it helped them empathize with their neighbors. Our findings demonstrate the potential of sensor- and visualization-based technology to help understand the relation between everyday practices and resources consumption, beyond individual eco-feedback. This work thus contributes to the development of a next generation of practices and technologies that support collective action for environmental sustainability.

As visualisations reach a broad range of audiences, designing visualisations that attract and engage becomes more critical. Prior work suggests that semantic icons entice and immerse the reader; however, little is known about their impact with informational tasks and when the viewer’s attention is divided because of a distracting element. To address this gap, we first explored a variety of semantic icons with various visualisation attributes. The findings of this exploration shaped the design of our primary comparative online user studies, where participants saw a target visualisation with a distracting visualisation on a web page and were asked to extract insights. Their engagement was measured through three dependent variables: (1) visual attention, (2) effort to write insights, and (3) self-reported engagement. In Study 1, we discovered that visualisations with semantic icons were consistently perceived to be more engaging than the plain version. However, we found no differences in visual attention and effort between the two versions. Thus, we ran Study 2 using visualisations with more salient semantic icons to achieve maximum contrast. The results were consistent with our first Study. Furthermore, we found that semantic icons elevated engagement with visualisations depicting less interesting and engaging topics from the participant’s perspective. We extended prior work by demonstrating the semantic value after performing an informational task (extracting insights) and reflecting on the visualisation, besides its value to the first impression. Our findings may be helpful to visualisation designers and storytellers keen on designing engaging visualisations with limited resources. We also contribute reflections on engagement measurements with visualisations and provide future directions.

Embellishments are features commonly used in everyday visualisations which are demonstrated to enhance assimilation and memorability. Despite their popularity, little is known about their impact on enticing readers to explore visualisations. To address this gap, we conducted 18 interviews with a diverse group of participants who were consumers of news media but non-experts in visualisation and design. Participants were shown ten embellished and plain visualisations collected from the news and asked to rank them based on enticement and ease of understanding. Extending prior work, our interview results suggest that visualisations with multiple embellishment types might make a visualisation perceived as more enticing. An important finding from our study is that the widespread of certain embellishments in the media might have made them part of visualisation conventions, making a visualisation appear more objective but less enticing. Based on these findings, we ran a follow-up online user study showing participants variations of the visualisations with multiple embellishments to isolate each embellishment type and investigate its effect. We found that variations with salient embellishments were perceived as more enticing. We argue that to unpack the concept of embellishments; we must consider two factors: embellishment saliency and editorial styles. Our study contributes concept and design considerations to the literature concerned with visualisation design for non-experts in visualisation and design.

Mutual awareness of visual attention is crucial for successful collaboration. Previous research has explored various ways to represent visual attention, such as field-of-view visualizations and cursor visualizations based on eye-tracking, but these methods have limitations. Verbal communication is often utilized as a complementary strategy to overcome such disadvantages. This paper proposes a novel method that combines verbal communication with the Cone of Vision to improve gaze inference and mutual awareness in VR. We conducted a within-group study with pairs of participants who performed a collaborative analysis of data visualizations in VR. We found that our proposed method provides a better approximation of eye gaze than the approximation provided by head direction. Furthermore, we release the first collaborative head, eyes, and verbal behaviour dataset. The results of this study provide a foundation for investigating the potential of verbal communication as a tool for enhancing visual cues for joint attention.

Eye behavior has gained much interest in the VR research community as an interactive input and support for collaboration. Researchers used head behavior and saliency to implement gaze inference models when eye-tracking is missing. However, these solutions are resource-demanding and thus unfit for untethered devices, and their angle accuracy is around 7°, which can be a problem in high-density informative areas. To address this issue, we propose a lightweight deep learning model that generates the probability density function of the gaze as a percentile contour. This solution allows us to introduce a visual attention representation based on a region rather than a point. In this way, we manage the trade-off between the ambiguity of a region and the error of a point. We tested our model in untethered devices with real-time performances; we evaluated its accuracy, outperforming our identified baselines (average fixation map and head direction).

Mutual awareness of visual attention is essential for collaborative work. In the field of collaborative virtual environments (CVE), it has been proposed to use Field-of-View (FoV) frustum visualisations as a cue to support mutual awareness during collaboration. Recent studies on FoV frustum visualisations focus on asymmetric collaboration with AR/VR hardware setups and 3D reconstructed environments. In contrast, we focus on the general-purpose CVEs (i.e., VR shared offices), whose popularity is increasing due to the availability of low-cost headsets, and the restrictions imposed by the pandemic. In these CVEs collaboration roles are symmetrical, and the same 2D content available on desktop computers is displayed on 2D surfaces in a 3D space (VR screens). We prototyped one such CVE to evaluate FoV frustrum visualisation within this collaboration scenario. We also implement a FoV visualisation generated from an average fixation map (AFM), therefore directly generated by users' gaze behaviour which we call Cone of Vision (CoV). Our approach to displaying the frustum visualisations is tailored for 2D surfaces in 3D space and allows for self-awareness of this visual cue. We evaluate CoV in the context of a general exploratory data analysis (EDA) with 10 pairs of participants. Our findings indicate that CoV is beneficial during shifts between independent and collaborative work and supports collaborative progression across the visualisation. Self-perception of the CoV improves visual attention coupling, reduces the number of times users watch the collaborator's avatars and offers a consistent representation of the shared reality.

The job of a camera operator is challenging, and potentially dangerous, when filming long moving camera shots. Broadly, the operator must keep the actors in frame while safely navigating around obstacles and while fulfilling an artistic vision. We propose a unified hardware and software system that distributes some of the camera operator’s burden, freeing the operator up to focus on safety and aesthetics during a take. Our real-time system provides solo operators with end-to-end control so that they can balance on-set responsiveness to action against planned storyboards and framing while looking where they are going. By default, we film without a field monitor. Our LookOut system is built around a lightweight commodity camera gimbal mechanism, with heavy modifications to the controller, which would normally just provide active stabilization. Our control algorithm reacts to speech commands, video, and a premade script. Specifically, our automatic monitoring of the live video feed saves the operator from distractions. In preproduction, an artist uses our graphical user interface (GUI) to design a sequence of high-level camera “behaviors.” Those can be specific, based on a storyboard, or looser objectives, such as “frame both actors.” Then, during filming, a machine-readable script, exported from the GUI, ties together with the sensor readings to drive the gimbal. To validate our algorithm, we compared tracking strategies, interfaces, and hardware protocols and collected impressions from (a) filmmakers who used all aspects of our system and (b) filmmakers who watched footage filmed using LookOut.

In this article, we report on one lab study and seven follow-up studies on a crowdsourcing platform designed to investigate the potential of animation cues to influence users’ perception of two smart systems: a handwriting recognition and a part-of-speech tagging system. Results from the first three studies indicate that animation cues can influence a participant’s perception of both systems’ performance. The subsequent three studies, designed to try and identify an explanation for this effect, suggest that this effect is related to the participants’ mental model of the smart system. The last two studies were designed to characterize the effect more in detail, and they revealed that different amounts of animation do not seem to create substantial differences and that the effect persists even when the system’s performance decreases, but only when the difference in performance level between the systems being compared is small.

UbiComp has been envisioned to bring about a future dominated by calm computing technologies making our everyday lives ever more convenient. Yet the same vision has also attracted criticism for encouraging a solitary and passive lifestyle. The aim of this paper is to explore and elaborate these tensions further by examining the human values surrounding future domestic UbiComp solutions. Drawing on envisioning and contravisioning, we probe members of the public (N=28) through the presentation and focus group discussion of two contrasting animated video scenarios, where one is inspired by 'calm' and the other by 'engaging' visions of future UbiComp technology. By analysing the reasoning of our participants, we identify and elaborate a number of relevant values involved in balancing the two perspectives. In conclusion, we articulate practically applicable takeaways in the form of a set of key design questions and challenges.

Many computational toolkits to promote early learning of basic computational concepts and practices are inaccessible to learners with reduced visual abilities. We report on the design of TIP-Toy, a tactile and inclusive open-source toolkit, to allow children with different visual abilities to learn about computational topics through music by combining a series of physical blocks. TIP-Toy was developed through two design consultations with experts and potential users. The first round of consultations was conducted with 3 visually impaired adults with significant programming experience; the second one involved 9 children with mixed visual abilities. Through these design consultations we collected feedback on TIP-Toy, and observed children's interactions with the toolkit. We discuss appropriate features for future iterations of TIP-toy to maximise the opportunities for accessible and enjoyable learning experiences.

Convolutional neural networks (CNNs) offer great machine learning performance over a range of applications, but their operation is hard to interpret, even for experts. Various explanation algorithms have been proposed to address this issue, yet limited research effort has been reported concerning their user evaluation. In this paper, we report on an online between-group user study designed to evaluate the performance of "saliency maps" - a popular explanation algorithm for image classification applications of CNNs. Our results indicate that saliency maps produced by the LRP algorithm helped participants to learn about some specific image features the system is sensitive to. However, the maps seem to provide very limited help for participants to anticipate the network's output for new images. Drawing on our findings, we highlight implications for design and further research on explainable AL In particular, we argue the HCI and AI communities should look beyond instance-level explanations.

Increasingly popular, long-distance running events (LDRE) attract not just runners but an exponentially increasing number of spectators. Due to the long duration and broad geographic spread of such events, interactions between them are limited to brief moments when runners (R) pass by their supporting spectators (S). Current technology is limited in its potential for supporting interactions and mainly measures and displays basic running information to spectators who passively consume it. In this paper, we conducted qualitative studies for an in-depth understanding of the R&S' shared experience during LDRE and how technology can enrich this experience. We propose a two-layer DyPECS framework, highlighting the rich dynamics of the R&S multi-faceted running journey and of their micro-encounters. DyPECS is enriched by the findings from our in depth qualitative studies. We finally present design implications for the multi-facet co-experience of R&S during LDRE.

Predictions of people's behaviour increasingly drive interactions with a new generation of IoT services designed to support everyday life in the home, from shopping to heating. Based on the premise that such automation is difficult due to the contingent nature of people's practices, in this work we explore the nature of these contingencies in depth. We have designed and conducted a technology probe that made use of simple linear predictions as a provocation, and invited people to track the life of their household essentials over a two-month period. Through a mixed-method approach we demonstrate the challenges of simple predictions, and in turn identify eight categories of contingencies that influenced prediction accuracy. We discuss strategies for how designers of future predictive IoT systems may take the contingencies into account by removing, hiding, revealing, managing, or exploiting the system uncertainty at the core of the issue.

Computer vision and pattern recognition are increasingly being employed by smartphone and tablet applications targeted at lay-users. An open design challenge is to make such systems intelligible without requiring users to become technical experts. This paper reports a lab study examining the role of visual feedback. Our findings indicate that the stage of processing from which feedback is derived plays an important role in users' ability to develop coherent and correct understandings of a system's operation. Participants in our study showed a tendency to misunderstand the meaning being conveyed by the feedback, relating it to processing outcomes and higher level concepts, when in reality the feedback represented low level features. Drawing on the experimental results and the qualitative data collected, we discuss the challenges of designing interactions around pattern matching algorithms.

The Internet of Things (IoT) promises to enable applications that foster a more efficient, sustainable, and healthy way of life. If end-users are to take full advantage of these developments we foresee the need for future IoT systems and services to include an element of autonomy and support the delegation of agency to software processes and connected devices. To inform the design of such future technology, we report on a breaching experiment designed to investigate how people integrate an unpredictable service, through the veg box scheme, in everyday life. Findings from our semi-structured interviews and a two-week diary study with 11 households reveal that agency delegation must be warranted, that it must be possible to incorporate delegated decisions into everyday activities, and that delegation is subject to constraint. We further discuss design implications on the need to support people's diverse values, and their coordinative and creative practices.

Increasing sophistication and ubiquity of digital devices is creating potential for the development of new kinds of actuated interfaces. In this paper, we explore the design space around movement as a form of gestural communication for information output, in simple actuated desktop devices. We were curious as to how people might envision interacting with autonomous technology in the office. Accordingly, we focused our attentions on one prevalent desktop object, an interactive lamp, with three actuated joints, which allowed us to explore the interaction space of such devices. We invited 13 participants to design and enact movements with the lamp to communicate 20 simple messages. We explored a subset of these generated gestures, using the lamp as a personal cueing device in an office setting with 14 new participants. We present our qualitative findings from both studies that let users imagine the usage of an interactive desktop lamp through actuation.

We present fieldwork findings from the deployment of an interactive sensing system that supports the work of energy advisors who give face-to-face advice to low-income households in the UK. We focus on how the system and the data it produced are articulated in the interactions between professional energy advisors and their clients, and how they collaboratively anticipate, rehearse, and perform data work. In addition to documenting how the system was appropriated in advisory work, we elaborate the 'overhead cost' of building collaborative action into connected devices and sensing systems, and the commensurate need to support discrete workflows and accountability systems to enable the methodical incorporation of the IoT into collaborative action. We contribute an elaboration of the social, collaborative methods of data work relevant to those who seek to design and study collaborative IoT systems.

Location-based services, narratives and interactive experiences have a substantial tradition in human–computer interaction. Building on this background, we report the iterative design process, prototyping and evaluation of Seven Stories, a location-based narrative experience. The design includes two novel features that aim to link the user experience more closely to its physical context: visual markers scanning and a narrator. We have evaluated Seven Stories with two studies. Study 1, with 12 participants, was conducted using qualitative methods to assess the overall user experience. Study 2, with 21 participants, was conducted using quantitative methods, to evaluate the role of the markers and narrator as specific design features. Our contribution is to illustrate, through both qualitative and quantitative findings, how these two novel features influence the user experience, and what implications they have for design.

Smart energy systems that leverage machine learning techniques are increasingly integrated in all aspects of our lives. To better understand how to design user interaction with such systems, we implemented three different smart thermostats that automate heating based on users' heating preferences and real-time price variations. We evaluated our designs through a field study, where 30 UK households used our thermostats to heat their homes over a month. Our findings through thematic analysis show that the participants formed different understandings and expectations of our smart thermostat, and used it in various ways to effectively respond to real-time prices while maintaining their thermal comfort. Based on the findings, we present a number of design and research implications, specifically for designing future smart thermostats that will assist us in controlling home heating with real-time pricing, and for future intelligent autonomous systems.

In this paper we present the design and evaluation of the Temperature Calendar -- a visualization of temperature variation within a workplace over the course of the past week. This highlights deviation from organizational temperature policy, and aims to bring staff "into the loop" of understanding and managing heating, and so reduce energy waste. The display was deployed for three weeks in five public libraries. Analysis of interaction logs, questionnaires and interviews shows that staff used the displays to understand heating in their buildings, and took action reflecting this new understanding. Bringing together our results, we discuss design implications for workplace displays, and an analysis of carbon emissions generated in constructing and operating our design. More in general, the findings helped us to reflect on the role of policy on energy consumption, and the potential for the HCI community to engage with its application, as well as its definition or modification.

This paper presents findings from a co-design project that aims to augment the practices of professional energy advisors with environmental data from sensors deployed in clients' homes. Premised on prior ethnographic observations we prototyped a sensor platform to support the work of tailoring advice-giving to particular homes. We report on the deployment process and the findings to emerge, particularly the work involved in making sense of or accounting for the data in the course of advice-giving. Our ethnomethodological analysis focuses on the ways in which data is drawn upon as a resource in the home visit, and how understanding and advice-giving turns upon unpacking the indexical relationship of the data to the situated goings-on in the home. This insight, coupled with further design workshops with the advisors, shaped requirements for an interactive system that makes the sensor data available for visual inspection and annotation to support the situated sense-making that is key to giving energy advice.

Autonomous systems are designed to take actions on behalf of users, acting autonomously upon data from sensors or online sources. As such, the design of interaction mechanisms that enable users to understand the operation of autonomous systems and flexibly delegate or regain control is an open challenge for HCI. Against this background, in this paper we report on a lab study designed to investigate whether displaying the confidence of an autonomous system about the quality of its work, which we call its confidence information, can improve user acceptance and interaction with autonomous systems. The results demonstrate that confidence information encourages the usage of the autonomous system we tested, compared to a situation where such information is not available. Furthermore, an additional contribution of our work is the method we employ to study users' incentives to do work in collaboration with the autonomous system. In experiments comparing different incentive strategies, our results indicate that our translation of behavioural economics research methods to HCI can support the study of interactions with autonomous systems in the lab.

Most work around technological interventions for energy conservation to date has focussed on changing individual behaviour. Hence, there is limited understanding of communal settings, such as office environments, as sites for intervention. Even when energy consumption in the workplace has been considered, the emphasis has typically been on the individual. To address this gap, we conducted a study of energy consumption and management in one workplace, based on a combination of workshops with a broad range of stakeholders, and quantitative data inspections. We report and discuss findings from this study, in light of prior literature, and we present a set of implications for design and further research. In particular, three themes, and associated intervention opportunities, emerged from our data: (1) energy wastage related to “errors”; (2) the role of company policies and the negotiation that surrounds their implementation; and (3) the bigger energy picture of procurement, construction and travel.

Autonomous robotic systems can automatically perform actions on behalf of users in the domestic environment to help people in their daily activities. Such systems aim to reduce users' cognitive and physical workload, and improve well-being. While the benefits of these systems are clear, recent studies suggest that users may misconstrue their performance of tasks. We see an opportunity in designing interaction techniques that improve how users perceive the performance of such systems. We report two lab studies (N=16 each) designed to investigate whether showing physical motion, which is showing the process of a system through movement (that is intrinsic to the system's task), of an autonomous system as it completes its task, affects how users perceive its performance. To ensure our studies are ecologically valid and to motivate participants to provide thoughtful responses we adopted consensus-oriented financial incentives. Our results suggest that physical presence does yield higher performance ratings.

Smart systems are becoming increasingly ubiquitous and consequently transforming our lives. The level of system autonomy plays a vital role in the development of smart systems as it profoundly affects how people and these systems interact with each other. However, to date, there are very few studies on human interaction with such systems. This paper presents findings from two field studies where two different prototypes for automating energy tariff-switching were developed and evaluated in the wild. Both prototypes offer flexible autonomy by which users can shift the system's level of autonomy among three options: suggestion-only, semi-autonomy, and full autonomy, whenever they like. Our findings based on thematic analysis show that flexible autonomy is a promising way to sustain users' engagement with smart systems, despite their occasional mistakes. The findings also suggest that users take responsibility for the undesired outcomes of automated actions when delegation of autonomy can be adjusted flexibly.

The availability of low-power Wi-Fi radio modules opens up opportunities to leverage the existing prevalent Wi-Fi infrastructure for large-scale trials and deployments of Ubicomp technology. In this paper we address the challenge of supporting end-users, especially when they are not technical experts, in connecting new low-power, low-cost Wi-Fi devices with very minimal UIs to an existing, secure Wi-Fi infrastructure. We report two usability studies through which 30 participants, with no formal technical training, compared 4 alternative configuration techniques, selected based on cost and consumption constraints, and on adoption in off-the-shelf products. Through an analysis of success rate and causes of failure, our results indicate that two techniques are noticeably more usable than others. These are a web-based configuration mechanism, where users connect to an access point on the Wi-Fi device, and one that makes use of a standard audio cable to connect a smartphone to the device to be configured.

Future energy systems that rely on renewable energy may bring about a radical shift in how we use energy in our homes. We developed and prototyped a future scenario with highly variable, real-time electricity prices due to a grid that mainly relies on renewables. We designed and deployed an agent-based interactive system that enables users to effectively operate the washing machine in this scenario. The system is used to book timeslots of washing machine use so that the agent can help to minimize the cost of a wash by charging a battery at times when electricity is cheap. We carried out a deployment in 10 households in order to uncover the socio-technical challenges around integrating new technologies into everyday routines. The findings reveal tensions that arise when deploying a rationalistic system to manage contingently and socially organized domestic practices. We discuss the trade-offs between utility and convenience inherent in smart grid applications; and illustrate how certain design choices position applications along this spectrum.

We present an ethnographic study of energy advisors working for a charity that provides support, particularly to people in fuel poverty. Our fieldwork comprises detailed observations that reveal the collaborative, interactional work of energy advisors and clients during home visits, supplemented with interviews and a participatory design workshop with advisors. We identify opportunities for Ubicomp technologies that focus on supporting the work of the advisor, including complementing the collaborative advice giving in home visits, providing help remotely, and producing evidence in support of accounts of practices and building conditions useful for interactions with landlords, authorities and other third parties. We highlight six specific design challenges that relate the domestic fuel poverty setting to the wider Ubicomp literature. Our work echoes a shift in attention from energy use and the individual consumer, specifically to matters of advice work practices and the domestic fuel poverty setting, and to the discourse around inclusive Ubicomp technologies.

We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.

Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy "in the wild" -- 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.

While previous research studied the high level attributes people consider when they assess the healthiness of food they are familiar with, little work has looked at how people assess arbitrary, potentially unfamiliar, food to decide whether it is a healthy choice. Since there is a growing body of work in Ubicomp around health practices, including systems to support healthy eating, it is important to understand how people apply the knowledge they have to food decisions. In our studies we identified 8 attributes participants use for determining if they think a food is "healthy" or not. Based upon our analysis, we reflect on current system designs and propose four future design opportunities: capturing context of healthy eating, preparation and reflection on healthy eating understanding, sharing understanding and in situ information support.

Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.

Wireless Sensor Networks (WSNs) allow the monitoring of activity or environmental conditions over a large area, from homes to industrial plants, from agriculture fields to forests and glaciers. They can support a variety of applications, from assisted living to natural disaster prevention. WSNs can, however, be challenging to setup and maintain, reducing the potential for real-world adoption. To address this limitation, this paper introduces SensorTune, a novel mobile interface to support non-expert users in iteratively setting up a WSN. SensorTune uses non-speech audio to present to its users information regarding the connectivity of the network they are setting up, allowing them to decide how to extend it. To simplify the interpretation of the data presented, the system adopts the metaphor of tuning a consumer analog radio, a very common and well known operation. A user study was conducted in which 20 subjects setup real multi-hop networks inside a large building using a limited number of wireless nodes. Subjects repeated the task with SensorTune and with a comparable mobile GUI interface. Experimental results show a statistically significant difference in the task completion time and a clear preference of users for the auditory interface.

Tangible user interfaces have been promoted and discussed in the Ubicomp and HCI communities for 15 years. In TUIs physical objects are used for the control and representation of digital information, similarly to how icons are used in graphical user interfaces for the same purpose. Most reported TUI systems have the nature of research prototypes, available in laboratories or museums. This paper reports an attempt to understand the impact of TUIs in users' everyday environments through 2 low-cost simple set-up tangible interfaces for music that can be freely downloaded from a website. The systems are based on computer vision, printed paper and audio output. A few hundreds of users downloaded them and played with them. We logged users interaction with the interfaces and analysed content posted by them on our own and other web sites to observe and evaluate how they relate to such novel systems, taking measures to protect their privacy. Both the interaction logs and the users' comments indicate that the tangible interfaces were accepted as normal: they were perceived just as interfaces to make music rather than esoteric systems. Its time to bring Ubicomp technology to the masses!

Visual markers are graphic symbols designed to be easily recognised by machines. They are traditionally used to track goods, but there is increasing interest in their application to mobile HCI. By scanning a visual marker through a camera phone users can retrieve localised information and access mobile services.

Mobile communication devices, such as mobile phones and networked personal digital assistants (PDAs), allow users to be constantly connected and communicate anywhere and at any time, often resulting in personal and private communication taking place in public spaces. This private -- public contrast can be problematic. As a remedy, we promote intimate interfaces: interfaces that allow subtle and minimal mobile interaction, without disruption of the surrounding environment. In particular, motionless gestures sensed through the electromyographic (EMG) signal have been proposed as a solution to allow subtle input in a mobile context. In this paper we present an expansion of the work on EMG-based motionless gestures including (1) a novel study of their usability in a mobile context for controlling a realistic, multimodal interface and (2) a formal assessment of how noticeable they are to informed observers. Experimental results confirm that subtle gestures can be profitably used within a multimodal interface and that it is difficult for observers to guess when someone is performing a gesture, confirming the hypothesis of subtlety.

Mobile devices are generally used in public, where the user is surrounded by others not involved in the interaction. Audible notification cues are often a cause of unnecessary disruption and distraction both for co-located people and even for the user to whom they are directed. We present a wearable peripheral display embedded in eyeglasses that delivers subtle, discreet and unobtrusive cues. The display is personal and intimate; it delivers visual cues in the wearers' periphery without disrupting their immediate environment. A user study conducted to validate the design reveals that the display is effective and subtle in notifying users. Experimental results show, with significance, that the cues can be designed to meet specific levels of visibility and disruption for the wearer, so that some cues are less noticeable when the user is not under high workload, which is highly desirable in many practical circumstances. Hence, peripheral notification displays can provide an effective solution for designing socially acceptable notification displays, unobtrusive to the user and the immediate environment.

Using a mobile device in a social context should not cause embarrassment and disruption to the immediate environment. Interaction with mobile and wearable devices needs to be subtle, discreet and unobtrusive. Therefore, we promote the idea of "intimate interfaces": discrete interfaces that allow control of mobile devices through subtle gestures in order to gain social acceptance. To achieve this goal, we present an electromyogram (EMG) based wearable input device which recognizes isometric muscular activity: activity related to very subtle or no movement at all. In the online experiment reported, the EMG device, worn on an armband around the bicep, was able to reliably recognize a motionless gesture without calibration or training across users with different muscle volumes. Hence, EMG-based input devices can provide an effective solution for designing mobile interfaces that are subtle and intimate, and therefore socially acceptable.